1
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Li Y, Xiao X, Li J, Han Y, Cheng C, Fernandes GF, Slewitzke SE, Rosenberg SM, Zhu M, Byun J, Bossé Y, McKay JD, Albanes D, Lam S, Tardon A, Chen C, Bojesen SE, Landi MT, Johansson M, Risch A, Bickeböller H, Wichmann HE, Christiani DC, Rennert G, Arnold SM, Goodman GE, Field JK, Davies MP, Shete S, Marchand LL, Liu G, Hung RJ, Andrew AS, Kiemeney LA, Sun R, Zienolddiny S, Grankvist K, Johansson M, Caporaso NE, Cox A, Hong YC, Lazarus P, Schabath MB, Aldrich MC, Schwartz AG, Gorlov I, Purrington KS, Yang P, Liu Y, Bailey-Wilson JE, Pinney SM, Mandal D, Willey JC, Gaba C, Brennan P, Xia J, Shen H, Amos CI. Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies. Cancer Epidemiol Biomarkers Prev 2024; 33:389-399. [PMID: 38180474 PMCID: PMC10905670 DOI: 10.1158/1055-9965.epi-23-0613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/03/2023] [Accepted: 01/03/2024] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. METHODS We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. RESULTS Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10-8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. CONCLUSIONS We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. IMPACT Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.
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Affiliation(s)
- Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Jianrong Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Gail F. Fernandes
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Shannon E. Slewitzke
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Susan M. Rosenberg
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, Canada
| | - James D. McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Stephen Lam
- Department of Integrative Oncology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Stig E. Bojesen
- Department of Clinical Biochemistry, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria T. Landi
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Angela Risch
- Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
- University of Salzburg and Cancer Cluster Salzburg, Salzburg, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | | | - David C. Christiani
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | | | | | - John K. Field
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Michael P.A. Davies
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Geoffrey Liu
- University Health Network- The Princess Margaret Cancer Centre, Toronto, California
| | - Rayjean J. Hung
- Luenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada
| | - Angeline S. Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, New Hampshire
| | | | - Ryan Sun
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | | | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of South Korea
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Melinda C. Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ann G. Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan
- Karmanos Cancer Institute, Detroit, Michigan
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Kristen S. Purrington
- Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan
- Karmanos Cancer Institute, Detroit, Michigan
| | - Ping Yang
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | | | - Susan M. Pinney
- University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, Louisiana
| | - James C. Willey
- College of Medicine and Life Sciences, University of Toledo, Toledo, Ohio
| | - Colette Gaba
- The University of Toledo College of Medicine, Toledo, Ohio
| | - Paul Brennan
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, Canada
| | - Jun Xia
- Creighton University School of Medicine, Omaha, Nebraska
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
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Craig DJ, Crawford EL, Chen H, Grogan EL, Deppen SA, Morrison T, Antic SL, Massion PP, Willey JC. TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk. BMC Cancer 2023; 23:783. [PMID: 37612638 PMCID: PMC10464352 DOI: 10.1186/s12885-023-11266-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case-control cohort. METHODS Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA. RESULTS TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749-0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846-0.986) and this was significantly higher than that for either factor alone (P < 0.03). CONCLUSIONS These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen.
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Affiliation(s)
- Daniel J Craig
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA
| | - Erin L Crawford
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA
| | - Heidi Chen
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Eric L Grogan
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
- Tennessee Valley VA Healthcare System, 1310 24Th Avenue South, Nashville, TN, 37212, USA
| | - Steven A Deppen
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Thomas Morrison
- Accugenomics Inc, 1410 Commonwealth Dr #105, Wilmington, NC, 28403, USA
| | - Sanja L Antic
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - Pierre P Massion
- Vanderbilt University Medical Center, 1301 Medical Center Dr., TN, 37232, Nashville, USA
| | - James C Willey
- University of Toledo College of Medicine, 3000 Arlington Ave, OH, 43614, Toledo, USA.
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Breidenbach JD, French BW, Gordon TT, Kleinhenz AL, Khalaf FK, Willey JC, Hammersley JR, Mark Wooten R, Crawford EL, Modyanov NN, Malhotra D, Teeguarden JG, Haller ST, Kennedy DJ. Microcystin-LR aerosol induces inflammatory responses in healthy human primary airway epithelium. Environ Int 2022; 169:107531. [PMID: 36137425 DOI: 10.1016/j.envint.2022.107531] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/24/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Harmful algal blooms plague bodies of freshwater globally. These blooms are often composed of outgrowths of cyanobacteria capable of producing the heptapeptide Microcystin-LR (MC-LR) which is a well-known hepatotoxin. Recently, MC-LR has been detected in aerosols generated from lake water. However, the risk for human health effects due to MC-LR inhalation exposure have not been extensively investigated. In this study, we exposed a fully differentiated 3D human airway epithelium derived from 14 healthy donors to MC-LR-containing aerosol once a day for 3 days. Concentrations of MC-LR ranged from 100 pM to 1 µM. Although there were little to no detrimental alterations in measures of the airway epithelial function (i.e. cell survival, tissue integrity, mucociliary clearance, or cilia beating frequency), a distinct shift in the transcriptional activity was found. Genes related to inflammation were found to be upregulated such as C-C motif chemokine 5 (CCL5; log2FC = 0.57, p = 0.03) and C-C chemokine receptor type 7 (CCR7; log2FC = 0.84, p = 0.03). Functionally, conditioned media from MC-LR exposed airway epithelium was also found to have significant chemo-attractive properties for primary human neutrophils. Additionally, increases were found in the concentration of secreted chemokine proteins in the conditioned media such as CCL1 (log2FC = 5.07, p = 0.0001) and CCL5 (log2FC = 1.02, p = 0.046). These results suggest that MC-LR exposure to the human airway epithelium is capable of inducing an inflammatory response that may potentiate acute or chronic disease.
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Affiliation(s)
| | - Benjamin W French
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Tamiya T Gordon
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Andrew L Kleinhenz
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Fatimah K Khalaf
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA; College of Pharmacy, University of Alkafeel, Najaf, Iraq
| | - James C Willey
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | | | - R Mark Wooten
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Erin L Crawford
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Nikolai N Modyanov
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Deepak Malhotra
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Justin G Teeguarden
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA
| | - Steven T Haller
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - David J Kennedy
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA.
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4
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Li Y, Xiao X, Li J, Byun J, Cheng C, Bossé Y, McKay J, Albanes D, Lam S, Tardon A, Chen C, Bojesen SE, Landi MT, Johansson M, Risch A, Bickeböller H, Wichmann HE, Christiani DC, Rennert G, Arnold S, Goodman G, Field JK, Davies MPA, Shete SS, Le Marchand L, Melander O, Brunnström H, Liu G, Hung RJ, Andrew AS, Kiemeney LA, Shen H, Sun R, Zienolddiny S, Grankvist K, Johansson M, Caporaso N, Teare DM, Hong YC, Lazarus P, Schabath MB, Aldrich MC, Schwartz AG, Gorlov I, Purrington K, Yang P, Liu Y, Han Y, Bailey-Wilson JE, Pinney SM, Mandal D, Willey JC, Gaba C, Brennan P, Amos CI. Genome-wide interaction analysis identified low-frequency variants with sex disparity in lung cancer risk. Hum Mol Genet 2022; 31:2831-2843. [PMID: 35138370 PMCID: PMC9402242 DOI: 10.1093/hmg/ddac030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/14/2022] [Accepted: 01/31/2022] [Indexed: 01/12/2023] Open
Abstract
Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene-sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequency < 0.05), rs17662871 [odds ratio (OR) = 0.71, P = 4.29×10-8); rs79942605 (OR = 2.17, P = 2.81×10-8) and rs208908 (OR = 0.70, P = 4.54×10-8) were identified with different risk effect of lung cancer between men and women. Further expression quantitative trait loci and functional annotation analysis suggested rs208908 affects lung cancer risk through differential regulation of Coxsackie virus and adenovirus receptor gene expression in lung tissues between men and women. Our study is one of the first studies to provide novel insights about the genetic and molecular basis for sex disparity in lung cancer development.
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Affiliation(s)
- Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jianrong Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City G1V 4G5, Canada
| | - James McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Stephen Lam
- Department of Integrative Oncology, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias 33003, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Stig E Bojesen
- Department of Clinical Biochemistry, Copenhagen University Hospital, Copenhagen 2600, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2177, Denmark
| | - Maria T Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Angela Risch
- Thoraxklinik at University Hospital Heidelberg, Heidelberg 69126, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg 69120, Germany
- University of Salzburg and Cancer Cluster Salzburg, 5020, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, 37099, Germany
| | - H-Erich Wichmann
- Institute of Medical Statistics and Epidemiology, Technical University Munich, 80333, Germany
| | - David C Christiani
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa 3436212, Israel
| | - Susanne Arnold
- University of Kentucky, Markey Cancer Center, Lexington, Kentucky 40536, USA
| | - Gary Goodman
- Swedish Cancer Institute, Seattle, WA 98104, USA
| | - John K Field
- Institute of Translational Medicine, University of Liverpool, Liverpool L69 7BE, United Kingdom
| | - Michael P A Davies
- Institute of Translational Medicine, University of Liverpool, Liverpool L69 7BE, United Kingdom
| | - Sanjay S Shete
- Department of Biostatistics, The University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030, USA
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Olle Melander
- Faculty of Medicine, Lund University, Lund 22184, Sweden
| | | | - Geoffrey Liu
- University Health Network- The Princess Margaret Cancer Centre, Toronto, CA ON, M5G 2C1, Canada
| | - Rayjean J Hung
- Luenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto ON, M5G 1X5, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto ON, M5T 3M7, Canada
| | - Angeline S Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, NH 03755, USA
| | | | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, P.R. China
| | - Ryan Sun
- Department of Biostatistics, The University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå 901 87, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Umeå University, Umeå 901 87, Sweden
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Dawn M Teare
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, Washington 99202, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Melinda C Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center Nashville, TN 37232, USA
| | - Ann G Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Karmanos Cancer Institute, Detroit, MI 48201, USA
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Ping Yang
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinics Rochester, MN, 55905, USA
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Susan M Pinney
- University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - James C Willey
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH 43614, USA
| | - Colette Gaba
- The University of Toledo College of Medicine, Toledo, OH 43614, USA
| | - Paul Brennan
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
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5
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Byun J, Han Y, Li Y, Xia J, Long E, Choi J, Xiao X, Zhu M, Zhou W, Sun R, Bossé Y, Song Z, Schwartz A, Lusk C, Rafnar T, Stefansson K, Zhang T, Zhao W, Pettit RW, Liu Y, Li X, Zhou H, Walsh KM, Gorlov I, Gorlova O, Zhu D, Rosenberg SM, Pinney S, Bailey-Wilson JE, Mandal D, de Andrade M, Gaba C, Willey JC, You M, Anderson M, Wiencke JK, Albanes D, Lam S, Tardon A, Chen C, Goodman G, Bojeson S, Brenner H, Landi MT, Chanock SJ, Johansson M, Muley T, Risch A, Wichmann HE, Bickeböller H, Christiani DC, Rennert G, Arnold S, Field JK, Shete S, Le Marchand L, Melander O, Brunnstrom H, Liu G, Andrew AS, Kiemeney LA, Shen H, Zienolddiny S, Grankvist K, Johansson M, Caporaso N, Cox A, Hong YC, Yuan JM, Lazarus P, Schabath MB, Aldrich MC, Patel A, Lan Q, Rothman N, Taylor F, Kachuri L, Witte JS, Sakoda LC, Spitz M, Brennan P, Lin X, McKay J, Hung RJ, Amos CI. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer. Nat Genet 2022; 54:1167-1177. [PMID: 35915169 PMCID: PMC9373844 DOI: 10.1038/s41588-022-01115-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/27/2022] [Indexed: 02/03/2023]
Abstract
To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.
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Affiliation(s)
- Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Jun Xia
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
| | - Wen Zhou
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Department of Molecular Medicine, Laval University, Quebec City, Quebec, Canada
| | - Zhuoyi Song
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ann Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine Lusk
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | | | | | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zhao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rowland W Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Xihao Li
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Kyle M Walsh
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Olga Gorlova
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Dakai Zhu
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Susan M Rosenberg
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Susan Pinney
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | | | - Colette Gaba
- The University of Toledo College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - James C Willey
- The University of Toledo College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Ming You
- Center for Cancer Prevention, Houston Methodist Research Institute, Houston, TX, USA
| | | | - John K Wiencke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephan Lam
- Department of Integrative Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Stig Bojeson
- Department of Clinical Biochemistry, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Thomas Muley
- Division of Cancer Epigenomics, DKFZ - German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Angela Risch
- Division of Cancer Epigenomics, DKFZ - German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
| | | | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | - David C Christiani
- Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, MA, USA
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Susanne Arnold
- University of Kentucky, Markey Cancer Center, Lexington, KY, USA
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Sanjay Shete
- Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | | | - Geoffrey Liu
- University Health Network- The Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Angeline S Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, NH, USA
| | | | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jian-Min Yuan
- UPMC Hillman Cancer Center and Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Melinda C Aldrich
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alpa Patel
- American Cancer Society, Atlanta, GA, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fiona Taylor
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Margaret Spitz
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Paul Brennan
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Xihong Lin
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - James McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
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6
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Zhang Y, Blomquist TM, Kusko R, Stetson D, Zhang Z, Yin L, Sebra R, Gong B, Lococo JS, Mittal VK, Novoradovskaya N, Yeo JY, Dominiak N, Hipp J, Raymond A, Qiu F, Arib H, Smith ML, Brock JE, Farkas DH, Craig DJ, Crawford EL, Li D, Morrison T, Tom N, Xiao W, Yang M, Mason CE, Richmond TA, Jones W, Johann DJ, Shi L, Tong W, Willey JC, Xu J. Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples. Genome Biol 2022; 23:141. [PMID: 35768876 PMCID: PMC9241261 DOI: 10.1186/s13059-022-02709-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. Results Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to “edge effects” seen in histology, while the inner samples display no quality degradation related to fixation time. Conclusions To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02709-8.
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Affiliation(s)
- Yifan Zhang
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Thomas M Blomquist
- (Formerly) Department of Pathology, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA.,Lucas County Coroner's Office, 2595 Arlington Ave, Toledo, OH, 43614, USA
| | - Rebecca Kusko
- Immuneering Corporation, 245 Main St, Cambridge, MA, 02142, USA
| | - Daniel Stetson
- Astrazeneca Pharmaceuticals, 35 Gatehouse Dr, Waltham, MA, 02451, USA
| | - Zhihong Zhang
- Research and Development, Burning Rock Biotech, Shanghai, 201114, China
| | - Lihui Yin
- (Formerly) Pathology and Laboratory Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Robert Sebra
- Icahn Institute and Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | - Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | | | - Vinay K Mittal
- Thermo Fisher Scientific, 110 Miller Ave, Ann Arbor, MI, 48104, USA
| | | | - Ji-Youn Yeo
- Department of Pathology, University of Toledo, 3000 Arlington Ave, Toledo, OH, 43614, USA
| | - Nicole Dominiak
- Department of Pathology, University of Toledo, 3000 Arlington Ave, Toledo, OH, 43614, USA
| | - Jennifer Hipp
- Department of Pathology, Strata Oncology, Inc., Ann Arbor, MI, 48103, USA
| | - Amelia Raymond
- Astrazeneca Pharmaceuticals, 35 Gatehouse Dr, Waltham, MA, 02451, USA
| | - Fujun Qiu
- Research and Development, Burning Rock Biotech, Shanghai, 201114, China
| | - Hanane Arib
- Icahn Institute and Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | - Melissa L Smith
- Icahn Institute and Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | - Jay E Brock
- Pathology and Laboratory Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Daniel H Farkas
- Pathology and Laboratory Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Daniel J Craig
- Department of Medicine, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA
| | - Erin L Crawford
- Department of Medicine, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA
| | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Tom Morrison
- Accugenomics, Inc., 1410 Commonwealth Drive, Suite 105, Wilmington, NC, 20403, USA
| | - Nikola Tom
- Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
| | - Wenzhong Xiao
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.,Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
| | - Mary Yang
- Department of Information Science, University of Arkansas at Little Rock, 2801 S. Univ. Ave, Little Rock, AR, 72204, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | - Todd A Richmond
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., 4300 Hacienda Dr, Pleasanton, CA, 94588, USA
| | - Wendell Jones
- Q2 Solutions - EA Genomics, 5927 S Miami Blvd, Morrisville, NC, 27560, USA
| | - Donald J Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR, 72205, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China.,Human Phenome Institute, Fudan University, Shanghai, 201203, China.,Fudan-Gospel Joint Research Center for Precision Medicine, Fudan University, Shanghai, 200438, China
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - James C Willey
- Departments of Medicine, Pathology, and Cancer Biology, College of Medicine and Life Sciences, University of Toledo Health Sciences Campus, 3000 Arlington Ave, Toledo, OH, 43614, USA.
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.
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Willey JC, Crawford E, Craig DJ, Xu J, Haseley N, Lococo J, Morrison T. Abstract 529: SNAQ-SEQ™: Use of synthetic internal standards in conjunction with poisson exact test to call variants in contrived circulating tumor DNA specimens. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Targeted next generation sequencing (NGS) analysis of circulating tumor (ct) DNA promises to significantly advance targeted therapy and potentially early diagnosis of cancer. However, targeted NGS has poor accuracy for calling known variants with VAF below 0.5%. The goal of this study from the FDA-Sequencing and Quality Control Phase 2 (SEQC2) consortium was to develop and validate bioinformatic and biostatistical methods that enable incorporation of synthetic competitive internal standards (IS) in targeted NGS analysis of actionable tumor mutations.
Methods: A synthetic IS spike-in was designed for each actionable mutation target, suitable for use in NGS following targeted PCR or hybrid-capture enrichment and either with unique molecular index (UMI) or non-UMI library preparation. Contrived ctDNA reference samples developed by the SEQC2 consortium containing actionable mutations at known variant allele fraction were used. An aliquot of each sample was mixed with a mixture of IS. Following Illumina TST170 enrichment and library preparation, each library was sequenced, then native template (NT) sequences were separated from IS sequences bioinformatically. In SNAQ-SEQ™ analysis, Poisson Exact Test (PET) analysis was used to calculate the statistical difference between each sample library NT variant VAF and respective IS variant VAF. Analysis was based on NT variant count and position coverage (i.e., copies recovered in library preparation) and IS count and position coverage. PET performed an exact test of a simple null hypothesis about the ratio between two rate parameters in Poisson distribution.
Results: Stochastic sampling effect on IS error detection was minimized by ensuring an IS/NT ratio of 2.5 or greater. Under the specified conditions, in which a minimum of two NT variant observations were required for a call, the IS was able to estimate NGS background error for each sample when a minimum IS:NT ratio of 2.5:1 was used. Without use of IS information, the Illumina pipeline called 73% (41/56) of known TP variants in the 0.1% - 0.3% VAF range. In contrast, SNAQ-SEQ™ analysis (PET analysis of IS and NT information) increased TP detection sensitivity to 86% (48/56), for a 13% increase in sensitivity, with no false positives.
Conclusion: Following mixture of contrived ctDNA reference samples with IS, PET analysis enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position, in each sample. Using this SNAQ-SEQ™ analysis, true positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity. SNAQ-SEQ™ provides QC that is already standard operating procedure in clinical laboratories for analysis by high pressure liquid chromatography and mass spectrometry.
Citation Format: James C. Willey, Erin Crawford, Daniel J. Craig, Joshua Xu, Nathan Haseley, Jennifer Lococo, Tom Morrison. SNAQ-SEQ™: Use of synthetic internal standards in conjunction with poisson exact test to call variants in contrived circulating tumor DNA specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 529.
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Affiliation(s)
| | | | | | - Joshua Xu
- 2National Center for Toxicological Research, Little Rock, AR
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8
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Willey JC, Morrison TB, Austermiller B, Crawford EL, Craig DJ, Blomquist TM, Jones WD, Wali A, Lococo JS, Haseley N, Richmond TA, Novoradovskaya N, Kusko R, Chen G, Li QZ, Johann DJ, Deveson IW, Mercer TR, Wu L, Xu J. Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA. Cell Rep Methods 2021; 1:100106. [PMID: 35475002 PMCID: PMC9017191 DOI: 10.1016/j.crmeth.2021.100106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/31/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022]
Abstract
The primary objective of the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) project is to develop standard analysis protocols and quality control metrics for use in DNA testing to enhance scientific research and precision medicine. This study reports a targeted next-generation sequencing (NGS) method that will enable more accurate detection of actionable mutations in circulating tumor DNA (ctDNA) clinical specimens. To accomplish this, a synthetic internal standard spike-in was designed for each actionable mutation target, suitable for use in NGS following hybrid capture enrichment and unique molecular index (UMI) or non-UMI library preparation. When mixed with contrived ctDNA reference samples, internal standards enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position in each sample. True-positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity.
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Affiliation(s)
- James C. Willey
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH 43614, USA
| | - Tom B. Morrison
- AccuGenomics Inc., The Atrium, Suite 105, 1410 Commonwealth Drive, Wilmington, NC 28403, USA
| | - Bradley Austermiller
- AccuGenomics Inc., The Atrium, Suite 105, 1410 Commonwealth Drive, Wilmington, NC 28403, USA
| | - Erin L. Crawford
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH 43614, USA
| | - Daniel J. Craig
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH 43614, USA
| | - Thomas M. Blomquist
- College of Medicine and Life Sciences, University of Toledo, Toledo, OH 43614, USA
| | | | - Aminah Wali
- Q Solutions, EA Genomics, Morrisville, NC 27560, USA
| | | | - Nathan Haseley
- Illumina Inc., 5200 Illumina Way, San Diego, CA 92122, USA
| | | | | | | | - Guangchun Chen
- University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Quan-Zhen Li
- University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Donald J. Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, 4301 W Markham Street, Little Rock, AR 72205, USA
| | - Ira W. Deveson
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
- St. Vincent’s Clinical School, University of New South Wales, Sydney, NSW 2010, Australia
| | - Timothy R. Mercer
- Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
- St. Vincent’s Clinical School, University of New South Wales, Sydney, NSW 2010, Australia
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
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Deveson IW, Gong B, Lai K, LoCoco JS, Richmond TA, Schageman J, Zhang Z, Novoradovskaya N, Willey JC, Jones W, Kusko R, Chen G, Madala BS, Blackburn J, Stevanovski I, Bhandari A, Close D, Conroy J, Hubank M, Marella N, Mieczkowski PA, Qiu F, Sebra R, Stetson D, Sun L, Szankasi P, Tan H, Tang LY, Arib H, Best H, Burgher B, Bushel PR, Casey F, Cawley S, Chang CJ, Choi J, Dinis J, Duncan D, Eterovic AK, Feng L, Ghosal A, Giorda K, Glenn S, Happe S, Haseley N, Horvath K, Hung LY, Jarosz M, Kushwaha G, Li D, Li QZ, Li Z, Liu LC, Liu Z, Ma C, Mason CE, Megherbi DB, Morrison T, Pabón-Peña C, Pirooznia M, Proszek PZ, Raymond A, Rindler P, Ringler R, Scherer A, Shaknovich R, Shi T, Smith M, Song P, Strahl M, Thodima VJ, Tom N, Verma S, Wang J, Wu L, Xiao W, Xu C, Yang M, Zhang G, Zhang S, Zhang Y, Shi L, Tong W, Johann DJ, Mercer TR, Xu J. Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology. Nat Biotechnol 2021; 39:1115-1128. [PMID: 33846644 PMCID: PMC8434938 DOI: 10.1038/s41587-021-00857-z] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 02/15/2021] [Indexed: 02/08/2023]
Abstract
Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.
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Affiliation(s)
- Ira W Deveson
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
- St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Kevin Lai
- Bioinformatics, Integrated DNA Technologies, Inc., Coralville, IA, USA
| | | | - Todd A Richmond
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., Pleasanton, CA, USA
| | | | - Zhihong Zhang
- Research and Development, Burning Rock Biotech, Shanghai, China
| | | | - James C Willey
- Departments of Medicine, Pathology, and Cancer Biology, College of Medicine and Life Sciences, University of Toledo Health Sciences Campus, Toledo, OH, USA
| | | | | | - Guangchun Chen
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bindu Swapna Madala
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - James Blackburn
- Cancer Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Igor Stevanovski
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | | | - Devin Close
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, Salt Lake City, UT, USA
| | | | - Michael Hubank
- NIHR Biomedical Research Centre, Royal Marsden Hospital, Sutton, Surrey, UK
| | | | | | - Fujun Qiu
- Research and Development, Burning Rock Biotech, Shanghai, China
| | - Robert Sebra
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Lihyun Sun
- Elim Biopharmaceuticals, Inc., Hayward, CA, USA
| | - Philippe Szankasi
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, Salt Lake City, UT, USA
| | - Haowen Tan
- Primbio Genes Biotechnology, East Lake High-tech Development Zone, Wuhan, Hubei, China
| | - Lin-Ya Tang
- Institute for Personalized Cancer Therapy, MD Anderson Cancer Center, Houston, TX, USA
| | - Hanane Arib
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hunter Best
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, Salt Lake City, UT, USA
- Departments of Pathology and Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | | | - Pierre R Bushel
- National Institute of Environmental Health Sciences, Research Triangle Park, Morrisville, NC, USA
| | - Fergal Casey
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., Pleasanton, CA, USA
| | - Simon Cawley
- Clinical Sequencing Division, Thermo Fisher Scientific, South San Francisco, CA, USA
| | - Chia-Jung Chang
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
| | - Jonathan Choi
- Roche Sequencing Solutions, Inc., Pleasanton, CA, USA
| | - Jorge Dinis
- Roche Sequencing Solutions, Inc., Pleasanton, CA, USA
| | | | - Agda Karina Eterovic
- Institute for Personalized Cancer Therapy, MD Anderson Cancer Center, Houston, TX, USA
| | - Liang Feng
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., Pleasanton, CA, USA
| | | | - Kristina Giorda
- Marketing, Integrated DNA Technologies, Inc., Coralville, IA, USA
| | | | | | | | | | - Li-Yuan Hung
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mirna Jarosz
- NGS Products and Services, Integrated DNA Technologies, Inc., Coralville, IA, USA
| | - Garima Kushwaha
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., Pleasanton, CA, USA
| | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Quan-Zhen Li
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Zhiguang Li
- Intramural Research Program, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Liang-Chun Liu
- Clinical Diagnostic Division, Thermo Fisher Scientific, Fremont, CA, USA
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Charles Ma
- Cancer Genetics, Inc., Rutherford, NJ, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Dalila B Megherbi
- CMINDS Research Center, Department of Electrical and Computer Engineering, College of Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | | | | | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paula Z Proszek
- NIHR Biomedical Research Centre, Royal Marsden Hospital, Sutton, Surrey, UK
| | | | - Paul Rindler
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, Salt Lake City, UT, USA
| | | | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, HiLIFE Unit, Biomedicum Helsinki 2U (D302b), University of Helsinki, Helsinki, Finland
- EATRIS ERIC- European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | | | - Tieliu Shi
- Center for Bioinformatics and Computational Biology and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
| | - Melissa Smith
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ping Song
- Institute for Personalized Cancer Therapy, MD Anderson Cancer Center, Houston, TX, USA
| | - Maya Strahl
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Nikola Tom
- EATRIS ERIC- European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
- Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | | | - Jiashi Wang
- Research and Development, Integrated DNA Technologies, Inc., Coralville, IA, USA
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenzhong Xiao
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chang Xu
- Research and Development, QIAGEN Sciences, Inc., Frederick, MD, USA
| | - Mary Yang
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA
| | | | - Sa Zhang
- Clinical Laboratory, Burning Rock Biotech, Guangzhou, China
| | - Yilin Zhang
- Elim Biopharmaceuticals, Inc., Hayward, CA, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
- Fudan-Gospel Joint Research Center for Precision Medicine, Fudan University, Shanghai, China
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Donald J Johann
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Timothy R Mercer
- St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia.
- Australian Institute of Bioengineering and Nanotechnology, University of Queensland, Queensland, QLD, Australia.
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
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Xiao W, Ren L, Chen Z, Fang LT, Zhao Y, Lack J, Guan M, Zhu B, Jaeger E, Kerrigan L, Blomquist TM, Hung T, Sultan M, Idler K, Lu C, Scherer A, Kusko R, Moos M, Xiao C, Sherry ST, Abaan OD, Chen W, Chen X, Nordlund J, Liljedahl U, Maestro R, Polano M, Drabek J, Vojta P, Kõks S, Reimann E, Madala BS, Mercer T, Miller C, Jacob H, Truong T, Moshrefi A, Natarajan A, Granat A, Schroth GP, Kalamegham R, Peters E, Petitjean V, Walton A, Shen TW, Talsania K, Vera CJ, Langenbach K, de Mars M, Hipp JA, Willey JC, Wang J, Shetty J, Kriga Y, Raziuddin A, Tran B, Zheng Y, Yu Y, Cam M, Jailwala P, Nguyen C, Meerzaman D, Chen Q, Yan C, Ernest B, Mehra U, Jensen RV, Jones W, Li JL, Papas BN, Pirooznia M, Chen YC, Seifuddin F, Li Z, Liu X, Resch W, Wang J, Wu L, Yavas G, Miles C, Ning B, Tong W, Mason CE, Donaldson E, Lababidi S, Staudt LM, Tezak Z, Hong H, Wang C, Shi L. Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing. Nat Biotechnol 2021; 39:1141-1150. [PMID: 34504346 PMCID: PMC8506910 DOI: 10.1038/s41587-021-00994-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 06/18/2021] [Indexed: 02/01/2023]
Abstract
Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.
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Affiliation(s)
- Wenming Xiao
- The Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhong Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Li Tai Fang
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Yongmei Zhao
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Justin Lack
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | | | | | - Thomas M Blomquist
- Departments of Medicine and Pathology, University of Toledo Medical Center, Toledo, OH, USA
| | | | - Marc Sultan
- Biomarker Development, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Kenneth Idler
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Charles Lu
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Andreas Scherer
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | | | - Malcolm Moos
- The Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Stephen T Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Ogan D Abaan
- Illumina Inc., Foster City, CA, USA
- Seven Bridges Genomics Inc., Cambridge, MA, USA
| | - Wanqiu Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Xin Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Jessica Nordlund
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ulrika Liljedahl
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Roberta Maestro
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Maurizio Polano
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Jiri Drabek
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- IMTM, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Petr Vojta
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- IMTM, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Sulev Kõks
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Perron Institute for Neurological and Translational Science, Nedlands, Perth, Western Australia, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, Perth, Western Australia, Australia
| | - Ene Reimann
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bindu Swapna Madala
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia
| | - Timothy Mercer
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia
| | - Chris Miller
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Howard Jacob
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | | | | | | | | | | | | | | | - Virginie Petitjean
- Biomarker Development, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Ashley Walton
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Tsai-Wei Shen
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Keyur Talsania
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Cristobal Juan Vera
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | | | - Jennifer A Hipp
- Departments of Medicine and Pathology, University of Toledo Medical Center, Toledo, OH, USA
| | - James C Willey
- Departments of Medicine and Pathology, University of Toledo Medical Center, Toledo, OH, USA
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Jyoti Shetty
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yuliya Kriga
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Arati Raziuddin
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Bao Tran
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Margaret Cam
- CCR Collaborative Bioinformatics Resource, Office of Science and Technology Resources, Center for Cancer Research, Bethesda, MD, USA
| | - Parthav Jailwala
- CCR Collaborative Bioinformatics Resource, Office of Science and Technology Resources, Center for Cancer Research, Bethesda, MD, USA
| | - Cu Nguyen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Daoud Meerzaman
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Qingrong Chen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Chunhua Yan
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | | | | | - Roderick V Jensen
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | | | - Jian-Liang Li
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Brian N Papas
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yun-Ching Chen
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fayaz Seifuddin
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zhipan Li
- Sentieon Inc., Mountain View, CA, USA
| | - Xuelu Liu
- Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Wolfgang Resch
- Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | | | - Leihong Wu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Gokhan Yavas
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Corey Miles
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Baitang Ning
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Eric Donaldson
- The Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Samir Lababidi
- Office of the Chief Scientist, Office of the Commissioner, US Food and Drug Information, Silver Spring, MD, USA
| | - Louis M Staudt
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zivana Tezak
- The Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Huixiao Hong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Charles Wang
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China.
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11
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Crawford EL, Chen T, Craig DJ, Willey JC. Abstract 2286: Use of a synthetic spike-in ladder to measure NGS library complexity. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-2286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Detection of rare variants in traditional and liquid biopsies is becoming increasingly important for cancer diagnosis, monitoring and treatment. Next Generation Sequencing (NGS) has the capacity to detect known and novel variants in small biological samples with a high level of sensitivity. However, the limit of detection is affected by sample DNA concentration and integrity, technical error and number of unique molecules captured and sequenced (library complexity). Unique molecular identifiers (UMl) and random fragment end analysis commonly are used to measure complexity yet both are reported to have systematic biases leading to a non-random read distribution when measured, altering interpretation of results and potentially skewing data. Here we describe the use of a spike in complexity calibration ladder comprising synthetic DNA internal standard competitors (IS) as an orthogonal measure of library complexity.
Methods: An Endogenous Complexity Calibration Ladder (ECCL) comprising multiple unique synthetic IS at different concentrations was created. All ECCL IS share homology with each other and endogenous human SCGB1A1 sequence but each contain nucleotide changes at different positions along the sequence string so that they can be distinguished. The ECCL was mixed with several IS targeting different TP53 exons in a fixed proportion. The ECCL/TP53 IS mixture is designed to be used in either amplicon or hybrid-capture library preparation. Here, amplicon libraries were generated. Molecule numbers and ratios between IS were determined using deep sequencing of multiple replicates. The ECCL/TP53 IS mix was spiked into a commercial human gDNA prior to NGS library preparation. Serial dilutions (1.5-, 3-, 6-, 12- and 24-fold) and intentional addition of inhibitors were conducted to stress and test the system. The ECCL/TP53 IS mixture also was spiked into gDNA from 60 primary airway epithelial cell (AEC) specimens prior to library preparation and NGS to measure TP53 variant fraction while controlling for complexity.
Results: Observed complexity measured using the ECCL was close to expected for each serially diluted mixture of gDNA and ECCL/TP53 IS. The precision of complexity measurement for less dilute and less inhibited samples was limited by absence of finer titration of the least concentrated IS in ECCL. Use of ECCL enabled measurement of inter-sample variation in complexity among the 60 AEC specimens.
Conclusions: The ECCL controls for both sample and library specific variation in complexity, and enables better estimation of the lower limit of detection for variant allele fraction (VAF). Additionally, it is compatible with the use of target specific IS which allow for improved measurement of technical sequencing error. The variation in complexity observed in primary AEC gDNA samples demonstrates the need to account for this when assessing rare variant allele fraction.
Citation Format: Erin L. Crawford, Tian Chen, Daniel J. Craig, James C. Willey. Use of a synthetic spike-in ladder to measure NGS library complexity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2286.
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12
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Craig DJ, Crawford EL, Massion PP, Morrison T, Willey JC. Abstract 2525: Low frequency TP53 mutations in airway epithelial cells serve as lung cancer risk biomarker. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-2525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Lung cancer is the leading cause of cancer-related death in men and women in the United States. Based on evidence from controlled trials in the United States and Europe that low dose CT (LDCT) screening significantly reduces lung cancer mortality, the United States Preventative Services Tasks Force (USPSTF) recommends LDCT screening for individuals with high demographic risk based on age and smoking history. However, a large fraction of lung cancers occur in individuals who do not meet LDCT screening threshold criteria. Thus, there is a need for biomarkers that supplement demographic factors to more accurately detect those at highest lung cancer risk and include additional individuals who will benefit from LDCT screening. An initial study from this lab demonstrated that TP53 mutations with 0.05-1.0% variant allele fraction (VAF) were significantly more prevalent (p<0.005) in grossly normal airway epithelial cell (AEC) specimens from lung cancer cases compared to non-cancer controls matched for smoking and age. Here, we present an expanded follow-up study aimed at testing this biomarker according to PRospective-specimen-collection, retrospective-Blinded-Evaluation (PRoBE) design.
Methods: AEC specimens were prospectively collected through the National Cancer Institute Early Detection Research Network (EDRN) program from 60 subjects at high demographic risk for lung cancer by bronchoscopic brush biopsy at Vanderbilt University Medical Center. Genomic (g)DNA was extracted from each AEC specimen and stored at -80 degrees Celsius. Subjects matched for age and smoking history who subsequently did or did not develop lung cancer were identified and gDNA specimens, blinded with respect to cancer status, were shipped to the University of Toledo. gDNA specimens were quantified and 50,000 gDNA copies were included in each NGS library preparation. Synthetic DNA internal standards (IS) were prepared for multiple lung cancer driver mutations within the TP53 gene and mixed with each AEC gDNA specimen prior to competitive multiplex PCR NGS library preparation. By controlling for technical error, this approach enables reliable detection of mutations with VAF as low as 5 x 10-4 (0.05%) (Craig et al, BMC Cancer, 2019).
Results: Following library preparation and sequencing on Illumina Novaseq, there was sufficient library complexity to detect TP53 VAF <0.05% in >90% of AEC specimens. The data are currently in pipeline analysis. After analysis is complete, VCF files will be sent to Vanderbilt for unblinding and validation of the biomarker.
Conclusions: Based on preliminary data, we expect that sufficient AEC gDNA is available from most subjects enrolled in the EDRN program for TP53 low VAF biomarker analysis, and the previously reported method (Craig et al, BMC Cancer, 2019) is reliable and feasible. The current study should provide additional information regarding validity of the biomarker.
Citation Format: Daniel J. Craig, Erin L. Crawford, Pierre P. Massion, Thomas Morrison, James C. Willey. Low frequency TP53 mutations in airway epithelial cells serve as lung cancer risk biomarker [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2525.
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Gong B, Li D, Kusko R, Novoradovskaya N, Zhang Y, Wang S, Pabón-Peña C, Zhang Z, Lai K, Cai W, LoCoco JS, Lader E, Richmond TA, Mittal VK, Liu LC, Johann DJ, Willey JC, Bushel PR, Yu Y, Xu C, Chen G, Burgess D, Cawley S, Giorda K, Haseley N, Qiu F, Wilkins K, Arib H, Attwooll C, Babson K, Bao L, Bao W, Lucas AB, Best H, Bhandari A, Bisgin H, Blackburn J, Blomquist TM, Boardman L, Burgher B, Butler DJ, Chang CJ, Chaubey A, Chen T, Chierici M, Chin CR, Close D, Conroy J, Cooley Coleman J, Craig DJ, Crawford E, Del Pozo A, Deveson IW, Duncan D, Eterovic AK, Fan X, Foox J, Furlanello C, Ghosal A, Glenn S, Guan M, Haag C, Hang X, Happe S, Hennigan B, Hipp J, Hong H, Horvath K, Hu J, Hung LY, Jarosz M, Kerkhof J, Kipp B, Kreil DP, Łabaj P, Lapunzina P, Li P, Li QZ, Li W, Li Z, Liang Y, Liu S, Liu Z, Ma C, Marella N, Martín-Arenas R, Megherbi DB, Meng Q, Mieczkowski PA, Morrison T, Muzny D, Ning B, Parsons BL, Paweletz CP, Pirooznia M, Qu W, Raymond A, Rindler P, Ringler R, Sadikovic B, Scherer A, Schulze E, Sebra R, Shaknovich R, Shi Q, Shi T, Silla-Castro JC, Smith M, López MS, Song P, Stetson D, Strahl M, Stuart A, Supplee J, Szankasi P, Tan H, Tang LY, Tao Y, Thakkar S, Thierry-Mieg D, Thierry-Mieg J, Thodima VJ, Thomas D, Tichý B, Tom N, Garcia EV, Verma S, Walker K, Wang C, Wang J, Wang Y, Wen Z, Wirta V, Wu L, Xiao C, Xiao W, Xu S, Yang M, Ying J, Yip SH, Zhang G, Zhang S, Zhao M, Zheng Y, Zhou X, Mason CE, Mercer T, Tong W, Shi L, Jones W, Xu J. Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions. Genome Biol 2021; 22:109. [PMID: 33863344 PMCID: PMC8051090 DOI: 10.1186/s13059-021-02315-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 03/18/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. RESULTS All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. CONCLUSION This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.
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Affiliation(s)
- Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Rebecca Kusko
- Immuneering Corporation, One Broadway, 14th Floor, Cambridge, MA, 02142, USA
| | | | - Yifan Zhang
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
- Department of Information Science, University of Arkansas at Little Rock, 2801 S. Univ. Ave, Little Rock, AR, 72204, USA
| | - Shangzi Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
| | - Carlos Pabón-Peña
- Agilent Technologies, 5301 Stevens Creek Blvd, Santa Clara, CA, 95051, USA
| | - Zhihong Zhang
- Research and Development, Burning Rock Biotech, Shanghai, 201114, China
| | - Kevin Lai
- Bioinformatics, Integrated DNA Technologies, Inc., 1710 Commercial Park, Coralville, IA, 52241, USA
| | - Wanshi Cai
- iGeneTech, 8 Shengmingyuan Rd., Zhongguancun Life Science Park, Changping District, Beijing, 100080, China
| | | | - Eric Lader
- Research and Development, QIAGEN Sciences Inc., Frederick, MD, 21703, USA
| | - Todd A Richmond
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., 4300 Hacienda Dr, Pleasanton, CA, 94588, USA
| | - Vinay K Mittal
- Thermo Fisher Scientific, 110 Miller Ave, Ann Arbor, MI, 48104, USA
| | - Liang-Chun Liu
- Clinical Diagnostic Division, Thermo Fisher Scientific, 46500 Kato Rd, Fremont, CA, 94538, USA
| | - Donald J Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR, 72205, USA
| | - James C Willey
- Departments of Medicine, Pathology, and Cancer Biology, College of Medicine and Life Sciences, University of Toledo Health Sciences Campus, 3000 Arlington Ave, Toledo, OH, 43614, USA
| | - Pierre R Bushel
- National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
| | - Chang Xu
- Research and Development, QIAGEN Sciences Inc., Frederick, MD, 21703, USA
| | - Guangchun Chen
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd, Dallas, TX, 75390, USA
| | - Daniel Burgess
- Research and Development, Roche Sequencing Solutions Inc., 500 South Rosa Rd, Madison, WI, 53719, USA
| | - Simon Cawley
- Clinical Sequencing Division, Thermo Fisher Scientific, 180 Oyster Point Blvd, South San Francisco, CA, 94080, USA
| | - Kristina Giorda
- Marketing, Integrated DNA Technologies, Inc., 1710 Commercial Park, Coralville, IA, 52241, USA
| | - Nathan Haseley
- Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Fujun Qiu
- Research and Development, Burning Rock Biotech, Shanghai, 201114, China
| | - Katherine Wilkins
- Agilent Technologies, 5301 Stevens Creek Blvd, Santa Clara, CA, 95051, USA
| | - Hanane Arib
- Icahn Institute and Dept. of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | | | - Kevin Babson
- Greenwood Genetic Center, 106 Gregor Mendel Circle, Greenwood, SC, 29646, USA
| | - Longlong Bao
- Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Wenjun Bao
- JMP Life Sciences, SAS Institute Inc., Cary, NC, 27519, USA
| | | | - Hunter Best
- Departments of Pathology and Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, 84108, USA
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, 500 Chipeta Way, Salt Lake City, UT, 84108, USA
| | | | - Halil Bisgin
- Department of Computer Science, Engineering and Physics, University of Michigan-Flint, Flint, MI, 48502, USA
| | - James Blackburn
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2010, Australia
| | - Thomas M Blomquist
- Department of Pathology, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA
- Lucas County Coroner's Office, 2595 Arlington Ave., Toledo, OH, 43614, USA
| | - Lisa Boardman
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Blake Burgher
- OmniSeq, Inc. 700 Ellicott St, Buffalo, NY, 14203, USA
| | - Daniel J Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | - Chia-Jung Chang
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
| | - Alka Chaubey
- Greenwood Genetic Center, 106 Gregor Mendel Circle, Greenwood, SC, 29646, USA
| | - Tao Chen
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | | | - Christopher R Chin
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | - Devin Close
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, 500 Chipeta Way, Salt Lake City, UT, 84108, USA
| | | | | | - Daniel J Craig
- Department of Medicine, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA
| | - Erin Crawford
- Department of Medicine, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA
| | - Angela Del Pozo
- Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, CIBERER Instituto de Salud Carlos III, 28046, Madrid, Spain
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
| | - Ira W Deveson
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Daniel Duncan
- Cancer Genetics Inc, 201 Route 17 N, Meadows Office Building, Rutherford, NJ, 07070, USA
| | - Agda Karina Eterovic
- Institute for Personalized Cancer Therapy, MD Anderson Cancer Center, 6565 MD Anderson Blvd, Houston, TX, 77030, USA
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | | | | | - Sean Glenn
- OmniSeq, Inc. 700 Ellicott St, Buffalo, NY, 14203, USA
| | - Meijian Guan
- JMP Life Sciences, SAS Institute Inc., Cary, NC, 27519, USA
| | - Christine Haag
- Molecular Laboratory, Prof. F. Raue, Im Weiher 12, Heidelberg, Germany
| | - Xinyi Hang
- iGeneTech, 8 Shengmingyuan Rd., Zhongguancun Life Science Park, Changping District, Beijing, 100080, China
| | - Scott Happe
- Agilent Technologies, 1834 State Hwy 71 West, Cedar Creek, TX, 78612, USA
| | - Brittany Hennigan
- Greenwood Genetic Center, 106 Gregor Mendel Circle, Greenwood, SC, 29646, USA
| | - Jennifer Hipp
- Department of Pathology, Strata Oncology, Inc., Ann Arbor, MI, 48103, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Kyle Horvath
- ResearchDx, Inc., 5 Mason, Irvine, CA, 92618, USA
| | - Jianhong Hu
- Human Genome Sequencing Center, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Li-Yuan Hung
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Mirna Jarosz
- NGS Products and Services, Integrated DNA Technologies, Inc., 1710 Commercial Park, Coralville, IA, 52241, USA
| | - Jennifer Kerkhof
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, 800 Commissioners Rd E, London, Ontario, N6A5W9, Canada
| | - Benjamin Kipp
- Division of Anatomic Pathology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - David Philip Kreil
- Bioinformatics Research, Institute of Molecular Biotechnology, Boku University Vienna, Vienna, Austria
| | - Paweł Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Department of Biotechnology, Boku University, Vienna, Austria
| | - Pablo Lapunzina
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
- Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, IdiPaz, CIBERER Instituto de Salud Carlos III, 28046, Madrid, Spain
- ITHACA, European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability, European Commission, Lille, France
| | - Peng Li
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Quan-Zhen Li
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd, Dallas, TX, 75390, USA
| | - Weihua Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Zhiguang Li
- Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China
| | - Yu Liang
- Geneis, 5 Guangshun North St., Chaoyang District, Beijing, 100102, China
| | - Shaoqing Liu
- GeneSmile Ltd Co., Jiangsu Cancer Hospital, 42 Baiziting St., Xuanwu District, Nanjing, 210009, Jiangsu, China
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Charles Ma
- Cancer Genetics Inc, 201 Route 17 N, Meadows Office Building, Rutherford, NJ, 07070, USA
| | - Narasimha Marella
- Cancer Genetics Inc, 201 Route 17 N, Meadows Office Building, Rutherford, NJ, 07070, USA
| | - Rubén Martín-Arenas
- Genycell Biotech España, Calle Garrido Atienza, 18320 Santa Fe, Granada, Spain
| | - Dalila B Megherbi
- CMINDS Research Center, Department of Electrical and Computer Engineering, College of Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA
| | - Qingchang Meng
- Human Genome Sequencing Center, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Piotr A Mieczkowski
- Department of Genetics, University of North Carolina, 250 Bell Tower Drive, Chapel Hill, NC, 27599, USA
| | - Tom Morrison
- Accugenomics, Inc., 1410 Commonwealth Drive, Suite 105, Wilmington, NC, 20403, USA
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Baitang Ning
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Barbara L Parsons
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Cloud P Paweletz
- Translational Research Laboratory, Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, 360 Longwood Ave, Boston, MA, 02215, USA
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Wubin Qu
- iGeneTech, 8 Shengmingyuan Rd., Zhongguancun Life Science Park, Changping District, Beijing, 100080, China
| | - Amelia Raymond
- Astrazeneca Pharmaceuticals, 35 Gatehouse Dr, Waltham, MA, 02451, USA
| | - Paul Rindler
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, 500 Chipeta Way, Salt Lake City, UT, 84108, USA
| | | | - Bekim Sadikovic
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, 800 Commissioners Rd E, London, Ontario, N6A5W9, Canada
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, N6A3K7, Canada
| | - Andreas Scherer
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, HiLIFE Unit, Biomedicum Helsinki 2U (D302b), P.O. Box 20, (Tukholmankatu 8), FI-00014 University of Helsinki, Helsinki, Finland
| | - Egbert Schulze
- Laboratory for Molecular Genetics, Endocrine Practice, Im Weiher 12, 69121, Heidelberg, Germany
| | - Robert Sebra
- Icahn Institute and Dept. of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | - Rita Shaknovich
- Cancer Genetics Inc, 201 Route 17 N, Meadows Office Building, Rutherford, NJ, 07070, USA
| | - Qiang Shi
- Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Rd, Shanghai, 200241, China
| | | | - Melissa Smith
- Icahn Institute and Dept. of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | - Mario Solís López
- Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, CIBERER Instituto de Salud Carlos III, 28046, Madrid, Spain
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
| | - Ping Song
- Institute for Personalized Cancer Therapy, MD Anderson Cancer Center, 6565 MD Anderson Blvd, Houston, TX, 77030, USA
| | - Daniel Stetson
- Astrazeneca Pharmaceuticals, 35 Gatehouse Dr, Waltham, MA, 02451, USA
| | - Maya Strahl
- Icahn Institute and Dept. of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA
| | - Alan Stuart
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, 800 Commissioners Rd E, London, Ontario, N6A5W9, Canada
| | - Julianna Supplee
- Translational Research Laboratory, Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, 360 Longwood Ave, Boston, MA, 02215, USA
| | - Philippe Szankasi
- R&D Genomics MPS, Institute for Clinical and Experimental Pathology ARUP Laboratories, 500 Chipeta Way, Salt Lake City, UT, 84108, USA
| | - Haowen Tan
- Primbio Genes Biotechnology, Building C6-501, Biolake, No.666 Gaoxin Ave., East Lake High-tech Development Zone, Wuhan, 430074, Hubei, China
| | - Lin-Ya Tang
- Institute for Personalized Cancer Therapy, MD Anderson Cancer Center, 6565 MD Anderson Blvd, Houston, TX, 77030, USA
| | - Yonghui Tao
- Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Shraddha Thakkar
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Venkat J Thodima
- Cancer Genetics Inc, 201 Route 17 N, Meadows Office Building, Rutherford, NJ, 07070, USA
| | - David Thomas
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2010, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Boris Tichý
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
- Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Nikola Tom
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
- Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Elena Vallespin Garcia
- Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, CIBERER Instituto de Salud Carlos III, 28046, Madrid, Spain
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
| | - Suman Verma
- ResearchDx, Inc., 5 Mason, Irvine, CA, 92618, USA
| | - Kimbley Walker
- Human Genome Sequencing Center, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA
| | - Charles Wang
- Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
- Division of Microbiology & Molecular Genetics, Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
| | - Junwen Wang
- Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
- Department of Health Sciences, Mayo Clinic, Scottsdale, AZ, 85259, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Yexun Wang
- Research and Development, QIAGEN Sciences Inc., Frederick, MD, 21703, USA
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Valtteri Wirta
- Science for Life Laboratory, Karolinska Institutet, Tomtebodavägen 23B, 171 65, Solna, Sweden
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 45 Center Drive, Bethesda, MD, 20894, USA
| | - Wenzhong Xiao
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Shibei Xu
- Department of Biostatistics, Columbia Mailman School of Public Health, 722 West 168th St., New York, NY, 10032, USA
| | - Mary Yang
- Department of Information Science, University of Arkansas at Little Rock, 2801 S. Univ. Ave, Little Rock, AR, 72204, USA
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shun H Yip
- Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Guangliang Zhang
- Clinical Laboratory, Burning Rock Biotech, Guangzhou, 510300, Guangdong, China
| | - Sa Zhang
- Clinical Laboratory, Burning Rock Biotech, Guangzhou, 510300, Guangdong, China
| | - Meiru Zhao
- Geneplus, PKUCare Industrial Park, Changping District, Beijing, 102206, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
| | - Xiaoyan Zhou
- Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | - Timothy Mercer
- Australian Institute of Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD, Australia
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China.
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
- Fudan-Gospel Joint Research Center for Precision Medicine, Fudan University, Shanghai, 200438, China.
| | - Wendell Jones
- Q2 Solutions - EA Genomics, 5927 S Miami Blvd, Morrisville, NC, 27560, USA.
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA.
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Jones W, Gong B, Novoradovskaya N, Li D, Kusko R, Richmond TA, Johann DJ, Bisgin H, Sahraeian SME, Bushel PR, Pirooznia M, Wilkins K, Chierici M, Bao W, Basehore LS, Lucas AB, Burgess D, Butler DJ, Cawley S, Chang CJ, Chen G, Chen T, Chen YC, Craig DJ, Del Pozo A, Foox J, Francescatto M, Fu Y, Furlanello C, Giorda K, Grist KP, Guan M, Hao Y, Happe S, Hariani G, Haseley N, Jasper J, Jurman G, Kreil DP, Łabaj P, Lai K, Li J, Li QZ, Li Y, Li Z, Liu Z, López MS, Miclaus K, Miller R, Mittal VK, Mohiyuddin M, Pabón-Peña C, Parsons BL, Qiu F, Scherer A, Shi T, Stiegelmeyer S, Suo C, Tom N, Wang D, Wen Z, Wu L, Xiao W, Xu C, Yu Y, Zhang J, Zhang Y, Zhang Z, Zheng Y, Mason CE, Willey JC, Tong W, Shi L, Xu J. A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency. Genome Biol 2021; 22:111. [PMID: 33863366 PMCID: PMC8051128 DOI: 10.1186/s13059-021-02316-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 03/18/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.
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Affiliation(s)
- Wendell Jones
- Q2 Solutions - EA Genomics, 5927 S Miami Blvd., Morrisville, NC, 27560, USA.
| | - Binsheng Gong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | | | - Dan Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Rebecca Kusko
- Immuneering Corporation, One Broadway, 14th Floor, Cambridge, MA, 02142, USA
| | - Todd A Richmond
- Market & Application Development Bioinformatics, Roche Sequencing Solutions Inc., 4300 Hacienda Dr., Pleasanton, CA, 94588, USA
| | - Donald J Johann
- Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, 4301 W Markham St., Little Rock, AR, 72205, USA
| | - Halil Bisgin
- Department of Computer Science, Engineering and Physics, University of Michigan-Flint, Flint, MI, 48502, USA
| | - Sayed Mohammad Ebrahim Sahraeian
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., 1301 Shoreway Rd., Suite 7 #300, Belmont, CA, 94002, USA
| | - Pierre R Bushel
- National Institute of Environmental Health Sciences, Research Triangle Park, Durham, NC, 27709, USA
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Katherine Wilkins
- Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | | | - Wenjun Bao
- JMP Life Sciences, SAS Institute Inc., Cary, NC, 27519, USA
| | - Lee Scott Basehore
- Agilent Technologies, 11011 N Torrey Pines Rd., La Jolla, CA, 92037, USA
| | | | - Daniel Burgess
- (formerly) Research and Development, Roche Sequencing Solutions Inc., 500 South Rosa Rd., Madison, WI, 53719, USA
| | - Daniel J Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | - Simon Cawley
- (formerly) Clinical Sequencing Division, Thermo Fisher Scientific, 180 Oyster Point Blvd., South San Francisco, CA, 94080, USA
| | - Chia-Jung Chang
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
| | - Guangchun Chen
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd., Dallas, TX, 75390, USA
| | - Tao Chen
- University of Texas Southwestern Medical Center, 2330 Inwood Rd., Dallas, TX, 75390, USA
| | - Yun-Ching Chen
- Bioinformatics and Computational Biology Laboratory, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Daniel J Craig
- Department of Medicine, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH, 43614, USA
| | - Angela Del Pozo
- Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, CIBERER Instituto de Salud Carlos III, 28046, Madrid, Spain
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | | | - Yutao Fu
- Thermo Fisher Scientific, 110 Miller Ave., Ann Arbor, MI, 48104, USA
| | | | - Kristina Giorda
- Marketing, Integrated DNA Technologies, Inc., 1710 Commercial Park, Coralville, IA, 52241, USA
| | - Kira P Grist
- Q2 Solutions - EA Genomics, 5927 S Miami Blvd., Morrisville, NC, 27560, USA
| | - Meijian Guan
- JMP Life Sciences, SAS Institute Inc., Cary, NC, 27519, USA
| | - Yingyi Hao
- College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Scott Happe
- Agilent Technologies, 1834 State Hwy 71 West, Cedar Creek, TX, 78612, USA
| | - Gunjan Hariani
- Q2 Solutions - EA Genomics, 5927 S Miami Blvd., Morrisville, NC, 27560, USA
| | - Nathan Haseley
- Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Jeff Jasper
- Q2 Solutions - EA Genomics, 5927 S Miami Blvd., Morrisville, NC, 27560, USA
| | | | - David Philip Kreil
- Bioinformatics Research, Institute of Molecular Biotechnology, Boku University Vienna, Vienna, Austria
| | - Paweł Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Department of Biotechnology, Boku University, Vienna, Austria
| | - Kevin Lai
- Bioinformatics, Integrated DNA Technologies, Inc., 1710 Commercial Park, Coralville, IA, 52241, USA
| | - Jianying Li
- Kelly Government Solutions, Inc., Research Triangle Park, NC, 27709, USA
| | - Quan-Zhen Li
- Department of Immunology, Genomics and Microarray Core Facility, University of Texas Southwestern Medical Center, 5323 Harry Hine Blvd., Dallas, TX, 75390, USA
| | - Yulong Li
- Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, Liaoning, China
| | - Zhiguang Li
- Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, Liaoning, China
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Mario Solís López
- Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, CIBERER Instituto de Salud Carlos III, 28046, Madrid, Spain
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
| | - Kelci Miclaus
- JMP Life Sciences, SAS Institute Inc., Cary, NC, 27519, USA
| | - Raymond Miller
- Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | - Vinay K Mittal
- Thermo Fisher Scientific, 110 Miller Ave., Ann Arbor, MI, 48104, USA
| | - Marghoob Mohiyuddin
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., 1301 Shoreway Rd., Suite 7 #300, Belmont, CA, 94002, USA
| | - Carlos Pabón-Peña
- Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, CA, 95051, USA
| | - Barbara L Parsons
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Fujun Qiu
- Research and Development, Burning Rock Biotech, Shanghai, 201114, China
| | - Andreas Scherer
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, HiLIFE Unit, Biomedicum Helsinki 2U (D302b), FI-00014 University of Helsinki, P.O. Box 20 (Tukholmankatu 8), Helsinki, Finland
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Rd, Shanghai, 200241, China
| | - Suzy Stiegelmeyer
- University of North Carolina Health, 101 Manning Drive, Chapel Hill, NC, 27514, USA
| | - Chen Suo
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China
| | - Nikola Tom
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
- Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Dong Wang
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Leihong Wu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Wenzhong Xiao
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Chang Xu
- Research and Development, QIAGEN Sciences Inc., Frederick, MD, 21703, USA
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
| | - Jiyang Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
| | - Yifan Zhang
- University of Arkansas at Little Rock, Little Rock, AR, 72204, USA
| | - Zhihong Zhang
- Research and Development, Burning Rock Biotech, Shanghai, 201114, China
| | - Yuanting Zheng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA
| | - James C Willey
- Departments of Medicine, Pathology, and Cancer Biology, College of Medicine and Life Sciences, University of Toledo Health Sciences Campus, 3000 Arlington Ave, Toledo, OH, 43614, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China
- Human Phenome Institute, Fudan University, Shanghai, 201203, China
- Fudan-Gospel Joint Research Center for Precision Medicine, Fudan University, Shanghai, 200438, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA.
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Craig DJ, Morrison T, Khuder S, Crawford EL, Wu L, Xu J, Blomquist TM, Willey JC. Abstract 4609: TP53, PIK3CA, and BRAF somatic mutations in airway epithelial field of injury associated with lung cancer risk. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-4609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Lung cancer is the leading cause of cancer-related death in men and women, and cigarette smoking is the most significant preventable risk factor. Early detection through annual low dose CT screening reduces mortality but has low positive predictive value and specificity, in part due to low lung cancer incidence (<10%) among those who currently meet screening criteria. Thus, there is a need for biomarkers that reliably detect those at highest lung cancer risk, thereby enabling more effectivescreening. The purpose of this study was to test the hypothesis that high risk for lung cancer is characterized by high prevalence of low variant allele frequency (VAF) somatic mutations among known lung cancer driver genes in normal airway epithelial cells (AEC).
Methods: Synthetic DNA internal standards (IS) were prepared for each of 11 lung cancer driver genes and mixed with each AEC genomic (g)DNA specimen prior to competitive multiplex PCR amplicon NGS library preparation. A custom Perl script was developed to separate IS reads and respective specimen gDNA reads from each target into separate files for parallel variant frequency analysis. This approach enabled reliable detection of mutations with VAF as low as 5 x 10-4 (0.05%). This method was then applied in a retrospective case-control study. Specifically, AEC specimens were collected by bronchoscopic brush biopsy from the normal airways of 19 subjects, including eleven lung cancer cases and eight non-cancer controls, and the association of lung cancer risk with AEC driver gene mutations was tested.
Results: TP53 mutations with 0.05-1.0% VAF were more prevalent (p<0.005) and significantly more enriched for tobacco smoke and age-associated mutation signatures in AEC from lung cancer cases compared to non-cancer controls matched for smoking and age. Further, PIK3CA and BRAF mutations in this VAF range were identified in AEC from cases but not controls.
Conclusions: Measurement of very low frequency mutations in the 0.05-1.0% VAF range enabled identification of an AEC somatic mutation field of injury associated with lung cancer risk. A biomarker comprising TP53, PIK3CA, and BRAF somatic mutations may better stratify individuals for optimal lung cancer screening and prevention outcomes.
Citation Format: Daniel J. Craig, Thomas Morrison, Sadik Khuder, Erin L. Crawford, Leihong Wu, Joshua Xu, Thomas M. Blomquist, James C. Willey. TP53, PIK3CA, and BRAF somatic mutations in airway epithelial field of injury associated with lung cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4609.
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Affiliation(s)
| | | | - Sadik Khuder
- 1University of Toledo Health Science Campus, Toledo, OH
| | | | - Leihong Wu
- 3U.S. Food and Drug Administration, Jefferson, AR
| | - Joshua Xu
- 3U.S. Food and Drug Administration, Jefferson, AR
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Meikle CK, Meisler AJ, Bird CM, Jeffries JA, Azeem N, Garg P, Crawford EL, Kelly CA, Gao TZ, Wuescher LM, Willey JC, Worth RG. Platelet-T cell aggregates in lung cancer patients: Implications for thrombosis. PLoS One 2020; 15:e0236966. [PMID: 32776968 PMCID: PMC7416940 DOI: 10.1371/journal.pone.0236966] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023] Open
Abstract
Platelet-leukocyte aggregates (PLAs) are associated with increased thrombosis risk. The influence of PLA formation is especially important for cancer patients, since thrombosis accounts for approximately 10% of cancer-associated deaths. Our objective was to characterize and quantify PLAs in whole blood samples from lung cancer patients compared to healthy volunteers with the intent to analyze PLA formation in the context of lung cancer-associated thrombosis. Consenting lung cancer patients (57) and healthy volunteers (56) were enrolled at the Dana Cancer Center at the University of Toledo Health Science Campus. Peripheral blood samples were analyzed by flow cytometry. Patient medical history was reviewed through electronic medical records. Most importantly, we found lung cancer patients to have higher percentages of platelet-T cell aggregates (PTCAs) than healthy volunteers among both CD4+ T lymphocyte and CD8+ T lymphocyte populations. Our findings demonstrate that characterization of PTCAs may have clinical utility in differentiating lung cancer patients from healthy volunteers and stratifying lung cancer patients by history of thrombosis.
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Affiliation(s)
- Claire K. Meikle
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Adam J. Meisler
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Cara M. Bird
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Joseph A. Jeffries
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Nabila Azeem
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Priyanka Garg
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Erin L. Crawford
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Clare A. Kelly
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Tess Z. Gao
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Leah M. Wuescher
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - James C. Willey
- Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
| | - Randall G. Worth
- Department of Medical Microbiology & Immunology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States of America
- * E-mail:
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17
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Craig DJ, Morrison T, Khuder SA, Crawford EL, Wu L, Xu J, Blomquist TM, Willey JC. Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells. BMC Cancer 2019; 19:1081. [PMID: 31711466 PMCID: PMC6844032 DOI: 10.1186/s12885-019-6313-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 10/30/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Standardized Nucleic Acid Quantification for SEQuencing (SNAQ-SEQ) is a novel method that utilizes synthetic DNA internal standards spiked into each sample prior to next generation sequencing (NGS) library preparation. This method was applied to analysis of normal appearing airway epithelial cells (AEC) obtained by bronchoscopy in an effort to define a somatic mutation field effect associated with lung cancer risk. There is a need for biomarkers that reliably detect those at highest lung cancer risk, thereby enabling more effective screening by annual low dose CT. The purpose of this study was to test the hypothesis that lung cancer risk is characterized by increased prevalence of low variant allele frequency (VAF) somatic mutations in lung cancer driver genes in AEC. METHODS Synthetic DNA internal standards (IS) were prepared for 11 lung cancer driver genes and mixed with each AEC genomic (g) DNA specimen prior to competitive multiplex PCR amplicon NGS library preparation. A custom Perl script was developed to separate IS reads and respective specimen gDNA reads from each target into separate files for parallel variant frequency analysis. This approach identified nucleotide-specific sequencing error and enabled reliable detection of specimen mutations with VAF as low as 5 × 10- 4 (0.05%). This method was applied in a retrospective case-control study of AEC specimens collected by bronchoscopic brush biopsy from the normal airways of 19 subjects, including eleven lung cancer cases and eight non-cancer controls, and the association of lung cancer risk with AEC driver gene mutations was tested. RESULTS TP53 mutations with 0.05-1.0% VAF were more prevalent (p < 0.05) and also enriched for tobacco smoke and age-associated mutation signatures in normal AEC from lung cancer cases compared to non-cancer controls matched for smoking and age. Further, PIK3CA and BRAF mutations in this VAF range were identified in AEC from cases but not controls. CONCLUSIONS Application of SNAQ-SEQ to measure mutations in the 0.05-1.0% VAF range enabled identification of an AEC somatic mutation field of injury associated with lung cancer risk. A biomarker comprising TP53, PIK3CA, and BRAF somatic mutations may better stratify individuals for optimal lung cancer screening and prevention outcomes.
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Affiliation(s)
- Daniel J. Craig
- Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH 43614 USA
| | - Thomas Morrison
- Accugenomics, Inc, 1410 Commonwealth Dr #105, Wilmington, NC 28403 USA
| | - Sadik A. Khuder
- Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH 43614 USA
| | - Erin L. Crawford
- Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH 43614 USA
| | - Leihong Wu
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR USA
| | - Joshua Xu
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR USA
| | - Thomas M. Blomquist
- Department of Pathology, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH 43614 USA
| | - James C. Willey
- Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH 43614 USA
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Craig DJ, Crawford EL, Xu J, Blomquist TM, Wu L, Morrison T, Willey JC. Abstract 432: Novel method for NGS analysis of actionable mutations in circulating tumor DNA specimens: improved quality control and 20-fold lower sequencing required. Cancer Res 2019. [DOI: 10.1158/1538-7445.am2019-432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Identification of actionable mutations in circulating tumor DNA (ctDNA) enables gene-targeted therapy of solid tumors based on a simple blood test. NGS methods that attach a unique molecular identifier (UMI) to each DNA molecule reduce variant allele fraction (VAF) limit of detection (LOD) to <0.01% at a cost of 10-20-fold higher sequencing requirement. Importantly, the VAF lower limit of detection (LOD) for analysis of ctDNA specimens does not typically extend below 0.5% due to limits of ctDNA specimen quantity. For example, the average ctDNA specimen is 32 ng (10,000 genome copies) and at 0.1% VAF, 10 mutant molecules would be added to library prep. At this level, signal loss during library prep (typically 70-90%) will be associated with stochastic sampling variation. For example, in the Sequencing Quality Control Consortium (SEQC) 2 study, we measured inter-site and inter-replicate reproducibility of VAF measurement in 50 ng (15,000 genome copies) of synthetic ctDNA reference material. Thus, even using UMI NGS, measurement, reproducibility at VAF >0.5% was >95%, but reproducibility at 0.1-0.5% VAF was about 70%. Given limited value of UMI NGS when size of ctDNA specimen restricted VAF LOD to 0.5%, we tested the hypothesis that synthetic internal standard (IS) spike-in molecules provide reliable alternative quality control while eliminating UMI-imposed sequencing burden.
Methods: We synthesized IS for 61 actionable mutations, cloned them into pUC vectors, confirmed each IS to be wild-type sequence, linearized, quantified abundance and combined at a 1:1 genome copy mixture. An aliquot of IS mixture was added to the ctDNA reference material at 1:1 genome copy prior to library prep, followed by Illumina HiSeq sequencing, separation of target from IS reads using custom splitter, then pipeline analysis on Qiagen CLC Genomics Workbench.
Results: Analysis of each target relative to IS reliably identified base-substitution sequencing errors at each measured actionable mutation site and determined that VAF for each was <0.2%. Further, although a typical UMI method requires >200,000 reads per target in each sample, with analysis relative to IS spike-ins, 20,000 reads was sufficient (10,000 reads for the specimen and 10,000 reads for the spike-in). Because the IS spike-in molecules were validated to be wild-type, any variants were assumed to be due to technical error. Thus, a z-score comparing sequence count for a) specimen wild-type, b) specimen mutant, c) IS wild-type, and d) IS mutant enabled calculation of confidence level regarding each biological variant call.
Conclusion: Preliminary data indicate that IS spike-in mixtures enable reliable analysis of ctDNA mutation fraction to LOD of 0.5% without need for UMI.
Disclaimer: The views expressed here are those of the authors only and do not necessarily express the views/policies of the FDA.
Citation Format: Daniel J. Craig, Erin L. Crawford, Joshua Xu, Thomas M. Blomquist, Leihong Wu, Thomas Morrison, James C. Willey. Novel method for NGS analysis of actionable mutations in circulating tumor DNA specimens: improved quality control and 20-fold lower sequencing required [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 432.
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Affiliation(s)
| | | | - Joshua Xu
- 2National Center for Toxicological Research, Jefferson, AR
| | | | - Leihong Wu
- 2National Center for Toxicological Research, Jefferson, AR
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Meikle CK, Meisler A, Garg P, Kelly CA, Jeffries JA, Gao T, Bird CM, Willey JC, Worth RG. Increased activated platelet binding to T cells in lung cancer patients is correlated with history of thrombosis. The Journal of Immunology 2019. [DOI: 10.4049/jimmunol.202.supp.194.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Abstract
This study aimed to characterize the role of platelet-leukocyte aggregation in lung cancer patients in context of history of thrombosis. Whole blood from lung cancer patients and healthy volunteers was labeled with fluorescently-conjugated antibodies, then fixed prior to flow cytometry. Platelet-leukocyte aggregates were quantified by detecting the number of platelets within all leukocyte-positive events.
Platelet-CD4 T cell and platelet-CD8 T cell aggregates were both significantly increased in lung cancer patients (both p < 0.0001). Lung cancer patients had significantly more CD4 and CD8 T cells aggregated with activated platelets compared to healthy volunteers (p < 0.01 and p < 0.001, respectively). Lung cancer patients were then separated into groups based on history of thrombosis: No previous thrombosis, arterial thrombotic event (ATE), and venous thromboembolism (VTE). ATE patients had significantly more CD8 T cells aggregated with platelets than patients with no history of thrombosis (p < 0.05). VTE patients had significantly higher expression of platelet activation markers in CD4 and CD8 T cell aggregates than patients with no history of thrombosis (p < 0.01 and p < 0.001, respectively) and ATE patients (p < 0.05 and p < 0.001, respectively).
Platelet-T cell aggregates were significantly increased in lung cancer patients compared to healthy volunteers, and platelet activation within aggregates was significantly correlated with history of VTE in patients with lung cancer. These findings indicate that interactions between platelets and T cells in cancer patients may contribute to a procoagulant phenotype.
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Craig DJ, Elsamaloty M, Blomquist TM, Crawford EL, Willey JC. Abstract 2222: Using rare variants to characterize lung cancer risk. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-2222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The human genome is exposed to a variety of exogenous and endogenous assaults capable of inducing DNA damage and contributing to diseases like cancer. Multiple DNA repair mechanisms are tasked with identifying and repairing these aberrant nucleotides, but damage is occasionally missed giving rise to somatic mutations. As we age, the relative abundance of somatic mutations increases, which may increase our risk for developing cancer. In addition, inter-individual variation in somatic mutation prevalence suggests an inter-individual variation in (a) inherited DNA repair capacity and/or (b) exposure to environmental mutagens. Therefore, somatic mutation prevalence may serve as an end-point biomarker for cancer risk, as it provides critical information regarding both.
While Next Generation Sequencing (NGS) has provided unparalleled access to the human genome and revolutionized the field of cancer genomics, its implementation into the clinical setting has been limited by technical errors introduced during library preparation and on the sequencing platform. This makes identification of rare variants below 10-4 mutations per nucleotide through NGS a challenge, and the use of somatic mutations to characterize cancer risk unfeasible. To address this issue, unique molecular indexes (UMI) were assigned to each genomic copy, enabling reliable identification of rare somatic mutations versus technical errors. We hypothesize that measurement of somatic mutation prevalence in normal bronchial epithelial cells (NBEC) will serve as an end-point biomarker for lung cancer risk by identifying both (a) inherited sub-optimal DNA repair/protection and (b) exposure to inhaled environmental mutagens including components of cigarette smoke, radon, and/or occupational hazards.
In our initial pilot study, we used genomic DNA derived from an A549 cell line to optimize an ERCC5 gene-specific, dual-indexed PCR method for UMI assignment, followed by sequencing on the Illumina MiSeq platform. In parallel analyses, UMI correction using 25,000 genomic copies provided over 10-fold greater sensitivity in identifying somatic mutations compared to no UMI correction. Based on this pilot, we obtained three clinical NBEC specimens via cytology brush biopsy of grossly normal (non-cancerous) airway from (a) heavy smoker with lung cancer, (b) heavy smoker without lung cancer, and (c) non-smoking control. UMIs were assigned to 50,000 genomic copies at 16 loci, increasing our theoretical limit of detection to approximately 1 in 4x107 nucleotides. Results thus far support further application of this approach in studies to assess NBEC somatic mutation prevalence as a biomarker for cancer risk.
Citation Format: Daniel J. Craig, Mazzin Elsamaloty, Thomas M. Blomquist, Erin L. Crawford, James C. Willey. Using rare variants to characterize lung cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2222.
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Zolondek R, Craig DJ, Crawford EL, Willey JC. Abstract 1488: Inter-individual variation in hTERT regulation pathway genes in normal bronchial epithelial cells. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-1488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Genome-wide association studies (GWAS) and candidate gene studies suggest that telomere maintenance genes participate in etiology of lung cancer, idiopathic pulmonary fibrosis, and COPD. Telomeres are nucleoprotein structures located at the end of chromosomes that are maintained by telomerase encoded by hTERT. Telomerase is active in cancers, has the ability to aid in rapid cell division, and is dysfunctional in chronic conditions induced by inflammation as such as diabetes, renal failure, and COPD. hTERT is a known downstream target of β-catenin and WNT signaling. However, little is known regarding expression levels of these genes in normal bronchial epithelial cells (NBEC). The purpose of this study is to investigate inter-individual variation in regulation of β-catenin and WNT in NBEC, and if present, identify variants in regulatory region of human telomerase reverse transcriptase (hTERT) that could be used as candidate biomarkers for lung cancer risk. Methods: NBEC specimens from subjects with COPD (n=5) or without COPD (n=9) were obtained by bronchoscopy brush under IRB approved protocol. β-catenin, and WNT4 were measured by competitive multiplex-RT-PCR. Results: Both β-catenin and WNT4 displayed significant inter-individual variation as measured by Intra-Class Correlation (ICC) analysis (β-catenin ICC = 0.47, 95% Confidence Interval (0.2, 0.55), p < 0.05; WNT4 ICC = 0.60, 95% Confidence Interval (0.35, 0.85), p < 0.05). In patients with COPD, WNT4 expression was significantly higher in current smokers compared to former smokers (p<0.05). Conclusions: Inter-individual variation in NBEC expression of β-catenin and/or WNT4 was observed and it is reasonable to test the hypothesis that these genes and related pathway genes may contribute to variation in regulation of hTERT in NBEC and risk for lung cancer, COPD, and/or other lung diseases.
Citation Format: Rose Zolondek, Daniel J. Craig, Erin L. Crawford, James C. Willey. Inter-individual variation in hTERT regulation pathway genes in normal bronchial epithelial cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1488.
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Yeo J, Morales DA, Chen T, Crawford EL, Zhang X, Blomquist TM, Levin AM, Massion PP, Arenberg DA, Midthun DE, Mazzone PJ, Nathan SD, Wainz RJ, Nana-Sinkam P, Willey PFS, Arend TJ, Padda K, Qiu S, Federov A, Hernandez DAR, Hammersley JR, Yoon Y, Safi F, Khuder SA, Willey JC. RNAseq analysis of bronchial epithelial cells to identify COPD-associated genes and SNPs. BMC Pulm Med 2018; 18:42. [PMID: 29506519 PMCID: PMC5838965 DOI: 10.1186/s12890-018-0603-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 02/23/2018] [Indexed: 01/09/2023] Open
Abstract
Background There is a need for more powerful methods to identify low-effect SNPs that contribute to hereditary COPD pathogenesis. We hypothesized that SNPs contributing to COPD risk through cis-regulatory effects are enriched in genes comprised by bronchial epithelial cell (BEC) expression patterns associated with COPD. Methods To test this hypothesis, normal BEC specimens were obtained by bronchoscopy from 60 subjects: 30 subjects with COPD defined by spirometry (FEV1/FVC < 0.7, FEV1% < 80%), and 30 non-COPD controls. Targeted next generation sequencing was used to measure total and allele-specific expression of 35 genes in genome maintenance (GM) genes pathways linked to COPD pathogenesis, including seven TP53 and CEBP transcription factor family members. Shrinkage linear discriminant analysis (SLDA) was used to identify COPD-classification models. COPD GWAS were queried for putative cis-regulatory SNPs in the targeted genes. Results On a network basis, TP53 and CEBP transcription factor pathway gene pair network connections, including key DNA repair gene ERCC5, were significantly different in COPD subjects (e.g., Wilcoxon rank sum test for closeness, p-value = 5.0E-11). ERCC5 SNP rs4150275 association with chronic bronchitis was identified in a set of Lung Health Study (LHS) COPD GWAS SNPs restricted to those in putative regulatory regions within the targeted genes, and this association was validated in the COPDgene non-hispanic white (NHW) GWAS. ERCC5 SNP rs4150275 is linked (D’ = 1) to ERCC5 SNP rs17655 which displayed differential allelic expression (DAE) in BEC and is an expression quantitative trait locus (eQTL) in lung tissue (p = 3.2E-7). SNPs in linkage (D’ = 1) with rs17655 were predicted to alter miRNA binding (rs873601). A classifier model that comprised gene features CAT, CEBPG, GPX1, KEAP1, TP73, and XPA had pooled 10-fold cross-validation receiver operator characteristic area under the curve of 75.4% (95% CI: 66.3%–89.3%). The prevalence of DAE was higher than expected (p = 0.0023) in the classifier genes. Conclusions GM genes comprised by COPD-associated BEC expression patterns were enriched for SNPs with cis-regulatory function, including a putative cis-rSNP in ERCC5 that was associated with COPD risk. These findings support additional total and allele-specific expression analysis of gene pathways with high prior likelihood for involvement in COPD pathogenesis. Electronic supplementary material The online version of this article (10.1186/s12890-018-0603-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jiyoun Yeo
- Department of Pathology, The University of Toledo College of Medicine, 3000 Arlington Avenue, HEB 219, Toledo, OH, 43614, USA
| | - Diego A Morales
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, HEB 219, Toledo, OH, 43614, USA
| | - Tian Chen
- Department of Mathematics and Statistics, The University of Toledo, 2801 W. Bancroft Street, Toledo, OH, 43606, USA
| | - Erin L Crawford
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, HEB 219, Toledo, OH, 43614, USA
| | - Xiaolu Zhang
- Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH, 43614, USA
| | - Thomas M Blomquist
- Department of Pathology, The University of Toledo College of Medicine, 3000 Arlington Avenue, HEB 219, Toledo, OH, 43614, USA
| | - Albert M Levin
- Department of Biostatistics, Henry Ford Health System, 1 Ford Place Detroit, MI, Detroit, MI, 48202, USA
| | - Pierre P Massion
- Thoracic Program, Vanderbilt Ingram Cancer Center, Nashville, TN, 37232, USA
| | | | - David E Midthun
- Department of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Peter J Mazzone
- Department of Pulmonary Medicine, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, 44195, USA
| | - Steven D Nathan
- Department of Pulmonary Medicine, Inova Fairfax Hospital, 3300 Gallows Road, Falls Church, VA, 22042-3300, USA
| | - Ronald J Wainz
- The Toledo Hospital, 2142 N Cove Blvd, Toledo, OH, 43606, USA
| | - Patrick Nana-Sinkam
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, USA, Richmond, VA, 23284-2512, USA.,Ohio State University James Comprehensive Cancer Center and Solove Research Institute, Columbus, OH, USA
| | - Paige F S Willey
- American Enterprise Institute, 1789 Massachusetts Ave NW, Washington, DC, 20036, USA
| | - Taylor J Arend
- The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH, 43614, USA
| | - Karanbir Padda
- Emory University School of Medicine, 1648 Pierce Dr NE, Atlanta, GA, 30307, USA
| | - Shuhao Qiu
- Department of Medicine, The University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH, 43614, USA
| | - Alexei Federov
- Department of Mathematics and Statistics, The University of Toledo, 2801 W. Bancroft Street, Toledo, OH, 43606, USA.,Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH, 43614, USA
| | - Dawn-Alita R Hernandez
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, RHC 0012, Toledo, OH, 43614, USA
| | - Jeffrey R Hammersley
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, RHC 0012, Toledo, OH, 43614, USA
| | - Youngsook Yoon
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, RHC 0012, Toledo, OH, 43614, USA
| | - Fadi Safi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, RHC 0012, Toledo, OH, 43614, USA
| | - Sadik A Khuder
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, RHC 0012, Toledo, OH, 43614, USA
| | - James C Willey
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH, 43614, USA.
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Yeo J, Crawford EL, Zhang X, Khuder S, Chen T, Levin A, Blomquist TM, Willey JC. A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells. BMC Cancer 2017; 17:301. [PMID: 28464886 PMCID: PMC5412061 DOI: 10.1186/s12885-017-3287-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 04/20/2017] [Indexed: 12/14/2022] Open
Abstract
Background Annual low dose CT (LDCT) screening of individuals at high demographic risk reduces lung cancer mortality by more than 20%. However, subjects selected for screening based on demographic criteria typically have less than a 10% lifetime risk for lung cancer. Thus, there is need for a biomarker that better stratifies subjects for LDCT screening. Toward this goal, we previously reported a lung cancer risk test (LCRT) biomarker comprising 14 genome-maintenance (GM) pathway genes measured in normal bronchial epithelial cells (NBEC) that accurately classified cancer (CA) from non-cancer (NC) subjects. The primary goal of the studies reported here was to optimize the LCRT biomarker for high specificity and ease of clinical implementation. Methods Targeted competitive multiplex PCR amplicon libraries were prepared for next generation sequencing (NGS) analysis of transcript abundance at 68 sites among 33 GM target genes in NBEC specimens collected from a retrospective cohort of 120 subjects, including 61 CA cases and 59 NC controls. Genes were selected for analysis based on contribution to the previously reported LCRT biomarker and/or prior evidence for association with lung cancer risk. Linear discriminant analysis was used to identify the most accurate classifier suitable to stratify subjects for screening. Results After cross-validation, a model comprising expression values from 12 genes (CDKN1A, E2F1, ERCC1, ERCC4, ERCC5, GPX1, GSTP1, KEAP1, RB1, TP53, TP63, and XRCC1) and demographic factors age, gender, and pack-years smoking, had Receiver Operator Characteristic area under the curve (ROC AUC) of 0.975 (95% CI: 0.96–0.99). The overall classification accuracy was 93% (95% CI 88%–98%) with sensitivity 93.1%, specificity 92.9%, positive predictive value 93.1% and negative predictive value 93%. The ROC AUC for this classifier was significantly better (p < 0.0001) than the best model comprising demographic features alone. Conclusions The LCRT biomarker reported here displayed high accuracy and ease of implementation on a high throughput, quality-controlled targeted NGS platform. As such, it is optimized for clinical validation in specimens from the ongoing LCRT blinded prospective cohort study. Following validation, the biomarker is expected to have clinical utility by better stratifying subjects for annual lung cancer screening compared to current demographic criteria alone. Electronic supplementary material The online version of this article (doi:10.1186/s12885-017-3287-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jiyoun Yeo
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, HEB 219, Toledo, OH, 43614, USA
| | - Erin L Crawford
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, HEB 219, Toledo, OH, 43614, USA
| | - Xiaolu Zhang
- Cancer Genetics and Comparative Genomics Branch (CGCGB), National Human Genomes Research Institute (NHGRI), National Institutes of Health (NIH), Bldg 50, Rm 5341, 50 South Dr., Bethesda, MD, 20892, USA
| | - Sadik Khuder
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, RHC 0012, Toledo, OH, 43614, USA
| | - Tian Chen
- Department of Mathematics and Statistics, The University of Toledo, 2801 W. Bancroft Street, Toledo, OH, 43606, USA
| | - Albert Levin
- Department of Biostatistics, Henry Ford Health System, 1 Ford Place, Detroit, MI, 48202, USA
| | - Thomas M Blomquist
- Department of Pathology, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH, 43614, USA
| | - James C Willey
- Ruppert 0012, Division of Pulmonary and Critical Care Medicine, Department of Medicine, The University of Toledo College of Medicine, 3000 Arlington Avenue, Toledo, OH, 43614, USA.
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Zhang X, Crawford EL, Blomquist TM, Khuder SA, Yeo J, Levin AM, Willey JC. Abstract 2890: ERCC5 variant rs2296147 T-allele creates a predicted TP53 binding site and up-regulates transcript abundance in normal bronchial epithelial cells, while rs17655 C-allele is linked to miRNA binding site variant and down regulates. Cancer Res 2016. [DOI: 10.1158/1538-7445.am2016-2890] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Excision repair cross-complementation group 5 (ERCC5) gene plays an important role in nucleotide excision repair (NER) and dysregulation of ERCC5 is associated with increased lung cancer risk. This study was conducted to characterize cis-acting genetic variants responsible for inter-individual variation in ERCC5 transcript regulation in normal bronchial epithelial cells (NBEC).
Methods: We determined genotypes at putative ERCC5 cis-regulatory single nucleotide polymorphic sites (SNP) rs751402 and rs2296147, and marker SNPs rs1047768 and rs17655. Using a recently developed targeted sequencing method, ERCC5 allele-specific transcript abundance was assessed in NBEC RNA from 55 individuals heterozygous for rs1047768 and 21 subjects heterozygous for rs17655. Syntenic relationships among alleles at rs751402, rs2296147 and rs1047768 were assessed by allele-specific PCR followed by Sanger sequencing. We assessed association of NBEC ERCC5 allele-specific expression at rs1047768 with haplotype and diplotype structure at putative ERCC5 promoter cis-regulatory SNPs rs751402 and rs2296147.
Results: Genotype analysis revealed higher inter-individual variation in allelic ratios in cDNA samples relative to matched gDNA samples at both rs1047768 and rs17655 (p<0.0001 and p = 0.0005 respectively). By haplotype analysis, mean expression was higher at the rs1047768 alleles syntenic with rs2296147 T allele compared to rs2296147 C allele (p = 0.0030). Sequence analysis predicts that T allele at SNP rs2296147 creates a TP53 binding site. Mean expression was higher at rs17655 G allele (p<0.0001) which is syntenic with G allele at a linked SNP rs873601 (r2 = 0.74). C allele at SNP rs873601 is predicted to create a miRNA binding site.
Conclusions: These data support the conclusion that T allele at SNP rs2296147 creates a TP53 binding site and up-regulates ERCC5 while C allele at SNP rs873601 creates a miRNA binding site and down-regulates ERCC5. Variation in ERCC5 transcript abundance associated with allelic variation at these SNPs is likely associated with variation in NER function in NBEC and lung cancer risk.
Citation Format: Xiaolu Zhang, Erin L. Crawford, Thomas M. Blomquist, Sadik A. Khuder, Jiyoun Yeo, Albert M. Levin, James C. Willey. ERCC5 variant rs2296147 T-allele creates a predicted TP53 binding site and up-regulates transcript abundance in normal bronchial epithelial cells, while rs17655 C-allele is linked to miRNA binding site variant and down regulates. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2890.
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Craig DJ, Zolondek RT, Zhang X, Yeo J, Crawford EL, Willey JC. Abstract 2905: Induction of hTERT and increased proliferative potential in conditionally reprogrammed normal bronchial epithelial cells. Cancer Res 2016. [DOI: 10.1158/1538-7445.am2016-2905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background. Based on increasing evidence from this laboratory and genome wide association studies (GWAS), single nucleotide polymorphisms (SNPs) responsible for inter-individual variation in normal bronchial epithelial cell (NBEC) cis-regulation of antioxidant, DNA repair, and cell cycle control genes are key determinants of lung cancer risk. Thus, there is a need for NBEC culture methods that enable extended population doublings without genetic alteration to enable experimental investigation of putative cis-regulatory SNPs in NBEC. Toward this goal, we assessed the effect of previously reported conditional reprogrammed culture (CRC) conditions on regulation of human telomerase reverse transcriptase (hTERT) transcript abundance and proliferative potential in NBEC. Methods. NBEC were obtained by bronchoscopic brush from eight individuals after obtaining informed consent to an IRB-approved protocol. NBEC were incubated in three different culture conditions: bronchial epithelial cell growth media (BEGM) only, co-cultured with irradiated mouse embryonic fibroblasts (IRR-MEF) + Rho kinase inhibitor (ROCKi) in BEGM, and conditioned BEGM + ROCKi. Media were changed every three days and cells were passaged and sub-cultured after ten days. Human telomerase reverse transcriptase (hTERT) was measured in three individuals after each passage in triplicate via qPCR. The proliferative capacity of all eight individuals was assessed using cell count and morphology at passage >3. Results. Co-culturing NBEC with IRR-MEF in BEGM supplemented with ROCKi produced a highly proliferative cell population while maintaining lineage commitment evident after removal of CRC conditions. Transcript abundance of hTERT was elevated 6.5-fold in NBEC in co-cultured conditions and 4.3-fold in NBEC in conditioned media compared to BEGM alone. Cell count in CRC conditions were up to 22-fold higher compared to BEGM alone. Cells were passaged and sub-cultured up to passage 4, followed by being frozen down in cell culture freezing media for further assessment. Conclusion. NBEC hTERT transcript abundance was up-regulated and cell population proliferative potential was extended in CRC conditions. It is likely that hTERT functions to protect the ends of linear chromosomes in dividing cells, enabling increased cell divisions while maintaining normal genome. These cell populations will be used in future studies to assess the effect of putative cis-regulatory single nucleotide polymorphisms (SNPs) on gene expression in NBEC.
Citation Format: Daniel J. Craig, Rose T. Zolondek, Xiaolu Zhang, Jiyoun Yeo, Erin L. Crawford, James C. Willey. Induction of hTERT and increased proliferative potential in conditionally reprogrammed normal bronchial epithelial cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2905.
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Zhang X, Crawford EL, Blomquist TM, Khuder SA, Yeo J, Levin AM, Willey JC. Haplotype and diplotype analyses of variation in ERCC5 transcription cis-regulation in normal bronchial epithelial cells. Physiol Genomics 2016; 48:537-43. [PMID: 27235448 PMCID: PMC4967224 DOI: 10.1152/physiolgenomics.00021.2016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 05/26/2016] [Indexed: 12/13/2022] Open
Abstract
Excision repair cross-complementation group 5 (ERCC5) gene plays an important role in nucleotide excision repair, and dysregulation of ERCC5 is associated with increased lung cancer risk. Haplotype and diplotype analyses were conducted in normal bronchial epithelial cells (NBEC) to better understand mechanisms responsible for interindividual variation in transcript abundance regulation of ERCC5 We determined genotypes at putative ERCC5 cis-regulatory SNPs (cis-rSNP) rs751402 and rs2296147, and marker SNPs rs1047768 and rs17655. ERCC5 allele-specific transcript abundance was assessed by a recently developed targeted sequencing method. Syntenic relationships among alleles at rs751402, rs2296147, and rs1047768 were assessed by allele-specific PCR followed by Sanger sequencing. We then assessed association of ERCC5 allele-specific expression at rs1047768 with haplotype and diplotype structure at cis-rSNPs rs751402 and rs2296147. Genotype analysis revealed significantly (P < 0.005) higher interindividual variation in allelic ratios in cDNA samples relative to matched gDNA samples at both rs1047768 and rs17655. By diplotype analysis, mean expression was higher at the rs1047768 alleles syntenic with rs2296147 T allele compared with rs2296147 C allele. Furthermore, mean expression was lower at rs17655 C allele, which is syntenic with G allele at a linked SNP rs873601 (D' = 0.95). These data support the conclusions that in NBEC, T allele at SNP rs2296147 upregulates ERCC5, variation at rs751402 does not alter ERCC5 regulation, and that C allele at SNP rs17655 downregulates ERCC5 Variation in ERCC5 transcript abundance associated with allelic variation at these SNPs could result in variation in NER function in NBEC and lung cancer risk.
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Affiliation(s)
- Xiaolu Zhang
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio
| | - Erin L Crawford
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio
| | - Thomas M Blomquist
- Department of Pathology, University of Toledo Health Sciences Campus, Toledo, Ohio
| | - Sadik A Khuder
- Departments of Medicine and Public Health and Homeland Security, University of Toledo Health Science Campus, Toledo, Ohio; and
| | - Jiyoun Yeo
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan
| | - James C Willey
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio; Department of Pathology, University of Toledo Health Sciences Campus, Toledo, Ohio;
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Crawford EL, Levin A, Safi F, Lu M, Baugh A, Zhang X, Yeo J, Khuder SA, Boulos AM, Nana-Sinkam P, Massion PP, Arenberg DA, Midthun D, Mazzone PJ, Nathan SD, Wainz R, Silvestri G, Tita J, Willey JC. Lung cancer risk test trial: study design, participant baseline characteristics, bronchoscopy safety, and establishment of a biospecimen repository. BMC Pulm Med 2016; 16:16. [PMID: 26801409 PMCID: PMC4722707 DOI: 10.1186/s12890-016-0178-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 01/12/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The Lung Cancer Risk Test (LCRT) trial is a prospective cohort study comparing lung cancer incidence among persons with a positive or negative value for the LCRT, a 15 gene test measured in normal bronchial epithelial cells (NBEC). The purpose of this article is to describe the study design, primary endpoint, and safety; baseline characteristics of enrolled individuals; and establishment of a bio-specimen repository. METHODS/DESIGN Eligible participants were aged 50-90 years, current or former smokers with 20 pack-years or more cigarette smoking history, free of lung cancer, and willing to undergo bronchoscopic brush biopsy for NBEC sample collection. NBEC, peripheral blood samples, baseline CT, and medical and demographic data were collected from each subject. DISCUSSION Over a two-year span (2010-2012), 403 subjects were enrolled at 12 sites. At baseline 384 subjects remained in study and mean age and smoking history were 62.9 years and 50.4 pack-years respectively, with 34% current smokers. Obstructive lung disease (FEV1/FVC <0.7) was present in 157 (54%). No severe adverse events were associated with bronchoscopic brushing. An NBEC and matched peripheral blood bio-specimen repository was established. The demographic composition of the enrolled group is representative of the population for which the LCRT is intended. Specifically, based on baseline population characteristics we expect lung cancer incidence in this cohort to be representative of the population eligible for low-dose Computed Tomography (LDCT) lung cancer screening. Collection of NBEC by bronchial brush biopsy/bronchoscopy was safe and well-tolerated in this population. These findings support the feasibility of testing LCRT clinical utility in this prospective study. If validated, the LCRT has the potential to significantly narrow the population of individuals requiring annual low-dose helical CT screening for early detection of lung cancer and delay the onset of screening for individuals with results indicating low lung cancer risk. For these individuals, the small risk incurred by undergoing once in a lifetime bronchoscopic sample collection for LCRT may be offset by a reduction in their CT-related risks. The LCRT biospecimen repository will enable additional studies of genetic basis for COPD and/or lung cancer risk. TRIAL REGISTRATION The LCRT Study, NCT 01130285, was registered with Clinicaltrials.gov on May 24, 2010.
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Affiliation(s)
- E L Crawford
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - A Levin
- Department of Biostatistics, Henry Ford Hospital System, Detroit, MI, USA
| | - F Safi
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - M Lu
- Department of Biostatistics, Henry Ford Hospital System, Detroit, MI, USA
| | - A Baugh
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - X Zhang
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - J Yeo
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - S A Khuder
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - A M Boulos
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA
| | - P Nana-Sinkam
- Ohio State University James Comprehensive Cancer Center and Solove Research Institute, Columbus, OH, USA
| | - P P Massion
- Thoracic Program, Vanderbilt Ingram Cancer Center, Nashville, TN, USA
| | | | | | | | - S D Nathan
- Inova Fairfax Hospital, Falls Church, VA, USA
| | - R Wainz
- The Toledo Hospital, Toledo, OH, USA
| | - G Silvestri
- Medical University of South Carolina, Charleston, SC, USA
| | - J Tita
- Mercy/St. Vincent's Hospital, Toledo, OH, USA
| | - J C Willey
- Department of Pulmonary and Critical Care, The University of Toledo Medical Center, Toledo, OH, USA.
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Blomquist T, Crawford EL, Yeo J, Zhang X, Willey JC. Control for stochastic sampling variation and qualitative sequencing error in next generation sequencing. Biomol Detect Quantif 2015; 5:30-37. [PMID: 26693143 PMCID: PMC4673681 DOI: 10.1016/j.bdq.2015.08.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Clinical implementation of Next-Generation Sequencing (NGS) is challenged by poor control for stochastic sampling, library preparation biases and qualitative sequencing error. To address these challenges we developed and tested two hypotheses. METHODS Hypothesis 1: Analytical variation in quantification is predicted by stochastic sampling effects at input of a) amplifiable nucleic acid target molecules into the library preparation, b) amplicons from library into sequencer, or c) both. We derived equations using Monte Carlo simulation to predict assay coefficient of variation (CV) based on these three working models and tested them against NGS data from specimens with well characterized molecule inputs and sequence counts prepared using competitive multiplex-PCR amplicon-based NGS library preparation method comprising synthetic internal standards (IS). Hypothesis 2: Frequencies of technically-derived qualitative sequencing errors (i.e., base substitution, insertion and deletion) observed at each base position in each target native template (NT) are concordant with those observed in respective competitive synthetic IS present in the same reaction. We measured error frequencies at each base position within amplicons from each of 30 target NT, then tested whether they correspond to those within the 30 respective IS. RESULTS For hypothesis 1, the Monte Carlo model derived from both sampling events best predicted CV and explained 74% of observed assay variance. For hypothesis 2, observed frequency and type of sequence variation at each base position within each IS was concordant with that observed in respective NTs (R2 = 0.93). CONCLUSION In targeted NGS, synthetic competitive IS control for stochastic sampling at input of both target into library preparation and of target library product into sequencer, and control for qualitative errors generated during library preparation and sequencing. These controls enable accurate clinical diagnostic reporting of confidence limits and limit of detection for copy number measurement, and of frequency for each actionable mutation.
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Affiliation(s)
- Thomas Blomquist
- Department of Pathology, University of Toledo Health Sciences Campus, Toledo, OH 43614
| | - Erin L Crawford
- Department of Medicine, University of Toledo Health Sciences Campus, Toledo, OH 43614
| | - Jiyoun Yeo
- Department of Medicine, University of Toledo Health Sciences Campus, Toledo, OH 43614
| | - Xiaolu Zhang
- Department of Medicine, University of Toledo Health Sciences Campus, Toledo, OH 43614
| | - James C Willey
- Department of Pathology, University of Toledo Health Sciences Campus, Toledo, OH 43614 ; Department of Medicine, University of Toledo Health Sciences Campus, Toledo, OH 43614
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Crawford EL, Blomquist T, Willey JC. Abstract 4892: Methods for accurate reporting of confidence intervals in clinical applications of next generation sequencing (NGS). Cancer Res 2015. [DOI: 10.1158/1538-7445.am2015-4892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
BACKGROUND: A challenge to clinical implementation of Next-Generation Sequencing (NGS) is lack of appropriate quality control including control for: a) adequate sample loading, b) variation in target amplification efficiency, and c) variation in loading of prepared NGS specimen onto sequencing platform. Polymerase chain reaction steps used in preparation for NGS can yield a large number of sequencing reads from a small number of starting nucleic acid molecules (e.g. small and/or degraded samples from FNA specimens or FFPE tissue), resulting in large stochastic sampling variation. At present, methods to quantify the analyzable fraction of target nucleic acid are not available or are not suitable for small specimens. As a result, current practice is to rely on sequencing coverage data that may provide false assurance of adequate specimen sampling during molecular analysis, and this has potential to negatively impact patient care. We hypothesize that coefficient of variation (CV) for amplicon-based NGS assay measurements is largely predicted by Poisson (i.e. stochastic) sampling effects for a nucleic acid target at two key points: 1) input molarity (i.e. number of intact molecules) and 2) sequencing coverage (i.e. read counts). METHODS: To test this hypothesis we developed three working models using Monte Carlo simulation and derived equations to predict expected CV based on sequence read count and/or intact molecules mesaured for a given nucleic acid target. These expectation models were tested against empirically derived data from cross-mixtures of two cell lines (H23 and H520) known to be homozygous for opposite alleles at four polymorphic sites (rs769217, rs1042522, rs735482 and rs2298881). Cell lines were mixed to produce limiting inputs of one allele relative to the other, then prepared for NGS such that a broad range of combinations of limiting allelic molecule inputs and/or sequence read counts were observed (46 sets of allelic measurement at all 4 loci). Intra-assay measurement of intact and amplifiable molecules was accomplished using recently described competitive multiplex-PCR amplicon-based NGS specimen preparation (Blomquist, et. al. 2013). RESULTS: Observed CV for measurement at varying input copies and sequencing read counts were compared to expected CV. Actual measured CV was on average 13- and 1.5-fold higher than expected CV based on sequencing reads or molecule input measurements alone, respectively. For the model derived from both sequencing reads and molecule input measurements, expected CV was very close to measured (average [measured CV/expected CV] = 1.01) and explained 74% of observed assay variance. CONCLUSIONS: NGS-based diagnostic tests that do not take into account both input concentration of intact target nucleic acid material and associated sequencing read coverage may not provide accurate reporting of confidence intervals in specimens with limited or degraded material.
Citation Format: Erin L. Crawford, Thomas Blomquist, James C. Willey. Methods for accurate reporting of confidence intervals in clinical applications of next generation sequencing (NGS). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4892. doi:10.1158/1538-7445.AM2015-4892
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Zhang X, Yeo J, Crawford E, Willey JC. Abstract 2084: Genetic variation at a cis-acting C/EBPG binding site is associated with allele-specific ERCC5 transcript expression. Cancer Res 2015. [DOI: 10.1158/1538-7445.am2015-2084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: CCAAT/enhancer-binding protein gamma (C/EBPG) transcription factor expression is correlated with that of ERCC5 and other key DNA repair genes in normal bronchial epithelial cells (NBEC) suggesting a regulatory role. In prior studies, ERCC5 transcript expression was increased in a human lung carcinoma cell line H23 following CEBPG overexpression and in NBEC from 81 subjects, A allele at putative ERCC5 cis-regulatory SNP (rSNP) rs751402 and T allele at rSNP rs2296147 were associated with higher expression of ERCC5 marker SNP rs1047768 T allele transcript. rs751402 is located in open chromatin region identified by FAIRE-seq in NBEC and variation at rs751402 is predicted to alter binding of C/EBP. These studies support the hypothesis that allelic differential affinity to C/EBPG at rs751402 contributes to hereditary inter-individual variation in regulation of ERCC5 either directly or through interaction with complexes bound at rs2296147. The purpose of this study was to further investigate the role of C/EBPG in ERCC5 cis-regulation in an independent cohort of subjects and lung cancer cell lines. Methods: We knocked-down C/EBPG transcript level by C/EBPG siRNA transfection in human non-small cell lung carcinoma cell line H1703. Total and allele-specific expression (ASE) at rs1047768 was measured through multiplex competitive PCR-based amplicon sequencing library preparation followed by Illumina HiSeq next generation sequencing (NGS). This NGS controls for inter-target variation in PCR amplification during library preparation by measuring each transcript native template relative to a known number of synthetic competitive template internal standard copies. The genotype at rs751402 and rs2296147 in NBEC from 78 subjects and 14 human lung carcinoma cell lines was determined by TaqMan SNP genotyping assays. Direct assessment of the syntenic relationship of alleles in gDNA from poly-heterozygous individuals was assessed by allele-specific PCR followed by sequencing. Results: CEBPG transcript expression was knocked-down by 93% in H1703 cells and this was associated with 4-fold reduction in the ERCC5 transcript level at rs1047768. ERCC5 displayed significant inter-individual variation in allele specific expression (ASE) in NBEC from 85 subjects. Thirty nine out of 92 subjects including 3 cell lines were heterozygous at rs751402 and rs2296147 rSNPs and rs1047768 marker SNP and will be assessed for haplotypes comprising those sites. Conclusions: Results are consistent with CEBPG regulation of ERCC5 in the cell line H1703. The results obtained will enable us to test the hypothesis that haplotypes comprising particular alleles syntenic between rs1047768 and rs751402 are associated with higher allele-specific ERCC5 transcript abundance. Cell lines heterozygous at three sites will be subjected to CEBPG up and/or down regulation to assess effect on allele-specific ERCC5 expression at rs1047768.
Citation Format: Xiaolu Zhang, Jiyoun Yeo, Erin Crawford, James C. Willey. Genetic variation at a cis-acting C/EBPG binding site is associated with allele-specific ERCC5 transcript expression. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2084. doi:10.1158/1538-7445.AM2015-2084
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Abstract
BACKGROUND Today, insurance insulates most patients from the true costs of the health care services they consume. Economists believe that the absence of price signals incentivizes patients to pursue more extensive care than they would otherwise. Reformers propose restoring price consciousness to patients as a way to tame the soaring costs of American health care. To test this idea, we decided to gauge the availability and variability of price quotes for a common elective surgery-bunion repair. METHODS Orthopedic clinics were sorted by state and randomly selected from an online directory maintained by the American Orthopaedic Foot and Ankle Society. Each selected clinic was contacted up to 3 times in an attempt to get a full, bundled price quote using a standardized patient script. If this was unavailable, an isolated quote for the physician fee alone was solicited. RESULTS Of the 141 clinics contacted, 56 (39.7%) could provide a physician price estimate and 12 (8.5%) could give a complete bundled estimate, including hospital fees. The overall mean bundled price quoted was $18 332, while the overall mean physician fee quoted was $2487. There was no statistically significant difference in the mean price quoted by academic and private clinics, nor was regional variation observed. CONCLUSION We found low price availability for elective bunion procedures. CLINICAL RELEVANCE However, the wide variation observed in the prices that were quoted suggests that a very determined patient may be able to spend substantially less on an elective surgery if they were willing to select a provider carefully.
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Affiliation(s)
- James C Willey
- Department of Orthopaedics and Rehabilitation, University of Iowa, Iowa City, IA, USA
| | - Lainee S Reuter
- Department of Orthopaedics and Rehabilitation, University of Iowa, Iowa City, IA, USA
| | - Daniel A Belatti
- Department of Orthopaedics and Rehabilitation, University of Iowa, Iowa City, IA, USA
| | - Phinit Phisitkul
- Department of Orthopaedics and Rehabilitation, University of Iowa, Iowa City, IA, USA
| | - Ned Amendola
- Department of Orthopaedics and Rehabilitation, University of Iowa, Iowa City, IA, USA
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Crawford EL, Yeo J, Zhang X, Padda K, Arend T, Blomquist TM, Levin AM, Lu M, Willey JC. Abstract 4251: Identification of expression quantitative trait loci at lung cancer and COPD risk genes in normal bronchial epithelial cells. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-4251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Lung cancer and chronic obstructive pulmonary disease (COPD) and are leading causes of morbidity and mortality both in the United States and worldwide. Inhalation of cigarette smoke is the primary known and preventable cause but only 10-15% of heavy smokers develop these diseases. This suggests that cigarette smoke exposure interacts with inherited susceptibility factors to determine risk. While some susceptibility genes are known, they account for less than 5% of risk for either disease. There is urgent need to identify heritable susceptibility factors that explain the majority of lung cancer and COPD risk. In this study we focused on discovery of cis-regulatory expression quantitative trait loci (eQTL) responsible for inter-individual variation in the expression of genes that we and/or others have reported to display altered regulation in subjects with lung cancer or COPD. Through funding in part from RC2 CA148572 and HL108016 we collected normal bronchial epithelial cell (NBEC) samples from over 500 subjects with lung cancer or COPD or demographically at risk for lung cancer or COPD. In this pilot study, we assessed allele-specific expression (ASE) and total expression of multiple putative risk genes in NBEC samples from 85 subjects.
Methods: A targeted competitive multiplex next generation sequencing (NGS) method (STAndardized RNA SEQuencing [STARSEQ]; Blomquist et al, PLOS one 2013) was used to quantify a) allele specific expression (ASE) and total expression at 140 single nucleotide polymorphism (SNP) sites among 41 target genes (including eleven of 14 genes comprised by the previously reported lung cancer risk test (Blomquist et al, Can. Res. 2009) in NBEC total RNA from 85 subjects (26 cancer cases and 59 non-cancer controls), and b) allelic representation and putative cis-regulatory single nucleotide polymorphisms (rSNPs) in matched peripheral blood leukocyte genomic (g)DNA from the same subjects. Heterozygosity was determined by gDNA analysis.
Results: Significant (p<0.05) inter-individual variation in ASE measured as allelic imbalance relative to matched gDNA was observed for: CAT, CCND2, ERCC4, ERCC5, KEAP1, MUC5B and TP73. Among these genes, for some the same allele was typically expressed at a higher level among the subjects studied (e.g. ERCC5 rs17655) which suggests a closely proximal cis-regulatory SNP and for others the over expressed allele was random (e.g. CAT rs1049982) which suggests a more distal cis regulatory SNP.
Conclusions: This small study of 85 subjects provides evidence for cis-regulatory eQTL in selected genes with high prior likelihood for contributing to lung cancer and COPD risk. The genes with ASE reported here will be among those included in additional studies in archived NBEC samples from 500 subjects aiming to further optimize current tests for lung cancer and COPD risk and better understand mechanisms of risk.
Citation Format: Erin L. Crawford, Jiyoun Yeo, Xiaolu Zhang, Karan Padda, Taylor Arend, Thomas M. Blomquist, Albert M. Levin, Mei Lu, James C. Willey. Identification of expression quantitative trait loci at lung cancer and COPD risk genes in normal bronchial epithelial cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4251. doi:10.1158/1538-7445.AM2014-4251
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Affiliation(s)
| | | | | | | | | | | | | | - Mei Lu
- 4Henry Ford Health Systems, Detroit, MI
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Zhang X, Yeo J, Crawford EL, Blomquist TM, Willey JC. Abstract 3401: Inter-individual variation in MUC5B allele specific expression in normal bronchial epithelial cells and relationship to lung cancer. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-3401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Familial interstitial pneumonia and idiopathic pulmonary fibrosis are associated with increased risk of lung cancer. Seibold et al (NEJM, 2011) recently identified a common variant in the putative promoter of MUC5B (rs35705950) associated with level of MUC5B expression in lung tissue and also with development of these diseases. The goal of this study was to determine whether the MUC5B rs35705950 variant is associated with a) MUC5B allele-specific expression (ASE) or total expression in normal bronchial epithelial cells (NBEC) or b) lung cancer risk. Through funding in part from RC2 CA148572 and HL108016 we collected NBEC samples from over 500 subjects with lung cancer or at risk for lung cancer. Methods: This was a pilot study of 85 subjects (26 cancer cases and 59 non-cancer controls). RNA was extracted from normal bronchial airway brush NBEC specimens of 85 subjects and reverse transcribed. Using next generation sequencing (NGS), allele-specific expression (ASE) was measured as allelic imbalance in each cDNA at three marker SNPs in MUC5B coding region (rs2075859, rs2943510, and rs4963059) selected for common (>0.05) minor allele frequency, using matched peripheral blood cell gDNA as a control. Specifically, each cDNA and matched gDNA sample was subjected to targeted competitive template multiplex PCR amplicon library generation followed by NGS (Blomquist et al, PLOS one, 2013) on Illumina Hiseq platform. This NGS method controlled for inter-target variation in PCR amplification during library preparation by measuring each transcript native template relative to a known number of synthetic competitive template internal standard copies. The genotype at putative cis-regulatory SNP rs35705950 was assessed in gDNA from 95 subjects (31 cancer cases and 64 non-cancer controls) including those assessed for ASE using a TaqMan® SNP assay. Results: There was significant (p<0.001) inter-individual variation in MUB5B allelic imbalance among heterozygotes at all three marker SNP loci and the level of imbalance measured at rs2075859 was greater in NBEC from cancer cases compared with non-cancer controls (P=0.0264). The genotype (i.e., heterozygote vs homozygote) at putative cis-regulatory SNP rs35705950 was not associated with MUC5B allelic imbalance or with lung cancer status. Inter-individual variation in MUC5B total expression was large (>3 log) but not associated with lung cancer. Conclusions: These results support previous observations that there is significant inter-individual variation in cis-regulation of MUC5B in NBEC and suggest that this variation also may contribute to inherited lung cancer risk. Lack of association between rs35705950 genotype and MUC5B ASE may have been due to role of other cis-regulatory factors, and/or environmentally associated transfactors that contributed to large inter-individual variation in total MUC5B expression.
Citation Format: Xiaolu Zhang, Jiyoun Yeo, Erin L. Crawford, Thomas M. Blomquist, James C. Willey. Inter-individual variation in MUC5B allele specific expression in normal bronchial epithelial cells and relationship to lung cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3401. doi:10.1158/1538-7445.AM2014-3401
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Zhang X, Yeo J, Crawford EL, Willey JC. Abstract 3381: Investigation of C/EBPG transcription factor role in regulation of ERCC4 and ERCC5 in human lung cancer cells. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-3381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: ERCC4 and ERCC5 are key nucleotide excision DNA repair genes and are expressed abundantly in normal bronchial epithelial cells (NBEC). DNA sequence variation in ERCC4 or ERCC5 is associated with risk for lung cancer in multiple independent studies. C/EBPG expression is correlated with that of ERCC4, ERCC5 and many other key genes in BEC, suggesting a regulatory role, supported by experimental observation that up-regulation of CCAAT/enhancer-binding protein gamma (C/EBPG) transcription factor up-regulates ERCC5 expression in H23 lung cancer cell line. The purpose of this study was to test the hypothesis that knockdown of C/EBPG in lung cancer cells would reduce transcription of ERCC5 and ERCC4. Methods: We knocked-down C/EBPG transcript level by C/EBPG siRNA transfection in three non-small cell lung cancer cell lines: H23, H520 and H1703. Following transfection, RNA was extracted after 24 and 48 hours. Reduction of C/EBPG transcript abundance measured by competitive multiplex RT-PCR in H23, H520, and H1703 was 56%, 84%, and 92% at 24 hours, and 82%, 89%, and 76% at 48 hours. After confirming C/EBPG knockdown, we measured allele-specific expression (ASE) and total expression of C/EBPG, ERCC4 ERCC5 through competitive multiplex PCR-based amplicon sequencing library preparation followed by Illumina HiSeq next generation sequencing (NGS) (Blomquist et al, PLOS one, 2013). This NGS method a) targets only the sequences of interest and b) controls for inter-target variation in PCR amplification during library preparation by measuring each transcript native template relative to a known number of synthetic competitive template internal standard copies. ASE was measured at ERCC4 SNPs rs2276466, rs3743538 and ERCC5 SNPs rs1047768, rs17655, rs4150316. Results: In H23 C/EBPG knock-down was associated with no change in expression at any of the SNPs for ERCC4 or ERCC5, while in H520 one ERCC5 rs1047768 allele decreased (3-fold) and the other allele had no change, and in H1703 both ERCC5 rs1047768 alleles increased (10-fold, and 2-fold). In H520, ERCC4 measured at two SNPs were different with increase at one allele but not the other at rs3743538 and decrease for both alleles at rs2276466. In H1703 each allele at ERCC4 rs3743538 increased, 2-fold and 45-fold respectively and each allele at ERCC4 rs2276466 increased, 5-fold and 2.7-fold respectively. Conclusions: These results support prior evidence that C/EBPG regulates ERCC5 transcription, and provides evidence that it also contributes to regulation of ERCC4. The inter-allelic and inter-cell line variation in response to C/EBPG knockdown data supports that hypothesis that cis-regulatory DNA variants interact with C/EBPG in regulation of these genes. The lack of response in H23 may be in part due to the low constitutive level of ERCC4 and ERCC5 expression in this cell line.
Citation Format: Xiaolu Zhang, Jiyoun Yeo, Erin L. Crawford, James C. Willey. Investigation of C/EBPG transcription factor role in regulation of ERCC4 and ERCC5 in human lung cancer cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3381. doi:10.1158/1538-7445.AM2014-3381
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Yeo J, Zhang X, Crawford EL, Willey JC. Abstract 4156: Inter-individual variation in allele specific expression of catalase (CAT) in normal bronchial epithelial cells and association of putative cis-regulatory CAT SNP rs12807961 with lung cancer risk. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-4156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Catalase (CAT) is a key antioxidant gene expressed at high levels in most human tissues, including normal bronchial epithelial cells (NBEC). NBEC CAT expression is more disperse (more high and low extreme values) among subjects with cancer compared to controls. CAT shares this property with 14 other antioxidant and DNA repair genes comprised by the Lung Cancer Risk Test (LCRT) reported from this laboratory. We hypothesize that inter-individual variation in CAT regulation in NBEC is in part due to inter-individual variation at one or more cis-regulatory single nucleotide polymorphisms (SNPs). If so, this should manifest as inter-individual variation in allele-specific CAT expression in NBEC. Through funding in part from RC2 CA148572 and HL108016 we collected NBEC samples from over 500 subjects at risk for lung cancer. In this pilot study, we assessed allele-specific and total expression of multiple genes in NBEC samples from 85 subjects and assessed the genotype at putative cis-regulatory CAT SNP rs12807961 in gDNA from 95 subjects. Methods: RNA extracted from normal bronchial airway brush specimens of 85 subjects (26 cancer cases and 59 non-cancer controls) was reverse transcribed. Using next generation sequencing (NGS), allele-specific expression (ASE) was measured as allelic imbalance in each cDNA at three marker SNPs in the CAT coding region (rs1049982, rs769217, and rs704724) and one putative regulatory SNP (rs12807961) that was 4364 bases upstream of transcription start site, using gDNA as control. Specifically, each cDNA and matched peripheral blood cell gDNA sample was subjected to targeted competitive template multiplex PCR amplicon library generation followed by NGS (Blomquist et al, PLOS one, 2013) on Illumina Hiseq platform. The genotype at putative cis-regulatory SNP rs12807961 was assessed in gDNA from 95 subjects including those assessed for ASE (a total of 31 cancer cases and 64 non-cancer controls) using a TaqMan® SNP Genotyping Assay. Results: Among heterozygotes, there was significant inter-individual variation in cDNA allelic imbalance at rs1049982 (p<0.001, n=40) and rs769217 (p<0.001, n=28) as measured by F-test using matched gDNA controls. In this cohort there was insufficient number of heterozygotes at rs704724 (n=2) to assess ASE. Among all 95 subjects assessed for rs12807961 genotype, nine were homozygous minor allele at the rs12807961 CAT SNP. Of these, 7/31 cancer cases and 2/64 non-cancer controls were homozygous minor allele. This difference was significant by two-tailed Fisher exact test (P<0.05) following Bonferroni adjustment for multiple testing. Conclusions: These data support the hypothesis that cis-regulatory DNA variants contribute to inter-individual variation in CAT regulation in NBEC and that this is associated with lung cancer risk.
Citation Format: Jiyoun Yeo, Xaiolu Zhang, Erin L. Crawford, James C. Willey. Inter-individual variation in allele specific expression of catalase (CAT) in normal bronchial epithelial cells and association of putative cis-regulatory CAT SNP rs12807961 with lung cancer risk. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4156. doi:10.1158/1538-7445.AM2014-4156
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Affiliation(s)
- Jiyoun Yeo
- University of Toledo Health Sciences Campus, Toledo, OH
| | - Xaiolu Zhang
- University of Toledo Health Sciences Campus, Toledo, OH
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Yeo J, Crawford EL, Blomquist TM, Stanoszek LM, Dannemiller RE, Zyrek J, De Las Casas LE, Khuder SA, Willey JC. A multiplex two-color real-time PCR method for quality-controlled molecular diagnostic testing of FFPE samples. PLoS One 2014; 9:e89395. [PMID: 24586747 PMCID: PMC3931751 DOI: 10.1371/journal.pone.0089395] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Accepted: 01/20/2014] [Indexed: 02/07/2023] Open
Abstract
Background Reverse transcription quantitative real-time PCR (RT-qPCR) tests support personalized cancer treatment through more clinically meaningful diagnosis. However, samples obtained through standard clinical pathology procedures are formalin-fixed, paraffin-embedded (FFPE) and yield small samples with low integrity RNA containing PCR interfering substances. RT-qPCR tests able to assess FFPE samples with quality control and inter-laboratory reproducibility are needed. Methods We developed an RT-qPCR method by which 1) each gene was measured relative to a known number of its respective competitive internal standard molecules to control for interfering substances, 2) two-color fluorometric hydrolysis probes enabled analysis on a real-time platform, 3) external standards controlled for variation in probe fluorescence intensity, and 4) pre-amplification maximized signal from FFPE RNA samples. Reagents were developed for four genes comprised by a previously reported lung cancer diagnostic test (LCDT) then subjected to analytical validation using synthetic native templates as test articles to assess linearity, signal-to-analyte response, lower detection threshold, imprecision and accuracy. Fitness of this method and these reagents for clinical testing was assessed in FFPE normal (N = 10) and malignant (N = 10) lung samples. Results Reagents for each of four genes, MYC, E2F1, CDKN1A and ACTB comprised by the LCDT had acceptable linearity (R2>0.99), signal-to-analyte response (slope 1.0±0.05), lower detection threshold (<10 molecules) and imprecision (CV <20%). Poisson analysis confirmed accuracy of internal standard concentrations. Internal standards controlled for experimentally introduced interference, prevented false-negatives and enabled pre-amplification to increase signal without altering measured values. In the fitness for purpose testing of this two-color fluorometric LCDT using surgical FFPE samples, the diagnostic accuracy was 93% which was similar to that previously reported for analysis of fresh samples. Conclusions This quality-controlled two-color fluorometric RT-qPCR approach will facilitate the development of reliable, robust RT-qPCR-based molecular diagnostic tests in FFPE clinical samples.
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Affiliation(s)
- Jiyoun Yeo
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Erin L. Crawford
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Thomas M. Blomquist
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Lauren M. Stanoszek
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Rachel E. Dannemiller
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Jill Zyrek
- Department of Pathology, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Luis E. De Las Casas
- Department of Pathology, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - Sadik A. Khuder
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
| | - James C. Willey
- Division of Pulmonary/Critical Care and Sleep Medicine, Department of Medicine, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
- Department of Pathology, University of Toledo Health Sciences Campus, Toledo, Ohio, United States of America
- * E-mail:
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Pierce LR, Willey JC, Palsule VV, Yeo J, Shepherd BS, Crawford EL, Stepien CA. Accurate detection and quantification of the fish viral hemorrhagic Septicemia virus (VHSv) with a two-color fluorometric real-time PCR assay. PLoS One 2013; 8:e71851. [PMID: 23977162 PMCID: PMC3748128 DOI: 10.1371/journal.pone.0071851] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Accepted: 07/03/2013] [Indexed: 01/08/2023] Open
Abstract
Viral Hemorrhagic Septicemia virus (VHSv) is one of the world's most serious fish pathogens, infecting >80 marine, freshwater, and estuarine fish species from Eurasia and North America. A novel and especially virulent strain - IVb - appeared in the Great Lakes in 2003, has killed many game fish species in a series of outbreaks in subsequent years, and shut down interstate transport of baitfish. Cell culture is the diagnostic method approved by the USDA-APHIS, which takes a month or longer, lacks sensitivity, and does not quantify the amount of virus. We thus present a novel, easy, rapid, and highly sensitive real-time quantitative reverse transcription PCR (qRT-PCR) assay that incorporates synthetic competitive template internal standards for quality control to circumvent false negative results. Results demonstrate high signal-to-analyte response (slope = 1.00±0.02) and a linear dynamic range that spans seven orders of magnitude (R(2) = 0.99), ranging from 6 to 6,000,000 molecules. Infected fishes are found to harbor levels of virus that range to 1,200,000 VHSv molecules/10(6) actb1 molecules with 1,000 being a rough cut-off for clinical signs of disease. This new assay is rapid, inexpensive, and has significantly greater accuracy than other published qRT-PCR tests and traditional cell culture diagnostics.
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Affiliation(s)
- Lindsey R. Pierce
- Great Lakes Genetics/Genomics Laboratory, Lake Erie Center and Department of Environmental Sciences, The University of Toledo, Toledo, Ohio, United States of America
| | - James C. Willey
- Department of Medicine, The University of Toledo, Toledo, Ohio, United States of America
| | - Vrushalee V. Palsule
- Great Lakes Genetics/Genomics Laboratory, Lake Erie Center and Department of Environmental Sciences, The University of Toledo, Toledo, Ohio, United States of America
| | - Jiyoun Yeo
- Department of Medicine, The University of Toledo, Toledo, Ohio, United States of America
| | - Brian S. Shepherd
- ARS/USDA/University of Wisconsin at Milwaukee/School of Freshwater Sciences, Milwaukee, Wisconsin, United States of America
| | - Erin L. Crawford
- Department of Medicine, The University of Toledo, Toledo, Ohio, United States of America
| | - Carol A. Stepien
- Great Lakes Genetics/Genomics Laboratory, Lake Erie Center and Department of Environmental Sciences, The University of Toledo, Toledo, Ohio, United States of America
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Blomquist TM, Brown RD, Crawford EL, de la Serna I, Williams K, Yoon Y, Hernandez DA, Willey JC. CEBPG Exhibits Allele-Specific Expression in Human Bronchial Epithelial Cells. Gene Regul Syst Bio 2013; 7:125-38. [PMID: 23888109 PMCID: PMC3712557 DOI: 10.4137/grsb.s11879] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Inter-individual variation in CCAAT/enhancer binding protein gamma (CEBPG) transcript expression in normal human bronchial epithelial cells (NBEC) is associated with predisposition to lung cancer. We hypothesize that this inter-individual variation is in part explained by cis-acting genetic variation in CEBPG. To test this hypothesis we measured transcript expression derived from each parental copy of CEBPG (ie, allele-specific expression; ASE). There was a significant 2.9-fold higher cell cycle-specific variation in ASE of CEBPG rs2772 A compared to C allele (P < 0.001). In 20% of NBEC samples, CEBPG rs2772 A allele was expressed on average 2.10 fold greater than rs2772 C allele. These data support the hypothesis that genetic variation in linkage disequilibrium with rs2772 influences regulation of CEBPG transcript expression through a trans-effect downstream of RNA polymerase II transcription and confirm that cis-acting genetic variation contributes to inter-individual variation in CEBPG transcript expression in NBEC, which is associated with variation in lung cancer risk.
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Affiliation(s)
- Thomas M Blomquist
- Department of Medicine, University of Toledo Medical Center, Toledo, USA
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Blomquist T, Crawford EL, Willey JC. Abstract 4150: Quantitative sequencing following PCR-driven library preparation with internal standard mixtures has improved analytical performance and lower cost. Cancer Res 2013. [DOI: 10.1158/1538-7445.am2013-4150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Next-generation sequencing (NGS) is amenable to a multitude of clinical applications by virtue of its automated and highly parallelized analysis of nucleic acid templates. However, prior studies have identified non-systematic biases introduced during preparation of NGS libraries as the primary source of technical variation preventing immediate application for measuring nucleic acid abundance in the clinical setting. We reasoned that a PCR-based NGS library preparation protocol that incorporated competitive internal amplification control (IAC) mixtures (i.e. internal standards) would control for the majority of bias introduced during NGS library preparation, enabling clinical laboratories to offer cost effective moderately complex diagnostic panels from quantitative NGS data. Methods: In order to test this approach, we obtained reference material RNA titration pools used in the FDA-sponsored Sequencing Quality Control (SEQC) project that have been characterized for nucleic acid abundance by multiple qPCR, Microarray and NGS platforms. Using Multiplex-PCR with primers and competitive IAC for 150-gene targets we prepared NGS libraries from: 1) gDNA to test general analytical performance, and 2) cDNA from reverse transcribed SEQC project reference material to determine accuracy in detecting fold change. Using gDNA mixed with serially titrated IAC mixtures as input, we observed a linear dynamic range over 106 orders of magnitude, with an average R2 = 0.995 (0.993 – 0.997; 95% CI). There was a high correlation coefficient (R2= 0.96) between measured values for copies of nucleic acid abundance in two separate library preparations (separate reverse transcriptions and Multiplex PCR-based library preparations) from the same reference RNA material (FDA SEQC project Sample A). Because the SEQC project RNA Samples C and D represent a known cross titration between SEQC project RNA Samples A and B, by comparing measured to expected values for expression of each gene in Samples C and D it is possible to determine accuracy of the method. In preliminary studies, the correlation coefficient of expected versus observed for Sample C was R2 = 0.96, with an ROC curve-determined accuracy to detect a 3-fold change of 97% (95 – 99%; 95% CI). Inter-platform and inter-laboratory comparisons are ongoing. Conclusion: The approach described here overcomes key sources of non-systematic bias introduced during NGS library preparation. This should enable reproducible inter-laboratory and inter-platform quantitative NGS results, and a clear path to regulatory approval for clinical diagnostic applications.
Citation Format: Thomas Blomquist, Erin L. Crawford, James C. Willey. Quantitative sequencing following PCR-driven library preparation with internal standard mixtures has improved analytical performance and lower cost. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4150. doi:10.1158/1538-7445.AM2013-4150
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Yeo J, Crawford EL, Blomquist TM, Stanoszek LM, Dannemiller RE, Jordan L, Zyrek J, de la Casas L, Willey JC. Abstract 61: Use of two-color fluorometric real-time PCR to develop molecular diagnostic tests with intrinsic quality control that augment cytomorphologic diagnosis of lung cancer. Cancer Res 2013. [DOI: 10.1158/1538-7445.am2013-61] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Quantitative RT-PCR tests that measure transcript abundance of selected genes in clinical specimens promise to improve cancer diagnostic accuracy and enable “personalized medicine” through selection of the most effective treatment for each cancer. However, most clinical samples are Formalin-Fixed, Paraffin-Embedded (FFPE) and these yield sub-optimal RNA integrity. Thus, there is a need for qPCR tests with increased robustness that also have intrinsic quality control and low cost. To address this need, we developed qPCR methods that enable simultaneous measurement of each target gene and reference gene transcript relative to a known number of gene-specific internal standard (IS) molecules using two-color fluorometric analysis on real-time platform. For each gene, a competitive template IS was synthesized containing 4-6 nucleotide changes relative to the native template (NT). During PCR, NT or IS signal was quantified with sequence-specific FAM-labeled probe or Quasar 670-labeled probe, respectively. To optimize measurement sensitivity for analysis of highly degraded FFPE samples; a) each RNA sample was reverse transcribed (RT) with gene-specific primer and b) cDNA was pre-amplified with 18 PCR cycles in the presence of internal standards. Results obtained with pre-amp or no pre-amp conditions were highly correspondent.
We conducted a validation study of this assay in 38 samples (10 malignant and 10 benign surgical FFPE tissues and 13 malignant and 5 benign fine needle aspirate (FNA) samples. Consistent with previous results in fresh frozen surgical samples, the lung cancer diagnostic test (LCDT) optimal cut-off value discriminated malignant from non-malignant tissues (p = 0.0009), had 92.9% specificity and 75.0% sensitivity, and a receiver operator characteristic area under the curve of 0.87 (95% confidence interval 0.74-0.99). Based on these data, we expect that this quality-controlled two color fluorometric qPCR approach will enable reliable analysis of the LCDT and other promising qPCR-based molecular diagnostic tests in small degraded RNA extracted from clinical FFPE and FNA cell block FFPE samples.
Citation Format: Jiyoun Yeo, Erin L. Crawford, Thomas M. Blomquist, Lauren M. Stanoszek, Rachel E. Dannemiller, Laura Jordan, Jill Zyrek, Luis de la Casas, James C. Willey. Use of two-color fluorometric real-time PCR to develop molecular diagnostic tests with intrinsic quality control that augment cytomorphologic diagnosis of lung cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 61. doi:10.1158/1538-7445.AM2013-61
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Stanoszek LM, Crawford EL, Blomquist TM, Warns JA, Willey PFS, Willey JC. Quality control methods for optimal BCR-ABL1 clinical testing in human whole blood samples. J Mol Diagn 2013; 15:391-400. [PMID: 23541592 DOI: 10.1016/j.jmoldx.2013.02.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2012] [Revised: 01/18/2013] [Accepted: 02/12/2013] [Indexed: 12/25/2022] Open
Abstract
Reliable breakpoint cluster region (BCR)--Abelson (ABL) 1 measurement is essential for optimal management of chronic myelogenous leukemia. There is a need to optimize quality control, sensitivity, and reliability of methods used to measure a major molecular response and/or treatment failure. The effects of room temperature storage time, different primers, and RNA input in the reverse transcription (RT) reaction on BCR-ABL1 and β-glucuronidase (GUSB) cDNA yield were assessed in whole blood samples mixed with K562 cells. BCR-ABL1 was measured relative to GUSB to control for sample loading, and each gene was measured relative to known numbers of respective internal standard molecules to control for variation in quality and quantity of reagents, thermal cycler conditions, and presence of PCR inhibitors. Clinical sample and reference material measurements with this test were concordant with results reported by other laboratories. BCR-ABL1 per 10(3) GUSB values were significantly reduced (P = 0.004) after 48-hour storage. Gene-specific primers yielded more BCR-ABL1 cDNA than random hexamers at each RNA input. In addition, increasing RNA inhibited the RT reaction with random hexamers but not with gene-specific primers. Consequently, the yield of BCR-ABL1 was higher with gene-specific RT primers at all RNA inputs tested, increasing to as much as 158-fold. We conclude that optimal measurement of BCR-ABL1 per 10(3) GUSB in whole blood is obtained when gene-specific primers are used in RT and samples are analyzed within 24 hours after blood collection.
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Affiliation(s)
- Lauren M Stanoszek
- Department of Medicine, University of Toledo Health Sciences Campus, Toledo, OH, USA
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Stanoszek LM, Crawford EL, Mann H, Moore M, Olivieri T, Lu M, Levin A, Willey JC. Abstract 5549: Normal bronchial epithelial cells (NBEC) sample RNA quality characteristics that will yield reliable measurement of lung cancer risk test. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-5549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Lung cancer is the leading cause of cancer mortality in the United States with cigarette smoking the primary risk factor. Lung cancer has a low survival rate because it typically is at an advanced stage when first detected and treated. In previous studies a Lung Cancer Risk Test (LCRT) was identified comprising transcript abundance measurement of 14 key antioxidant, DNA repair and transcription factor genes measured in normal bronchial epithelial cells (NBEC) sampled after bronchoscopy. The LCRT promises to identify high-risk individuals who will develop lung cancer. This will enable even more focused selection for closer monitoring, further reduce risk of false positive findings and markedly reduce cost of implementation. An ongoing multi-institutional prospective cohort nested case control trial, funded by NCI grant RC2148572 is intended to test the validity of LCRT as an accurate test for lung cancer risk. More than twelve test sites are providing NBEC samples for the LCRT study. Accurate LCRT measurement will be dependent upon sample RNA quality. Thus, the purpose of this study is to identify cut-off criteria according to which NBEC RNA biospecimens can be expected to yield reliable quantitative, reverse-transcriptase polymerase chain reaction (qRT-PCR) results for the genes comprised by the LCRT. RNA quality of each collected NBEC sample was measured using three parameters: quantity, purity and integrity. RNA integrity was measured by one of two tests, GAPD and GUSB. Each test used one reverse primer and two forward primers and a sequence specific fluorescent labeled probe for signal detection. The GAPD test yielded 70bp and 230bp products and the GUSB test yielded 60bp and 120bp products. The integrity of each unknown RNA sample was determined by dividing the yield of longer product by that of shorter product. Of 126 NBEC samples assessed so far, 77.8% provided > 500 ng total RNA, 94.4% had A260/280 values > 1.5 and 95% gave integrity test values > 0.5. To determine the specific cut-off threshold for each NBEC RNA quality criterion above which LCRT results will be reliable, we established an A549 cell line RNA integrity reference model by incubating cell populations at room temperature for different amounts of time following cytolysis, generating samples with varying decreasing integrity. The LCRT will be measured in these intentionally degraded RNA samples, allowing comparison between integrity levels and LCRT values. The integrity level above which measurement of each gene comprised by the LCRT can be expected to be reliable and accurate will be determined.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5549. doi:1538-7445.AM2012-5549
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Affiliation(s)
| | | | | | | | | | - Mei Lu
- 3Henry Ford Health System, Detroit, MI
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Yeo J, Crawford EL, Zyrek-Betts J, Khuder SA, Austermiller B, Willey JC. Abstract 5541: Molecular diagnostic tests to augment cytomorphologic diagnosis of lung cancer. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-5541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Morphologic analysis of cytology samples obtained by transthoracic needle aspiration (TTNA) is one of the primary methods for diagnosing bronchogenic carcinoma. However, across multiple studies the false negative rate for cytomorphological analysis ranges from 0.2-0.3, and among those diagnosed with lung cancer, the false positive rate for diagnosing the sub-class of small cell carcinoma averages 0.09. These false results lead to additional invasive diagnostic studies and delay treatment. In an effort to augment accuracy and utility of cytopathologic evaluation of TTNA lung samples, we measured the previously reported c-myc x E2F-1/p21WAF1/CIP1 (p21 hereafter) gene expression index in cDNA samples from 78 non-malignant lung tissues and 57 lung cancer tissues. Using the optimal cut-off value, this test correctly classified 72/78 non-malignant and 50/57 malignant samples for a correct classification rate of 90% (95% CI 83.6% - 94.3%), sensitivity of 88%, and specificity of 92%. A CDKN2C/FOSL1 test for distinguishing non-small cell lung cancer (NSCLC) from small cell lung cancer (SCLC) had a PPV of 100% with 63% sensitivity. In order to optimize the robustness and utility of these tests, we developed qPCR methods that enable simultaneous measurement of each target gene and reference gene transcript relative to a known number of internal standard competitive template (CT) molecules using two-color flurometric analysis on real-time platform. For each gene, the native template was quantified with a sequence-specific FAM-labeled probe and the CT was quantified with a sequence-specific Quasar670 labeled probe. Results for each gene thus far demonstrate excellent linearity (R2 > 0.99, slope 1.0 +/- 0.05), relative accuracy (variance < 0.2), signal-to-analyte response (1.0 +/- 0.05) and precision (CV < 30%) over six orders of magnitude, and reliable detection of as few as 10 molecules. Assessing both the c-myc x E2F-1/p21 index and the CDKN2C/FOSL1 test promises to augment cytomorphologic diagnosis of bronchogenic carcinoma in TTNA samples biopsy samples.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5541. doi:1538-7445.AM2012-5541
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Stanoszek LM, Crawford EL, Blomquist T, Willey P, Willey JC. Abstract 5538: Development of RNA quality control methods to improve BCR-ABL measurement in whole blood samples. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-5538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Measurement of BCR-ABL fusion transcripts in whole blood by quantitative, reverse-transcriptase PCR is a clinically validated method to monitor treatment response in patients with chronic myelogenous leukemia. For example, achieving a three-log or greater reduction in BCR-ABL expression from baseline is considered a Maximum Molecular Response (MMR). A 0.5-log increase from MMR signifies treatment failure sufficient to motivate change in treatment. The purpose of this study was to develop a BCR-ABL test with improved analytical performance characteristics including adequate quality control for RNA degradation or reverse transcription interference and reliable comparison of results across testing sites. To test whether RNA degradation could mask a 0.5-log increase in expression, an RNA degradation model was established by incubating whole blood from each of six individuals with K562 cells at room temperature for various times post-venipuncture. When data from six subjects were combined, the BCR-ABL/103 GUSB value trended down at 24h but was not significantly decreased until 48h (51% decrease, p = 0.004). To quantify the amount of whole blood RNA that can be loaded into an RT reaction without reducing RT efficiency, six different amounts of total RNA extracted from whole blood mixed with K562 cells were added into RT reactions. To measure RT efficiency, an RT Standards Mixture (RTSM) containing known copy numbers of External RNA Control Consortium (ERCC) 171 RNA and ERCC 113 cDNA was added to each RT reaction. RT efficiency was measured as the yield of PCR product from ERCC 171 RNA relative to ERCC 113 cDNA. Based on data from three subjects, compared to RT efficiency at reference concentration of 30 ng/µl RNA in RT, there was a 35% decrease (p = 0.043) at 167 ng/μl RNA and 82% reduction at 1000 ng/μl RNA. The maximum yield of BCR-ABL (molecules/μL cDNA) was observed at 300 ng/µl RNA/RT with a 3-fold increase (p=0.003) compared to 30 ng/µl RNA. Greater than 300 ng/µl RNA/RT did not further increase yield of BCR-ABL molecules/μL cDNA, likely due to reduced RT efficiency. We conclude that BCR-ABL/103 GUSB should be measured within 24 hours following blood collection. Because decreasing RT efficiency is associated with increasing RNA input into RT reactions, including 300 ng/µl RNA in RT reactions provides the maximum sensitivity in measuring BCR-ABL expression.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5538. doi:1538-7445.AM2012-5538
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Stanoszek L, Blomquist T, Crawford EL, Austermiller B, Spitzer C, Willey PFS, Willey JC. Abstract 2232: Effectiveness evaluation of an in vitro nucleic acid amplification test for quantification of BCR-ABL fusion transcript variants in human whole blood. Cancer Res 2011. [DOI: 10.1158/1538-7445.am2011-2232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Quantitative measurement of BCR-ABL fusion gene transcripts in whole blood by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) is a clinically validated method to monitor tyrosine kinase inhibitor drug response in patients diagnosed with chronic myelogenous leukemia (CML). Monitoring of BCR-ABL expression in blood of cML patients under treatment with a tyrosine kinase inhibitor of BCR-ABL is considered standard of care. A three log or greater reduction in BCR-ABL expression from baseline is considered a Maximum Molecular Response (MMR). A subsequent 0.5 log increase in BCR-ABL from MMR is considered a sign of treatment failure sufficient to trigger change in treatment. The challenge is to develop a test with sufficient analytical performance characteristics and quality control to reliably measure BCRABL in MMR samples in which there may be less than 10 molecules in 300 of RNA extracted from whole blood. A method validation protocol was developed using the International Committee on Harmonization (ICH) Q2(R1) international validation guidelines. BCRABL variants b2a2 and b3a2 as well as the housekeeping gene GUSB were measured in KCL22 cell line RNA and Stratagene Universal Human Reference RNA test articles in extreme linear dilution assay, robustness testing conditions, and in multiple laboratories. Quality controls were developed to enable loading as much whole blood RNA into the assay as possible without assay interference. These included a reverse transcription (RT) standardization mixture comprising External RNA Control Consortium (ERCC) reagents to control for RT interference, and controls for genomic DNA contamination, RNA integrity, and PCR interference. Results: This test for b2a2 and b3a2 variants of BCR-ABL meets ICHQ2(R1) guidelines for specificity, linearity, accuracy, LOD and LOQ, imprecision, robustness, and reproducibility. Each analyte comprised by the test was linear (R2 > 0.97) over more than 3 logs10, and demonstrated inter-experimental and inter-laboratory imprecision low enough to enable measurement of a 3-fold difference as significant (P <0.05) from a value as low as five molecules/assay. Experiments are underway to quantify the amount of whole blood RNA that can be loaded into RT reaction without interference.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2232. doi:10.1158/1538-7445.AM2011-2232
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Willey JC. Abstract 3168: An antioxidant and DNA repair gene expression pattern associated with lung cancer risk is also associated with COPD risk. Cancer Res 2011. [DOI: 10.1158/1538-7445.am2011-3168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Lung cancer and Chronic Obstructive Pulmonary Disease (COPD) are leading causes of morbidity and mortality both in the United States and worldwide. Inhalation of cigarette smoke is the primary known and preventable cause but only 10-15% of heavy smokers develop these diseases. This suggests that cigarette smoke exposure interacts with inherited susceptibility factors to determine risk. While some susceptibility genes are known, they account for less than 5% of risk for either disease. There is urgent need to identify additional heritable susceptibility factors for these diseases that explain the majority of COPD and lung cancer risk. For example, the National Lung Screening Trial (NLST) recently reported a 20% reduction in mortality from lung cancer. Because there are over 50 million individuals in the US who meet the criteria for the NLST, at a cost of $400/CT scan, the yearly cost of screening could be $20 billion. The objective in this study is to determine the role of inter-individual variation in antioxidant and DNA repair gene transcript regulation in conferring risk for both COPD and lung cancer. The central hypothesis is that inherited DNA variants are associated with increased risk for both COPD and lung cancer and that many of these manifest as increased transcript abundance dispersion in key antioxidant, DNA repair, and transcription factor genes. In support of our hypothesis, we recently identified a lung cancer risk test (LCRT) that comprises transcript abundance values of 14 genes, including nine anti-oxidant, three DNA repair, and two transcription factor genes. For each of these genes, transcript abundance values were dispersed over a greater range in lung cancer compared to matched controls (Blomquist et al, 2009). In addition, inherited DNA variation causes variation in transcript regulation of genes comprised by the LCRT (Crawford et al, 2007;Blomquist et al, 2010). Here we report that in a small number of non-lung cancer cases studied thus far N=13), a subject with positive LCRT value has a 6.7-fold increased likelihood of having COPD (defined by FEV1/FVC<0.7). Development of biomarkers for lung cancer and/or COPD risk will enable prioritization for screening and/or chemoprevention trials and provide drug targets for development of chemo-preventative and therapeutic pharmaceuticals. These developments are expected to reduce mortality and health care costs.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3168. doi:10.1158/1538-7445.AM2011-3168
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Stanoszek LM, Crawford EL, Blomquist T, Willey JC. Abstract 825: Development of RNA quality-control methods to improve tissue archiving and molecular diagnostic testing. Cancer Res 2010. [DOI: 10.1158/1538-7445.am10-825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Biospecimen collection and processing methods practiced today yield clinical samples with a wide variation in RNA quality. Efforts to improve these methods are inadequate due to insufficient means to measure RNA quality. Improved RNA quality control (QC) measurement methods will enable establishment of cut-off criteria to determine which biospecimens will yield reliable RT-PCR results. Implementation of such criteria will enable existing banks of samples with variable RNA quality to be used for reverse transcriptase polymerase chain reaction (RT-PCR)-based diagnostic test development. Sources of inter-sample variation in RNA quality include RNA integrity, genomic DNA (gDNA) contamination, and substances that either a) interfere with RT efficiency and/or b) carry over to cDNA and cause gene-specific inhibition of PCR efficiency. An RNA integrity reference model was established by incubating A549 cell line populations at room temperature for different amounts of time following cytolysis. Between 0 and 100 min. post cytolysis, ACTB degradation, as well as the degradation of other major housekeeping genes, was more rapid than decline in RNA Integrity Number (RIN) Score, a measurement based on microcapillary electropheresis. Quantification of single-copy CC10 gene gDNA in RNA controlled for gDNA contamination and measurement of transcript abundance of each gene relative to known number of respective cDNA internal standards controlled for PCR inhibitors. The RT inhibition test uses a Reverse Transcription Standards Mixture (RTSM) that contains a known number of copies of ERCC (External RNA Control Consortium) RNA and cDNA standards. A known quantity of RTSM is added to an RNA sample and measurement of PCR product from the RNA standard relative to its cDNA standard following RT-PCR assesses RT efficiency. These RT-PCR tests have been implemented on an Agilent 2100 Bioanalyzer® and are being developed for implementation on high throughput RT-PCR platforms such as the Biotrove OpenArray® nanoarray and the Gene Express Standardized Expression Measurement (SEM) Center™. Improved methods for biospecimen collection will be selected and will then be used to identify which samples collected according to present methods are suitable for analysis by RT-PCR-based molecular diagnostic tests.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 825.
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Blomquist TM, Brown R, Crawford EL, Willey JC. Abstract 4988: Cis-acting genetic variation is associated with altered allele-specific expression of CEBPG transcript in normal human bronchial epithelium. Cancer Res 2010. [DOI: 10.1158/1538-7445.am10-4988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
CEBPG (CCAAT/enhancer binding protein gamma) is one of six known members of the CEBP family of bZIP transcription factors. Gene targeted knock-out of CEBPG transcription factor in mice results in early perinatal death, presumably due to observed emphysematous lung histology and abnormal development of alveolar pneumocytes (Kaisho et al, 1999). In recent studies, we observed that: 1) CEBPG transcript expression in normal human bronchial epithelial cells (NBEC) varies greater than 20-fold among individuals (Mullins et al, 2005), 2) the pattern of CEBPG transcript expression in NBEC is different between lung cancer cases and controls (Blomquist et al, 2009), and 3) transcript expression of a key DNA repair gene, ERCC5 (XPG), is partly regulated by CEBPG in NBEC (Crawford et al, 2007). In this study we hypothesized that variation in CEBPG transcript expression patterns in NBEC may, in part, be due to cis-acting genetic variation. In order to test this hypothesis, Allele-Specific Expression (ASE) was measured at transcribed polymorphic site rs2772 in Exon 2 of CEBPG in NBEC cDNA of fifteen individuals heterozygous at rs2772 using allele-specific competitive PCR. ASE at rs2772 was then assessed for association with genotype at the seven polymorphic sites (rs736682, rs17530479, rs17530508, rs1469084, rs16968029, rs3745968, rs36101103) representing the most common haplotypes in the promoter, intron and 3′-untranslated regions of CEBPG gene. CEBPG rs2772 A allele was expressed, on average, 2.34 fold greater than C allele (p < 0.001) in 20% (3 out of 15) of NBEC cDNA samples, among individuals heterozygous at both rs2772 and rs17530479 in the 3′-untranslated and promoter regions of CEBPG, respectively. These findings are consistent with previous reports that in 20% of samples, CEBPG rs2772 A allele was expressed 2.42 fold greater than C allele (Lo et al, 2003). We also observed that 3 out of 9 NBEC samples obtained from individuals diagnosed with lung cancer exhibited rs2772 A:C allelic ratios significantly higher than genomic DNA controls, while 0 out 6 samples from individuals without lung cancer exhibited altered ASE. Of note, rs2772 and rs17530479 polymorphic sites are in linkage disequilibrium with rs10518275 polymorphic site, which was recently associated with severity of cystic fibrosis lung disease (Gu et al, 2009). Here we demonstrate that cis-acting variation in linkage with rs2772 and rs17530479 polymorphic sites is associated with variation in ASE of CEBPG and lung cancer diagnosis.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4988.
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Blomquist TM, Crawford EL, Willey JC. Cis-acting genetic variation at an E2F1/YY1 response site and putative p53 site is associated with altered allele-specific expression of ERCC5 (XPG) transcript in normal human bronchial epithelium. Carcinogenesis 2010; 31:1242-50. [PMID: 20233728 DOI: 10.1093/carcin/bgq057] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
ERCC5 (XPG) is a key component of the nucleotide excision DNA repair pathway. In two recent case-control studies, we determined that ERCC5 transcript expression pattern in grossly normal human bronchial epithelial cells (NBEC) was different in individuals diagnosed with lung cancer compared with non-lung cancer controls. In this study, we tested the hypothesis that variation at cis-acting sites contributed to observed variation in ERCC5 transcript expression in NBEC. Allele-specific expression (ASE) was measured at transcribed polymorphic site rs1047768 in exon 2 of ERCC5 in NBEC complementary DNA (cDNA) of 22 individuals using allele-specific competitive polymerase chain reaction. ASE at rs1047768 was then assessed for association with allelotype at polymorphic sites rs751402 (E2F1 and YY1 recognition and response site) and rs2296147 (putative P53 recognition site) in the proximal promoter and 5' untranslated region, respectively, of ERCC5. Interindividual variation in recombination between rs751402, rs2296147 and rs1047768 in poly-heterozygotes was controlled for by allele-specific sequencing. Measured rs1047768 T:C allelic ratio was (i) significantly higher in NBEC cDNA compared with genomic DNA controls (P < 0.001) among samples heterozygous at both rs751402 and rs2296147; (ii) less high (P = 0.02) for samples homozygous at rs751402 but heterozygous at rs2296147 and (iii) not significantly different (P = 0.18) for doubly homozygous individuals. Here, we demonstrate that rs751402 A allele and rs2296147 T allele are associated with higher ASE of ERCC5 T allele transcript at rs1047768 in NBEC.
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Affiliation(s)
- Thomas M Blomquist
- Department of Medicine, University of Toledo Medical Center, Toledo, OH 43614, USA
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