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Yang X, Eriksson M, Czene K, Lee A, Leslie G, Lush M, Wang J, Dennis J, Dorling L, Carvalho S, Mavaddat N, Simard J, Schmidt MK, Easton DF, Hall P, Antoniou AC. Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study. J Med Genet 2022; 59:1196-1205. [PMID: 36162852 PMCID: PMC9691822 DOI: 10.1136/jmg-2022-108806] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/24/2022] [Indexed: 01/13/2023]
Abstract
BACKGROUND The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort. METHODS We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45-63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC). RESULTS Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%). CONCLUSION The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.
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Affiliation(s)
- Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Jean Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Sara Carvalho
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Jacques Simard
- Department of Molecular Medicine, Université Laval and CHU de Québec-Université Laval Research Center, Quebec City, Quebec, Canada
| | - Marjanka K Schmidt
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Devision of Molecular Pathology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
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202
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"It Will Lead You to Make Better Decisions about Your Health"-A Focus Group and Survey Study on Women's Attitudes towards Risk-Based Breast Cancer Screening and Personalised Risk Assessments. Curr Oncol 2022; 29:9181-9198. [PMID: 36547133 PMCID: PMC9776908 DOI: 10.3390/curroncol29120719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Singapore launched a population-based organised mammography screening (MAM) programme in 2002. However, uptake is low. A better understanding of breast cancer (BC) risk factors has generated interest in shifting from a one-size-fits-all to a risk-based screening approach. However, public acceptability of the change is lacking. Focus group discussions (FGD) were conducted with 54 women (median age 37.5 years) with no BC history. Eight online sessions were transcribed, coded, and thematically analysed. Additionally, we surveyed 993 participants in a risk-based MAM study on how they felt in anticipation of receiving their risk profiles. Attitudes towards MAM (e.g., fear, low perceived risk) have remained unchanged for ~25 years. However, FGD participants reported that they would be more likely to attend routine mammography after having their BC risks assessed, despite uncertainty and concerns about risk-based screening. This insight was reinforced by the survey participants reporting more positive than negative feelings before receiving their risk reports. There is enthusiasm in knowing personal disease risk but concerns about the level of support for individuals learning they are at higher risk for breast cancer. Our results support the empowering of Singaporean women with personal health information to improve MAM uptake.
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203
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Plym A, Zhang Y, Stopsack KH, Jee YH, Wiklund F, Kibel AS, Kraft P, Giovannucci E, Penney KL, Mucci LA. Family History of Prostate and Breast Cancer Integrated with a Polygenic Risk Score Identifies Men at Highest Risk of Dying from Prostate Cancer before Age 75 Years. Clin Cancer Res 2022; 28:4926-4933. [PMID: 36103261 PMCID: PMC9660541 DOI: 10.1158/1078-0432.ccr-22-1723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/04/2022] [Accepted: 09/12/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE Family history of prostate cancer is one of the few universally accepted risk factors for prostate cancer. How much an assessment of inherited polygenic risk for prostate cancer adds to lifetime risk stratification beyond family history is unknown. EXPERIMENTAL DESIGN We followed 10,120 men in the Health Professionals Follow-up Study with existing genotype data for risk of prostate cancer and prostate cancer-specific death. We assessed to what extent family history of prostate or breast cancer, combined with a validated polygenic risk score (PRS) including 269 prostate cancer risk variants, identifies men at risk of prostate cancer and prostate cancer death across the age span. RESULTS During 20 years of follow-up, 1,915 prostate cancer and 166 fatal prostate cancer events were observed. Men in the top PRS quartile with a family history of prostate or breast cancer had the highest rate of both prostate cancer and prostate cancer-specific death. Compared with men at lowest genetic risk (bottom PRS quartile and no family history), the HR was 6.95 [95% confidence interval (CI), 5.57-8.66] for prostate cancer and 4.84 (95% CI, 2.59-9.03) for prostate cancer death. Men in the two upper PRS quartiles (50%-100%) or with a family history of prostate or breast cancer (61.8% of the population) accounted for 97.5% of prostate cancer deaths by age 75 years. CONCLUSIONS Our study shows that prostate cancer risk stratification on the basis of family history and inherited polygenic risk can identify men at highest risk of dying from prostate cancer before age 75 years.
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Affiliation(s)
- Anna Plym
- Urology Division, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Corresponding Author: Anna Plym, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, Stockholm SE-171 77, Sweden. Phone: 468-5248-0000; Fax: 468-314-975; E-mail:
| | - Yiwen Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Konrad H. Stopsack
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yon Ho Jee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Adam S. Kibel
- Urology Division, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Edward Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Kathryn L. Penney
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Lorelei A. Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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204
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Hughes E, Wagner S, Pruss D, Bernhisel R, Probst B, Abkevich V, Simmons T, Hullinger B, Judkins T, Rosenthal E, Roa B, Domchek SM, Eng C, Garber J, Gary M, Klemp J, Mukherjee S, Offit K, Olopade OI, Vijai J, Weitzel JN, Whitworth P, Yehia L, Gordon O, Pederson H, Kurian A, Slavin TP, Gutin A, Lanchbury JS. Development and Validation of a Breast Cancer Polygenic Risk Score on the Basis of Genetic Ancestry Composition. JCO Precis Oncol 2022; 6:e2200084. [PMID: 36331239 PMCID: PMC9666117 DOI: 10.1200/po.22.00084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 07/11/2022] [Accepted: 09/08/2022] [Indexed: 08/12/2023] Open
Abstract
PURPOSE Polygenic risk scores (PRSs) for breast cancer (BC) risk stratification have been developed primarily in women of European ancestry. Their application to women of non-European ancestry has lagged because of the lack of a formal approach to incorporate genetic ancestry and ancestry-dependent variant frequencies and effect sizes. Here, we propose a multiple-ancestry PRS (MA-PRS) that addresses these issues and may be useful in the development of equitable PRSs across other cancers and common diseases. MATERIALS AND METHODS Women referred for hereditary cancer testing were divided into consecutive cohorts for development (n = 189,230) and for independent validation (n = 89,126). Individual genetic composition as fractions of three reference ancestries (African, East Asian, and European) was determined from ancestry-informative single-nucleotide polymorphisms. The MA-PRS is a combination of three ancestry-specific PRSs on the basis of genetic ancestral composition. Stratification of risk was evaluated by multivariable logistic regression models controlling for family cancer history. Goodness-of-fit analysis compared expected with observed relative risks by quantiles of the MA-PRS distribution. RESULTS In independent validation, the MA-PRS was significantly associated with BC risk in the full cohort (odds ratio, 1.43; 95% CI, 1.40 to 1.46; P = 8.6 × 10-308) and within each major ancestry. The top decile of the MA-PRS consistently identified patients with two-fold increased risk of developing BC. Goodness-of-fit tests showed that the MA-PRS was well calibrated and predicted BC risk accurately in the tails of the distribution for both European and non-European women. CONCLUSION The MA-PRS uses genetic ancestral composition to expand the utility of polygenic risk prediction to non-European women. Inclusion of genetic ancestry in polygenic risk prediction presents an opportunity for more personalized treatment decisions for women of varying and mixed ancestries.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Susan M. Domchek
- Basser Center for BRCA, University of Pennsylvania, Philadelphia, PA
| | - Charis Eng
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH
| | | | | | - Jennifer Klemp
- The University of Kansas Cancer Center, The University of Kansas Medical Center, Kansas City, KS
| | | | - Kenneth Offit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Joseph Vijai
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Lamis Yehia
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH
| | - Ora Gordon
- Providence Health and Services, Renton, WA
| | - Holly Pederson
- Medical Breast Services, Cleveland Clinic, Cleveland, OH
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205
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Dornbos P, Koesterer R, Ruttenburg A, Nguyen T, Cole JB, Leong A, Meigs JB, Florez JC, Rotter JI, Udler MS, Flannick J. A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels. Nat Genet 2022; 54:1609-1614. [PMID: 36280733 PMCID: PMC9995082 DOI: 10.1038/s41588-022-01200-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 09/07/2022] [Indexed: 11/08/2022]
Abstract
Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.
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Affiliation(s)
- Peter Dornbos
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Ryan Koesterer
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
| | - Andrew Ruttenburg
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
| | - Trang Nguyen
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
| | - Joanne B Cole
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
- Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Aaron Leong
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jose C Florez
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Miriam S Udler
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jason Flannick
- Programs in Metabolism Program, Broad Institute, Cambridge, MA, USA.
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA.
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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206
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Kumuthini J, Zick B, Balasopoulou A, Chalikiopoulou C, Dandara C, El-Kamah G, Findley L, Katsila T, Li R, Maceda EB, Monye H, Rada G, Thong MK, Wanigasekera T, Kennel H, Marimuthu V, Williams MS, Al-Mulla F, Abramowicz M. The clinical utility of polygenic risk scores in genomic medicine practices: a systematic review. Hum Genet 2022; 141:1697-1704. [PMID: 35488921 PMCID: PMC9055005 DOI: 10.1007/s00439-022-02452-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/26/2022] [Indexed: 12/21/2022]
Abstract
Genomic medicine aims to improve health using the individual genomic data of people to inform care. While clinical utility of genomic medicine in many monogenic, Mendelian disorders is amply demonstrated, clinical utility is less evident in polygenic traits, e.g., coronary artery disease or breast cancer. Polygenic risk scores (PRS) are subsets of individual genotypes designed to capture heritability of common traits, and hence to allow the stratification of risk of the trait in a population. We systematically reviewed the PubMed database for unequivocal evidence of clinical utility of polygenic risk scores, using stringent inclusion and exclusion criteria. While we identified studies demonstrating clinical validity in conditions where medical intervention based on a PRS is likely to benefit patient outcome, we did not identify a single study demonstrating unequivocally such a benefit, i.e. clinical utility. We conclude that while the routine use of PRSs hold great promise, translational research is still needed before they should enter mainstream clinical practice.
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Affiliation(s)
- Judit Kumuthini
- South African National Bioinformatics Institute (SANBI), University of Western Cape, Cape Town, South Africa
| | - Brittany Zick
- Global Genomic Medicine Collaborative, Durham, NC USA
| | - Angeliki Balasopoulou
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
| | | | - Collet Dandara
- Division of Human Genetics, Department of Pathology & Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ghada El-Kamah
- Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt
| | - Laura Findley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Theodora Katsila
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
| | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Ebner Bon Maceda
- Center for Human Genetics Services, Institute of Human Genetics, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | - Henrietta Monye
- Department of Ophthalmology, University College Hospital, Ibadan, Nigeria
| | | | - Meow-Keong Thong
- Genetic and Metabolism Unit, Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Hannah Kennel
- Global Genomic Medicine Collaborative, Durham, NC USA
| | - Veeramani Marimuthu
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, P.O.Box 1180, 15462 Dasman, Kuwait
| | - the G2MC Evidence investigators
- South African National Bioinformatics Institute (SANBI), University of Western Cape, Cape Town, South Africa
- Global Genomic Medicine Collaborative, Durham, NC USA
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
- Division of Human Genetics, Department of Pathology & Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
- Center for Human Genetics Services, Institute of Human Genetics, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
- Department of Ophthalmology, University College Hospital, Ibadan, Nigeria
- Epistemonikos Foundation, Santiago, Chile
- Genetic and Metabolism Unit, Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Ministry of Health of Sri Lanka, Colombo, Sri Lanka
- Global Genomic Medicine Collaborative, Durham, NC USA
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, P.O.Box 1180, 15462 Dasman, Kuwait
- Genomic Medicine Institute, Geisinger, Danville, PA 17822 USA
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
- Department of Genetic Medicine and Development, Faculty of Medicine, Université de Genève, Geneva, Switzerland
| | | | - Fahd Al-Mulla
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Marc Abramowicz
- Department of Genetic Medicine and Development, Faculty of Medicine, Université de Genève, Geneva, Switzerland
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Lapointe J, Buron AC, Mbuya-Bienge C, Dorval M, Pashayan N, Brooks JD, Walker MJ, Chiquette J, Eloy L, Blackmore K, Turgeon A, Lambert-Côté L, Leclerc L, Dalpé G, Joly Y, Knoppers BM, Chiarelli AM, Simard J, Nabi H. Polygenic risk scores and risk-stratified breast cancer screening: Familiarity and perspectives of health care professionals. Genet Med 2022; 24:2380-2388. [PMID: 36057905 DOI: 10.1016/j.gim.2022.08.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 10/14/2022] Open
Abstract
PURPOSE Health care professionals are expected to take on an active role in the implementation of risk-based cancer prevention strategies. This study aimed to explore health care professionals' (1) self-reported familiarity with the concept of polygenic risk score (PRS), (2) perceived level of knowledge regarding risk-stratified breast cancer (BC) screening, and (3) preferences for continuing professional development. METHODS A cross-sectional survey was conducted using a bilingual-English/French-online questionnaire disseminated by health care professional associations across Canada between November 2020 and May 2021. RESULTS A total of 593 professionals completed more than 2 items and 453 responded to all questions. A total of 432 (94%) participants were female, 103 (22%) were physicians, and 323 (70%) were nurses. Participants reported to be unfamiliar with (20%), very unfamiliar (32%) with, or did not know (41%) the concept of PRS. Most participants reported not having enough knowledge about risk-stratified BC screening (61%) and that they would require more training (77%). Online courses and webinar conferences were the preferred continuing professional development modalities. CONCLUSION The study indicates that health care professionals are currently not familiar with the concept of PRS or a risk-stratified approach for BC screening. Online information and training seem to be an essential knowledge transfer modality.
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Affiliation(s)
- Julie Lapointe
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada
| | - Anne-Catherine Buron
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada
| | - Cynthia Mbuya-Bienge
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada; Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec City, Québec, Canada
| | - Michel Dorval
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada; Faculty of Pharmacy, Université Laval, Québec City, Québec, Canada; CISSS de Chaudière-Appalaches Research Center, Lévis, Québec, Canada
| | - Nora Pashayan
- Department of Applied Health Research, Institute of Epidemiology and Healthcare, University College London, United Kingdom
| | - Jennifer D Brooks
- Dalla Lana School of Public Health Science, University of Toronto, Toronto, Ontario, Canada
| | - Meghan J Walker
- Dalla Lana School of Public Health Science, University of Toronto, Toronto, Ontario, Canada; Cancer Care Ontario, Ontario Health, Toronto, Ontario, Canada
| | - Jocelyne Chiquette
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada; CHU de Québec-Université Laval, Québec City, Québec, Canada
| | - Laurence Eloy
- Programme Québécois de Cancérologie, Ministère de la Santé et des Services Sociaux, Québec City, Québec, Canada
| | | | - Annie Turgeon
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada
| | - Laurence Lambert-Côté
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada
| | - Lucas Leclerc
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada
| | - Gratien Dalpé
- Centre of Genomics and Policy, McGill University, Montréal, Québec, Canada
| | - Yann Joly
- Centre of Genomics and Policy, McGill University, Montréal, Québec, Canada; Human Genetics Department and Bioethics Unit, Faculty of Medicine, McGill University, Montréal, Québec, Canada
| | | | - Anna Maria Chiarelli
- Dalla Lana School of Public Health Science, University of Toronto, Toronto, Ontario, Canada; Cancer Care Ontario, Ontario Health, Toronto, Ontario, Canada
| | - Jacques Simard
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada; Department of Molecular Medicine, Faculty of Medicine, Université Laval, Québec City, Québec, Canada
| | - Hermann Nabi
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec City, Québec, Canada; Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec City, Québec, Canada.
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208
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Harrison H, Li N, Saunders CL, Rossi SH, Dennis J, Griffin SJ, Stewart GD, Usher‐Smith JA. The current state of genetic risk models for the development of kidney cancer: a review and validation. BJU Int 2022; 130:550-561. [PMID: 35460182 PMCID: PMC9790357 DOI: 10.1111/bju.15752] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. METHODS Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). RESULTS A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. CONCLUSIONS Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.
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Affiliation(s)
- Hannah Harrison
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Nicole Li
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
- Deanary of Biomedical SciencesUniversity of EdinburghEdinburghUK
| | | | - Sabrina H. Rossi
- Department of SurgeryUniversity of CambridgeAddenbrooke’s HospitalCambridgeUK
| | - Joe Dennis
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Simon J. Griffin
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
| | - Grant D. Stewart
- Department of SurgeryUniversity of CambridgeAddenbrooke’s HospitalCambridgeUK
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209
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Barnett GC, Kerns SL, Dorling L, Fachal L, Aguado-Barrera ME, Martínez-Calvo L, Jandu HK, Welsh C, Tyrer J, Coles CE, Haviland JS, Parker C, Gómez-Caamaño A, Calvo-Crespo P, Sosa-Fajardo P, Burnet NG, Summersgill H, Webb A, De Ruysscher D, Seibold P, Chang-Claude J, Talbot CJ, Rattay T, Parliament M, De Ruyck K, Rosenstein BS, Pharoah PDP, Dunning AM, Vega A, West CML. No Association Between Polygenic Risk Scores for Cancer and Development of Radiation Therapy Toxicity. Int J Radiat Oncol Biol Phys 2022; 114:494-501. [PMID: 35840111 DOI: 10.1016/j.ijrobp.2022.06.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/16/2022] [Accepted: 06/26/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE Our aim was to test whether updated polygenic risk scores (PRS) for susceptibility to cancer affect risk of radiation therapy toxicity. METHODS AND MATERIALS Analyses included 9,717 patients with breast (n=3,078), prostate (n=5,748) or lung (n=891) cancer from Radiogenomics and REQUITE Consortia cohorts. Patients underwent potentially curative radiation therapy and were assessed prospectively for toxicity. Germline genotyping involved genome-wide single nucleotide polymorphism (SNP) arrays with nontyped SNPs imputed. PRS for each cancer were generated by summing literature-identified cancer susceptibility risk alleles: 352 breast, 136 prostate, and 24 lung. Weighted PRS were generated using log odds ratio (ORs) for cancer susceptibility. Standardized total average toxicity (STAT) scores at 2 and 5 years (breast, prostate) or 6 to 12 months (lung) quantified toxicity. Primary analysis tested late STAT, secondary analyses investigated acute STAT, and individual endpoints and SNPs using multivariable regression. RESULTS Increasing PRS did not increase risk of late toxicity in patients with breast (OR, 1.000; 95% confidence interval [CI], 0.997-1.002), prostate (OR, 0.99; 95% CI, 0.98-1.00; weighted PRS OR, 0.93; 95% CI, 0.83-1.03), or lung (OR, 0.93; 95% CI, 0.87-1.00; weighted PRS OR, 0.68; 95% CI, 0.45-1.03) cancer. Similar results were seen for acute toxicity. Secondary analyses identified rs138944387 associated with breast pain (OR, 3.05; 95% CI, 1.86-5.01; P = 1.09 × 10-5) and rs17513613 with breast edema (OR, 0.94; 95% CI, 0.92-0.97; P = 1.08 × 10-5). CONCLUSIONS Patients with increased polygenic predisposition to breast, prostate, or lung cancer can safely undergo radiation therapy with no anticipated excess toxicity risk. Some individual SNPs increase the likelihood of a specific toxicity endpoint, warranting validation in independent cohorts and functional studies to elucidate biologic mechanisms.
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Affiliation(s)
- Gillian C Barnett
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, United Kingdom.
| | - Sarah L Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Miguel E Aguado-Barrera
- Fundación Pública Galega de Medicina Xenómica (FPGMX)-SERGAS, Santiago de Compostela, A Coruña, Spain; Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, A Coruña, Spain
| | - Laura Martínez-Calvo
- Fundación Pública Galega de Medicina Xenómica (FPGMX)-SERGAS, Santiago de Compostela, A Coruña, Spain; Grupo Genética en Cáncer y Enfermedades Raras, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, A Coruña, Spain
| | - Harkeran K Jandu
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Ceilidh Welsh
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Jonathan Tyrer
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom
| | - Charlotte E Coles
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, United Kingdom
| | - Joanne S Haviland
- Clinical Trials and Statistics Unit, Institute of Cancer Research, London, United Kingdom
| | - Christopher Parker
- Institute of Cancer Research & Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Antonio Gómez-Caamaño
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela, Spain
| | - Patricia Calvo-Crespo
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela, Spain
| | - Paloma Sosa-Fajardo
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela, Spain
| | - Neil G Burnet
- Proton Beam Therapy Centre, Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Holly Summersgill
- Translational Radiobiology Group, Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Adam Webb
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Center, GROW School for Oncology and Developmental Biology, Maastricht, The Netherlands; Radiation Oncology, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Petra Seibold
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Germany
| | - Christopher J Talbot
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Tim Rattay
- Leicester Cancer Research Centre, University of Leicester, Leicester, United Kingdom
| | - Matthew Parliament
- Division of Radiation Oncology, Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
| | - Kim De Ruyck
- Departments of Basic Medical Sciences and Radiotherapy, Ghent University Hospital, Ghent, Belgium
| | - Barry S Rosenstein
- Departments of Radiation Oncology and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom
| | - Ana Vega
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom; Fundación Pública Galega de Medicina Xenómica (FPGMX)-SERGAS, Santiago de Compostela, A Coruña, Spain; Biomedical Network on Rare Diseases (CIBERER), Spain
| | - Catharine M L West
- Translational Radiobiology Group, Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Christie NHS Foundation Trust, Manchester, United Kingdom
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210
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Giardiello D, Hooning MJ, Hauptmann M, Keeman R, Heemskerk-Gerritsen BAM, Becher H, Blomqvist C, Bojesen SE, Bolla MK, Camp NJ, Czene K, Devilee P, Eccles DM, Fasching PA, Figueroa JD, Flyger H, García-Closas M, Haiman CA, Hamann U, Hopper JL, Jakubowska A, Leeuwen FE, Lindblom A, Lubiński J, Margolin S, Martinez ME, Nevanlinna H, Nevelsteen I, Pelders S, Pharoah PDP, Siesling S, Southey MC, van der Hout AH, van Hest LP, Chang-Claude J, Hall P, Easton DF, Steyerberg EW, Schmidt MK. PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. BREAST CANCER RESEARCH : BCR 2022; 24:69. [PMID: 36271417 PMCID: PMC9585761 DOI: 10.1186/s13058-022-01567-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/07/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. METHODS We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. RESULTS The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | | | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.,Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.,Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Nicola J Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Jonine D Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK.,Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.,Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Floor E Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden.,Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.,Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ines Nevelsteen
- Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium
| | - Saskia Pelders
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands.,Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Annemieke H van der Hout
- Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands
| | - Liselotte P van Hest
- Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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211
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Li S, MacInnis RJ, Lee A, Nguyen-Dumont T, Dorling L, Carvalho S, Dite GS, Shah M, Luccarini C, Wang Q, Milne RL, Jenkins MA, Giles GG, Dunning AM, Pharoah PDP, Southey MC, Easton DF, Hopper JL, Antoniou AC. Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction. Am J Hum Genet 2022; 109:1777-1788. [PMID: 36206742 PMCID: PMC9606477 DOI: 10.1016/j.ajhg.2022.09.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/12/2022] [Indexed: 01/25/2023] Open
Abstract
Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3051, Australia.
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Sara Carvalho
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Genetic Technologies Ltd., Fitzroy, VIC 3065, Australia
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
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Corpas M, Megy K, Metastasio A, Lehmann E. Implementation of individualised polygenic risk score analysis: a test case of a family of four. BMC Med Genomics 2022; 15:207. [PMID: 36192731 PMCID: PMC9531350 DOI: 10.1186/s12920-022-01331-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
Background Polygenic risk scores (PRS) have been widely applied in research studies, showing how population groups can be stratified into risk categories for many common conditions. As healthcare systems consider applying PRS to keep their populations healthy, little work has been carried out demonstrating their implementation at an individual level. Case presentation We performed a systematic curation of PRS sources from established data repositories, selecting 15 phenotypes, comprising an excess of 37 million SNPs related to cancer, cardiovascular, metabolic and autoimmune diseases. We tested selected phenotypes using whole genome sequencing data for a family of four related individuals. Individual risk scores were given percentile values based upon reference distributions among 1000 Genomes Iberians, Europeans, or all samples. Over 96 billion allele effects were calculated in order to obtain the PRS for each of the individuals analysed here. Conclusions Our results highlight the need for further standardisation in the way PRS are developed and shared, the importance of individual risk assessment rather than the assumption of inherited averages, and the challenges currently posed when translating PRS into risk metrics.
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Affiliation(s)
- Manuel Corpas
- Cambridge Precision Medicine Limited, ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK. .,Institute of Continuing Education, University of Cambridge, Cambridge, UK. .,Facultad de Ciencias de La Salud, Universidad Internacional de La Rioja, Madrid, Spain.
| | - Karyn Megy
- Cambridge Precision Medicine Limited, ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK.,Department of Haematology, University of Cambridge & NHS Blood and Transplant, Cambridge, UK
| | - Antonio Metastasio
- Cambridge Precision Medicine Limited, ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK.,Camden and Islington NHS Foundation Trust, London, UK
| | - Edmund Lehmann
- Cambridge Precision Medicine Limited, ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK
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213
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Breast cancer polygenic risk scores are associated with short-term risk of poor prognosis breast cancer. Breast Cancer Res Treat 2022; 196:389-398. [PMID: 36138293 DOI: 10.1007/s10549-022-06739-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/04/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE Polygenic risk scores (PRS) for breast cancer may help guide screening decisions. However, few studies have examined whether PRS are associated with risk of short-term or poor prognosis breast cancers. The study purpose was to evaluate the association of the 313 SNP breast cancer PRS with 2-year risk of poor prognosis breast cancer. METHODS We evaluated the association of breast cancer PRS with breast cancer overall, ER + and ER- breast cancer, and poor prognosis breast cancer diagnosed within 2 years of a negative mammogram among a cohort of 3657 women using logistic regression adjusted for age, breast density, race/ethnicity, year of screening, and genetic ancestry principal components. Breast cancers were considered poor prognosis if they were metastatic, positive lymph nodes, ER/PR + HER2- and > 2 cm, ER/PR/HER2-, or HER2 + and > 1 cm. RESULTS Of the 308 breast cancers, 137 (44%) were poor prognosis. The overall breast cancer PRS was significantly associated with breast cancer diagnosis within 2 years (OR 1.39, 95% CI 1.23-1.57, p < 0.001). The breast cancer PRS was also associated specifically with diagnosis of poor prognosis disease (OR 1.24, 95% CI 1.03-1.49, p = 0.018), but was more strongly associated with good prognosis cancer (OR 1.52 95% CI 1.29-1.80 p = 3.60 × 10-7) The ER + PRS was significantly associated with ER/PR + breast cancer (OR 1.41, 95% CI 1.24-1.61, p < 0.001) and the ER- PRS was significantly associated with ER- breast cancer (OR 1.48, 95% CI 1.08-2.02, p = 0.015). CONCLUSION Breast cancer PRS was independently and significantly associated with diagnosis of both breast cancer overall and poor prognosis breast cancer within 2 years of a negative mammogram, suggesting PRS may help guide decisions about screening intervals and supplemental screening.
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214
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Kim MS, Naidoo D, Hazra U, Quiver MH, Chen WC, Simonti CN, Kachambwa P, Harlemon M, Agalliu I, Baichoo S, Fernandez P, Hsing AW, Jalloh M, Gueye SM, Niang L, Diop H, Ndoye M, Snyper NY, Adusei B, Mensah JE, Abrahams AOD, Biritwum R, Adjei AA, Adebiyi AO, Shittu O, Ogunbiyi O, Adebayo S, Aisuodionoe-Shadrach OI, Nwegbu MM, Ajibola HO, Oluwole OP, Jamda MA, Singh E, Pentz A, Joffe M, Darst BF, Conti DV, Haiman CA, Spies PV, van der Merwe A, Rohan TE, Jacobson J, Neugut AI, McBride J, Andrews C, Petersen LN, Rebbeck TR, Lachance J. Testing the generalizability of ancestry-specific polygenic risk scores to predict prostate cancer in sub-Saharan Africa. Genome Biol 2022; 23:194. [PMID: 36100952 PMCID: PMC9472407 DOI: 10.1186/s13059-022-02766-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 09/05/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Genome-wide association studies do not always replicate well across populations, limiting the generalizability of polygenic risk scores (PRS). Despite higher incidence and mortality rates of prostate cancer in men of African descent, much of what is known about cancer genetics comes from populations of European descent. To understand how well genetic predictions perform in different populations, we evaluated test characteristics of PRS from three previous studies using data from the UK Biobank and a novel dataset of 1298 prostate cancer cases and 1333 controls from Ghana, Nigeria, Senegal, and South Africa. RESULTS Allele frequency differences cause predicted risks of prostate cancer to vary across populations. However, natural selection is not the primary driver of these differences. Comparing continental datasets, we find that polygenic predictions of case vs. control status are more effective for European individuals (AUC 0.608-0.707, OR 2.37-5.71) than for African individuals (AUC 0.502-0.585, OR 0.95-2.01). Furthermore, PRS that leverage information from African Americans yield modest AUC and odds ratio improvements for sub-Saharan African individuals. These improvements were larger for West Africans than for South Africans. Finally, we find that existing PRS are largely unable to predict whether African individuals develop aggressive forms of prostate cancer, as specified by higher tumor stages or Gleason scores. CONCLUSIONS Genetic predictions of prostate cancer perform poorly if the study sample does not match the ancestry of the original GWAS. PRS built from European GWAS may be inadequate for application in non-European populations and perpetuate existing health disparities.
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Affiliation(s)
- Michelle S Kim
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Dr, Atlanta, GA, 30332, USA
| | - Daphne Naidoo
- Centre for Proteomic and Genomic Research, Cape Town, South Africa
| | - Ujani Hazra
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Dr, Atlanta, GA, 30332, USA
| | - Melanie H Quiver
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Dr, Atlanta, GA, 30332, USA
| | - Wenlong C Chen
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa
| | - Corinne N Simonti
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Dr, Atlanta, GA, 30332, USA
| | | | - Maxine Harlemon
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Dr, Atlanta, GA, 30332, USA
| | - Ilir Agalliu
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Pedro Fernandez
- Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Ann W Hsing
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | | | | | - Lamine Niang
- Universite Cheikh Anta Diop de Dakar, Dakar, Senegal
| | | | - Medina Ndoye
- Universite Cheikh Anta Diop de Dakar, Dakar, Senegal
| | | | | | - James E Mensah
- Korle-Bu Teaching Hospital and University of Ghana Medical School, Accra, Ghana
| | - Afua O D Abrahams
- Korle-Bu Teaching Hospital and University of Ghana Medical School, Accra, Ghana
| | - Richard Biritwum
- Korle-Bu Teaching Hospital and University of Ghana Medical School, Accra, Ghana
| | - Andrew A Adjei
- Department of Pathology, University of Ghana Medical School, Accra, Ghana
| | | | | | | | - Sikiru Adebayo
- College of Medicine, University of Ibadan, Ibadan, Nigeria
| | | | - Maxwell M Nwegbu
- College of Health Sciences, University of Abuja and University of Abuja Teaching Hospital, Abuja, Nigeria
| | - Hafees O Ajibola
- College of Health Sciences, University of Abuja and University of Abuja Teaching Hospital, Abuja, Nigeria
| | - Olabode P Oluwole
- College of Health Sciences, University of Abuja and University of Abuja Teaching Hospital, Abuja, Nigeria
| | - Mustapha A Jamda
- College of Health Sciences, University of Abuja and University of Abuja Teaching Hospital, Abuja, Nigeria
| | - Elvira Singh
- National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa
| | - Audrey Pentz
- Non-Communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd, Johannesburg, South Africa
| | - Maureen Joffe
- Non-Communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd, Johannesburg, South Africa.,MRC Developmental Pathways to Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - Burcu F Darst
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David V Conti
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher A Haiman
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Petrus V Spies
- Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - André van der Merwe
- Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Thomas E Rohan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Judith Jacobson
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - Alfred I Neugut
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - Jo McBride
- Centre for Proteomic and Genomic Research, Cape Town, South Africa
| | | | | | - Timothy R Rebbeck
- Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joseph Lachance
- School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Dr, Atlanta, GA, 30332, USA.
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215
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Gao G, Zhao F, Ahearn TU, Lunetta KL, Troester MA, Du Z, Ogundiran TO, Ojengbede O, Blot W, Nathanson KL, Domchek SM, Nemesure B, Hennis A, Ambs S, McClellan J, Nie M, Bertrand K, Zirpoli G, Yao S, Olshan AF, Bensen JT, Bandera EV, Nyante S, Conti DV, Press MF, Ingles SA, John EM, Bernstein L, Hu JJ, Deming-Halverson SL, Chanock SJ, Ziegler RG, Rodriguez-Gil JL, Sucheston-Campbell LE, Sandler DP, Taylor JA, Kitahara CM, O’Brien KM, Bolla MK, Dennis J, Dunning AM, Easton DF, Michailidou K, Pharoah PDP, Wang Q, Figueroa J, Biritwum R, Adjei E, Wiafe S, Ambrosone CB, Zheng W, Olopade OI, García-Closas M, Palmer JR, Haiman CA, Huo D. Polygenic risk scores for prediction of breast cancer risk in women of African ancestry: a cross-ancestry approach. Hum Mol Genet 2022; 31:3133-3143. [PMID: 35554533 PMCID: PMC9476624 DOI: 10.1093/hmg/ddac102] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/29/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.
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Affiliation(s)
- Guimin Gao
- Department of Public Health Sciences, The University of Chicago, Chicago, IL 60637, USA
| | - Fangyuan Zhao
- Department of Public Health Sciences, The University of Chicago, Chicago, IL 60637, USA
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Melissa A Troester
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhaohui Du
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Temidayo O Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oladosu Ojengbede
- Centre for Population & Reproductive Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Katherine L Nathanson
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Susan M Domchek
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Anselm Hennis
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- University of the West Indies, Bridgetown, Bardados
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, MD 20892, USA
| | - Julian McClellan
- Department of Public Health Sciences, The University of Chicago, Chicago, IL 60637, USA
| | - Mark Nie
- Department of Public Health Sciences, The University of Chicago, Chicago, IL 60637, USA
| | | | - Gary Zirpoli
- Slone Epidemiology Center, Boston University, Boston, MA 02215, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA
| | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jeannette T Bensen
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Sarah Nyante
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - David V Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Michael F Press
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Sue A Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Esther M John
- Departments of Epidemiology & Population Health and of Medicine (Oncology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Leslie Bernstein
- Biomarkers of Early Detection and Prevention, Department of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
| | - Jennifer J Hu
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sandra L Deming-Halverson
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Regina G Ziegler
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Jorge L Rodriguez-Gil
- Genomics, Development and Disease Section, Genetic Disease Research Branch, National Human Genome Research Institute, Bethesda, MD 20894, USA
| | - Lara E Sucheston-Campbell
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Katie M O’Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Joe Dennis
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Alison M Dunning
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia 2371, Cyprus
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Qin Wang
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Jonine Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh EH16 5TJ, UK
- Cancer Research UK Edinburgh Centre, Edinburgh EH4 2XR, UK
| | | | | | - Seth Wiafe
- School of Public Health, Loma Linda University, Loma Linda, CA 92350, USA
| | | | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL 60637, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA 02215, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL 60637, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL 60637, USA
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216
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Zhai S, Zhang H, Mehrotra DV, Shen J. Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods. Nat Commun 2022; 13:5278. [PMID: 36075892 PMCID: PMC9458667 DOI: 10.1038/s41467-022-32407-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022] Open
Abstract
Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches. To try to predict an individual’s drug response using genetic data, most studies have used traditional polygenic risk score (PRS) methods. Here, the authors develop a pharmacogenomics-specific PRS method, which can improve drug response prediction and patient stratification in pharmacogenomics studies.
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Affiliation(s)
- Song Zhai
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, 07065, USA
| | - Hong Zhang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA, 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, 07065, USA.
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217
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Willson ML, Srinivasa S, Fatema K, Lostumbo L, Carbine NE, Egger SJ, Goodwin A. Risk-reducing mastectomy for unaffected women with a strong family history of breast cancer. Hippokratia 2022. [DOI: 10.1002/14651858.cd015020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Melina L Willson
- Evidence Integration; NHMRC Clinical Trials Centre, The University of Sydney; Sydney Australia
| | - Shweta Srinivasa
- Department of Cancer Genetics; Royal Prince Alfred Hospital and Concord Repatriation General Hospital; Sydney Australia
| | - Kaniz Fatema
- Cancer Services; Royal Prince Alfred Hospital; Sydney Australia
| | - Liz Lostumbo
- Author of the original review; Gaithersburg Maryland USA
| | - Nora E Carbine
- Georgetown Breast Cancer Research Advocates (GBCA); Georgetown University Lombardi Cancer Center; Washington DC USA
| | - Sam J Egger
- Cancer Research Division; Cancer Council NSW; Sydney Australia
| | - Annabel Goodwin
- Department of Cancer Genetics; Royal Prince Alfred Hospital and Concord Repatriation General Hospital; Sydney Australia
- Concord Clinical School; The University of Sydney; Concord Australia
- Department of Medical Oncology; Concord Repatriation General Hospital; Concord Australia
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218
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Page ML, Vance EL, Cloward ME, Ringger E, Dayton L, Ebbert MTW, Miller JB, Kauwe JSK. The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores. Commun Biol 2022; 5:899. [PMID: 36056235 PMCID: PMC9438378 DOI: 10.1038/s42003-022-03795-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
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Affiliation(s)
- Madeline L Page
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Elizabeth L Vance
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | | | - Ed Ringger
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Louisa Dayton
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Mark T W Ebbert
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.,Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.,Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | | | - Justin B Miller
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.,Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.,Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT, USA.
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219
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Bafligil C, Thompson DJ, Lophatananon A, Ryan NAJ, Smith MJ, Dennis J, Mekli K, O'Mara TA, Evans DG, Crosbie EJ. Development and evaluation of polygenic risk scores for prediction of endometrial cancer risk in European women. Genet Med 2022; 24:1847-1856. [PMID: 35704044 DOI: 10.1016/j.gim.2022.05.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Single-nucleotide variations (SNVs) (formerly single-nucleotide polymorphism [SNV]) influence genetic predisposition to endometrial cancer. We hypothesized that a polygenic risk score (PRS) comprising multiple SNVs may improve endometrial cancer risk prediction for targeted screening and prevention. METHODS We developed PRSs from SNVs identified from a systematic review of published studies and suggestive SNVs from the Endometrial Cancer Association Consortium. These were tested in an independent study of 555 surgically-confirmed endometrial cancer cases and 1202 geographically-matched controls from Manchester, United Kingdom and validated in 1676 cases and 116,960 controls from the UK Biobank (UKBB). RESULTS Age and body mass index predicted endometrial cancer in both data sets (Manchester: area under the receiver operator curve [AUC] = 0.77, 95% CI = 0.74-0.80; UKBB: AUC = 0.74, 95% CI = 0.73-0.75). The AUC for PRS19, PRS24, and PRS72 were 0.58, 0.55, and 0.57 in the Manchester study and 0.56, 0.54, and 0.54 in UKBB, respectively. For PRS19, women in the third tertile had a 2.1-fold increased risk of endometrial cancer compared with those in the first tertile of the Manchester study (odds ratio = 2.08, 95% CI = 1.61-2.68, Ptrend = 5.75E-9). Combining PRS19 with age and body mass index improved discriminatory power (Manchester study: AUC = 0.79, 95% CI = 0.76-0.82; UKBB: AUC =0.75, 95% CI = 0.73-0.76). CONCLUSION An endometrial cancer risk prediction model incorporating a PRS derived from multiple SNVs may help stratify women for screening and prevention strategies.
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Affiliation(s)
- Cemsel Bafligil
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester, United Kingdom
| | - Deborah J Thompson
- Strangeways Research Laboratory, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Neil A J Ryan
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester, United Kingdom; Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Miriam J Smith
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester, United Kingdom
| | - Joe Dennis
- Strangeways Research Laboratory, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Krisztina Mekli
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Tracy A O'Mara
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - D Gareth Evans
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, North West Laboratory Genetics Hub, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Emma J Crosbie
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary's Hospital, Manchester, United Kingdom; Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom.
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Kullo IJ, Lewis CM, Inouye M, Martin AR, Ripatti S, Chatterjee N. Polygenic scores in biomedical research. Nat Rev Genet 2022; 23:524-532. [PMID: 35354965 PMCID: PMC9391275 DOI: 10.1038/s41576-022-00470-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2022] [Indexed: 12/12/2022]
Abstract
Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.
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Affiliation(s)
- Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre & Department of Medical & Molecular, King's College London, London, UK.
| | - Michael Inouye
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Samuli Ripatti
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Paranjpe MD, Chaffin M, Zahid S, Ritchie S, Rotter JI, Rich SS, Gerszten R, Guo X, Heckbert S, Tracy R, Danesh J, Lander ES, Inouye M, Kathiresan S, Butterworth AS, Khera AV. Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer's disease. PLoS Genet 2022; 18:e1010294. [PMID: 36048760 PMCID: PMC9436054 DOI: 10.1371/journal.pgen.1010294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/14/2022] [Indexed: 11/18/2022] Open
Abstract
For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.
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Affiliation(s)
- Manish D. Paranjpe
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Mark Chaffin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Sohail Zahid
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Scott Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California, United States of America
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Robert Gerszten
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California, United States of America
| | - Susan Heckbert
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Russ Tracy
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont, United States of America
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Eric S. Lander
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
- The Alan Turing Institute, London, United Kingdom
| | - Sekar Kathiresan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Verve Therapeutics, Cambridge, Massachusetts, United States of America
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Amit V. Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Verve Therapeutics, Cambridge, Massachusetts, United States of America
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
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222
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Kang KW, Cho YW, Lee SK, Jung KY, Kim JH, Kim DW, Lee SA, Hong SB, Na IS, Lee SH, Baek WK, Choi SY, Kim MK. Multidimensional Early Prediction Score for Drug-Resistant Epilepsy. J Clin Neurol 2022; 18:553-561. [PMID: 36062773 PMCID: PMC9444554 DOI: 10.3988/jcn.2022.18.5.553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/16/2022] [Accepted: 03/16/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Purpose Achieving favorable postoperative outcomes in patients with drug-resistant epilepsy (DRE) requires early referrals for preoperative examinations. The purpose of this study was to investigate the possibility of a user-friendly early DRE prediction model that is easy for nonexperts to utilize. Methods A two-step genotype analysis was performed, by applying 1) whole-exome sequencing (WES) to the initial test set (n=243) and 2) target sequencing to the validation set (n=311). Based on a multicenter case–control study design using the WES data set, 11 genetic and 2 clinical predictors were selected to develop the DRE risk prediction model. The early prediction scores for DRE (EPS-DRE) was calculated for each group of the selected genetic predictors (EPS-DREgen), clinical predictors (EPS-DREcln), and two types of predictor mix (EPS-DREmix) in both the initial test set and the validation set. Results The multidimensional EPS-DREmix of the predictor mix group provided a better match to the outcome data than did the unidimensional EPS-DREgen or EPS-DREcln. Unlike previous studies, the EPS-DREmix model was developed using only 11 genetic and 2 clinical predictors, but it exhibited good discrimination ability in distinguishing DRE from drug-responsive epilepsy. These results were verified using an unrelated validation set. Conclusions Our results suggest that EPS-DREmix has good performance in early DRE prediction and is a user-friendly tool that is easy to apply in real clinical trials, especially by nonexperts who do not have detailed knowledge or equipment for assessing DRE. Further studies are needed to improve the performance of the EPS-DREmix model.
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Affiliation(s)
- Kyung Wook Kang
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Yong Won Cho
- Department of Neurology, Dongsan Medical Center, Keimyung University School of Medicine, Daegu, Korea
| | - Sang Kun Lee
- Department of Neurology, Comprehensive Epilepsy Center, Laboratory for Neurotherapeutics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea Program in Neuroscience, Seoul National University College of Medicine, Seoul, Korea
| | - Ki-Young Jung
- Department of Neurology, Comprehensive Epilepsy Center, Laboratory for Neurotherapeutics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea Program in Neuroscience, Seoul National University College of Medicine, Seoul, Korea
| | - Ji Hyun Kim
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Dong Wook Kim
- Department of Neurology, Konkuk University School of Medicine, Seoul, Korea
| | - Sang-Ahm Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Bong Hong
- Department of Neurology, Samsung Medical Center, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University School of Medicine, Samsung Biomedical Research Institute (SBRI), Seoul, Korea.,National Epilepsy Care Center, Seoul, Korea
| | - In-Seop Na
- National Program of Excellence in Software Centre, Chosun University, Gwangju, Korea
| | - So-Hyun Lee
- Department of Biomedical Science, Chonnam National University Medical School, Hwasun, Korea
| | - Won-Ki Baek
- Department of Microbiology, Keimyung University School of Medicine, Daegu, Korea
| | - Seok-Yong Choi
- Department of Biomedical Science, Chonnam National University Medical School, Hwasun, Korea.
| | - Myeong-Kyu Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea.
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Jobanputra V, Wrzeszczynski KO, Buttner R, Caldas C, Cuppen E, Grimmond S, Haferlach T, Mullighan C, Schuh A, Elemento O. Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology. Semin Cancer Biol 2022; 84:23-31. [PMID: 34256129 DOI: 10.1016/j.semcancer.2021.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 07/01/2021] [Accepted: 07/07/2021] [Indexed: 02/08/2023]
Abstract
Whole-genome sequencing either alone or in combination with whole-transcriptome sequencing has started to be used to analyze clinical tumor samples to improve diagnosis, provide risk stratification, and select patient-specific therapies. Compared with current genomic testing strategies, largely focused on small number of genes tested individually or targeted panels, whole-genome and transcriptome sequencing (WGTS) provides novel opportunities to identify and report a potentially much larger number of actionable alterations with diagnostic, prognostic, and/or predictive impact. Such alterations include point mutations, indels, copy- number aberrations and structural variants, but also germline variants, fusion genes, noncoding alterations and mutational signatures. Nevertheless, these comprehensive tests are accompanied by many challenges ranging from the extent and diversity of sequence alterations detected by these methods to the complexity and limited existing standardization in interpreting them. We describe the challenges of WGTS interpretation and the opportunities with comprehensive genomic testing.
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Affiliation(s)
- Vaidehi Jobanputra
- New York Genome Center, 101 Avenue of the Americas, New York, NY 100132, United States; Columbia University Medical Center, 650 W 168th St, New York, NY 10032, United States.
| | | | | | - Carlos Caldas
- Cancer Research UK Cambridge Institute and Department of Oncology, University of Cambridge, United Kingdom
| | - Edwin Cuppen
- Hartwig Medical Foundation, Amsterdam, Netherlands; Center for Molecular Medicine and Oncode Institute, University Medical Center, Utrecht, Netherlands
| | - Sean Grimmond
- Centre for Cancer Research, University of Melbourne, Melbourne, Australia
| | | | - Charles Mullighan
- Department of Pathology, St. Jude Children's Research Hospital, United States
| | - Anna Schuh
- NIHR Oxford Biomedical Research Centre and Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Olivier Elemento
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, United States; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, United States.
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224
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Siemens A, Anderson SJ, Rassekh SR, Ross CJD, Carleton BC. A Systematic Review of Polygenic Models for Predicting Drug Outcomes. J Pers Med 2022; 12:jpm12091394. [PMID: 36143179 PMCID: PMC9505711 DOI: 10.3390/jpm12091394] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/21/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.
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Affiliation(s)
- Angela Siemens
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Spencer J. Anderson
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - S. Rod Rassekh
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, Canada
- Division of Oncology, Hematology and Bone Marrow Transplant, University of British Columbia, Vancouver, BC V6H 3V4, Canada
| | - Colin J. D. Ross
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Bruce C. Carleton
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, Canada
- Pharmaceutical Outcomes Programme, British Columbia Children’s Hospital, Vancouver, BC V5Z 4H4, Canada
- Correspondence:
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225
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Thompson M, Hill BL, Rakocz N, Chiang JN, Geschwind D, Sankararaman S, Hofer I, Cannesson M, Zaitlen N, Halperin E. Methylation risk scores are associated with a collection of phenotypes within electronic health record systems. NPJ Genom Med 2022; 7:50. [PMID: 36008412 PMCID: PMC9411568 DOI: 10.1038/s41525-022-00320-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 07/18/2022] [Indexed: 12/20/2022] Open
Abstract
Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. We measured methylation across a large cohort (n = 831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs, and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10−7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.
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Affiliation(s)
- Mike Thompson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
| | - Brian L Hill
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
| | - Nadav Rakocz
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Jeffrey N Chiang
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Daniel Geschwind
- Institute of Precision Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.,Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
| | - Ira Hofer
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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226
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Pan J, Ma B, Hou X, Li C, Xiong T, Gong Y, Song F. The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12353-12370. [PMID: 36654001 DOI: 10.3934/mbe.2022576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer. METHODS The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. RESULTS The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients. CONCLUSIONS Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.
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Affiliation(s)
- Jianqiao Pan
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Baoshan Ma
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Xiaoyu Hou
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Chongyang Li
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Tong Xiong
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Yi Gong
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
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227
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Laza-Vásquez C, Martínez-Alonso M, Forné-Izquierdo C, Vilaplana-Mayoral J, Cruz-Esteve I, Sánchez-López I, Reñé-Reñé M, Cazorla-Sánchez C, Hernández-Andreu M, Galindo-Ortego G, Llorens-Gabandé M, Pons-Rodríguez A, Rué M. Feasibility and Acceptability of Personalized Breast Cancer Screening (DECIDO Study): A Single-Arm Proof-of-Concept Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10426. [PMID: 36012059 PMCID: PMC9407798 DOI: 10.3390/ijerph191610426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40-50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants' intention to participate in risk-based screening and satisfaction at 1-year were very high.
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Affiliation(s)
- Celmira Laza-Vásquez
- Department of Nursing and Physiotherapy and Health Care Research Group (GRECS), IRBLleida—Institut de Recerca Biomèdica de Lleida, University of Lleida, 25198 Lleida, Spain
| | - Montserrat Martínez-Alonso
- IRBLleida—Institut de Recerca Biomèdica de Lleida, Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
| | - Carles Forné-Izquierdo
- Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
- Heorfy Consulting, 25007 Lleida, Spain
| | - Jordi Vilaplana-Mayoral
- Department of Computing and Industrial Engineering, University of Lleida, 25001 Lleida, Spain
| | - Inés Cruz-Esteve
- Primer de Maig Basic Health Area, Catalan Institute of Health, 25003 Lleida, Spain
| | | | - Mercè Reñé-Reñé
- Department of Radiology, Arnau de Vilanova University Hospital, 25198 Lleida, Spain
| | | | | | | | | | - Anna Pons-Rodríguez
- Example Basic Health Area, Catalan Institute of Health, 25006 Lleida, Spain
- Health PhD Program, University of Lleida, 25198 Lleida, Spain
| | - Montserrat Rué
- IRBLleida—Institut de Recerca Biomèdica de Lleida, Department of Basic Medical Sciences, University of Lleida, 25198 Lleida, Spain
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van der Made CI, Netea MG, van der Veerdonk FL, Hoischen A. Clinical implications of host genetic variation and susceptibility to severe or critical COVID-19. Genome Med 2022; 14:96. [PMID: 35986347 PMCID: PMC9390103 DOI: 10.1186/s13073-022-01100-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/03/2022] [Indexed: 01/08/2023] Open
Abstract
Since the start of the coronavirus disease 2019 (COVID-19) pandemic, important insights have been gained into virus biology and the host factors that modulate the human immune response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 displays a highly variable clinical picture that ranges from asymptomatic disease to lethal pneumonia. Apart from well-established general risk factors such as advanced age, male sex and chronic comorbidities, differences in host genetics have been shown to influence the individual predisposition to develop severe manifestations of COVID-19. These differences range from common susceptibility loci to rare genetic variants with strongly predisposing effects, or proven pathogenic variants that lead to known or novel inborn errors of immunity (IEI), which constitute a growing group of heterogeneous Mendelian disorders with increased susceptibility to infectious disease, auto-inflammation, auto-immunity, allergy or malignancies. The current genetic findings point towards a convergence of common and rare genetic variants that impact the interferon signalling pathways in patients with severe or critical COVID-19. Monogenic risk factors that impact IFN-I signalling have an expected prevalence between 1 and 5% in young, previously healthy individuals (<60 years of age) with critical COVID-19. The identification of these IEI such as X-linked TLR7 deficiency indicates a possibility for targeted genetic screening and personalized clinical management. This review aims to provide an overview of our current understanding of the host genetic factors that predispose to severe manifestations of COVID-19 and focuses on rare variants in IFN-I signalling genes and their potential clinical implications.
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229
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Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annu Rev Biomed Data Sci 2022; 5:293-320. [PMID: 35576555 PMCID: PMC9828290 DOI: 10.1146/annurev-biodatasci-111721-074830] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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230
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Role of Polygenic Risk Score in Cancer Precision Medicine of Non-European Populations: A Systematic Review. Curr Oncol 2022; 29:5517-5530. [PMID: 36005174 PMCID: PMC9406904 DOI: 10.3390/curroncol29080436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/28/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022] Open
Abstract
The development of new screening methods and diagnostic tests for traits, common diseases, and cancer is linked to the advent of precision genomic medicine, in which health care is individually adjusted based on a person’s lifestyle, environmental influences, and genetic variants. Based on genome-wide association study (GWAS) analysis, rapid and continuing progress in the discovery of relevant single nucleotide polymorphisms (SNPs) for traits or complex diseases has increased interest in the potential application of genetic risk models for routine health practice. The polygenic risk score (PRS) estimates an individual’s genetic risk of a trait or disease, calculated by employing a weighted sum of allele counts combined with non-genetic variables. However, 98.38% of PRS records held in public databases relate to the European population. Therefore, PRSs for multiethnic populations are urgently needed. We performed a systematic review to discuss the role of polygenic risk scores in advancing precision medicine for different cancer types in multiethnic non-European populations.
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231
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Dixon P, Keeney E, Taylor JC, Wordsworth S, Martin RM. Can polygenic risk scores contribute to cost-effective cancer screening? A systematic review. Genet Med 2022; 24:1604-1617. [PMID: 35575786 PMCID: PMC7614235 DOI: 10.1016/j.gim.2022.04.020] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE Polygenic risk influences susceptibility to cancer. We assessed whether polygenic risk scores could be used in conjunction with other predictors of future disease status in cost-effective risk-stratified screening for cancer. METHODS We undertook a systematic review of papers that evaluated the cost-effectiveness of screening interventions informed by polygenic risk scores compared with more conventional screening modalities. We included papers reporting cost-effectiveness outcomes with no restriction on type of cancer or form of polygenic risk modeled. We evaluated studies using the Quality of Health Economic Studies checklist. RESULTS A total of 10 studies were included in the review, which investigated 3 cancers: prostate (n = 5), colorectal (n = 3), and breast (n = 2). Of the 10 papers, 9 scored highly (score >75 on a 0-100 scale) when assessed using the quality checklist. Of the 10 studies, 8 concluded that polygenic risk-informed cancer screening was likely to be more cost-effective than alternatives. CONCLUSION Despite the positive conclusions of the included studies, it is unclear if polygenic risk stratification will contribute to cost-effective cancer screening given the absence of robust evidence on the costs of polygenic risk stratification, the effects of differential ancestry, potential downstream economic sequalae, and how large volumes of polygenic risk data would be collected and used.
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Affiliation(s)
- Padraig Dixon
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.
| | - Edna Keeney
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jenny C Taylor
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; National Institute for Health and Care Research Biomedical Research Centre, Oxford, United Kingdom
| | - Sarah Wordsworth
- The Health Economics Research Centre (HERC), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; National Institute for Health Research (NIHR) Health Research Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, Oxford, United Kingdom
| | - Richard M Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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232
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Herzig AF, Clerget-Darpoux F, Génin E. The False Dawn of Polygenic Risk Scores for Human Disease Prediction. J Pers Med 2022; 12:jpm12081266. [PMID: 36013215 PMCID: PMC9409868 DOI: 10.3390/jpm12081266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/24/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022] Open
Abstract
Polygenic risk scores (PRSs) are being constructed for many diseases and are presented today as a promising avenue in the field of human genetics. These scores aim at predicting the risk of developing a disease by leveraging the many genome-wide association studies (GWAS) conducted during the two last decades. Important investments are being made to improve score estimates by increasing GWAS sample sizes, by developing more sophisticated methods, and by proposing different corrections for potential biases. PRSs have entered the market with direct-to-consumer companies proposing to compute them from saliva samples and even recently to help parents select the healthiest embryos. In this paper, we recall how PRSs arose and question the credit they are given by revisiting underlying assumptions in light of the history of human genetics and by comparing them with estimated breeding values (EBVs) used for selection in livestock.
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Affiliation(s)
- Anthony F. Herzig
- Inserm, Université de Brest, EFS, CHU Brest, UMR 1078, GGB, F-29200 Brest, France;
| | - Françoise Clerget-Darpoux
- Université Paris Cité, Inserm, Institut Imagine, Laboratoire Embryologie et Génétique des Malformations, F-75015 Paris, France
- Correspondence: (F.C.-D.); (E.G.)
| | - Emmanuelle Génin
- Inserm, Université de Brest, EFS, CHU Brest, UMR 1078, GGB, F-29200 Brest, France;
- Correspondence: (F.C.-D.); (E.G.)
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233
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Polygenic risk scores: improving the prediction of future disease or added complexity? Br J Gen Pract 2022; 72:396-398. [PMID: 35902257 PMCID: PMC9343049 DOI: 10.3399/bjgp22x720437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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234
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Clifton L, Collister JA, Liu X, Littlejohns TJ, Hunter DJ. Assessing agreement between different polygenic risk scores in the UK Biobank. Sci Rep 2022; 12:12812. [PMID: 35896674 PMCID: PMC9329440 DOI: 10.1038/s41598-022-17012-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/19/2022] [Indexed: 11/16/2022] Open
Abstract
Polygenic risk scores (PRS) are proposed for use in clinical and research settings for risk stratification. However, there are limited investigations on how different PRS diverge from each other in risk prediction of individuals. We compared two recently published PRS for each of three conditions, breast cancer, hypertension and dementia, to assess the stability of using these algorithms for risk prediction in a single large population. We used imputed genotyping data from the UK Biobank prospective cohort, limited to the White British subset. We found that: (1) 20% or more of SNPs in the first PRS were not represented in the more recent PRS for all three diseases, by the same SNP or a surrogate with R2 > 0.8 by linkage disequilibrium (LD). (2) Although the difference in the area under the receiver operating characteristic curve (AUC) obtained using the two PRS is hardly appreciable for all three diseases, there were large differences in individual risk prediction between the two PRS. For instance, for each disease, of those classified in the top 5% of risk by the first PRS, over 60% were not so classified by the second PRS. We found substantial discordance between different PRS for the same disease, indicating that individuals could receive different medical advice depending on which PRS is used to assess their genetic susceptibility. It is desirable to resolve this uncertainty before using PRS for risk stratification in clinical settings.
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Affiliation(s)
- Lei Clifton
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | | | - Xiaonan Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
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235
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Nabi H. Personalized Approaches for the Prevention and Treatment of Breast Cancer. J Pers Med 2022; 12:jpm12081201. [PMID: 35893295 PMCID: PMC9331702 DOI: 10.3390/jpm12081201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
Breast cancer (BC) remains a major public health issue worldwide [...]
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Affiliation(s)
- Hermann Nabi
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada; ; Tel.: +1-418-682-7511 (ext. 82800)
- Université Laval Cancer Research Center (CRC), Université Laval, Quebec City, QC G1S 4L8, Canada
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1S 4L8, Canada
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Abstract
PURPOSE OF REVIEW Testicular germ cell tumours (TGCTs) are the most common solid malignant cancer diagnosed in young males and the incidence is increasing. Understanding the genetic basis of this disease will help us to navigate the challenges of early detection, diagnosis, treatment, surveillance, and long-term outcomes for patients. RECENT FINDINGS TGCTs are highly heritable. Current understanding of germline risk includes the identification of one moderate-penetrance predisposition gene, checkpoint kinase 2 (CHEK2), and 78 low-to-moderate-risk single nucleotide polymorphisms identified in genome-wide-associated studies, which account for 44% of familial risk. Biomarker research in TGCTs has been challenging for multiple reasons: oncogenesis is complex, actionable mutations are uncommon, clonal evolution unpredictable and tumours can be histologically and molecularly heterogeneous. Three somatic mutations have thus far been identified by DNA exome sequencing, exclusively in seminomas: KIT, KRAS and NRAS. Several genetic markers appear to be associated with risk of TGCT and treatment resistance. TP53 mutations appear to be associated with platinum resistance. MicroRNA expression may be a useful biomarker of residual disease and relapse in future. SUMMARY The biology of testicular germ cells tumours is complex, and further research is needed to fully explain the high heritability of these cancers, as well as the molecular signatures which may drive their biological behaviour.
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237
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TGFBR1*6A as a modifier of breast cancer risk and progression: advances and future prospects. NPJ Breast Cancer 2022; 8:84. [PMID: 35853889 PMCID: PMC9296458 DOI: 10.1038/s41523-022-00446-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022] Open
Abstract
There is growing evidence that germline mutations in certain genes influence cancer susceptibility, tumor evolution, as well as clinical outcomes. Identification of a disease-causing genetic variant enables testing and diagnosis of at-risk individuals. For breast cancer, several genes such as BRCA1, BRCA2, PALB2, ATM, and CHEK2 act as high- to moderate-penetrance cancer susceptibility genes. Genotyping of these genes informs genetic risk assessment and counseling, as well as treatment and management decisions in the case of high-penetrance genes. TGFBR1*6A (rs11466445) is a common variant of the TGF-β receptor type I (TGFBR1) that has a global minor allelic frequency (MAF) of 0.051 according to the 1000 Genomes Project Consortium. It is emerging as a high frequency, low penetrance tumor susceptibility allele associated with increased cancer risk among several cancer types. The TGFBR1*6A allele has been associated with increased breast cancer risk in women, OR 1.15 (95% CI 1.01–1.31). Functionally, TGFBR1*6A promotes breast cancer cell proliferation, migration, and invasion through the regulation of the ERK pathway and Rho-GTP activation. This review discusses current findings on the genetic, functional, and mechanistic associations between TGFBR1*6A and breast cancer risk and proposes future directions as it relates to genetic association studies and mechanisms of action for tumor growth, metastasis, and immune suppression.
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238
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Naret O, Kutalik Z, Hodel F, Xu ZM, Marques-Vidal P, Fellay J. Improving polygenic prediction with genetically inferred ancestry. HGG ADVANCES 2022; 3:100109. [PMID: 35571679 PMCID: PMC9095896 DOI: 10.1016/j.xhgg.2022.100109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Genome-wide association studies (GWASs) have demonstrated that most common diseases have a strong genetic component from many genetic variants each with a small effect size. GWAS summary statistics have allowed the construction of polygenic scores (PGSs) estimating part of the individual risk for common diseases. Here, we propose to improve PGS-based risk estimation by incorporating genetic ancestry derived from genome-wide genotyping data. Our method involves three cohorts: a base (or discovery) for association studies, a target for phenotype/risk prediction, and a map for ancestry mapping; successively, (1) it generates for each individual in the base and target cohorts a set of principal components based on the map cohort-called mapped PCs, (2) it associates in the base cohort the phenotype with the mapped-PCs, and (3) it uses the mapped PCs in the target cohort to generate a phenotypic predictor called the ancestry score. We evaluated the ancestry score by comparing a predictive model using a PGS with one combining a PGS and an ancestry score. First, we performed simulations and found that the ancestry score has a greater impact on traits that correlate with ancestry-specific variants. Second, we showed, using UK Biobank data, that the ancestry score improves genetic prediction for our nine phenotypes to very different degrees. Third, we performed simulations and found that the more heterogeneous the base and target cohorts, the more beneficial the ancestry score is. Finally, we validated our approach under realistic conditions with UK Biobank as the base cohort and Swiss individuals from the CoLaus|PsyCoLaus study as the target cohort.
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Affiliation(s)
- Olivier Naret
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Flavia Hodel
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zhi Ming Xu
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jacques Fellay
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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239
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Spendlove SJ, Bondhus L, Lluri G, Sul JH, Arboleda VA. Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease. HGG ADVANCES 2022; 3:100112. [PMID: 35599848 PMCID: PMC9118152 DOI: 10.1016/j.xhgg.2022.100112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/19/2022] [Indexed: 01/28/2023] Open
Abstract
Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n1 = 711 trios, n2 = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10-3; fetal cardiac SNVs, p = 8.00 × 10-3) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10-3; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases.
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Affiliation(s)
- Sarah J Spendlove
- Interdepartmental Bioinformatics Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Leroy Bondhus
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gentian Lluri
- Ahmanson/UCLA Adult Congenital Heart Disease Center, Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jae Hoon Sul
- Interdepartmental Bioinformatics Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Valerie A Arboleda
- Interdepartmental Bioinformatics Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
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240
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases. Genet Med 2022; 24:1545-1555. [PMID: 35460399 DOI: 10.1016/j.gim.2022.03.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases. METHODS Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history. RESULTS Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%. CONCLUSION Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - John Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, Gerald Bronfman Department of Oncology, McGill University, McGill University, Montreal, Quebec, Canada.
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241
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Li X, Schöttker B, Holleczek B, Brenner H. Associations of DNA methylation algorithms of aging and cancer risk: Results from a prospective cohort study. EBioMedicine 2022; 81:104083. [PMID: 35636319 PMCID: PMC9157462 DOI: 10.1016/j.ebiom.2022.104083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 11/18/2022] Open
Abstract
Background Previous studies have shown that three DNA methylation (DNAm) based algorithms of aging, DNAm PhenoAge acceleration (AgeAccelPheno), DNAm GrimAge acceleration (AgeAccelGrim), and mortality risk score (MRscore), to be strong predictors of mortality and aging related outcomes. We aimed to investigate if and to what extent these algorithms predict cancer. Methods In four subsets (n = 727, 1003, 910, and 412) of a population-based cohort from Germany, DNA methylation in whole blood was measured using the Infinium Methylation EPIC BeadChip kit or the Infinium HumanMethylation450K BeadChip Assay (Illumina.Inc, San Diego, CA, USA). AgeAccelPheno, AgeAccelGrim, and a revised MRscore based on 8 CpGs only (MRscore-8CpGs), were calculated. Hazard ratios (HRs) were calculated to assess associations of the three DNAm algorithms with total cancer risk and risk of invasive breast, lung, prostate, and colorectal cancer incidence. Findings During 17 years of follow-up, a total of 697 malignant tumors (thereof breast cancer = 75, lung cancer = 146, prostate cancer = 114, colorectal cancer = 155) were observed. All three algorithms showed strong positive associations with lung cancer risk in a dose response manner, with adjusted HRs per SD increase in AgeAccelPheno, AgeAccelGrim, and MRscore-8CpGs, of 1·37 (1·03-1·82), 1·74 (1·11-2·73), and 2·06 (1·39-3·06), respectively. By contrast, strong inverse associations were seen with breast cancer risk [adjusted HRs 0·65 (0·49-0·86), 0·45 (0·25-0·80), and 0·42 (0·25-0·70), respectively]. Weak positive associations of MRscore-8CpGs were seen with total cancer risk. Interpretation The DNAm algorithms, particularly the MRscore-8CpGs, have potential to contribute to site-specific cancer risk prediction. Funding The ESTHER study was funded by grants from the Baden-Württemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrücken, Germany). The work of Xiangwei Li was supported by a grant from Fondazione Cariplo (Bando Ricerca Malattie invecchiamento, #2017-0653).
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Affiliation(s)
- Xiangwei Li
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Im Neuenheimer Feld 672, 69120, Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Network Aging Research, Heidelberg University, Bergheimer Straße 20, 69115 Heidelberg, Germany
| | | | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany; Network Aging Research, Heidelberg University, Bergheimer Straße 20, 69115 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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Khan A, Turchin MC, Patki A, Srinivasasainagendra V, Shang N, Nadukuru R, Jones AC, Malolepsza E, Dikilitas O, Kullo IJ, Schaid DJ, Karlson E, Ge T, Meigs JB, Smoller JW, Lange C, Crosslin DR, Jarvik GP, Bhatraju PK, Hellwege JN, Chandler P, Torvik LR, Fedotov A, Liu C, Kachulis C, Lennon N, Abul-Husn NS, Cho JH, Ionita-Laza I, Gharavi AG, Chung WK, Hripcsak G, Weng C, Nadkarni G, Irvin MR, Tiwari HK, Kenny EE, Limdi NA, Kiryluk K. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat Med 2022; 28:1412-1420. [PMID: 35710995 PMCID: PMC9329233 DOI: 10.1038/s41591-022-01869-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/11/2022] [Indexed: 01/03/2023]
Abstract
Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Michael C Turchin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Rajiv Nadukuru
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alana C Jones
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ozan Dikilitas
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tian Ge
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - David R Crosslin
- Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jacklyn N Hellwege
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paulette Chandler
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Laura Rasmussen Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Alex Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA
| | - Cong Liu
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Noura S Abul-Husn
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nita A Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Gynecologic Cancer Risk and Genetics: Informing an Ideal Model of Gynecologic Cancer Prevention. Curr Oncol 2022; 29:4632-4646. [PMID: 35877228 PMCID: PMC9322111 DOI: 10.3390/curroncol29070368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/09/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022] Open
Abstract
Individuals with proven hereditary cancer syndrome (HCS) such as BRCA1 and BRCA2 have elevated rates of ovarian, breast, and other cancers. If these high-risk people can be identified before a cancer is diagnosed, risk-reducing interventions are highly effective and can be lifesaving. Despite this evidence, the vast majority of Canadians with HCS are unaware of their risk. In response to this unmet opportunity for prevention, the British Columbia Gynecologic Cancer Initiative convened a research summit “Gynecologic Cancer Prevention: Thinking Big, Thinking Differently” in Vancouver, Canada on 26 November 2021. The aim of the conference was to explore how hereditary cancer prevention via population-based genetic testing could decrease morbidity and mortality from gynecologic cancer. The summit invited local, national, and international experts to (1) discuss how genetic testing could be more broadly implemented in a Canadian system, (2) identify key research priorities in this topic and (3) outline the core essential elements required for such a program to be successful. This report summarizes the findings from this research summit, describes the current state of hereditary genetic programs in Canada, and outlines incremental steps that can be taken to improve prevention for high-risk Canadians now while developing an organized population-based hereditary cancer strategy.
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244
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Analysis of the Role of Continuous Early Intervention in Improving the Quality of Life of Breast Cancer Patients. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3054587. [PMID: 35755738 PMCID: PMC9225834 DOI: 10.1155/2022/3054587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 05/25/2022] [Indexed: 12/03/2022]
Abstract
In order to improve the quality of life of breast cancer patients, this paper will apply continuous early intervention to the nursing of breast cancer patients. The continuous early intervention nursing model can propose countermeasures for the health problems faced by patients with postoperative chemotherapy. In order to analyze the effect of continuous early intervention nursing on negative emotions and quality of life in breast cancer patients, the effect of continuous early intervention in improving the quality of life of breast cancer patients is analyzed by means of experimental research. The control group is given routine nursing, and the observation group is given continuous early intervention nursing intervention. Moreover, this paper obtains data statistics in combination with statistical analysis. Through the comparison of the test results, it can be seen that the implementation of continuous early intervention nursing intervention in breast cancer patients can improve the nursing effect, effectively relieve the negative emotions of patients, and improve the quality of life of patients.
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Borde J, Laitman Y, Blümcke B, Niederacher D, Weber-Lassalle K, Sutter C, Rump A, Arnold N, Wang-Gohrke S, Horváth J, Gehrig A, Schmidt G, Dutrannoy V, Ramser J, Hentschel J, Meindl A, Schroeder C, Wappenschmidt B, Engel C, Kuchenbaecker K, Schmutzler RK, Friedman E, Hahnen E, Ernst C. Polygenic risk scores indicate extreme ages at onset of breast cancer in female BRCA1/2 pathogenic variant carriers. BMC Cancer 2022; 22:706. [PMID: 35761208 PMCID: PMC9238030 DOI: 10.1186/s12885-022-09780-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 06/14/2022] [Indexed: 11/10/2022] Open
Abstract
Background Clinical management of women carrying a germline pathogenic variant (PV) in the BRCA1/2 genes demands for accurate age-dependent estimators of breast cancer (BC) risks, which were found to be affected by a variety of intrinsic and extrinsic factors. Here we assess the contribution of polygenic risk scores (PRSs) to the occurrence of extreme phenotypes with respect to age at onset, namely, primary BC diagnosis before the age of 35 years (early diagnosis, ED) and cancer-free survival until the age of 60 years (late/no diagnosis, LD) in female BRCA1/2 PV carriers. Methods Overall, estrogen receptor (ER)-positive, and ER-negative BC PRSs as developed by Kuchenbaecker et al. for BC risk discrimination in female BRCA1/2 PV carriers were employed for PRS computation in a curated sample of 295 women of European descent carrying PVs in the BRCA1 (n=183) or the BRCA2 gene (n=112), and did either fulfill the ED criteria (n=162, mean age at diagnosis: 28.3 years, range: 20 to 34 years) or the LD criteria (n=133). Binomial logistic regression was applied to assess the association of standardized PRSs with either ED or LD under adjustment for patient recruitment criteria for germline testing and localization of BRCA1/2 PVs in the corresponding BC or ovarian cancer (OC) cluster regions. Results For BRCA1 PV carriers, the standardized overall BC PRS displayed the strongest association with ED (odds ratio (OR) = 1.62; 95% confidence interval (CI): 1.16–2.31, p<0.01). Additionally, statistically significant associations of selection for the patient recruitment criteria for germline testing and localization of pathogenic PVs outside the BRCA1 OC cluster region with ED were observed. For BRCA2 PV carriers, the standardized PRS for ER-negative BC displayed the strongest association (OR = 2.27, 95% CI: 1.45–3.78, p<0.001). Conclusions PRSs contribute to the development of extreme phenotypes of female BRCA1/2 PV carriers with respect to age at primary BC diagnosis. Construction of optimized PRS SNP sets for BC risk stratification in BRCA1/2 PV carriers should be the task of future studies with larger, well-defined study samples. Furthermore, our results provide further evidence, that localization of PVs in BC/OC cluster regions might be considered in BC risk calculations for unaffected BRCA1/2 PV carriers. Supplementary Information The online version contains supplementary material available at (10.1186/s12885-022-09780-1).
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Affiliation(s)
- Julika Borde
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany
| | - Yael Laitman
- Oncogenetics Unit, Sheba Medical Center, Tel Hashomer, Israel.,Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Britta Blümcke
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany
| | - Dieter Niederacher
- Department of Gynaecology and Obstetrics, University Hospital Duesseldorf, Heinrich-Heine University, Duesseldorf, Germany
| | - Konstantin Weber-Lassalle
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany
| | - Christian Sutter
- Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
| | - Andreas Rump
- Institute of Clinical Genetics, Technische Universitaet Dresden, Dresden, Germany
| | - Norbert Arnold
- Institute of Clinical Molecular Biology, Department of Gynaecology and Obstetrics, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel, Germany
| | - Shan Wang-Gohrke
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Judit Horváth
- Institute for Human Genetics, University Hospital Muenster, Muenster, Germany
| | - Andrea Gehrig
- Institute of Human Genetics, Julius-Maximilians University, Wuerzburg, Germany
| | - Gunnar Schmidt
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Véronique Dutrannoy
- Institute of Medical and Human Genetics, Charité Universitaetsmedizin, Berlin, Germany
| | - Juliane Ramser
- Department for Gynaecology and Obstetrics, Division of Tumor Genetics, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Julia Hentschel
- Institute of Human Genetics, University of Leipzig Hospitals and Clinics, Leipzig, Germany
| | - Alfons Meindl
- Department of Gynaecology and Obstetrics, LMU Munich, University Hospital Munich, Munich, Germany
| | - Christopher Schroeder
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Barbara Wappenschmidt
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig, Germany
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK.,UCL Genetics Institute, University College London, London, UK
| | - Rita K Schmutzler
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany
| | - Eitan Friedman
- Oncogenetics Unit, Sheba Medical Center, Tel Hashomer, Israel.,Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany
| | - Corinna Ernst
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Straße 62, Cologne, 50937, Germany.
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Capturing additional genetic risk from family history for improved polygenic risk prediction. Commun Biol 2022; 5:595. [PMID: 35710731 PMCID: PMC9203758 DOI: 10.1038/s42003-022-03532-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/24/2022] [Indexed: 12/01/2022] Open
Abstract
Family history of complex traits may reflect transmitted rare pathogenic variants, intra-familial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. For 941 children in the Avon Longitudinal Study of Parents and Children cohort, a joint predictor combining a polygenic risk score for height and mid-parental height was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Marginal yet consistent risk prediction improvements were also achieved among ~400,000 European ancestry participants for 11 complex diseases in the UK Biobank. Our work showcases a paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada.
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Department of Human Genetics, McGill University, Montreal, QC, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada. .,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
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247
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Chen DP, Jaing TH, Hour AL, Lin WT, Hsu FP. Single-Nucleotide Polymorphisms Within Non-HLA Regions Are Associated With Engraftment Effectiveness for Patients With Unrelated Cord Blood Transplantation. Front Immunol 2022; 13:888204. [PMID: 35769457 PMCID: PMC9234117 DOI: 10.3389/fimmu.2022.888204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/28/2022] [Indexed: 11/18/2022] Open
Abstract
Clinically, stem cells with matched human leukocyte antigens (HLAs) must be selected for allogeneic transplantation to avoid graft rejection. However, adverse reactions still occur after cord blood transplantation (CBT). It was inferred that the HLA system is not the only regulatory factor that may influence CBT outcomes. Therefore, we plan to investigate whether the single-nucleotide polymorphisms (SNPs) located in non-HLA genes are associated with the effectiveness of CBT. In this study, the samples of 65 donors from CBT cases were collected for testing. DNA sequencing was focused on the SNPs of non-HLA genes, cytotoxic T-lymphocyte-associated protein 4 (CTLA4), CD28, tumor necrosis factor ligand superfamily 4 (TNFSF4), and programmed cell death protein 1 (PDCD1), which were selected in regard to the literatures published in 2017 and 2018, which indicated that they were related to stem cell transplantation. Then, in combination with the detailed follow-up transplantation tracking database, these SNPs were analyzed with the risk of mortality, relapse, cytomegalovirus (CMV) infection, and graft-versus-host disease (GVHD). We found that there were 2 SNPs of CTLA4, 1 SNP of TNFSF4, and 2 SNPs of PDCD1 associated with the effectiveness of unrelated CBT. These statistically significant SNPs and haplotypes would be used in clinical to choose the best donor for the patient receiving CBT. Moreover, the polygenic risk scores (PRSs) with these SNPs could be used to predict the risk of CBT adverse reactions with an area under the receiver operating characteristic curve (AUC) of 0.7692. Furthermore, these SNPs were associated with several immune-related diseases or cancer susceptibility, which implied that SNPs play an important role in immune regulation.
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Affiliation(s)
- Ding-Ping Chen
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- *Correspondence: Ding-Ping Chen,
| | - Tang-Her Jaing
- Department of Pediatrics, Division of Hematology/Oncology, Chang Gung Children’s Hospital, Chang Gung University, Taoyuan, Taiwan
| | - Ai-Ling Hour
- Department of Life Science, Fu Jen Catholic University, Taipei, Taiwan
| | - Wei-Tzu Lin
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Fang-Ping Hsu
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
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248
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Sehovic E, Urru S, Chiorino G, Doebler P. Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection. BMC Cancer 2022; 22:634. [PMID: 35681127 PMCID: PMC9178880 DOI: 10.1186/s12885-022-09698-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 05/24/2022] [Indexed: 01/17/2023] Open
Abstract
Background Breast cancer (BC) is the most frequently diagnosed cancer among women. Numerous studies explored cell-free circulating microRNAs as diagnostic biomarkers of BC. As inconsistent and rarely intersecting microRNA panels have been reported thus far, we aim to evaluate the overall diagnostic performance as well as the sources of heterogeneity between studies. Methods Based on the search of three online search engines performed up to March 21st 2022, 56 eligible publications that investigated diagnostic circulating microRNAs by utilizing Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) were obtained. Primary studies’ potential for bias was evaluated with the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). A bivariate generalized linear mixed-effects model was applied to obtain pooled sensitivity and specificity. A novel methodology was utilized in which the sample and study models’ characteristics were analysed to determine the potential preference of studies for sensitivity or specificity. Results Pooled sensitivity and specificity of 0.85 [0.81—0.88] and 0.83 [0.79—0.87] were obtained, respectively. Subgroup analysis showed a significantly better performance of multiple (sensitivity: 0.90 [0.86—0.93]; specificity: 0.86 [0.80—0.90]) vs single (sensitivity: 0.82 [0.77—0.86], specificity: 0.83 [0.78—0.87]) microRNA panels and a comparable pooled diagnostic performance between studies using serum (sensitivity: 0.87 [0.81—0.91]; specificity: 0.83 [0.78—0.87]) and plasma (sensitivity: 0.83 [0.77—0.87]; specificity: 0.85 [0.78—0.91]) as specimen type. In addition, based on bivariate and univariate analyses, miRNA(s) based on endogenous normalizers tend to have a higher diagnostic performance than miRNA(s) based on exogenous ones. Moreover, a slight tendency of studies to prefer specificity over sensitivity was observed. Conclusions In this study the diagnostic ability of circulating microRNAs to diagnose BC was reaffirmed. Nonetheless, some subgroup analyses showed between-study heterogeneity. Finally, lack of standardization and of result reproducibility remain the biggest issues regarding the diagnostic application of circulating cell-free microRNAs. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09698-8.
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Affiliation(s)
- Emir Sehovic
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, 13900, Biella, Italy. .,Department of Life Sciences and Systems Biology, University of Turin, 10100, Turin, Italy.
| | - Sara Urru
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, 13900, Biella, Italy.,Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35121, Padova, Italy
| | - Giovanna Chiorino
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, 13900, Biella, Italy
| | - Philipp Doebler
- Department of Statistics, TU Dortmund University, 44227, Dortmund, Germany
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Li S, Nguyen TL, Nguyen-Dumont T, Dowty JG, Dite GS, Ye Z, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Hopper JL, Southey MC. Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:cancers14112767. [PMID: 35681745 PMCID: PMC9179294 DOI: 10.3390/cancers14112767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 04/27/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022] Open
Abstract
Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - James G. Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Genetic Technologies Limited, Fitzroy, VIC 3065, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Ho N. Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Christopher F. Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Correspondence:
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
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Picard M. Why Do We Care More About Disease than Health? PHENOMICS (CHAM, SWITZERLAND) 2022; 2:145-155. [PMID: 36939781 PMCID: PMC9590501 DOI: 10.1007/s43657-021-00037-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/26/2021] [Accepted: 12/03/2021] [Indexed: 04/30/2023]
Abstract
Modern Western biomedical research and clinical practice are primarily focused on disease. This disease-centric approach has yielded an impressive amount of knowledge around what goes wrong in illness. However, in comparison, researchers and physicians know little about health. What is health? How do we quantify it? And how do we improve it? We currently do not have good answers to these questions. Our lack of fundamental knowledge about health is partly driven by three main factors: (i) a lack of understanding of the dynamic processes that cause variations in health/disease states over time, (ii) an excessive focus on genes, and (iii) a pervasive psychological bias towards additive solutions. Here I briefly discuss potential reasons why scientists and funders have generally adopted a gene- and disease-centric framework, how medicine has ended up practicing "diseasecare" rather than healthcare, and present cursory evidence that points towards an alternative energetic view of health. Understanding the basis of human health with a similar degree of precision that has been deployed towards mapping disease processes could bring us to a point where we can actively support and promote human health across the lifespan, before disease shows up on a scan or in bloodwork.
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Affiliation(s)
- Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY 10032 USA
- Department of Neurology, Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY 10032 USA
- New York State Psychiatric Institute, New York, NY 10032 USA
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