1
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Sicotte H, Kalari KR, Qin S, Dehm SM, Bhargava V, Gormley M, Tan W, Sinnwell JP, Hillman DW, Li Y, Vedell PT, Carlson RE, Bryce AH, Jimenez RE, Weinshilboum RM, Kohli M, Wang L. Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment. Mol Cancer Res 2022; 20:1739-1750. [PMID: 36135372 PMCID: PMC9716248 DOI: 10.1158/1541-7786.mcr-22-0099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 01/30/2022] [Revised: 07/05/2022] [Accepted: 09/02/2022] [Indexed: 01/15/2023]
Abstract
We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study. We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC). At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4-4.5) or long-term (median 29 months; range 23.5-41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders. IMPLICATIONS Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.
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Affiliation(s)
- Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Krishna R. Kalari
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Sisi Qin
- Department of Pathology, The University of Chicago., Chicago, Illinois
| | - Scott M. Dehm
- Masonic Cancer Center and Departments of Laboratory Medicine and Pathology and Urology, University of Minnesota, Minneapolis, Minnesota
| | - Vipul Bhargava
- Janssen Research and Development, Spring House, Pennsylvania
| | - Michael Gormley
- Janssen Research and Development, Spring House, Pennsylvania
| | - Winston Tan
- Department of Medicine, Mayo Clinic, Jacksonville, Florida
| | - Jason P. Sinnwell
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - David W. Hillman
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Ying Li
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Peter T. Vedell
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Rachel E. Carlson
- Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Alan H. Bryce
- Division of Hematology & Medical Oncology, Mayo Clinic, Rochester, Minnesota
| | | | - Richard M. Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Manish Kohli
- Department of Internal Medicine, University of Utah and Huntsman Cancer Institute, Salt Lake City, Utah
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
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2
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Wang L, Scherer SE, Bielinski SJ, Muzny DM, Jones LA, Black JL, Moyer AM, Giri J, Sharp RR, Matey ET, Wright JA, Oyen LJ, Nicholson WT, Wiepert M, Sullard T, Curry TB, Vitek CRR, McAllister TM, Sauver JL, Caraballo PJ, Lazaridis KN, Venner E, Qin X, Hu J, Kovar CL, Korchina V, Walker K, Doddapaneni H, Wu TJ, Raj R, Denson S, Liu W, Chandanavelli G, Zhang L, Wang Q, Kalra D, Karow MB, Harris KJ, Sicotte H, Peterson SE, Barthel AE, Moore BE, Skierka JM, Kluge ML, Kotzer KE, Kloke K, Vander Pol JM, Marker H, Sutton JA, Kekic A, Ebenhoh A, Bierle DM, Schuh MJ, Grilli C, Erickson S, Umbreit A, Ward L, Crosby S, Nelson EA, Levey S, Elliott M, Peters SG, Pereira N, Frye M, Shamoun F, Goetz MP, Kullo IJ, Wermers R, Anderson JA, Formea CM, El Melik RM, Zeuli JD, Herges JR, Krieger CA, Hoel RW, Taraba JL, Thomas SR, Absah I, Bernard ME, Fink SR, Gossard A, Grubbs PL, Jacobson TM, Takahashi P, Zehe SC, Buckles S, Bumgardner M, Gallagher C, Fee-Schroeder K, Nicholas NR, Powers ML, Ragab AK, Richardson DM, Stai A, Wilson J, Pacyna JE, Olson JE, Sutton EJ, Beck AT, Horrow C, Kalari KR, Larson NB, Liu H, Wang L, Lopes GS, Borah BJ, Freimuth RR, Zhu Y, Jacobson DJ, Hathcock MA, Armasu SM, McGree ME, Jiang R, Koep TH, Ross JL, Hilden M, Bosse K, Ramey B, Searcy I, Boerwinkle E, Gibbs RA, Weinshilboum RM. Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study. Genet Med 2022; 24:1062-1072. [PMID: 35331649 PMCID: PMC9272414 DOI: 10.1016/j.gim.2022.01.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [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: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.
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Affiliation(s)
- Liewei Wang
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Division of Clinical Pharmacology, Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN
| | - Steven E. Scherer
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Suzette J. Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Donna M. Muzny
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Leila A. Jones
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - John Logan Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Ann M. Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Jyothsna Giri
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | | | - Wayne T. Nicholson
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Mathieu Wiepert
- Department of Information Technology, Mayo Clinic, Rochester, MN
| | - Terri Sullard
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Timothy B. Curry
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Jennifer L. Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN,Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Pedro J. Caraballo
- Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Konstantinos N. Lazaridis
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Eric Venner
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Xiang Qin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Jianhong Hu
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Christie L. Kovar
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Viktoriya Korchina
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Kimberly Walker
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | | | - Tsung-Jung Wu
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Ritika Raj
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Shawn Denson
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Wen Liu
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Gauthami Chandanavelli
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Lan Zhang
- Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX
| | - Qiaoyan Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Divya Kalra
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Mary Beth Karow
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Hugues Sicotte
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Sandra E. Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Amy E. Barthel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Brenda E. Moore
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Michelle L. Kluge
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Katrina E. Kotzer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Karen Kloke
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Heather Marker
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Joseph A. Sutton
- Department of Information Technology, Mayo Clinic, Rochester, MN
| | | | | | - Dennis M. Bierle
- Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | - Audrey Umbreit
- Department of Pharmacy, Mayo Clinic Health System, Mankato, MN
| | - Leah Ward
- Department of Pharmacy, Mayo Clinic, Jacksonville, FL
| | - Sheena Crosby
- Department of Pharmacy, Mayo Clinic, Jacksonville, FL
| | | | - Sharon Levey
- Department of Clinical Genomics, Mayo Clinic, Scottsdale, AZ
| | - Michelle Elliott
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Steve G. Peters
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Naveen Pereira
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mark Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN
| | - Fadi Shamoun
- Department of Cardiovascular Medicine Mayo Clinic, Phoenix, AZ
| | - Matthew P. Goetz
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, MN
| | | | - Robert Wermers
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | | | | | | | | | | | | | - Scott R. Thomas
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Imad Absah
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | - Stephanie R. Fink
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Andrea Gossard
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Paul Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | | | - Susan Buckles
- Department of Public Affairs, Mayo Clinic, Rochester, MN
| | | | | | | | | | - Melody L. Powers
- Biospecimens Accessioning and Processing Laboratory, Mayo Clinic, Rochester, MN
| | - Ahmed K. Ragab
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | | | - Anthony Stai
- Department of Information Technology, Mayo Clinic, Rochester, MN
| | - Jaymi Wilson
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN
| | - Joel E. Pacyna
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Janet E. Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN,Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Erica J. Sutton
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Annika T. Beck
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Caroline Horrow
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN
| | - Krishna R. Kalari
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Nicholas B. Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Hongfang Liu
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Liwei Wang
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Guilherme S. Lopes
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN,Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Bijan J. Borah
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN,Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Robert R. Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Ye Zhu
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Debra J. Jacobson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Matthew A. Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Sebastian M. Armasu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Michaela E. McGree
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | | | | | | | | | | | | | - Eric Boerwinkle
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX,Human Genome Sequencing Center Clinical Laboratory, Baylor College of Medicine, Houston, TX,School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Richard A. Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX,Corresponding Authors (), ()
| | - Richard M. Weinshilboum
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN,Division of Clinical Pharmacology, Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN,Corresponding Authors (), ()
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3
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Olson R, Morales-Rosado J, Safgren S, Sicotte H, Zieman A, Hansen A, Karow MB, Kruisselbrink T, Lazaridis K, Klee E, Ferber M. eP383: Mayo Clinic GeneGuide: A population-scale genetic interpretation software for reporting pathogenic and likely pathogenic variants impacting the CDC Tier1 genes. Genet Med 2022. [DOI: 10.1016/j.gim.2022.01.418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Horn IP, Marks DL, Koenig AN, Hogenson TL, Almada LL, Goldstein LE, Romecin Duran PA, Vera R, Vrabel AM, Cui G, Rabe KG, Bamlet WR, Mer G, Sicotte H, Zhang C, Li H, Petersen GM, Fernandez-Zapico ME. A rare germline CDKN2A variant (47T>G; p16-L16R) predisposes carriers to pancreatic cancer by reducing cell cycle inhibition. J Biol Chem 2021; 296:100634. [PMID: 33823155 PMCID: PMC8121974 DOI: 10.1016/j.jbc.2021.100634] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 09/27/2020] [Revised: 03/29/2021] [Accepted: 04/01/2021] [Indexed: 11/30/2022] Open
Abstract
Germline mutations in CDKN2A, encoding the tumor suppressor p16, are responsible for a large proportion of familial melanoma cases and also increase risk of pancreatic cancer. We identified four families through pancreatic cancer probands that were affected by both cancers. These families bore a germline missense variant of CDKN2A (47T>G), encoding a p16-L16R mutant protein associated with high cancer occurrence. Here, we investigated the biological significance of this variant. When transfected into p16-null pancreatic cancer cells, p16-L16R was expressed at lower levels than wild-type (WT) p16. In addition, p16-L16R was unable to bind CDK4 or CDK6 compared with WT p16, as shown by coimmunoprecipitation assays and also was impaired in its ability to inhibit the cell cycle, as demonstrated by flow cytometry analyses. In silico molecular modeling predicted that the L16R mutation prevents normal protein folding, consistent with the observed reduction in expression/stability and diminished function of this mutant protein. We isolated normal dermal fibroblasts from members of the families expressing WT or L16R proteins to investigate the impact of endogenous p16-L16R mutant protein on cell growth. In culture, p16-L16R fibroblasts grew at a faster rate, and most survived until later passages than p16-WT fibroblasts. Further, western blotting demonstrated that p16 protein was detected at lower levels in p16-L16R than in p16-WT fibroblasts. Together, these results suggest that the presence of a CDKN2A (47T>G) mutant allele contributes to an increased risk of pancreatic cancer as a result of reduced p16 protein levels and diminished p16 tumor suppressor function.
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Affiliation(s)
- Isaac P Horn
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - David L Marks
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Amanda N Koenig
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Tara L Hogenson
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Luciana L Almada
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Lauren E Goldstein
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Paola A Romecin Duran
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Renzo Vera
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Anne M Vrabel
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Gaofeng Cui
- Division of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kari G Rabe
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - William R Bamlet
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Georges Mer
- Division of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Cheng Zhang
- Division of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Hu Li
- Division of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Martin E Fernandez-Zapico
- Division of Oncology Research, Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.
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5
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Antwi SO, Bamlet WR, Cawthon RM, Rabe KG, Druliner BR, Sicotte H, Jatoi A, Mahipal A, Boardman LA, Oberg AL, Petersen GM. Shorter Treatment-Naïve Leukocyte Telomere Length is Associated with Poorer Overall Survival of Patients with Pancreatic Ductal Adenocarcinoma. Cancer Epidemiol Biomarkers Prev 2020; 30:210-216. [PMID: 33187969 DOI: 10.1158/1055-9965.epi-20-1279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/14/2020] [Accepted: 11/02/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Critically shortened telomeres contribute to chromosomal instability and neoplastic transformation and are associated with early death of patients with certain cancer types. Shorter leukocyte telomere length (LTL) has been associated with higher risk for pancreatic ductal adenocarcinoma (PDAC) and might be associated also with survival of patients with PDAC. We investigated the association between treatment-naïve LTL and overall survival of patients with incident PDAC. METHODS The study included 642 consecutively enrolled PDAC patients in the Mayo Clinic Biospecimen Resource for Pancreas Research. Blood samples were obtained at the time of diagnosis, before the start of cancer treatment, from which LTL was assayed by qRT-PCR. LTL was first modeled as a continuous variable (per-interquartile range decrease in LTL) and then as a categorized variable (short, medium, long). Multivariable-adjusted HRs and 95% confidence intervals (CI) were calculated for overall mortality using Cox proportional hazard models. RESULTS Shorter treatment-naïve LTL was associated with higher mortality among patients with PDAC (HRcontinuous = 1.13, 95% CI: 1.01-1.28, P = 0.03; HRshortest vs. longest LTL = 1.29, 95% CI: 1.05-1.59, P trend = 0.01). There was a difference in the association between LTL and overall mortality by tumor stage at diagnosis; resectable tumors (HRcontinuous = 0.91; 95% CI: 0.73-1.12), locally advanced tumors (HRcontinuous = 1.29; 95% CI: 1.07-1.56), and metastatic tumors (HRcontinuous = 1.17; 95% CI: 0.96-1.42), P interaction = 0.04. CONCLUSION Shorter treatment-naïve LTL is associated with poorer overall survival of patients with incident PDAC. IMPACT Peripheral blood LTL might be a prognostic marker for PDAC.
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Affiliation(s)
- Samuel O Antwi
- Division of Epidemiology, Mayo Clinic, Jacksonville, Florida.
| | - William R Bamlet
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Richard M Cawthon
- Department of Human Genetics, University of Utah, Salt Lake City, Utah
| | - Kari G Rabe
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | | | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Aminah Jatoi
- Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota
| | - Amit Mahipal
- Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota
| | - Lisa A Boardman
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Ann L Oberg
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
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6
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Morales-Rosado JA, Goel K, Zhang L, Åkerblom A, Baheti S, Black JL, Eriksson N, Wallentin L, James S, Storey RF, Goodman SG, Jenkins GD, Eckloff BW, Bielinski SJ, Sicotte H, Johnson S, Roger VL, Wang L, Weinshilboum R, Klee EW, Rihal CS, Pereira NL. Next-Generation Sequencing of CYP2C19 in Stent Thrombosis: Implications for Clopidogrel Pharmacogenomics. Cardiovasc Drugs Ther 2020; 35:549-559. [PMID: 32623598 DOI: 10.1007/s10557-020-06988-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE Describe CYP2C19 sequencing results in the largest series of clopidogrel-treated cases with stent thrombosis (ST), the closest clinical phenotype to clopidogrel resistance. Evaluate the impact of CYP2C19 genetic variation detected by next-generation sequencing (NGS) with comprehensive annotation and functional studies. METHODS Seventy ST cases on clopidogrel identified from the PLATO trial (n = 58) and Mayo Clinic biorepository (n = 12) were matched 1:1 with controls for age, race, sex, diabetes mellitus, presentation, and stent type. NGS was performed to cover the entire CYP2C19 gene. Assessment of exonic variants involved measuring in vitro protein expression levels. Intronic variants were evaluated for potential splicing motif variations. RESULTS Poor metabolizers (n = 4) and rare CYP2C19*8, CYP2C19*15, and CYP2C19*11 alleles were identified only in ST cases. CYP2C19*17 heterozygote carriers were observed more frequently in cases (n = 29) than controls (n = 18). Functional studies of CYP2C19 exonic variants (n = 11) revealed 3 cases and only 1 control carrying a deleterious variant as determined by in vitro protein expression studies. Greater intronic variation unique to ST cases (n = 169) compared with controls (n = 84) was observed with predictions revealing 13 allele candidates that may lead to a potential disruption of splicing and a loss-of-function effect of CYP2C19 in ST cases. CONCLUSION NGS detected CYP2C19 poor metabolizers and paradoxically greater number of so-called rapid metabolizers in ST cases. Rare deleterious exonic variation occurs in 4%, and potentially disruptive intronic alleles occur in 16% of ST cases. Additional studies are required to evaluate the role of these variants in platelet aggregation and clopidogrel metabolism.
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Affiliation(s)
- Joel A Morales-Rosado
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kashish Goel
- Vanderbilt University School of Medicine, Nashville, TN, 37215, USA
| | - Lingxin Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Axel Åkerblom
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Saurabh Baheti
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - John L Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Niclas Eriksson
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Lars Wallentin
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Stefan James
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Robert F Storey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Shaun G Goodman
- St. Michael's Hospital, University of Toronto, Toronto, Canada.,Canadian VIGOUR Centre, University of Alberta , Edmonton, Canada
| | - Gregory D Jenkins
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Suzette J Bielinski
- Division of Epidemiology, Mayo Clinic, Department of Health Sciences Research, Rochester, MN, USA
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Stephen Johnson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Veronique L Roger
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Eric W Klee
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Charanjit S Rihal
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Naveen L Pereira
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
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7
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Burgenske DM, Yang J, Decker PA, Kollmeyer TM, Kosel ML, Mladek AC, Caron AA, Vaubel RA, Gupta SK, Kitange GJ, Sicotte H, Youland RS, Remonde D, Voss JS, Fritcher EGB, Kolsky KL, Ida CM, Meyer FB, Lachance DH, Parney IJ, Kipp BR, Giannini C, Sulman EP, Jenkins RB, Eckel-Passow JE, Sarkaria JN. Molecular profiling of long-term IDH-wildtype glioblastoma survivors. Neuro Oncol 2020; 21:1458-1469. [PMID: 31346613 DOI: 10.1093/neuonc/noz129] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Glioblastoma (GBM) represents an aggressive cancer type with a median survival of only 14 months. With fewer than 5% of patients surviving 5 years, comprehensive profiling of these rare patients could elucidate prognostic biomarkers that may confer better patient outcomes. We utilized multiple molecular approaches to characterize the largest patient cohort of isocitrate dehydrogenase (IDH)-wildtype GBM long-term survivors (LTS) to date. METHODS Retrospective analysis was performed on 49 archived formalin-fixed paraffin embedded tumor specimens from patients diagnosed with GBM at the Mayo Clinic between December 1995 and September 2013. These patient samples were subdivided into 2 groups based on survival (12 LTS, 37 short-term survivors [STS]) and subsequently examined by mutation sequencing, copy number analysis, methylation profiling, and gene expression. RESULTS Of the 49 patients analyzed in this study, LTS were younger at diagnosis (P = 0.016), more likely to be female (P = 0.048), and MGMT promoter methylated (UniD, P = 0.01). IDH-wildtype STS and LTS demonstrated classic GBM mutations and copy number changes. Pathway analysis of differentially expressed genes showed LTS enrichment for sphingomyelin metabolism, which has been linked to decreased GBM growth, invasion, and angiogenesis. STS were enriched for DNA repair and cell cycle control networks. CONCLUSIONS While our findings largely report remarkable similarity between these LTS and more typical STS, unique attributes were observed in regard to altered gene expression and pathway enrichment. These attributes may be valuable prognostic markers and are worth further examination. Importantly, this study also underscores the limitations of existing biomarkers and classification methods in predicting patient prognosis.
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Affiliation(s)
| | - Jie Yang
- Department of Radiation Oncology, NYU Langone School of Medicine, New York, New York
| | - Paul A Decker
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Thomas M Kollmeyer
- Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Matthew L Kosel
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Ann C Mladek
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Alissa A Caron
- Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Rachael A Vaubel
- Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Shiv K Gupta
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Gaspar J Kitange
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Ryan S Youland
- Department of Radiation Oncology, Gundersen Health System, La Crosse, Wisconsin
| | - Dioval Remonde
- Department of Radiation Oncology, Mays Cancer Center, University of Texas Health San Antonio, San Antonio, Texas
| | - Jesse S Voss
- Molecular Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Emily G Barr Fritcher
- Molecular Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Kathryn L Kolsky
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Cristiane M Ida
- Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Fredric B Meyer
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota
| | | | - Ian J Parney
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota
| | - Benjamin R Kipp
- Molecular Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Caterina Giannini
- Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Erik P Sulman
- Department of Radiation Oncology, NYU Langone School of Medicine, New York, New York
| | - Robert B Jenkins
- Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
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8
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Antwi SO, Bamlet WR, Rabe KG, Cawthon RM, Umudi I, Druliner BR, Sicotte H, Oberg AL, Jatoi A, Boardman LA, Petersen GM. Leukocyte Telomere Length and Its Interaction with Germline Variation in Telomere-Related Genes in Relation to Pancreatic Adenocarcinoma Risk. Cancer Epidemiol Biomarkers Prev 2020; 29:1492-1500. [PMID: 32312758 DOI: 10.1158/1055-9965.epi-19-1597] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 03/18/2020] [Accepted: 04/15/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Leukocyte telomere length (LTL) has been associated with risk of multiple cancers, but its association with pancreatic ductal adenocarcinoma (PDAC) is unclear. We therefore investigated the association between peripheral blood LTL and PDAC risk, and examined effect modification by candidate SNPs previously reported to be associated with variation in LTL. METHODS A case-control study of 1,460 PDAC cases and 1,459 frequency-matched controls was performed using biospecimens and data from the Mayo Clinic Biospecimen Resource for Pancreas Research. Quantitative PCR was used to measure LTL and categorized into tertiles based on sex-specific control distribution. Eleven telomere-related SNPs also were genotyped. Logistic regression was used to calculate ORs and 95% confidence intervals (CI). RESULTS Shorter peripheral blood LTL was associated with a higher risk of PDAC (ORT1vsT3 = 1.26, 95% CI = 1.03-1.54, P trend = 0.02; ORcontinuous = 1.14, 95% CI = 1.02-1.28), but the association was restricted to cases with treatment-naïve blood samples (ORT1vsT3 = 1.51, 95% CI = 1.16-1.96, P trend = 0.002; ORcontinuous = 1.25, 95% CI = 1.08-1.45) and not cases whose blood samples were collected after initiation of cancer therapy (ORT1vsT3 = 1.10, 95% CI = 0.87-1.39, P trend = 0.42; ORcontinuous = 1.08, 95% CI = 0.94-1.23). Three SNPs (TERC-rs10936599, ACYP2-rs11125529, and TERC-rs1317082) were each associated with interindividual variation in LTL among controls, but there was no evidence of effect modification by these SNPs. CONCLUSIONS Treatment-naïve short LTL is associated with a higher risk of PDAC, and the association does not differ by germline variation in the candidate telomere-related SNPs examined. IMPACT Peripheral blood LTL might serve as a molecular marker for risk modeling to identify persons at high risk of PDAC.
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Affiliation(s)
- Samuel O Antwi
- Division of Epidemiology, Mayo Clinic, Jacksonville, Florida.
| | - William R Bamlet
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Kari G Rabe
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Richard M Cawthon
- Department of Human Genetics, University of Utah, Salt Lake City, Utah
| | - Isoken Umudi
- Division of Epidemiology, Mayo Clinic, Jacksonville, Florida
| | - Brooke R Druliner
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Ann L Oberg
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Aminah Jatoi
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Lisa A Boardman
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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9
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Giridhar K, Sosa C, Sicotte H, Wang L, Wang L, Sinnwell JP, Tan W, Costello BA, Quevedo F, Pitot HC, Bryce AH, Jimenez RE, Weinshilboum RM, Dehm S, Kalari KR, Kohli M. Evolution of androgen receptor variant (ARV) profiles in serial metastatic solid and liquid biopsies in metastatic castrate resistant prostate cancer (mCRPC) treated with abiraterone acetate/ prednisone (AA/P). J Clin Oncol 2019. [DOI: 10.1200/jco.2019.37.15_suppl.e16559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e16559 Background: ARVs that develop early during treatment with abiraterone acetate/ prednisone (AA/P) may play a role in treatment resistance. We evaluated metastatic site and whole blood mRNA expression of full-length AR (AR-FL) and ARVs to characterize treatment-emergent ARVs (TE-ARVs) from men with mCRPC pre- and post- 12 weeks of AA/P collected in a prospective clinical trial (NCT#01953640). Methods: RNAseq performed on paired metastatic site biopsy (n = 40) and whole blood (n = 25) passed quality control. Reads were aligned to the GRCh38 reference genome with the spliced-alignment TopHat2 package. AR-FL or ARVs were detected if ≥ 2 splice reads aligned to unique splice junctions for AR-FL, AR-8, AR-45, AR-23, AR-V3, AR-V5V6, AR-V7, AR-V8, AR-V9, AR-V10, AR-V12, AR-V13, and AR-V14, and normalized to splice reads per million (SRPM). Cox proportional hazard regression analysis was performed on AR-FL and AR-Vs with ≥1 SRPM for association with time to treatment change (TTTC). Results: In metastatic site biopsies post-AA/P, the average number of splice reads was 27,376,541 (range 7,753,998 to 62,456,773). The median number of ARVs detected was 2 (range 0-8), with a total of total of 110 ARVs identified (Table). Dynamic shifts in ARV profiles were observed post-AAP, with 41 TE-ARVs identified in 17 unique patients. The most common TE-ARV was AR-8 (n = 8), followed by AR-45 (n = 5), AR-23 (n = 5), and AR-V7 (n = 4). The presence of AR-V7 post-AAP was adversely associated with TTTC (hazard ratio 2.46, p = 0.013). The identification of early TE- ARV was not associated with TTTC. In whole blood samples, post-AA/P detection of AR-FL was low (n = 3) and no ARVs were detected. Conclusions: No specific patterns were observed in ARV profiles obtained in the metastatic biopsies after 12 weeks of treatment with AA/P. Clinical trial information: 01953640. [Table: see text]
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Scott Dehm
- University of Minnesota, Minneapolis, MN
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10
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Wang L, Dehm SM, Hillman DW, Sicotte H, Tan W, Gormley M, Bhargava V, Jimenez R, Xie F, Yin P, Qin S, Quevedo F, Costello BA, Pitot HC, Ho T, Bryce AH, Ye Z, Li Y, Eiken P, Vedell PT, Barman P, McMenomy BP, Atwell TD, Carlson RE, Ellingson M, Eckloff BW, Qin R, Ou F, Hart SN, Huang H, Jen J, Wieben ED, Kalari KR, Weinshilboum RM, Wang L, Kohli M. A prospective genome-wide study of prostate cancer metastases reveals association of wnt pathway activation and increased cell cycle proliferation with primary resistance to abiraterone acetate-prednisone. Ann Oncol 2019; 29:352-360. [PMID: 29069303 DOI: 10.1093/annonc/mdx689] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [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] [Indexed: 01/16/2023] Open
Abstract
Background Genomic aberrations have been identified in metastatic castration-resistant prostate cancer (mCRPC), but molecular predictors of resistance to abiraterone acetate/prednisone (AA/P) treatment are not known. Patients and methods In a prospective clinical trial, mCRPC patients underwent whole-exome sequencing (n = 82) and RNA sequencing (n = 75) of metastatic biopsies before initiating AA/P with the objective of identifying genomic alterations associated with resistance to AA/P. Primary resistance was determined at 12 weeks of treatment using criteria for progression that included serum prostate-specific antigen measurement, bone and computerized tomography imaging and symptom assessments. Acquired resistance was determined using the end point of time to treatment change (TTTC), defined as time from enrollment until change in treatment from progressive disease. Associations of genomic and transcriptomic alterations with primary resistance were determined using logistic regression, Fisher's exact test, single and multivariate analyses. Cox regression models were utilized for determining association of genomic and transcriptomic alterations with TTTC. Results At 12 weeks, 32 patients in the cohort had progressed (nonresponders). Median study follow-up was 32.1 months by which time 58 patients had switched treatments due to progression. Median TTTC was 10.1 months (interquartile range: 4.4-24.1). Genes in the Wnt/β-catenin pathway were more frequently mutated and negative regulators of Wnt/β-catenin signaling were more frequently deleted or displayed reduced mRNA expression in nonresponders. Additionally, mRNA expression of cell cycle regulatory genes was increased in nonresponders. In multivariate models, increased cell cycle proliferation scores (≥ 50) were associated with shorter TTTC (hazard ratio = 2.11, 95% confidence interval: 1.17-3.80; P = 0.01). Conclusions Wnt/β-catenin pathway activation and increased cell cycle progression scores can serve as molecular markers for predicting resistance to AA/P therapy.
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Affiliation(s)
- L Wang
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, USA
| | - S M Dehm
- Masonic Cancer Center, University of Minnesota, Minneapolis, USA; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, USA; Department of Urology, University of Minnesota, Minneapolis, USA
| | - D W Hillman
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - H Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - W Tan
- Department of Medicine, Mayo Clinic, Jacksonville, USA
| | - M Gormley
- Janssen Research and Development, Spring House, Philadelphia, USA
| | - V Bhargava
- Janssen Research and Development, Spring House, Philadelphia, USA
| | - R Jimenez
- Department of Pathology and Lab Medicine, Mayo Clinic, Rochester, USA
| | - F Xie
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, USA
| | - P Yin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, USA
| | - S Qin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, USA
| | - F Quevedo
- Department of Oncology, Mayo Clinic, Rochester, USA
| | - B A Costello
- Department of Oncology, Mayo Clinic, Rochester, USA
| | - H C Pitot
- Department of Oncology, Mayo Clinic, Rochester, USA
| | - T Ho
- Department of Medicine, Mayo Clinic, Scottsdale, USA
| | - A H Bryce
- Department of Medicine, Mayo Clinic, Scottsdale, USA
| | - Z Ye
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, USA
| | - Y Li
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - P Eiken
- Department of Radiology, Mayo Clinic, Rochester, USA
| | - P T Vedell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - P Barman
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - B P McMenomy
- Department of Radiology, Mayo Clinic, Rochester, USA
| | - T D Atwell
- Department of Radiology, Mayo Clinic, Rochester, USA
| | - R E Carlson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - M Ellingson
- Medical Genetics, Mayo Clinic, Rochester, USA
| | - B W Eckloff
- Medical Genome Facility, Mayo Clinic, Rochester, USA
| | - R Qin
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - F Ou
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - S N Hart
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - H Huang
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, USA
| | - J Jen
- Medical Genome Facility, Mayo Clinic, Rochester, USA; Division of Experimental Pathology and Laboratory Medicine, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, USA; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, USA
| | - E D Wieben
- Medical Genome Facility, Mayo Clinic, Rochester, USA
| | - K R Kalari
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, USA
| | - R M Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, USA
| | - L Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, USA.
| | - M Kohli
- Department of Oncology, Mayo Clinic, Rochester, USA.
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11
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McWilliams RR, Wieben ED, Chaffee KG, Antwi SO, Raskin L, Olopade OI, Li D, Highsmith WE, Colon-Otero G, Khanna LG, Permuth JB, Olson JE, Frucht H, Genkinger J, Zheng W, Blot WJ, Wu L, Almada LL, Fernandez-Zapico ME, Sicotte H, Pedersen KS, Petersen GM. CDKN2A Germline Rare Coding Variants and Risk of Pancreatic Cancer in Minority Populations. Cancer Epidemiol Biomarkers Prev 2018; 27:1364-1370. [PMID: 30038052 DOI: 10.1158/1055-9965.epi-17-1065] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/13/2018] [Accepted: 07/11/2018] [Indexed: 12/20/2022] Open
Abstract
Background: Pathogenic germline mutations in the CDKN2A tumor suppressor gene are rare and associated with highly penetrant familial melanoma and pancreatic cancer in non-Hispanic whites (NHW). To date, the prevalence and impact of CDKN2A rare coding variants (RCV) in racial minority groups remain poorly characterized. We examined the role of CDKN2A RCVs on the risk of pancreatic cancer among minority subjects.Methods: We sequenced CDKN2A in 220 African American (AA) pancreatic cancer cases, 900 noncancer AA controls, and 183 Nigerian controls. RCV frequencies were determined for each group and compared with that of 1,537 NHW patients with pancreatic cancer. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for both a case-case comparison of RCV frequencies in AAs versus NHWs, and case-control comparison between AA cases versus noncancer AA controls plus Nigerian controls. Smaller sets of Hispanic and Native American cases and controls also were sequenced.Results: One novel missense RCV and one novel frameshift RCV were found among AA patients: 400G>A and 258_278del. RCV carrier status was associated with increased risk of pancreatic cancer among AA cases (11/220; OR, 3.3; 95% CI, 1.5-7.1; P = 0.004) compared with AA and Nigerian controls (17/1,083). Further, AA cases had higher frequency of RCVs: 5.0% (OR, 13.4; 95% CI, 4.9-36.7; P < 0.001) compared with NHW cases (0.4%).Conclusions: CDKN2A RCVs are more common in AA than in NHW patients with pancreatic cancer and associated with moderately increased pancreatic cancer risk among AAs.Impact: RCVs in CDKN2A are frequent in AAs and are associated with risk for pancreatic cancer. Cancer Epidemiol Biomarkers Prev; 27(11); 1364-70. ©2018 AACR.
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Affiliation(s)
| | - Eric D Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota.,Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Kari G Chaffee
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Samuel O Antwi
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida
| | - Leon Raskin
- Division of Epidemiology, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
| | - Olufunmilayo I Olopade
- Departments of Medicine and Human Genetics, University of Chicago Medical Center, Chicago, Illinois
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - W Edward Highsmith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Gerardo Colon-Otero
- Department of Medicine, Division of Hematology/Oncology, Mayo Clinic, Jacksonville, Florida
| | - Lauren G Khanna
- Department of Medicine, Columbia University Medical Center, New York, New York
| | - Jennifer B Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Harold Frucht
- Department of Medicine, Columbia University Medical Center, New York, New York
| | - Jeanine Genkinger
- Department of Epidemiology, Columbia University Medical Center, New York, New York.,Herbert Irving Comprehensive Cancer Center, New York, New York
| | - Wei Zheng
- Division of Epidemiology, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
| | - William J Blot
- Division of Epidemiology, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
| | - Lang Wu
- Division of Epidemiology, Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
| | - Luciana L Almada
- Schulze Center for Novel Therapeutics, Division of Oncology Research, Mayo Clinic, Rochester, Minnesota
| | - Martin E Fernandez-Zapico
- Schulze Center for Novel Therapeutics, Division of Oncology Research, Mayo Clinic, Rochester, Minnesota
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | | | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
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12
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Kohli M, Dehm S, Bhargava V, Gormley M, Sinnwell JP, Tan W, Hillman DW, Li Y, Jimenez RE, Weinshilboum RM, Kalari KR, Wang L, Sicotte H. A transcriptome analysis of castration resistant prostate cancer metastases in a prospective cohort study reveals high expression of AKT pathway genes predictive of long term response to abiraterone acetate/prednisone. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.15_suppl.5038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Scott Dehm
- University of Minnesota, Minneapolis, MN
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13
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Burgenske D, Eckel-Passow J, Decker P, Kosel M, Youland R, Remonde D, Kollmeyer T, Sicotte H, Caron A, Giannini C, Lachance D, Jenkins R, Sarkaria J. GENE-28. CLINICAL AND MOLECULAR ANALYSES OF LONG-TERM SURVIVORS OF GLIOBLASTOMA. Neuro Oncol 2017. [DOI: 10.1093/neuonc/nox168.402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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14
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Druliner BR, Ruan X, Sicotte H, O'Brien D, Liu H, Kocher JPA, Boardman L. Early genetic aberrations in patients with sporadic colorectal cancer. Mol Carcinog 2017; 57:114-124. [PMID: 28926134 DOI: 10.1002/mc.22738] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [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: 09/12/2016] [Revised: 09/01/2017] [Accepted: 09/18/2017] [Indexed: 01/10/2023]
Abstract
Chromosome instability (CIN) is widely observed in both sporadic and hereditary colorectal cancer (CRC). Defects in APC and WNT signaling are primarily associated with CIN in hereditary CRC, but the genetic causes for CIN in sporadic CRC remain elusive. Using high-density SNP array and exome data from The Cancer Genome Atlas (TCGA), we characterized loss of heterozygosity (LOH) and copy number variation (CNV) in the peripheral blood, normal colon, and corresponding tumor tissue in 15 CRC patients with proficient mismatch repair (MMR) and 24 CRC patients with deficient MMR. We found a high frequency of 18q LOH in tumors and arm-specific enrichment of genetic aberrations on 18q in the normal colon (primarily copy neutral LOH) and blood (primarily copy gain). These aberrations were specific to the sporadic, pMMR CRC. Though in tumor samples genetic aberrations were observed for genes commonly mutated in hereditary CRC (eg, APC, CTNNB1, SMAD4, BRAF), none of them showed LOH or CNV in the normal colon or blood. DCC located on 18q21.1 topped the list of genes with genetic aberrations in the tumor. In an independent cohort of 13 patients subjected to Whole Genome Sequencing (WGS), we found LOH and CNV on 18q in adenomatous polyp and tumor tissues. Our data suggests that patients with sporadic CRC may have genetic aberrations preferentially enriched on 18q in their blood, normal colon epithelium, and non-malignant polyp lesions that may prove useful as a clinical marker for sporadic CRC detection and risk assessment.
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Affiliation(s)
- Brooke R Druliner
- Division of Internal Medicine, Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Xiaoyang Ruan
- Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Hugues Sicotte
- Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Daniel O'Brien
- Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Hongfang Liu
- Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Jean-Pierre A Kocher
- Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Lisa Boardman
- Division of Internal Medicine, Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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15
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Bielinski SJ, Berardi C, Decker PA, Larson NB, Bell EJ, Pankow JS, Sale MM, Tang W, Hanson NQ, Wassel CL, de Andrade M, Budoff MJ, Polak JF, Sicotte H, Tsai MY. Hepatocyte growth factor demonstrates racial heterogeneity as a biomarker for coronary heart disease. Heart 2017; 103:1185-1193. [PMID: 28572400 DOI: 10.1136/heartjnl-2016-310450] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 01/19/2017] [Accepted: 01/27/2017] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To determine if hepatocyte growth factor (HGF), a promising biomarker of coronary heart disease (CHD) given its release into circulation in response to endothelial damage, is associated with subclinical and clinical CHD in a racial/ethnic diverse population. METHODS HGF was measured in 6738 participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Highest mean HGF values (pg/mL) were observed in Hispanic, followed by African, non-Hispanic white, then Chinese Americans. RESULTS In all races/ethnicities, HGF levels were associated with older age, higher systolic blood pressure (SBP) and body mass index, lower high-density lipoprotein, diabetes and current smoking. In fully adjusted models, each SD higher HGF was associated with an average increase in coronary artery calcium (CAC) of 55 Agatston units for non-Hispanic whites (p<0.001) and 51 Agatston units for African-Americans (p=0.007) but was not in the other race/ethnic groups (interaction p=0.02). There were 529 incident CHD events, and CHD risk was 41% higher in African (p<0.001), 17% in non-Hispanic white (p=0.026) and Chinese (p=0.36), and 6% in Hispanic Americans (p=0.56) per SD increase in HGF. CONCLUSION In a large and diverse population-based cohort, we report that HGF is associated with subclinical and incident CHD. We demonstrate evidence of racial/ethnic heterogeneity within these associations, as the results are most compelling in African-Americans and non-Hispanic white Americans. We provide evidence that HGF is a biomarker of atherosclerotic disease that is independent of traditional risk factors.
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Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Cecilia Berardi
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.,Department of Internal Medicine, Albert Einstein College of Medicine, and Montefiore Medical Center, Bronx, New York, USA
| | - Paul A Decker
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Elizabeth J Bell
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michele M Sale
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Naomi Q Hanson
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christina L Wassel
- Department of Pathology and Laboratory Medicine, University of Vermont College of Medicine, Colchester, Vermont, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew J Budoff
- Los Angeles Biomedical Research Institute, Harbor-UCLA, Torrance, California, USA
| | - Joseph F Polak
- Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael Y Tsai
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
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16
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Pekmezci M, Rice T, Molinaro AM, Walsh KM, Decker PA, Hansen H, Sicotte H, Kollmeyer TM, McCoy LS, Sarkar G, Perry A, Giannini C, Tihan T, Berger MS, Wiemels JL, Bracci PM, Eckel-Passow JE, Lachance DH, Clarke J, Taylor JW, Luks T, Wiencke JK, Jenkins RB, Wrensch MR. Adult infiltrating gliomas with WHO 2016 integrated diagnosis: additional prognostic roles of ATRX and TERT. Acta Neuropathol 2017; 133:1001-1016. [PMID: 28255664 PMCID: PMC5432658 DOI: 10.1007/s00401-017-1690-1] [Citation(s) in RCA: 207] [Impact Index Per Article: 29.6] [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: 12/16/2016] [Revised: 02/06/2017] [Accepted: 02/24/2017] [Indexed: 01/07/2023]
Abstract
The "integrated diagnosis" for infiltrating gliomas in the 2016 revised World Health Organization (WHO) classification of tumors of the central nervous system requires assessment of the tumor for IDH mutations and 1p/19q codeletion. Since TERT promoter mutations and ATRX alterations have been shown to be associated with prognosis, we analyzed whether these tumor markers provide additional prognostic information within each of the five WHO 2016 categories. We used data for 1206 patients from the UCSF Adult Glioma Study, the Mayo Clinic and The Cancer Genome Atlas (TCGA) with infiltrative glioma, grades II-IV for whom tumor status for IDH, 1p/19q codeletion, ATRX, and TERT had been determined. All cases were assigned to one of 5 groups following the WHO 2016 diagnostic criteria based on their morphologic features, and IDH and 1p/19q codeletion status. These groups are: (1) Oligodendroglioma, IDH-mutant and 1p/19q-codeleted; (2) Astrocytoma, IDH-mutant; (3) Glioblastoma, IDH-mutant; (4) Glioblastoma, IDH-wildtype; and (5) Astrocytoma, IDH-wildtype. Within each group, we used univariate and multivariate Cox proportional hazards models to assess associations of overall survival with patient age at diagnosis, grade, and ATRX alteration status and/or TERT promoter mutation status. Among Group 1 IDH-mutant 1p/19q-codeleted oligodendrogliomas, the TERT-WT group had significantly worse overall survival than the TERT-MUT group (HR: 2.72, 95% CI 1.05-7.04, p = 0.04). In both Group 2, IDH-mutant astrocytomas and Group 3, IDH-mutant glioblastomas, neither TERT mutations nor ATRX alterations were significantly associated with survival. Among Group 4, IDH-wildtype glioblastomas, ATRX alterations were associated with favorable outcomes (HR: 0.36, 95% CI 0.17-0.81, p = 0.01). Among Group 5, IDH-wildtype astrocytomas, the TERT-WT group had significantly better overall survival than the TERT-MUT group (HR: 0.48, 95% CI 0.27-0.87), p = 0.02). Thus, we present evidence that in certain WHO 2016 diagnostic groups, testing for TERT promoter mutations or ATRX alterations may provide additional useful prognostic information.
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Affiliation(s)
- Melike Pekmezci
- Department of Pathology, University of California, Box 0102, 505 Parnassus Avenue, Room M-551, San Francisco, CA, 94143, USA.
- Department of Anatomic Pathology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
| | - Terri Rice
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Kyle M Walsh
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Paul A Decker
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Helen Hansen
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Thomas M Kollmeyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Lucie S McCoy
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Gobinda Sarkar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Arie Perry
- Department of Pathology, University of California, Box 0102, 505 Parnassus Avenue, Room M-551, San Francisco, CA, 94143, USA
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Caterina Giannini
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Tarik Tihan
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Joseph L Wiemels
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Department of Radiology, University of California, San Francisco, CA, USA
| | - Paige M Bracci
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | | | - Jennifer Clarke
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Jennie W Taylor
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Tracy Luks
- Department of Radiology, University of California, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Institute of Human Genetics, University of California, San Francisco, CA, USA
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Margaret R Wrensch
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Institute of Human Genetics, University of California, San Francisco, CA, USA
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17
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Kohli M, Wang L, Dehm S, Hillman DW, Sicotte H, Gormley M, Bhargava V, Li W, Tan W, Pitot HC, Ho TH, Costello BA, Bryce AH, Zhenqing Y, Vedell PT, Barman P, Jimenez RE, Carlson R, Wang L. Genome-wide analysis of metastases to reveal association of pathway activation with abiraterone acetate/prednisone (AA/P) primary resistance and cell cycle proliferation pathway activation with response duration in metastatic castrate resistant prostate cancer (mCRPC). J Clin Oncol 2017. [DOI: 10.1200/jco.2017.35.15_suppl.5053] [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: 11/20/2022] Open
Abstract
5053 Background: Genomic aberrations associated with resistance/response to AA/P are not known. In a prospective study we assessed whole-exome/RNA-seq based aberrations in CRPC metastatic biopsies for identifying molecular markers associated with primary resistance and response duration. Methods: Sequencing of metastatic biopsies was performed for analyzing molecular aberrations that predict primary resistance (defined as progression at 12-weeks of therapy (non-responders) using PSA, RECIST, bone scan criteria per PCWG2). Gene network analysis was performed in genes mutated more frequently in non-responders and in genes differentially expressed between non-responders and responders using a “risk ratio” (RR) of ≥2. Cox regression models with multiple gene network pathways were used for determining association with time to treatment change (TTTC). Results: Of 92 enrolled pts 82 had complete whole-exome, RNA-seq & 12-week outcome data available for analysis. At 12-weeks 33/82 had progressed. Using a RR of ≥2, 113 genes were more frequently mutated in non-responders & 292 in responders. In non-responders, gene network analysis revealed frequent mutations in Wnt/β-catenin pathway genes; frequent deletion of negative regulators of Wnt pathway ( DKK4, SFRP2, LRP6). Gene expression analyses revealed significantly reduced expression levels of Wnt/β-catenin pathway inhibitors and increased expression levels of cell cycle proliferation (CCP) genes in non-responders. Median study follow up was 32 months during which time 58/82 pts progressed and switched treatments. Median TTTC was 10.1 months (IQR:4.4-24.1). In multivariate analysis CCP scores of ≥50 predicted shorter TTTC (HR = 2.11, 95% CI: 1.17-3.80; p = 0.01). Conclusions: In metastases Wnt/β-catenin pathway activation is associated with primary AA/P resistance and increased CCP with acquired drug resistance. These findings offer molecular based predictive biomarkers in CRPC stage treatment. Clinical trial information: NCT#01953640.
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Affiliation(s)
| | | | - Scott Dehm
- University of Minnesota, Minneapolis, MN
| | | | | | | | | | - Weimin Li
- Janssen Research and Development, LLC, Spring House, PA
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18
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Kohli M, Ho Y, Hillman DW, Van Etten JL, Henzler C, Yang R, Sperger JM, Li Y, Tseng E, Hon T, Clark T, Tan W, Carlson RE, Wang L, Sicotte H, Thai H, Jimenez R, Huang H, Vedell PT, Eckloff BW, Quevedo JF, Pitot HC, Costello BA, Jen J, Wieben ED, Silverstein KAT, Lang JM, Wang L, Dehm SM. Androgen Receptor Variant AR-V9 Is Coexpressed with AR-V7 in Prostate Cancer Metastases and Predicts Abiraterone Resistance. Clin Cancer Res 2017; 23:4704-4715. [PMID: 28473535 DOI: 10.1158/1078-0432.ccr-17-0017] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 04/13/2017] [Accepted: 04/27/2017] [Indexed: 01/22/2023]
Abstract
Purpose: Androgen receptor (AR) variant AR-V7 is a ligand-independent transcription factor that promotes prostate cancer resistance to AR-targeted therapies. Accordingly, efforts are under way to develop strategies for monitoring and inhibiting AR-V7 in castration-resistant prostate cancer (CRPC). The purpose of this study was to understand whether other AR variants may be coexpressed with AR-V7 and promote resistance to AR-targeted therapies.Experimental Design: We utilized complementary short- and long-read sequencing of intact AR mRNA isoforms to characterize AR expression in CRPC models. Coexpression of AR-V7 and AR-V9 mRNA in CRPC metastases and circulating tumor cells was assessed by RNA-seq and RT-PCR, respectively. Expression of AR-V9 protein in CRPC models was evaluated with polyclonal antisera. Multivariate analysis was performed to test whether AR variant mRNA expression in metastatic tissues was associated with a 12-week progression-free survival endpoint in a prospective clinical trial of 78 CRPC-stage patients initiating therapy with the androgen synthesis inhibitor, abiraterone acetate.Results: AR-V9 was frequently coexpressed with AR-V7. Both AR variant species were found to share a common 3' terminal cryptic exon, which rendered AR-V9 susceptible to experimental manipulations that were previously thought to target AR-V7 uniquely. AR-V9 promoted ligand-independent growth of prostate cancer cells. High AR-V9 mRNA expression in CRPC metastases was predictive of primary resistance to abiraterone acetate (HR = 4.0; 95% confidence interval, 1.31-12.2; P = 0.02).Conclusions: AR-V9 may be an important component of therapeutic resistance in CRPC. Clin Cancer Res; 23(16); 4704-15. ©2017 AACR.
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Affiliation(s)
- Manish Kohli
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota.
| | - Yeung Ho
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - David W Hillman
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, Minnesota
| | - Jamie L Van Etten
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | - Christine Henzler
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Rendong Yang
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Jamie M Sperger
- Department of Medicine, Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Yingming Li
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
| | | | - Ting Hon
- Pacific Biosciences, Menlo Park, California
| | | | - Winston Tan
- Department of Medicine, Mayo Clinic, Jacksonville, Florida
| | - Rachel E Carlson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, Minnesota
| | - Liguo Wang
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, Minnesota.,Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, Minnesota
| | - Ho Thai
- Department of Medicine, Mayo Clinic, Scottsdale, Arizona
| | - Rafael Jimenez
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Haojie Huang
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota
| | - Peter T Vedell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Rochester, Minnesota
| | | | - Jorge F Quevedo
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota
| | - Henry C Pitot
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota
| | - Brian A Costello
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota
| | - Jin Jen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.,Medical Genome Facility, Mayo Clinic, Rochester, Minnesota.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Eric D Wieben
- Medical Genome Facility, Mayo Clinic, Rochester, Minnesota
| | | | - Joshua M Lang
- Department of Medicine, Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Scott M Dehm
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota. .,Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota.,Department of Urology, University of Minnesota, Minneapolis, Minnesota
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19
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Melin BS, Barnholtz-Sloan JS, Wrensch MR, Johansen C, Il'yasova D, Kinnersley B, Ostrom QT, Labreche K, Chen Y, Armstrong G, Liu Y, Eckel-Passow JE, Decker PA, Labussière M, Idbaih A, Hoang-Xuan K, Di Stefano AL, Mokhtari K, Delattre JY, Broderick P, Galan P, Gousias K, Schramm J, Schoemaker MJ, Fleming SJ, Herms S, Heilmann S, Nöthen MM, Wichmann HE, Schreiber S, Swerdlow A, Lathrop M, Simon M, Sanson M, Andersson U, Rajaraman P, Chanock S, Linet M, Wang Z, Yeager M, Wiencke JK, Hansen H, McCoy L, Rice T, Kosel ML, Sicotte H, Amos CI, Bernstein JL, Davis F, Lachance D, Lau C, Merrell RT, Shildkraut J, Ali-Osman F, Sadetzki S, Scheurer M, Shete S, Lai RK, Claus EB, Olson SH, Jenkins RB, Houlston RS, Bondy ML. Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors. Nat Genet 2017; 49:789-794. [PMID: 28346443 PMCID: PMC5558246 DOI: 10.1038/ng.3823] [Citation(s) in RCA: 205] [Impact Index Per Article: 29.3] [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: 05/20/2016] [Accepted: 03/01/2017] [Indexed: 01/07/2023]
Abstract
Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10-9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10-10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10-8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10-11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10-10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10-9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10-10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10-10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10-9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10-8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10-10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10-11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10-9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.
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Affiliation(s)
| | - Jill S Barnholtz-Sloan
- Case Comprehensive Cancer Center, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Margaret R Wrensch
- Department of Neurological Surgery, School of Medicine, University of California, San Francisco, San Francisco, California, USA
- Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA
| | - Christoffer Johansen
- Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark and Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Dora Il'yasova
- Department of Epidemiology and Biostatistics, School of Public Health, Georgia State University, Atlanta, Georgia, USA
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA
- Cancer Control and Prevention Program, Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Quinn T Ostrom
- Case Comprehensive Cancer Center, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Karim Labreche
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
| | - Yanwen Chen
- Case Comprehensive Cancer Center, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Georgina Armstrong
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Yanhong Liu
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Jeanette E Eckel-Passow
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Paul A Decker
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Marianne Labussière
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
| | - Ahmed Idbaih
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
- AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, Paris, France
| | - Khe Hoang-Xuan
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
- AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, Paris, France
| | - Anna-Luisa Di Stefano
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
- AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, Paris, France
| | - Karima Mokhtari
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
- AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, Paris, France
| | - Jean-Yves Delattre
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
- AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, Paris, France
| | - Peter Broderick
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Pilar Galan
- Université Paris 13 Sorbonne Paris Cité, INSERM U557, INRA U1125, CNAM, Paris, France
| | | | - Johannes Schramm
- Department of Neurosurgery, University of Bonn Medical Center, Bonn, Germany
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Sarah J Fleming
- Centre for Epidemiology and Biostatistics, Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Stefan Herms
- Centre for Epidemiology and Biostatistics, Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | | | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Heinz-Erich Wichmann
- Helmholtz Center Munich, Institute of Epidemiology I, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, Germany
- Institute of Medical Statistics and Epidemiology, Technical University Munich, Munich, Germany
| | - Stefan Schreiber
- 1st Medical Department, University Clinic Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Anthony Swerdlow
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
- Division of Breast Cancer Research, Institute of Cancer Research, London, UK
| | - Mark Lathrop
- Génome Québec, Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Matthias Simon
- Department of Neurosurgery, University of Bonn Medical Center, Bonn, Germany
| | - Marc Sanson
- Sorbonne Universités UPMC Univ Paris 06, INSERM CNRS, U1127, UMR 7225, ICM, Paris, France
- AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, Paris, France
| | | | - Preetha Rajaraman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Martha Linet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - John K Wiencke
- Department of Neurological Surgery, School of Medicine, University of California, San Francisco, San Francisco, California, USA
- Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA
| | - Helen Hansen
- Department of Neurological Surgery, School of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Lucie McCoy
- Department of Neurological Surgery, School of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Terri Rice
- Department of Neurological Surgery, School of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Matthew L Kosel
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Faith Davis
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Dan Lachance
- Department of Neurology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Ching Lau
- Department of Pediatrics, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Ryan T Merrell
- Department of Neurology, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Joellen Shildkraut
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA
- Cancer Control and Prevention Program, Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Francis Ali-Osman
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA
- Department of Surgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Siegal Sadetzki
- Cancer and Radiation Epidemiology Unit, Gertner Institute, Chaim Sheba Medical Center, Tel Hashomer, Israel
- Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Michael Scheurer
- Department of Pediatrics, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Sanjay Shete
- Department of Biostatistics, University of Texas Maryland Anderson Cancer Center, Houston, Texas, USA
| | - Rose K Lai
- Departments of Neurology and Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Elizabeth B Claus
- School of Public Health, Yale University, New Haven, Connecticut, USA
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
- Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Melissa L Bondy
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
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20
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Kohli M, Wang L, Dehm S, Hillman DW, Sicotte H, Gormley M, Bhargava V, Ricci DS, Li W, Tan W, Costello BA, Pitot HC, Dronca RS, Ho TH, Bryce AH, Zhenqing Y, Vedell PT, Barman P, Carlson R, Wang L. Association of Wnt pathway activation with prechemotherapy abiraterone acetate resistance in metastatic castration-resistant prostate cancer (mCRPC) by genome-wide analysis of metastases. J Clin Oncol 2017. [DOI: 10.1200/jco.2017.35.6_suppl.175] [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: 11/20/2022] Open
Abstract
175 Background: Genome and transcriptome aberrations associated with primary resistance to abiraterone acetate/prednisone (AA/P) in mCRPC are not known. In a prospective trial (NCT#01953640) we assessed whole-exome and RNA-seq based aberrations in metastases of CRPC stage patients (pts) for identifying markers associated with primary resistance to AA/P. Methods: Whole-exome and RNA-seq of biopsies from metastases was performed followed by analyses for association between genomic aberrations & primary resistance. Primary resistance was defined by progression on AA/P after 12-weeks of therapy(non-responders) using PSA, RECIST, bone scan and symptom criteria (per PCWG2). Gene network analysis was performed in genes mutated more frequently in non-responders, and also in genes that were differentially expressed between non-responders and responders and a “risk ratio” (RR) was calculated thereof. Results: Between 6/2013 & 8/2015, 92 pts were enrolled of which 82 had complete whole-exome, RNA-seq and 12-week outcome data available for analysis. Median age was 72.5 yrs (IQR: 68.5-78); median PSA-18 ng/ml (IQR: 8.1- 46.6). At 12-weeks 33/82 had progressed. Using the RR of 2 as threshold, we identified 113 and 292 genes that were more frequently mutated in non-responders & responders respectively. Among the 113 genes, OBSCN, ADAM21, LPHN3, DOCK10 ( P = 0.08, RR= inf) & USP42 ( P = 0.16, RR = 5.71) were the top candidates.Gene network analysis revealed that in non-responders, genes involved in the Wnt/β-catenin pathway were frequently mutated and negative regulators of Wnt pathway ( DKK4, SFRP2 & LRP6) were also frequently deleted. Gene expression analyses revealed the expression levels of Wnt/β-catenin pathway inhibitors were significantly reduced while expression levels of cell cycle regulatory genes were significantly increased in non-responders. Conclusions: In this study we observed Wnt/β-catenin pathway activation to be associated with primary AA/P resistance. This finding offers a potential for the development of predictive biomarkers and modulation of targeted pathways to overcome AA/P resistance. Clinical trial information: NCT# 01953640.
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Affiliation(s)
| | | | - Scott Dehm
- University of Minnesota, Minneapolis, MN
| | | | | | | | | | | | - Weimin Li
- Janssen Research & Development, Spring House, PA
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21
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Heit JA, Armasu SM, McCauley BM, Kullo IJ, Sicotte H, Pathak J, Chute CG, Gottesman O, Bottinger EP, Denny JC, Roden DM, Li R, Ritchie MD, de Andrade M. Identification of unique venous thromboembolism-susceptibility variants in African-Americans. Thromb Haemost 2017; 117:758-768. [PMID: 28203683 DOI: 10.1160/th16-08-0652] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [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: 08/19/2016] [Accepted: 01/12/2017] [Indexed: 12/30/2022]
Abstract
To identify novel single nucleotide polymorphisms (SNPs) associated with venous thromboembolism (VTE) in African-Americans (AAs), we performed a genome-wide association study (GWAS) of VTE in AAs using the Electronic Medical Records and Genomics (eMERGE) Network, comprised of seven sites each with DNA biobanks (total ~39,200 unique DNA samples) with genome-wide SNP data (imputed to 1000 Genomes Project cosmopolitan reference panel) and linked to electronic health records (EHRs). Using a validated EHR-driven phenotype extraction algorithm, we identified VTE cases and controls and tested for an association between each SNP and VTE using unconditional logistic regression, adjusted for age, sex, stroke, site-platform combination and sickle cell risk genotype. Among 393 AA VTE cases and 4,941 AA controls, three intragenic SNPs reached genome-wide significance: LEMD3 rs138916004 (OR=3.2; p=1.3E-08), LY86 rs3804476 (OR=1.8; p=2E-08) and LOC100130298 rs142143628 (OR=4.5; p=4.4E-08); all three SNPs validated using internal cross-validation, parametric bootstrap and meta-analysis methods. LEMD3 rs138916004 and LOC100130298 rs142143628 are only present in Africans (1000G data). LEMD3 showed a significant differential expression in both NCBI Gene Expression Omnibus (GEO) and the Mayo Clinic gene expression data, LOC100130298 showed a significant differential expression only in the GEO expression data, and LY86 showed a significant differential expression only in the Mayo expression data. LEMD3 encodes for an antagonist of TGF-β-induced cell proliferation arrest. LY86 encodes for MD-1 which down-regulates the pro-inflammatory response to lipopolysaccharide; LY86 variation was previously associated with VTE in white women; LOC100130298 is a non-coding RNA gene with unknown regulatory activity in gene expression and epigenetics.
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Affiliation(s)
- John A Heit
- John A. Heit, MD, Stabile 6-Hematology Research, Mayo Clinic, 200 First Street, SW, Rochester, MN 55905, USA, Tel.: +1 507 284 4634, Fax: +1 507 266 9302, E-mail:
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22
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Earp M, Winham SJ, Larson N, Permuth JB, Sicotte H, Chien J, Anton-Culver H, Bandera EV, Berchuck A, Cook LS, Cramer D, Doherty JA, Goodman MT, Levine DA, Monteiro ANA, Ness RB, Pearce CL, Rossing MA, Tworoger SS, Wentzensen N, Bisogna M, Brinton L, Brooks-Wilson A, Carney ME, Cunningham JM, Edwards RP, Fogarty ZC, Iversen ES, Kraft P, Larson MC, Le ND, Lin HY, Lissowska J, Modugno F, Moysich KB, Olson SH, Pike MC, Poole EM, Rider DN, Terry KL, Thompson PJ, van den Berg D, Vierkant RA, Vitonis AF, Wilkens LR, Wu AH, Yang HP, Ziogas A, Phelan CM, Schildkraut JM, Chen YA, Sellers TA, Fridley BL, Goode EL. A targeted genetic association study of epithelial ovarian cancer susceptibility. Oncotarget 2016; 7:7381-9. [PMID: 26848776 PMCID: PMC4884925 DOI: 10.18632/oncotarget.7121] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 01/24/2016] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Genome-wide association studies have identified several common susceptibility alleles for epithelial ovarian cancer (EOC). To further understand EOC susceptibility, we examined previously ungenotyped candidate variants, including uncommon variants and those residing within known susceptibility loci. RESULTS At nine of eleven previously published EOC susceptibility regions (2q31, 3q25, 5p15, 8q21, 8q24, 10p12, 17q12, 17q21.31, and 19p13), novel variants were identified that were more strongly associated with risk than previously reported variants. Beyond known susceptibility regions, no variants were found to be associated with EOC risk at genome-wide statistical significance (p <5x10(-8)), nor were any significant after Bonferroni correction for 17,000 variants (p< 3x10-6). METHODS A customized genotyping array was used to assess over 17,000 variants in coding, non-coding, regulatory, and known susceptibility regions in 4,973 EOC cases and 5,640 controls from 13 independent studies. Susceptibility for EOC overall and for select histotypes was evaluated using logistic regression adjusted for age, study site, and population substructure. CONCLUSION Given the novel variants identified within the 2q31, 3q25, 5p15, 8q21, 8q24, 10p12, 17q12, 17q21.31, and 19p13 regions, larger follow-up genotyping studies, using imputation where necessary, are needed for fine-mapping and confirmation of low frequency variants that fall below statistical significance.
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Affiliation(s)
- Madalene Earp
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Stacey J Winham
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Larson
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Jeremy Chien
- Department of Cancer Biology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Hoda Anton-Culver
- Department of Epidemiology, University of California Irvine, Irvine, CA, USA
| | - Elisa V Bandera
- Rutgers Cancer Institute of New Jersey and Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Andrew Berchuck
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Linda S Cook
- Division of Epidemiology and Biostatistics, University of New Mexico, Albuquerque, NM, USA
| | - Daniel Cramer
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Jennifer A Doherty
- Section of Biostatistics and Epidemiology, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Marc T Goodman
- Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Douglas A Levine
- Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Alvaro N A Monteiro
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Roberta B Ness
- The University of Texas School of Public Health, Houston, TX, USA
| | - Celeste L Pearce
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mary Anne Rossing
- Department of Epidemiology, University of Washington, Seattle, WA, USA.,Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Shelley S Tworoger
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Maria Bisogna
- Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Louise Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Angela Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Michael E Carney
- Clinical and Translational Research Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Division of Experimental Pathology, Mayo Clinic, Rochester, MN, USA
| | - Robert P Edwards
- Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Zachary C Fogarty
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Edwin S Iversen
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Peter Kraft
- Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Melissa C Larson
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Nhu D Le
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
| | - Hui-Yi Lin
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Warsaw, Poland
| | - Francesmary Modugno
- Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.,Cancer Research Program, Magee-Women's Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, US
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Malcolm C Pike
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, US
| | - Elizabeth M Poole
- Channing Division of Network Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - David N Rider
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Kathryn L Terry
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Pamela J Thompson
- Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - David van den Berg
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Robert A Vierkant
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Allison F Vitonis
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lynne R Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hannah P Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Argyrios Ziogas
- Department of Epidemiology, Center for Cancer Genetics Research and Prevention, School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Catherine M Phelan
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Joellen M Schildkraut
- Department of Community and Family Medicine, Duke University Medical Center, Durham, NC, USA.,Cancer Prevention, Detection and Control Research Program, Duke Cancer Institute, Durham, NC, USA
| | - Yian Ann Chen
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Thomas A Sellers
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brooke L Fridley
- Kansas IDeA Network of Biomedical Research Excellence Bioinformatics Core, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Ellen L Goode
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
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23
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Hu L, Ning S, Eschbacher J, Baxter L, Gaw N, Ranjbar S, Plasencia J, Dueck A, Peng S, Smith K, Nakaji P, Karis J, Quarles C, Wu T, Loftus J, Jenkins R, Sicotte H, Kollmeyer T, O’Neill B, Elmquist W, Hoxworth J, Frakes D, Sarkaria J, Swanson K, Tran N, Li J, Mitchell R. NIMG-05. RADIOGENOMICS TO CHARACTERIZE REGIONAL GENETIC HETEROGENEITY IN GLIOBLASTOMA. Neuro Oncol 2016. [DOI: 10.1093/neuonc/now212.517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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24
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Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 2016; 19:128-137. [PMID: 27502248 DOI: 10.1093/neuonc/now135] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. METHODS We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). RESULTS We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). CONCLUSION MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Shuluo Ning
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jennifer M Eschbacher
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nathan Gaw
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jonathan Plasencia
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Amylou C Dueck
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sen Peng
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kris A Smith
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Peter Nakaji
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - John P Karis
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - C Chad Quarles
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph C Loftus
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Robert B Jenkins
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Hugues Sicotte
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Thomas M Kollmeyer
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Brian P O'Neill
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - William Elmquist
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph M Hoxworth
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - David Frakes
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jann Sarkaria
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kristin R Swanson
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nhan L Tran
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - J Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
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Sicotte H. Deep sequencing reveals that low level TP53 mutations are ubiquitous in ovarian cancer patients and controls: successes and challenges for early detection. Transl Cancer Res 2016. [DOI: 10.21037/tcr.2016.08.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Berardi C, Wassel CL, Decker PA, Larson NB, Kirsch PS, Andrade MD, Tsai MY, Pankow JS, Sale MM, Sicotte H, Tang W, Hanson NQ, McDermott MM, Criqui MH, Allison MA, Bielinski SJ. Elevated Levels of Adhesion Proteins Are Associated With Low Ankle-Brachial Index. Angiology 2016; 68:322-329. [PMID: 27436494 DOI: 10.1177/0003319716659178] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Inflammation plays a pivotal role in peripheral artery disease (PAD). Cellular adhesion proteins mediate the interaction of leukocytes with endothelial cells during inflammation. To determine the association of cellular adhesion molecules with ankle-brachial index (ABI) and ABI category (≤1.0 vs >1.0) in a diverse population, 15 adhesion proteins were measured in the Multi-Ethnic Study of Atherosclerosis (MESA). To assess multivariable associations of each protein with ABI and ABI category, linear and logistic regression was used, respectively. Among 2364 participants, 23 presented with poorly compressible arteries (ABI > 1.4) and were excluded and 261 had ABI ≤ 1.0. Adjusting for traditional risk factors, elevated levels of soluble P-selectin, hepatocyte growth factor, and secretory leukocyte protease inhibitor were associated with lower ABI ( P = .0004, .001, and .002, respectively). Per each standard deviation of protein, we found 26%, 20%, and 19% greater odds of lower ABI category ( P = .001, .01, and .02, respectively). Further investigation into the adhesion pathway may shed new light on biological mechanisms implicated in PAD.
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Affiliation(s)
- Cecilia Berardi
- 1 Department of Internal Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA.,2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Christine L Wassel
- 3 Department of Pathology and Laboratory Medicine, University of Vermont College of Medicine, Colchester, VT, USA
| | - Paul A Decker
- 2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Nicholas B Larson
- 2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Phillip S Kirsch
- 2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Mariza de Andrade
- 2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Michael Y Tsai
- 4 Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - James S Pankow
- 5 Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Michele M Sale
- 6 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Hugues Sicotte
- 2 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Weihong Tang
- 5 Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Naomi Q Hanson
- 4 Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Mary M McDermott
- 7 Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Michael H Criqui
- 8 Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Michael A Allison
- 8 Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
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Kohli M, Hillman DW, Wang L, Li Y, Sicotte H, Carlson R, Tan W, Wu K, Eiken PW, Jimenez RE, Cernigliaro J, Quevedo F, Costello BA, Pitot HC, Moynihan TJ, Ho TH, Bryce AH, Wang L, Dehm S. Association of androgen receptor variant 9 ( AR-V9) mRNA expression levels in metastatic tissue with resistance to abiraterone acetate/prednisone (AA/P). J Clin Oncol 2016. [DOI: 10.1200/jco.2016.34.15_suppl.5036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Scott Dehm
- University of Minnesota, Minneapolis, MN
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Hart SN, Ellingson MS, Schahl K, Vedell PT, Carlson RE, Sinnwell JP, Barman P, Sicotte H, Eckel-Passow JE, Wang L, Kalari KR, Qin R, Kruisselbrink TM, Jimenez RE, Bryce AH, Tan W, Weinshilboum R, Wang L, Kohli M. Determining the frequency of pathogenic germline variants from exome sequencing in patients with castrate-resistant prostate cancer. BMJ Open 2016; 6:e010332. [PMID: 27084275 PMCID: PMC4838679 DOI: 10.1136/bmjopen-2015-010332] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES To determine the frequency of pathogenic inherited mutations in 157 select genes from patients with metastatic castrate-resistant prostate cancer (mCRPC). DESIGN Observational. SETTING Multisite US-based cohort. PARTICIPANTS Seventy-one adult male patients with histological confirmation of prostate cancer, and had progressive disease while on androgen deprivation therapy. RESULTS Twelve patients (17.4%) showed evidence of carrying pathogenic or likely pathogenic germline variants in the ATM, ATR, BRCA2, FANCL, MSR1, MUTYH, RB1, TSHR and WRN genes. All but one patient opted in to receive clinically actionable results at the time of study initiation. We also found that pathogenic germline BRCA2 variants appear to be enriched in mCRPC compared to familial prostate cancers. CONCLUSIONS Pathogenic variants in cancer-susceptibility genes are frequently observed in patients with mCRPC. A substantial proportion of patients with mCRPC or their family members would derive clinical utility from mutation screening. TRIAL REGISTRATION NUMBER NCT01953640; Results.
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Affiliation(s)
- Steven N Hart
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Kim Schahl
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter T Vedell
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Rachel E Carlson
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Jason P Sinnwell
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Poulami Barman
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Hugues Sicotte
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Liguo Wang
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Krishna R Kalari
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Rui Qin
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Rafael E Jimenez
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Alan H Bryce
- Division of Hematology/Oncology, Mayo Clinic, Mayo Clinic Cancer Center, Scottsdale, Arizona, USA
| | - Winston Tan
- Division of Hematology and Oncology, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Richard Weinshilboum
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Liewei Wang
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Manish Kohli
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, USA
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29
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Bancks MP, Bielinski SJ, Decker PA, Hanson NQ, Larson NB, Sicotte H, Wassel CL, Pankow JS. Circulating level of hepatocyte growth factor predicts incidence of type 2 diabetes mellitus: The Multi-Ethnic Study of Atherosclerosis (MESA). Metabolism 2016; 65:64-72. [PMID: 26892517 PMCID: PMC4857763 DOI: 10.1016/j.metabol.2015.10.023] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Revised: 10/12/2015] [Accepted: 10/15/2015] [Indexed: 10/22/2022]
Abstract
BACKGROUND Hepatocyte growth factor (HGF) is a pleotropic factor posited to have metabolic homeostatic properties. The purpose of this study is to examine whether level of HGF is associated with the development of type 2 diabetes. METHODS Data from the Multi-Ethnic Study of Atherosclerosis (MESA) were used to examine the prospective association between serum level of HGF and incident diabetes. Fasting HGF was measured at Exam 1 (2000-2002) in 5395 participants free from diabetes (61.5±10.2 years old) and incidence of diabetes was determined at four subsequent follow-up exams over 12 years. Hazard ratios (HR) for incident diabetes were estimated according to 1 standard deviation (SD) unit increment of HGF (1 SD=26 μg/l), before and after adjustment for age, sex, race/ethnicity, education, study center, smoking status, alcohol consumption, body mass index, waist circumference, fasting glucose and insulin, C-reactive protein, and interleukin-6 levels. RESULTS A 1 SD increment of baseline HGF was associated with a 46% (95% CI=1.37, 1.56) increased risk of diabetes before adjustment. After adjustment, diabetes risk per 1 SD increment of HGF was attenuated but remained significantly increased (HR=1.21; 95% CI=1.12, 1.32). Men had a significantly greater HR compared to women per equivalent increase of HGF (p-value for sex interaction=0.04). There was no evidence of effect modification by race/ethnicity. CONCLUSIONS This study advances understanding from cross-sectional studies and investigation of incident insulin resistance, demonstrating higher level of HGF is associated with incident diabetes and may reflect a unique type of impaired metabolism.
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Affiliation(s)
- Michael P Bancks
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, 55454, USA.
| | - Suzette J Bielinski
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Paul A Decker
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Naomi Q Hanson
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, 55454, USA.
| | - Nicholas B Larson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Christina L Wassel
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Colchester, VT, 05446, USA.
| | - James S Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, 55454, USA.
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Wang L, Nie J, Sicotte H, Li Y, Eckel-Passow JE, Dasari S, Vedell PT, Barman P, Wang L, Weinshiboum R, Jen J, Huang H, Kohli M, Kocher JPA. Measure transcript integrity using RNA-seq data. BMC Bioinformatics 2016; 17:58. [PMID: 26842848 PMCID: PMC4739097 DOI: 10.1186/s12859-016-0922-z] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [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: 10/02/2015] [Accepted: 01/29/2016] [Indexed: 11/21/2022] Open
Abstract
Background Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling. Result We developed the transcript integrity number (TIN) to measure RNA degradation. When applied to 3 independent RNA-seq datasets, we demonstrated TIN is a reliable and sensitive measure of the RNA degradation at both transcript and sample level. Through comparing 10 prostate cancer clinical samples with lower RNA integrity to 10 samples with higher RNA quality, we demonstrated that calibrating gene expression counts with TIN scores could effectively neutralize RNA degradation effects by reducing false positives and recovering biologically meaningful pathways. When further evaluating the performance of TIN correction using spike-in transcripts in RNA-seq data generated from the Sequencing Quality Control consortium, we found TIN adjustment had better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sensitivity = 0.98, specificity = 0.50, accuracy = 0.86). Conclusion TIN is a reliable measurement of RNA integrity and a valuable approach used to neutralize in vitro RNA degradation effect and improve differential gene expression analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0922-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Liguo Wang
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Jinfu Nie
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Ying Li
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | | | - Surendra Dasari
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Peter T Vedell
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Poulami Barman
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Richard Weinshiboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Jin Jen
- Department of laboratory medicine and pathology, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Haojie Huang
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Manish Kohli
- Department of Oncology, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Jean-Pierre A Kocher
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
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31
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Kohli M, Hillman DW, Carlson R, Wang L, Li Y, Tan W, Ho TH, Sicotte H, Wang L, Costello BA, Pitot HC, Quevedo F, Dronca RS, Moynihan TJ, Atwell TD, Eiken PW, McMenomy BP, Bryce AH, Wu K, Dehm S. Association of androgen receptor V9 (ARV9) mRNA expression in metastatic tissue with early resistance to pre-chemotherapy abiraterone acetate/prednisone (AA/P). J Clin Oncol 2016. [DOI: 10.1200/jco.2016.34.2_suppl.237] [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: 11/20/2022] Open
Abstract
237 Background: AA/P is an approved treatment for mCRPC but there are no known predictive markers of response or resistance. We conducted a prospective trial to evaluate if Androgen Receptor (AR) & AR-variant (ARV) expression in tissue metastases can predict resistance to AA/P. Methods: mCRPC stage patients (pts) initiating pre-chemo AA/P underwent metastatic site biopsies prior to (pre-AA/P) and after 12 weeks of treatment. Composite progression at 12 weeks, (primary endpoint) was evaluated with PSA, RECIST, bone scan and symptoms (per PCWG2). mRNA expressions of pre-AA/P ARFL, ARV3, ARV7, ARV9, ARV23, ARV45, four cell cycle division genes, Chromogranin-A (CHGA) together with PSA/testosterone levels, Gleason Score (GS) at initial diagnosis; high versus low volume disease; time from starting hormone therapy to mCRPC stage and serum CHGA levels were evaluated using a logistic regression model for predicting resistance at 12 weeks of therapy. A final multivariate model fitted only those factors thought to be clinically relevant or with an entry threshold of p ≤ 0.3 in univariate analysis. Results: Between 6/2013 & 3/2015, 82 pts were enrolled of which 52 had complete mRNA expression & disease assessment data at the12-week time point for analysis. Median age of the cohort was 72.5 yrs (IQR: 68.5-78); median pre-AA/P PSA was 18 ng/ml (IQR: 8.1- 46.6); GS distribution at initial diagnosis for GS 2-6; 7; 8-10 was 11; 14; 27 respectively. Progression was observed in 21/52 pts after 12 weeks. At the univariate level elevated pre-AA/P expression of ARV3 (p = 0.08), ARV7 (p = 0.26), ARV9 (p = 0.04), and cell division cycle gene CDC45 (p = 0.19) along with GS at diagnosis (p = 0.29) met the threshold for inclusion into multivariate analysis. Elevated expression of pre-therapy ARV9 in metastases alone was associated with progression at 12 weeks (OR: 3.9; CI 1.07 – 14.16; C-Index: 0.63). The 12-week biopsy of pts with progression had increased ARV9 mRNA expression compared to pts responding at 12 weeks (p = 0.14). Conclusions: Increased ARV9 mRNA expression in metastases is associated with early resistance to AA/P. This observation will need further validation in comparable datasets. Clinical trial information: NCT #0195364.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Scott Dehm
- University of Minnesota, Minneapolis, MN
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Christoph MJ, Allison MA, Pankow JS, Decker PA, Kirsch PS, Tsai MY, Sale MM, de Andrade M, Sicotte H, Tang W, Hanson NQ, Berardi C, Wassel CL, Larson NB, Bielinski SJ. Impact of adiposity on cellular adhesion: The Multi-Ethnic Study of atherosclerosis (MESA). Obesity (Silver Spring) 2016; 24:223-30. [PMID: 26638193 PMCID: PMC4688228 DOI: 10.1002/oby.21245] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 07/13/2015] [Indexed: 11/09/2022]
Abstract
OBJECTIVE At the cellular level, how excess adiposity promotes atherogenesis is not fully understood. One pathway involves secretion of adipokines that stimulate endothelial dysfunction through increased expression of adhesion molecules. However, the relationship of adiposity to adhesion molecules that promote atherosclerosis is largely unknown. METHODS Linear regression models were used to assess the sex-specific associations of soluble cellular adhesion molecules (sP- and sL-selectin, sICAM-1, sVCAM-1, and sHGF) and adiposity in 5,974 adults examined as part of the Multi-Ethnic Study of Atherosclerosis (MESA). Adiposity measures included body mass index (BMI), waist-to-hip-ratio (WHR), and computed tomography measures of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). RESULTS The mean age was 64 years and 52% were female. In multivariable models adjusting for traditional cardiovascular risk factors, sHGF was positively associated with BMI, WHR, and VAT in both males and females, and sP-selectin with WHR and VAT in males. sVCAM-1 was inversely associated with VAT in females only. CONCLUSIONS Our results showed the relation of adiposity to soluble cellular adhesion proteins was similar across adiposity measures and for both sexes. However, the relationship between adiposity and sVCAM-1 and P-selectin may be modified by sex and the measure used to assess adiposity.
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Affiliation(s)
- Mary J. Christoph
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Matthew A. Allison
- Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Paul A. Decker
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Phillip S. Kirsch
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Michael Y. Tsai
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Michele M. Sale
- Center for Public Health Genomics, University of Virginia, VA, USA
| | - Mariza de Andrade
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Naomi Q. Hanson
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Cecilia Berardi
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Montefiore Medical Center, Bronx, NY, USA
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Kohli M, Wang L, Xie F, Sicotte H, Yin P, Dehm SM, Hart SN, Vedell PT, Barman P, Qin R, Mahoney DW, Carlson RE, Eckel-Passow JE, Atwell TD, Eiken PW, McMenomy BP, Wieben ED, Jha G, Jimenez RE, Weinshilboum R, Wang L. Mutational Landscapes of Sequential Prostate Metastases and Matched Patient Derived Xenografts during Enzalutamide Therapy. PLoS One 2015; 10:e0145176. [PMID: 26695660 PMCID: PMC4687867 DOI: 10.1371/journal.pone.0145176] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [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: 08/10/2015] [Accepted: 11/30/2015] [Indexed: 12/22/2022] Open
Abstract
Developing patient derived models from individual tumors that capture the biological heterogeneity and mutation landscape in advanced prostate cancer is challenging, but essential for understanding tumor progression and delivery of personalized therapy in metastatic castrate resistant prostate cancer stage. To demonstrate the feasibility of developing patient derived xenograft models in this stage, we present a case study wherein xenografts were derived from cancer metastases in a patient progressing on androgen deprivation therapy and prior to initiating pre-chemotherapy enzalutamide treatment. Tissue biopsies from a metastatic rib lesion were obtained for sequencing before and after initiating enzalutamide treatment over a twelve-week period and also implanted subcutaneously as well as under the renal capsule in immuno-deficient mice. The genome and transcriptome landscapes of xenografts and the original patient tumor tissues were compared by performing whole exome and transcriptome sequencing of the metastatic tumor tissues and the xenografts at both time points. After comparing the somatic mutations, copy number variations, gene fusions and gene expression we found that the patient's genomic and transcriptomic alterations were preserved in the patient derived xenografts with high fidelity. These xenograft models provide an opportunity for predicting efficacy of existing and potentially novel drugs that is based on individual metastatic tumor expression signature and molecular pharmacology for delivery of precision medicine.
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Affiliation(s)
- Manish Kohli
- Department of Oncology, Mayo Clinic, Rochester, Minnesota, United States of America
- * E-mail: (MK); (Liguo Wang)
| | - Liguo Wang
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
- * E-mail: (MK); (Liguo Wang)
| | - Fang Xie
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Ping Yin
- Department of Oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Scott M. Dehm
- Masonic Cancer Center and Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Steven N. Hart
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Peter T. Vedell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Poulami Barman
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Rui Qin
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Douglas W. Mahoney
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Rachel E. Carlson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Jeanette E. Eckel-Passow
- Division of Biomedical Statistics and Informatics, Department of Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Thomas D. Atwell
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Patrick W. Eiken
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Brendan P. McMenomy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Eric D. Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Gautam Jha
- Division of Hematology-Oncology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Rafael E. Jimenez
- Department of Pathology and Lab Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
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Walsh K, Pekmezci M, Decker P, Hansen H, Sicotte H, Rice T, Kollmeyer T, McCoy L, Sarkar G, Perry A, Giannini C, Tihan T, Berger M, Molinaro A, Wiemels J, Eckel-Passow J, Lachance D, Wiencke J, Jenkins R, Wrensch M. EPID-31ATRX AND TERT ASSAYS PROVIDE NON-REDUNDANT INFORMATION IN ADULT GLIOMA PATIENTS: INCORPORATING TELOMERE MARKERS INTO NEUROPATHOLOGY PRACTICE. Neuro Oncol 2015. [DOI: 10.1093/neuonc/nov213.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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35
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Jimenez R, Sicotte H, Barman P, Sinnwell J, Eiken P, Atwell T, McMenomy B, Tan W, Wu K, Bryce A, Ho T, Pitot H, Quevedo J, Costello B, Dronca R, Moynihan T, Wang L, Qin R, Carlson R, Kohli M. 2523 Feasibility analysis of pathology and genetic yield from a prospective trial of tissue biopsies in metastatic castrate resistant prostate cancer. Eur J Cancer 2015. [DOI: 10.1016/s0959-8049(16)31342-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Chien J, Sicotte H, Fan JB, Humphray S, Cunningham JM, Kalli KR, Oberg AL, Hart SN, Li Y, Davila JI, Baheti S, Wang C, Dietmann S, Atkinson EJ, Asmann YW, Bell DA, Ota T, Tarabishy Y, Kuang R, Bibikova M, Cheetham RK, Grocock RJ, Swisher EM, Peden J, Bentley D, Kocher JPA, Kaufmann SH, Hartmann LC, Shridhar V, Goode EL. TP53 mutations, tetraploidy and homologous recombination repair defects in early stage high-grade serous ovarian cancer. Nucleic Acids Res 2015; 43:6945-58. [PMID: 25916844 PMCID: PMC4538798 DOI: 10.1093/nar/gkv111] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [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: 11/04/2014] [Revised: 01/23/2015] [Accepted: 02/02/2015] [Indexed: 12/30/2022] Open
Abstract
To determine early somatic changes in high-grade serous ovarian cancer (HGSOC), we performed whole genome sequencing on a rare collection of 16 low stage HGSOCs. The majority showed extensive structural alterations (one had an ultramutated profile), exhibited high levels of p53 immunoreactivity, and harboured a TP53 mutation, deletion or inactivation. BRCA1 and BRCA2 mutations were observed in two tumors, with nine showing evidence of a homologous recombination (HR) defect. Combined Analysis with The Cancer Genome Atlas (TCGA) indicated that low and late stage HGSOCs have similar mutation and copy number profiles. We also found evidence that deleterious TP53 mutations are the earliest events, followed by deletions or loss of heterozygosity (LOH) of chromosomes carrying TP53, BRCA1 or BRCA2. Inactivation of HR appears to be an early event, as 62.5% of tumours showed a LOH pattern suggestive of HR defects. Three tumours with the highest ploidy had little genome-wide LOH, yet one of these had a homozygous somatic frame-shift BRCA2 mutation, suggesting that some carcinomas begin as tetraploid then descend into diploidy accompanied by genome-wide LOH. Lastly, we found evidence that structural variants (SV) cluster in HGSOC, but are absent in one ultramutated tumor, providing insights into the pathogenesis of low stage HGSOC.
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Affiliation(s)
- Jeremy Chien
- Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Sean Humphray
- Illumina Cambridge Ltd, Little Chesterford, Essex CB10 1, UK
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Ann L Oberg
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Steven N Hart
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Ying Li
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Jaime I Davila
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Saurabh Baheti
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Chen Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Sabine Dietmann
- Wellcome Trust, Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge CB2 1TN, UK
| | | | - Yan W Asmann
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Debra A Bell
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Takayo Ota
- Department of Internal Medicine, Rinku General Medical Center, Izumi-sano, 598-8577, Japan
| | - Yaman Tarabishy
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Rui Kuang
- Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN 55414, USA
| | | | | | | | - Elizabeth M Swisher
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA 98109, USA
| | - John Peden
- Illumina Cambridge Ltd, Little Chesterford, Essex CB10 1, UK
| | - David Bentley
- Illumina Cambridge Ltd, Little Chesterford, Essex CB10 1, UK
| | | | | | - Lynn C Hartmann
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Viji Shridhar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen L Goode
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
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Larson NB, Berardi C, Decker PA, Wassel CL, Kirsch PS, Pankow JS, Sale MM, de Andrade M, Sicotte H, Tang W, Hanson NQ, Tsai MY, Taylor KD, Bielinski SJ. Trans-ethnic meta-analysis identifies common and rare variants associated with hepatocyte growth factor levels in the Multi-Ethnic Study of Atherosclerosis (MESA). Ann Hum Genet 2015; 79:264-74. [PMID: 25998175 PMCID: PMC4474777 DOI: 10.1111/ahg.12119] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [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: 02/06/2015] [Accepted: 04/08/2015] [Indexed: 01/03/2023]
Abstract
Hepatocyte growth factor (HGF) is a mesenchyme-derived pleiotropic factor that regulates cell growth, motility, mitogenesis, and morphogenesis in a variety of cells, and increased serum levels of HGF have been linked to a number of clinical and subclinical cardiovascular disease phenotypes. However, little is currently known regarding which genetic factors influence HGF levels, despite evidence of substantial genetic contributions to HGF variation. Based upon ethnicity-stratified single-variant association analysis and trans-ethnic meta-analysis of 6201 participants of the Multi-Ethnic Study of Atherosclerosis (MESA), we discovered five statistically significant common and low-frequency variants: HGF missense polymorphism rs5745687 (p.E299K) as well as four variants (rs16844364, rs4690098, rs114303452, rs3748034) within or in proximity to HGFAC. We also identified two significant ethnicity-specific gene-level associations (A1BG in African Americans; FASN in Chinese Americans) based upon low-frequency/rare variants, while meta-analysis of gene-level results identified a significant association for HGFAC. However, identified single-variant associations explained modest proportions of the total trait variation and were not significantly associated with coronary artery calcium or coronary heart disease. Our findings indicate that genetic factors influencing circulating HGF levels may be complex and ethnically diverse.
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Affiliation(s)
- Nicholas B. Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Cecilia Berardi
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Paul A. Decker
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Phillip S. Kirsch
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Michele M. Sale
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Naomi Q. Hanson
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Michael Y. Tsai
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute and Department of Pediatrics at Harbor-UCLA, Torrance, CA, USA
| | - Suzette J. Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Eckel-Passow JE, Lachance DH, Molinaro AM, Walsh KM, Decker PA, Sicotte H, Pekmezci M, Rice T, Kosel ML, Smirnov IV, Sarkar G, Caron AA, Kollmeyer TM, Praska CE, Chada AR, Halder C, Hansen HM, McCoy LS, Bracci PM, Marshall R, Zheng S, Reis GF, Pico AR, O'Neill BP, Buckner JC, Giannini C, Huse JT, Perry A, Tihan T, Berger MS, Chang SM, Prados MD, Wiemels J, Wiencke JK, Wrensch MR, Jenkins RB. Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors. N Engl J Med 2015; 372:2499-508. [PMID: 26061753 PMCID: PMC4489704 DOI: 10.1056/nejmoa1407279] [Citation(s) in RCA: 1367] [Impact Index Per Article: 151.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND The prediction of clinical behavior, response to therapy, and outcome of infiltrative glioma is challenging. On the basis of previous studies of tumor biology, we defined five glioma molecular groups with the use of three alterations: mutations in the TERT promoter, mutations in IDH, and codeletion of chromosome arms 1p and 19q (1p/19q codeletion). We tested the hypothesis that within groups based on these features, tumors would have similar clinical variables, acquired somatic alterations, and germline variants. METHODS We scored tumors as negative or positive for each of these markers in 1087 gliomas and compared acquired alterations and patient characteristics among the five primary molecular groups. Using 11,590 controls, we assessed associations between these groups and known glioma germline variants. RESULTS Among 615 grade II or III gliomas, 29% had all three alterations (i.e., were triple-positive), 5% had TERT and IDH mutations, 45% had only IDH mutations, 7% were triple-negative, and 10% had only TERT mutations; 5% had other combinations. Among 472 grade IV gliomas, less than 1% were triple-positive, 2% had TERT and IDH mutations, 7% had only IDH mutations, 17% were triple-negative, and 74% had only TERT mutations. The mean age at diagnosis was lowest (37 years) among patients who had gliomas with only IDH mutations and was highest (59 years) among patients who had gliomas with only TERT mutations. The molecular groups were independently associated with overall survival among patients with grade II or III gliomas but not among patients with grade IV gliomas. The molecular groups were associated with specific germline variants. CONCLUSIONS Gliomas were classified into five principal groups on the basis of three tumor markers. The groups had different ages at onset, overall survival, and associations with germline variants, which implies that they are characterized by distinct mechanisms of pathogenesis. (Funded by the National Institutes of Health and others.).
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Affiliation(s)
- Jeanette E Eckel-Passow
- From the Departments of Health Sciences Research (J.E.E.-P., P.A.D., H.S., M.L.K.), Laboratory Medicine and Pathology (D.H.L., G.S., A.A.C., T.M.K., C.E.P., A.R.C., C.H., C.G., R.B.J.), Neurology (D.H.L., B.P.O.), and Oncology (J.C.B.), Mayo Clinic, Rochester, MN; the Departments of Neurological Surgery (A.M.M., K.M.W., T.R., I.V.S., H.M.H., L.S.M., S.Z., A.P., M.S.B., S.M.C., M.D.P., J.K.W., M.R.W.), Epidemiology and Biostatistics (A.M.M., P.M.B., J.W., J.K.W., M.R.W.) and Pathology (M.P., R.M., G.F.R., A.P., T.T.) and the Institute of Human Genetics (J.W., J.K.W., M.R.W.), University of California, San Francisco, and the Bioinformatics Core, Gladstone Institutes (A.R.P.) - all in San Francisco; and the Department of Pathology and Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York (J.T.H.)
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Kohli M, Qin R, Wang L, Sicotte H, Carlson R, Tan W, Jimenez RE, Wang L, Eckel-Passow J, Costello BA, Pitot HC, Quevedo F, Dronca RS, Wu K, Moynihan TJ, Ho TH, Bryce AH, Atwell TD, McMenomy BP, Dehm S. A molecular and clinico-pathological model for predicting abiraterone acetate/prednisone (AA/P) efficacy in metastatic castrate resistant prostate cancer (mCRPC). J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.15_suppl.5056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Rui Qin
- Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN, Rochester, MN
| | - Liguo Wang
- Mayo Clinic, Rochester, MN, Rochester, MN
| | | | | | - Winston Tan
- Mayo Clinic, Jacksonville, FL, Jacksonville, FL
| | | | | | | | | | | | | | | | - Kevin Wu
- Mayo Clinic, Jacksonville, FL, Jacksonville, FL
| | | | | | | | | | | | - Scott Dehm
- University of Minnesota, Minneapolis, MN
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Wu L, Chaffee KG, Parker AS, Sicotte H, Petersen GM. Zinc transporter genes and urological cancers: integrated analysis suggests a role for ZIP11 in bladder cancer. Tumour Biol 2015; 36:7431-7. [PMID: 25900876 DOI: 10.1007/s13277-015-3459-2] [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] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Accepted: 04/13/2015] [Indexed: 01/01/2023] Open
Abstract
Although zinc transporters were shown to play roles in the development of prostate, bladder, and renal cancer, no study has evaluated the genetic variants in zinc transporter genes with risk of urological cancers. A candidate gene association study using genome-wide association study (GWAS) datasets was conducted for variants in 24 zinc transporter genes. Genotypes were analyzed using logistic regression models adjusted for covariates. The function of identified variants was assessed by using the Encyclopedia of DNA Elements (ENCODE). We further evaluated tumors for somatic change of the implicated gene(s) and the associations between identified variants and patient survival from data in The Cancer Genome Atlas (TCGA). A ZIP11 variant, rs8081059, was significantly associated with increased risk of renal cell carcinoma (odds ratios (OR) = 1.28, 95 % confidence intervals (CI) (1.13-1.45), p = 0.049). No zinc transporter variants were associated with prostate cancer risk. Four variants within ZIP11 were significantly associated with bladder cancer risk: rs11871756 (OR = 1.43, 95 % CI (1.24-1.63), p = 0.0002), rs11077654 (OR = 0.76, 95 % CI (0.68-0.85), p = 0.001), rs9913017 (OR = 0.76, 95 % CI (0.68-0.85), p = 0.002), and rs4969054 (OR = 0.78, 95 % CI (0.69-0.88), p = 0.02); the three protective variants were co-located and highly correlated. These variants were located within predicted transcribed or enhancer regions. Among the 253 bladder cancer patients in TCGA, two had tumors that contained deleterious missense mutations in ZIP11. Moreover, rs11077654 was significantly associated with survival of bladder cancer patients (p = 0.046). In conclusion, zinc transporter gene, ZIP11, may play an important role in bladder cancer. Further studies of the gene are warranted.
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Affiliation(s)
- Lang Wu
- Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.,Center for Clinical and Translational Science, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.,Mayo Graduate School, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Kari G Chaffee
- Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Alexander S Parker
- Department of Health Sciences Research, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
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Jimenez RE, Sicotte H, Barman P, Sinnwell JP, Eiken PW, Atwell TD, McMenomy BP, Tan W, Wu K, Bryce AH, Ho TH, Pitot HC, Quevedo F, Costello BA, Dronca RS, Moynihan TJ, Wang L, Qin R, Carlson R, Kohli M. Feasibility analysis of pathology and genetic yield from a prospective trial of tissue biopsies in metastatic castrate-resistant prostate cancer (mCRPC). J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.7_suppl.249] [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: 11/20/2022] Open
Abstract
249 Background: Evaluating tumor specific pathologic and genetic profiles in mCRPC stage is difficult due to the limited availability of mCRPC tissue. We describe findings from a prospective cohort study performed to obtain concomitant histopathology and genetic information in mCRPC. Methods: Patients with mCRPC initiating abiraterone acetate therapy underwent 2 serial (3 months apart) metastatic (met) site needle core biopsies (NCB1/2). Bone lesions were biopsied using 11-13G core needles and 18G core biopsy devices were used for non-osseous masses. Up to 4 cores were obtained at each biopsy with the 1st core (S1) sent for DNA sequencing, the 2nd (S2) for RNA sequencing, and the 3rd/4th (X3, X4) submitted for xenograft implantation. From each, 1-2 mm segments were separated and formalin-fixed for histopathologic examination (HPE). Results: A total of 54 patients, enrolled between June 2013 and July 2014, underwent 94 NCB (54 NCB1, 40 NCB2), rendering a total of 259 samples (94 S1; 75 S2; 59 X3 and 31 X4) of which 85 (32%) were positive (pos) for tumor by HPE. Positivity for tumor in S1, S2, X3, and X4 cores was 42%, 33%, 22%, and 19%, respectively. At least one core pos for met tumor was observed in 62% NCB1 and 52% NCB2 (overall 52/94; 56%). Met sites biopsied include bone (71), lymph nodes (18), liver (3), penile (1) and pelvic (1) soft tissues. HPE revealed met adenocarcinoma in 44/52, and poorly differentiated carcinoma in 8/52. Gleason grade applied to the met ranged from 3+4 to 5+5. 45/85 pos samples (53%) had > 50% tumor cellularity. Pos NCB in bone lesions was observed in 23/71 (32%), compared to 17/23 (73%) of non-bone sites (p=.0004). 85 S1 samples yielded DNA material, of which 66 (77%) had ≥10% tumoral DNA. 24 cases with negative HPE had ≥10% tumoral DNA; 7 cases with pos HPE had <10 tumoral DNA. In all, 76/94 (81%) NCB yielded tumor material either by HPE or DNA analysis. Conclusions: NCB of mCRPC is feasible and provides adequate tissue for pursuing HPE and sequencing studies. HPE of extracted material correlates with DNA sequencing data and provides complementary information on tumor features. Bone lesions yield significantly less tumoral material than non-osseous sites.
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Affiliation(s)
| | | | | | | | | | | | | | - Winston Tan
- Mayo Clinic, Jacksonville, FL, Jacksonville, FL
| | - Kevin Wu
- Mayo Clinic, Jacksonville, FL, Jacksonville, FL
| | | | | | | | | | | | | | | | | | - Rui Qin
- Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN, Rochester, MN
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Kohli M, Wang L, Sicotte H, Qin R, Carlson R, Eckel-Passow J, Tan W, Wu K, Dehm S, Eiken PW, Jimenez RE, Cernigliaro J, Quevedo F, Costello BA, Pitot HC, Moynihan TJ, Ho TH, Dronca RS, Bryce AH, Wang L. Androgen receptor (AR) based biomarker association with response to abiraterone acetate/prednisone (AA/P) in metastatic castrate resistant prostate cancer (mCRPC). J Clin Oncol 2015. [DOI: 10.1200/jco.2015.33.7_suppl.174] [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: 11/20/2022] Open
Abstract
174 Background: AA/P is an FDA approved treatment for mCRPC. Since markers of early resistance to AA/P are unknown, we report initial findings of androgen receptor (AR) based associations with short term (12 week) progression on AA/P. Methods: mCRPC stage patients (pts) initiating pre-chemotherapy AA/P underwent metastatic site biopsies at baseline (pre AA/P) and after 12 weeks. Baseline somatic whole exome DNA, tumor gene expression for AR full length (ARFL), AR splice variant 7 (ARV7) and ARV7/ARFL ratios were compared in progessors versus non-progressors. Progression at or within 12 weeks of AA/P therapy was defined as death or disease progression by PCWG2 “composite progression (CP)” and/or “radiographic progression” endpoints. Wilcoxon rank-sum tests were used to test for differences in the two groups for comparing ARFL, ARV7 expressions and ARV7/ARFL ratios and chi square tests were used for differences in copy number variation. Results: Between 1/2013 and 6/2014, 59 pts were enrolled of which 44 have disease assessment data at the12-week time point. CP was observed in 17/44 patients. DNA seq and clinical data was available for 42/44 pts. Using radiographic progression at 12 weeks, AR Amplification/Gain was observed in 20/26 non-progressors (13 with Amplification) and in 9/16 progressors (P-value = 0.19; OR 2.5). ARFL and ARV7 gene expressions in both groups is provided in table. Conclusions: A trend towards higher ARV7/ARFL gene expression ratio in metastases was observed with early progression on AA/P. AR gain/amplification is observed less often in pts with early progression. Validation of these findings is on-going in this prospective trial. Clinical trial information: NCT# 01953640. [Table: see text]
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Affiliation(s)
| | - Liguo Wang
- Mayo Clinic, Rochester, MN, Rochester, MN
| | | | - Rui Qin
- Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN, Rochester, MN
| | | | | | - Winston Tan
- Mayo Clinic, Jacksonville, FL, Jacksonville, FL
| | - Kevin Wu
- Mayo Clinic, Jacksonville, FL, Jacksonville, FL
| | - Scott Dehm
- University of Minnesota, Minneapolis, MN
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Bielinski SJ, Berardi C, Decker PA, Kirsch PS, Larson NB, Pankow JS, Sale M, de Andrade M, Sicotte H, Tang W, Hanson NQ, Wassel CL, Polak JF, Tsai MY. P-selectin and subclinical and clinical atherosclerosis: the Multi-Ethnic Study of Atherosclerosis (MESA). Atherosclerosis 2015; 240:3-9. [PMID: 25744700 DOI: 10.1016/j.atherosclerosis.2015.02.036] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 02/06/2015] [Accepted: 02/14/2015] [Indexed: 12/14/2022]
Abstract
OBJECTIVE P-selectin is a cellular adhesion molecule that has been shown to be crucial in development of coronary heart disease (CHD). We sought to determine the role of P-selectin on the risk of atherosclerosis in a large multi-ethnic population. METHODS Data from the Multi-Ethnic Study of Atherosclerosis (MESA), including 1628 African, 702 Chinese, 2393 non-Hispanic white, and 1302 Hispanic Americans, were used to investigate the association of plasma P-selectin with CHD risk factors, coronary artery calcium (CAC), intima-media thickness, and CHD. Regression models were used to investigate the association between P-selectin and risk factors, Tobit model for CAC, and Cox regression for CHD events. RESULTS Mean levels of P-selectin differed by ethnicity and were higher in men (P<0.001). For all ethnic groups, P-selectin was positively associated with measures of adiposity, blood pressure, current smoking, LDL, and triglycerides and inversely with HDL. A significant ethnic interaction was observed for the association of P-selectin and prevalent diabetes; however, P-selectin was positively associated with HbA1c in all groups. Higher P-selectin levels were associated with greater prevalence of CAC. Over 10.1 years of follow-up, there were 335 incident CHD events. There was a positive linear association between P-selectin levels and rate of incident CHD after adjustment for traditional risk factors. However, association was only significant in non-Hispanic white Americans (HR: 1.81, 95% CI 1.07 to 3.07, P=0.027). CONCLUSION We observed ethnic heterogeneity in the association of P-selectin and risk of CHD.
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Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Cecilia Berardi
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Montefiore Medical Center, Bronx, NY, USA.
| | - Paul A Decker
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Phillip S Kirsch
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - James S Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA.
| | - Michele Sale
- Center for Public Health Genomics, University of Virginia, VA, USA.
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Hugues Sicotte
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA.
| | - Naomi Q Hanson
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
| | - Christina L Wassel
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Joseph F Polak
- Department of Radiology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA.
| | - Michael Y Tsai
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
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Berardi C, Larson NB, Decker PA, Wassel CL, Kirsch PS, Pankow JS, Sale MM, de Andrade M, Sicotte H, Tang W, Hanson NQ, Tsai MY, Chen YDI, Bielinski SJ. Multi-ethnic analysis reveals soluble L-selectin may be post-transcriptionally regulated by 3'UTR polymorphism: the Multi-Ethnic Study of Atherosclerosis (MESA). Hum Genet 2015; 134:393-403. [PMID: 25576479 DOI: 10.1007/s00439-014-1527-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 12/29/2014] [Indexed: 01/01/2023]
Abstract
L-Selectin is constitutively expressed on leukocytes and mediates their interaction with endothelial cells during inflammation. Previous studies on the association of soluble L-selectin (sL-selectin) with cardiovascular disease (CVD) are inconsistent. Genetic variants associated with sL-selectin levels may be a better surrogate of levels over a lifetime. We explored the association of genetic variants and sL-selectin levels in a race/ethnicity stratified random sample of 2,403 participants in the Multi-Ethnic Study of Atherosclerosis (MESA). Through a genome-wide analysis with additive linear regression models, we found that rs12938 on the SELL gene accounted for a significant portion of the protein level variance across all four races/ethnicities. To evaluate potential additional associations, elastic net models were used for variants located in the SELL/SELP/SELE genetic region and an additional two SNPs, rs3917768 and rs4987361, were associated with sL-selectin levels in African Americans. These variants accounted for a portion of protein variance that ranged from 4 % in Hispanic to 14 % in African Americans. To investigate the relationship of these variants with CVD, 6,317 subjects were used. No significant association was found between any of the identified SNPs and carotid intima-media thickness or presence of carotid plaque using linear and logistic regression, respectively. Similarly no significant results were found for coronary artery calcium or coronary heart disease events. In conclusion, we found that variants within the SELL gene are associated with sL-selectin levels. Despite accounting for a significant portion of the protein level variance, none of the variants was associated with clinical or subclinical CVD.
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Affiliation(s)
- Cecilia Berardi
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Harwick Building 6-56, 200 First Street SW, Rochester, MN, 55905, USA
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Wu L, Schaid DJ, Sicotte H, Wieben ED, Li H, Petersen GM. Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations. J Med Genet 2014; 52:10-6. [PMID: 25371537 DOI: 10.1136/jmedgenet-2014-102697] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.
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Affiliation(s)
- Lang Wu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA Center for Clinical and Translational Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Hugues Sicotte
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric D Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
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Thompson KJ, Tang X, Sun Z, Sinnwell JP, Sicotte H, Mahoney DW, Hart S, Vedell PT, Barman P, Passow JEE, Wieben ED, Ingle JN, Boughey JC, Wang L, Weinshilboum R, Kalari KR, Goetz MP. Abstract 5592: Molecular classification of triple negative breast cancer via RNA-sequencing data. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-5592] [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
Introduction:
Triple negative breast cancers (TNBC) are characterized as lacking estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression and TNBC patients have higher rates of recurrence and death compared with other breast cancer subtypes. For TNBC patients who fail standard chemotherapy, there are a lack of novel drug therapies, given the absence of well-defined molecular targets. Recently, a microarray meta-analysis identified 7 triple negative subtypes, including the validation of the luminal androgen receptor (LAR) positive subtype [Lehmann, 2011]. However, microarray technology is dependent on probe-target specificity and the 7 subtypes have yet to be validated using RNA sequencing data, and the presences of recurrent genomic alterations in the 7 subtypes are unknown.
METHODS:
We obtained 1106 breast cancer RNA-Seq bam files from The Cancer Genome Atlas (TCGA) and aligned with Tophat v1.3. The PAM50 intrinsic gene signature was used to extract a cohort of 128 TNBC samples. Consensus clustering of genes, greater than 75th percentile of variance, was performed using Kmeans clustering in Spearman's correlation space. A nearest centroid prediction model was developed from genes differentially expressed among the clusters. Eighty independent TNBC RNA sequencing samples were obtained (British Colombia; BC) [Shah, 2012] which were calibrated to our TNBC conditional quantile normalized cohort and sub-typed by our model.
RESULTS:
Using RNA-Seq gene expression count data, we identified 5 clusters, all of which were stable, including the LAR cluster. Signaling pathway impact analysis (SPIA) implicated cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and regulation of actin cytoskeleton pathways commonly altered in the non-LAR TNBC subtypes. In contrast, cell cycle, ECM-receptor interaction, endocrine regulated calcium reabsorption, and insulin signaling pathways were altered in the LAR versus non-LAR subtypes. Neuroactive ligand-receptor interactions were observed to be altered commonly between all sub-types. We then applied our model to the Shah, et al cohort. In this cohort, the LAR subtype was consistent with Shah's classification of ‘other’ TNBC and contained no basal samples by PAM50 intrinsic modeling. Analysis of sub-type specific mutation data from the BC cohort demonstrates an increased mutational load in ECM-related proteins, particularly the myosins, along with increased TP53 clonality in the non-LAR subtypes.
CONCLUSIONS:
Using TCGA RNASeq data, we have confirmed the presence of 5 major TNBC subtypes, including the LAR; which was negligible in basal composition by PAM50 intrinsic modeling. SPIA pathway analysis indicates a core set of pathways demonstrating altered expression across the TNBC sub-types and the identification of molecular targets within each subtype is ongoing.
Citation Format: Kevin J. Thompson, Xiaojia Tang, Zhifu Sun, Jason P. Sinnwell, Hugues Sicotte, Douglas W. Mahoney, Steven Hart, Peter T. Vedell, Poulami Barman, Jeanette E. Eckel Passow, Eric D. Wieben, James N. Ingle, Judy C. Boughey, Liewei Wang, Richard Weinshilboum, Krishna R. Kalari, Matthew P. Goetz. Molecular classification of triple negative breast cancer via RNA-sequencing data. [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 5592. doi:10.1158/1538-7445.AM2014-5592
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Kalari KR, Tang X, Thompson KJ, Mahoney DW, Barman P, Sinnwell JP, Sicotte H, Vedell P, Hart SN, Dockter TJ, Jones KN, Conners AL, Moyer AM, Visscher DW, Yu J, Gao B, McLaughlin SA, Copland JA, Moreno-Aspitia A, Northfelt DW, Gray RJ, Suman VJ, Passow JEE, Kocher JPA, Wieben ED, Farrugia G, Schultz CG, Ingle JN, Weinshilboum R, Goetz MP, Wang L, Boughey JC. Abstract 4185: Analysis of sequencing data to identify potential drug targets for an individual newly diagnosed with basal breast cancer who failed to respond to current standard neoadjuvant chemotherapy. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-4185] [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
INTRODUCTION
Next generation sequencing (NGS) of patients has significantly changed our ways to study cancer genomics as it provides precise estimates of gene expression, fusion transcripts, expressed single nucleotide variants (eSNVs), splice variants and copy number variants. The Breast Cancer Genome Guided Therapy (BEAUTY) is an ongoing clinical study in which RNA sequencing (RNAseq) and whole exome sequencing (WES) are performed prior to, during and after neoadjuvant chemotherapy. Here we report the use of these sequencing technologies to investigate gene expression levels and mutational profiles in a triple negative breast cancer (TNBC) patient enrolled in BEAUTY whose disease did not respond to neoadjuvant paclitaxel and anthracycline/cycphosphamide.
METHODS
Computational approaches were used to integrate WES and RNAseq data obtained before therapy (V1T), after 12 weekly paclitaxel treatments (V2T) and after anthracycline-based regimen at surgical resection (V3T) to study a single patient with persistent TNBC disease after neoadjuvant chemotherapy.
RESULTS
Using RNA-Seq data, we identified an inter-chromosomal fusion transcript between chromosome 20 and 22 (GNAS-TTC38) that was highly expressed at V1T, V2T and V3T. We also identified intra-chromosomal fusion transcripts that were expressed at two time points, such as fusion transcript (KANSL1-ARL17A) on chromosome 17 for V1T and V2T and fusion transcript (RBM12B-LINC00535) on chromosome 8 for V2T and V3T time points. Several gene expression changes were also observed. Gene expression analysis of V1T, V2T and V3T tumors was performed. Differential temporal gene expression profiles of 9884 genes that were significant and varying at different time points were obtained for pathway analysis. Pathway analysis of 9884 genes identified up regulation and down regulation of several transcription factors with a fold change of 2x or more. When compared to blood, DNA tumor and RNA-Seq data, we identified 81 common somatic eSNVs that were expressed in both V1T and V2T time points and we are in the process of investigating V3T data. We found alterations of key transporter domains (CD225, Coatamer_beta_C, DUF2435, Dynamitin, EI24, GLTP, LMF1, Porin_3, V-ATPase_C) in our V1T and V2T SNV data. Similar to gene expression analysis, we are in the process of obtaining the list of mutations at various time points to identify driver and passenger mutation candidate genes for this specific TNBC patient.
CONCLUSIONS
Our initial time-series analysis of eSNV, fusion transcripts and gene expression data demonstrate that intensive analysis for individual patients is feasible. Further investigation of drug transporters and transcription regulators may help develop personalized treatment strategies for patients with disease resistant to current regimens.
Citation Format: Krishna R. Kalari, Xiaojia Tang, Kevin J. Thompson, Douglas W. Mahoney, Poulami Barman, Jason P. Sinnwell, Hugues Sicotte, Peter Vedell, Steven N. Hart, Travis J. Dockter, Katie N. Jones, Amy L. Conners, Ann M. Moyer, Daniel W. Visscher, Jia Yu, Bowen Gao, Sarah A. McLaughlin, John A. Copland, Alvaro Moreno-Aspitia, Donald W. Northfelt, Richard J. Gray, Vera J. Suman, Jeanette E. Eckel Passow, Jean-Pierre A. Kocher, Eric D. Wieben, Gianrico Farrugia, Cloann G. Schultz, James N. Ingle, Richard Weinshilboum, Matthew P. Goetz, Liewei Wang, Judy C. Boughey. Analysis of sequencing data to identify potential drug targets for an individual newly diagnosed with basal breast cancer who failed to respond to current standard neoadjuvant chemotherapy. [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 4185. doi:10.1158/1538-7445.AM2014-4185
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jia Yu
- 1Mayo Clinic, Rochester, MN
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Eckel-Passow JE, Kollmeyer TM, Sarkar G, Chada A, Decker PA, Kosel ML, Caron AA, Sicotte H, Nandakumar K, Prodduturi N, O'Neill BP, Lachance DH, Jenkins RB. Abstract 4714: The association of glioma germline risk SNPs with mutation-based molecular subgroups. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-4714] [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: Specific germline alterations within the TERT, EGFR, CCDC26, CDKN2A/B, PHLDB1, TP53 and RTEL1 regions are associated with development of glioma. While some of these germline variants are associated with all gliomas (e.g. rs2736100 in TERT), others are associated with a specific morphologic and/or molecular subtype. For example, the RTEL1 region variants are associated with primary glioblastoma and rs55705857 in CCDC26 is associated with 1p/19q co-deleted oligodendrogliomas and with IDH mutant astrocytic gliomas. We hypothesized that germline variants in these regions will be associated with other molecular subtypes. The Cancer Genome Atlas (TCGA) and other groups have described molecular subtypes of glioma based on acquired somatic mutation patterns. Herein, we grouped glioma patients into mutation-based molecular subgroups based on three molecular alterations and determined if germline polymorphisms are associated with these mutation-based molecular subtypes.
Methods: Five molecular subtypes were defined based on combinations of the following molecular alterations: TERT promoter mutation (TERTmut vs. TERTwt), IDH1/2 mutation (IDH mut vs. IDH wt) and 1p/19q co-deletion (1p19qcodel vs. 1p19q noncodel). Germline single nucleotide polymorphisms (SNPs) were evaluated by custom Illumina array analysis of blood-derived DNA and 1K genome imputation.
Results: One hundred ninety-nine gliomas (24 oligodendrogliomas, 59 mixed oligoastrocytomas, 41 grade 2-3 astrocytomas and 75 glioblastomas) had both SNP data and mutation data available in order to assign them into the five molecular subgroups. The prevalence of each of the molecular subtypes was as follows: TERTmut/IDHmut/1p19qcodel (20%), TERTmut/IDHmut/1p19qnoncodel (5%), TERTmut/IDHwt/1p19qnoncodel (32%), TERTwt/IDHmut/1p19qnondel (35%) and TERTwt/IDHwt/1p19qnoncodel (8%). The TERT SNP (rs2736100) was protective for TERTmut/IDHmut/1p19qnoncodel and TERTwt/IDHmut/1p19qnoncodel gliomas. The CCDC26 SNP (rs55705857) was strongly associated with the risk of developing TERTmut/IDHmut/1p19qcodel and TERTwt/IDHmut/1p19qnoncodel gliomas. The TP53 SNP (rs7837822) was strongly associated with risk of developing TERTmut/IDHmut/1p19qnoncodel and TERTwt/IDHmut/1p19qnoncodel gliomas. Interestingly, while one RTEL1 region SNP (rs6062297) was strongly associated with glioma risk, RTEL1 SNPs rs6010620 and rs2297440 were protective for the development of TERTmut/IDHwt/1p19qnoncodel gliomas. We are currently in the process of validating these results using the TCGA glioblastoma and low grade glioma data.
Conclusions: Glioma patients can be meaningfully classified according to their acquired mutation subtype. Importantly, our results suggest that there are significant associations between germline polymorphisms and mutation-based glioma molecular subtypes.
Citation Format: Jeanette E. Eckel-Passow, Thomas M. Kollmeyer, Gobinda Sarkar, Anisha Chada, Paul A. Decker, Matthew L. Kosel, Alissa A. Caron, Hugues Sicotte, Kannabiran Nandakumar, Naresh Prodduturi, Brian P. O'Neill, Daniel H. Lachance, Robert B. Jenkins. The association of glioma germline risk SNPs with mutation-based molecular subgroups. [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 4714. doi:10.1158/1538-7445.AM2014-4714
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Purrington KS, Yannoukakos D, Carpenter J, Nevanlinna H, Cox A, Severi G, Ambrosone C, Toland AE, Godwin AK, Brauch H, Fasching PA, Miron P, Chang-Claude J, Martin NG, Montgomery GW, Kristensen V, Anton-Culver H, Goodfellow P, Olson JE, Sicotte H, Prodduturi N, Visscher DW, Eckel-Passow JE, Anderson SK, Slettedahl S, Olswold C, Wang X, Pankratz VS, Slager S, Zheng W, Mannermaa A, Hamann U, Eccles DM, Vachon CM, Couch FJ. Abstract 3266: Expression quantitative trait locus analysis of triple negative breast cancer. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-3266] [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
Recent studies have shown that associations between common genetic variation and gene expression in breast tumors provide insight into the functional mechanisms underlying breast cancer risk loci. However, the relationship between genetic variation, gene expression, and risk of triple negative (TN) breast cancer remains largely unexplored. We performed a genome-wide expression quantitative trait locus (eQTL) analysis of TN breast cancer using 668 formalin-fixed paraffin embedded TN tumors from the Triple Negative Breast Cancer Consortium (TNBCC) to identify novel genes relevant to TN breast cancer risk and to further explore the biology underlying known TN risk loci. Cis-eQTLs were defined as correlations between single nucleotide polymorphisms (SNP) and expression of genes within 1Mb, and trans-eQTLs were defined as the remainder of all SNP-gene correlations. In a genome-wide analysis, we identified 68,012 cis-eQTLs, representing correlations between 42,193 SNPs and 2,092 genes that were significant at a 10% false discovery rate (FDR). We also identified 70 trans-eQTLs across two unique genes at the same FDR threshold. Five cis-eQTLs involved SNPs previously associated with TN breast cancer risk (p<5.0x10-5) in 3,700 TN cases and 4,700 controls from the TNBCC. Three of these, located on chromosomes 5p (cis-eQTL p=4.8x10-34; risk p=1.1x10-7), 7p (cis-eQTL p=6.1x10-10; risk p=1.3x10-7), and 17q (cis-eQTL p=1.8x10-15; risk p=2.9x10-7), have previously been reported in public databases of normal tissue. The remaining two TN breast cancer cis-eQTLs, located on chromosomes 8q (cis-eQTL p=6.3x10-6, risk p=8.3x10-6) and 14q (cis-eQTL p=3.1x10-6, risk p=1.2x10-5), have not been reported as eQTLs in other tissues. In addition, 12 of 25 known TN breast cancer risk loci (PEX14, MDM4, 2q31.1, ESR1, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, 19p13.1, MLK1), contained at least one significant cis-eQTL. In summary, we have identified five cis-eQTLs that may identify novel risk loci for TN breast cancer.
Citation Format: Kristen S. Purrington, Drakoulis Yannoukakos, Jane Carpenter, Heli Nevanlinna, Angela Cox, Gianluca Severi, Christine Ambrosone, Amanda Ewart Toland, Andrew K. Godwin, Hiltrud Brauch, Peter A. Fasching, Penelope Miron, Jenny Chang-Claude, Nicholas G. Martin, Grant W. Montgomery, Vessela Kristensen, Hoda Anton-Culver, Paul Goodfellow, Janet E. Olson, Hugues Sicotte, Naresh Prodduturi, Daniel W. Visscher, Jeanette E. Eckel-Passow, S. Keith Anderson, Seth Slettedahl, Curtis Olswold, Xianshu Wang, V. Shane Pankratz, Susan Slager, Wei Zheng, Arto Mannermaa, Ute Hamann, Diana M. Eccles, Celine M. Vachon, Fergus J. Couch. Expression quantitative trait locus analysis of triple negative breast 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 3266. doi:10.1158/1538-7445.AM2014-3266
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Affiliation(s)
| | | | - Jane Carpenter
- 3University of Sydney at the Westmead Millennium Institute, Westmead, Australia
| | - Heli Nevanlinna
- 4University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Angela Cox
- 5Cancer Research UK/Yorkshire Cancer Research Sheffield Cancer Research Centre, University of Sheffield, Sheffield, United Kingdom
| | | | | | | | | | - Hiltrud Brauch
- 10Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart and University of Tuebingen, Germany, Suttgart, Germany
| | - Peter A. Fasching
- 11University Breast Center Franconia, University Hospital Erlangen; Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | | | | | | | | | - Vessela Kristensen
- 15Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway
| | | | - Paul Goodfellow
- 17Washington University School of Medicine, Barnes-Jewish Hospital and Siteman Cancer Center, St. Louis, MO
| | | | | | | | | | | | | | | | | | | | | | | | - Wei Zheng
- 19Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN
| | | | - Ute Hamann
- 13German Cancer Research Center (DKFZ), Heidelberg, Germany
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Yu J, Yin P, Gao B, Sinnwell JP, Moyer AM, Visscher DW, Conners AL, Dockter TJ, Kalari KR, Tang X, Thompson KJ, Sicotte H, Mahoney DW, Hart SN, Vedell PT, Barman P, Jones KN, McLaughlin SA, Copland JA, Aspitia AM, Northfelt DW, Gray RJ, Suman VJ, Passow JEE, Wieben ED, Ingle JN, Lou Z, Farrugia G, Weinshilboum R, Goetz MP, Boughey JC, Wang L. Abstract 1195: Feasibility of using percutaneous tumor biopsies from a prospective neoadjuvant breast cancer study to develop patient derived xenografts and assess in vivo chemotherapy sensitivity. Cancer Res 2014. [DOI: 10.1158/1538-7445.am2014-1195] [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
INTRODUCTION
Patient derived xenografts (PDX) may better reflect individual patient (pt) tumor biology; however, the feasibility of collecting PDX from percutaneous tumor biopsies (PTB) in the neoadjuvant setting is unknown. Furthermore, drug response phenotypes observed in PDX have not been prospectively compared to the corresponding pt clinical outcomes.
METHODS
The Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective Mayo study of pts with high-risk breast cancer treated with neoadjuvant weekly paclitaxel (T) +/- trastuzumab followed by anthracycline based chemotherapy. PTB (at baseline) and residual surgical tissue (after all chemotherapy) are obtained for next generation sequencing (NGS) and PDX. Tumor biopsies (1-2 cores from 14 gauge needle) were implanted with Matrigel <1 hour of collection in the flanks of NOD-SCID or NSG mice. Low dose estradiol was supplemented in the drinking water. Primary outgrowth rate was defined as PDX tumor volume >50 mm3. Take rate was defined as development of at least 1 stably transplantable xenograft line/pt. To determine whether clinical T response assessed by MRI corresponded with in vivo T response, pretreatment PDX from 5 pts were injected into NOD-SCID mice (20 mice per pt PDX) and when tumors reached 100-200mm3, mice were randomized to no treatment vs T (20 mg/kg, ip. every 3-4 days). Two of these 5 patients had a MRI response defined as >30% decrease in longest lesion.
RESULTS
Pretreatment PTB from 81 unique pts were implanted in 251 mice (2-4 mice/pt). PDX outgrowth rates were 33.3% (27/81 pts) and 22 stable PDX were established (overall take rate 27.2%). Take rates were as follows: triple negative breast cancer (46%; 13/28); HER2 (27%; 6/22), Luminal B (13%; 3/22), and luminal A (0%; 0/9). Residual surgical tumor (after all treatment) from 17 pts was injected into 85 mice (average 5 mice/pt) and the initial outgrowth rate was 23% (4/17) with 3 stably transplantable lines established. PDX, derived from pretreatment PTB of 5 pts (2 responders and 3 non-responders), were assessed for in vivo T response. The size of the T treated group was significantly smaller than the no treatment group for the PDX derived from the 2 clinical responders, with complete disappearance of tumor by 18 days. In contrast, the PDX derived from the 3 clinical non-responders had no evidence for T response.
CONCLUSIONS
We have demonstrated the feasibility of using PTB to establish PDX in a prospective neoadjuvant clinical study and have demonstrated similar T drug response phenotypes in in the PDX as seen in the corresponding pt. These data suggest that PDX generated prospectively may be useful for biomarker validation and the development and individualization of new drug therapy.
Funded by the Mayo Clinic Center for Individualized Medicine and the Mayo Clinic Cancer Center
Citation Format: Jia Yu, Ping Yin, Bowen Gao, Jason P. Sinnwell, Ann M. Moyer, Daniel W. Visscher, Amy L. Conners, Travis J. Dockter, Krishna R. Kalari, Xiaojia Tang, Kevin J. Thompson, Hugues Sicotte, Douglas W. Mahoney, Steven N. Hart, Peter T. Vedell, Poulami Barman, Katie N. Jones, Sarah A. McLaughlin, John A. Copland, Alvaro Moreno Aspitia, Donald W. Northfelt, Richard J. Gray, Vera J. Suman, Jeanette E. Eckel Passow, Eric D. Wieben, James N. Ingle, Zhenkun Lou, Gianrico Farrugia, Richard Weinshilboum, Matthew P. Goetz, Judy C. Boughey, Liewei Wang. Feasibility of using percutaneous tumor biopsies from a prospective neoadjuvant breast cancer study to develop patient derived xenografts and assess in vivo chemotherapy sensitivity. [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 1195. doi:10.1158/1538-7445.AM2014-1195
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Affiliation(s)
- Jia Yu
- 1Mayo Clinic, Rochester, MN
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