Genetic Susceptibility to Cancer the GWAS era. Program in Genetic Epidemiology i and Statistical. Channing Laboratory, Brigham and Women s Hospital

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1 Genetic Susceptibility to Cancer the GWAS era David Hunter Program in Genetic Epidemiology i and Statistical ti ti Genetics Harvard School of Public Health Channing Laboratory, Brigham and Women s Hospital Broad Institute of MIT and Harvard

2 Common variants and breast cancer April 2007 CASP8

3 Genome-wide CGEMS Scan Breast Cancer FGFR2 P< P<0.01 Hunter et al, Nat Gen 2007

4 Common variants and breast cancer May 2007 FGFR2 2q35 TOX3 MAP3K1 8q24 LSP1 ngwas ~ 1600

5 The case of the missing heritability NATURE Vol November 2008

6 Holtzman and Marteau, Will genetics revolutionize medicine? NEJM 2000

7 Common variants and breast cancer Sept 2010 ngwas ~ 5000 FGFR2 2q35 TOX3 MAP3K1 8q24 LSP1 5p12 16q12 1p11.2 RAD51L1 3p24 17q23.2 CASP8 9p21 10p14 10q21 11q13 ESR1 5p12

8 Common variants and breast cancer April 2012 ngwas ~ 7000 FGFR2 12p11 2q35 12q24 TOX3 21q21 MAP3K1 9q31 8q24 LSP1 5p12 16q12 1p11.2 RAD51L1 3p24 17q23.2 CASP8 9p21 10p14 10q21 11q13 ESR1 5p12

9 Common variants and breast cancer April 2013 ngwas ~ FGFR2 12p11 C19orf61-2q35 12q24 MLLT10-DNAJC1 TOX3 21q21 DNAJC1 MAP3K1 9q31 TCF7L2 8q24 PEX14 EMID1- LSP1 MKL1 PTPN22-5p12 HNF4G METAP1D- 16q12 CDCA7 ARHGEF5-NOBOX 1p11.2 DIRC3 DKFZp761E198- RAD51L1 ITPR1-EGOT NTN4 3p24 TGFBR2 BRCA2-17q23.2 TET2 PAX9-SLC25A21 CASP8 ADAM29 RAD51L1 9p21 RAB3C CCDC88C 10p14 PDE4D MIR FTO 10q21 EBF1 CDYL2 11q13 FOXQ1 CHST9 ESR1 RANBP9 SSBP4-5p12 MIR1208 Chr 2, 8, 8, 9, 10, 11, 12, 18

10 1000 genomes 17 further imputation hits (>100 - Manhattan in total) plots from 1000 genomes imputation+meta-analysis (62,890 cases and 61,872 controls, GWAS+iCOGS) Combine GWAS+iCOGS Excluding known regions Kyriaki Michailidou et a

11 Genetic Variants and Breast Cancer Risk Jan 2014 Easton, D in press Unexplained: 54%* ~28 new hits from fine-mapping (7%) ~19 new SNPs post icogs (2%) 45 icogs SNPs (5%) 27 pre-icogs SNPs (9%) TP53 PTEN LKB1 CHEK2 ATM PALB2 BRCA1 BRCA2 * For overall breast cancer in Europeans Lower for ER- disease, early onset disease, and breast cancer in non-europeans

12 GAME-ON OncoArray

13 Published Genome-Wide Associations through 12/2012 Published GWA at p 5X10-8 for 17 trait categories NHGRI GWA Catalog

14 73 variants: a OR in EAs vs AAs OR in EAs OR in AAs Haiman et al.

15 Value of tumor-subset GWAS Kraft and Haiman, Nat Gen, Oct 2010

16 Lessons from the GWAS o Small Relative Risks (RR ) 1 1) can be discovered and reproduced o Very large sample sizes are necessary to maximize power and reproducibility o False positives can be minimized with very large sample sizes pooled and analyzed de novo o Beware results reported from small studies Hunter DJ. Epidemiology (3):363-7.

17 GENE-ENVIRONMENT INTERACTIONS DO CLASSIC BREAST CANCER RISK FACTORS SYNERGIZE WITH GWAS SNPS? 16,285 BC cases and 19,376 controls 39 GWAS-assoc SNPS x 8 Env Risk Factors AAM Parity AAMeno Height BMI FH Smoking Alcohol After correction for multiple testing, no significant [multiplicative] interaction between SNPs and established risk factors...was found. Barrdahl et al, BPC3, in preparation

18 GENE-ENVIRONMENT INTERACTIONS Good examples of supramultiplicative (synergistic) interactions between strong exogenous environmental risk factors (e.g. smoking, alcohol) and genetic variants known to be on the pharmacogenetic pathway (e.g. NAT2, ALDH2). Few examples of synergistic interactions between lifestyle and environmental risk factors and GWAS-associated SNPs.

19 GENE-ENVIRONMENT INTERACTIONS Good examples of supramultiplicative (synergistic) interactions between strong exogenous environmental risk factors (e.g. smoking, alcohol) and genetic variants known to be on the pharmacogenetic pathway (e.g. NAT2, ALDH2). Few examples of synergistic interactions between lifestyle and environmental risk factors and GWAS-associated SNPs.

20 GENE-ENVIRONMENT INTERACTIONS Good examples of supramultiplicative (synergistic) interactions between strong exogenous environmental risk factors (e.g. smoking, alcohol) and genetic variants known to be on the pharmacogenetic pathway (e.g. NAT2, ALDH2). Few examples of synergistic interactions between lifestyle and environmental risk factors and GWAS-associated SNPs. Actually, this is good news multiplicativity makes risk modelling much more robust and predictable An indication that the GWAS variants represent biological processes independent of what we know from established risk factors Absence of G-E interaction simplifies our public health messages on E

21 RISK PREDICTION

22 Clinical Utility of breast ca risk scores? To select women at higher risk for prevention trials To stratify screening? As modifiers of high penetrance alleles

23 Loci of proven relevance to etiology of cancers May lead to new understanding of gene-specific mechanisms May lead to new biologic understanding e.g. role of intergenic regions

24 Loci of proven relevance to etiology of cancers May lead to new understanding of gene-specific mechanism May lead to new biologic understanding e.g. role of intergenic regions

25 SUMMARY Hundreds of new cancer risk factors, many more to come Extremes of risk prediction approaching clinical utility Sample size rules Whole genome sequence data much harder to interpret due to the very large number of potentially functional variants found per genome Absence of G-E synergy on the multiplicative scale make our life easier Insights into biology and mechanism likely to be the major contribution of genetic epidemiology

26 Acknowledgements CGEMS & DCEG Stephen Chanock Gilles Thomas Robert Hoover Kevin Jacobs Meredith Yeager Richard Hayes Joseph Fraumeni Daniela Gerhard Patricia Hartge Demetrius Albanes Sholom Wacholder Nilanjan Chatterjee Zhaoming Wang Kai Yu Margaret Tucker Jesus Gonzalez Bosquet Montse Garcia-Closas Charles Chung Julia Ciampi HSPH-BWH Peter Kraft David Cox Sue Hankinson Sara Lindstrom Rulla Tamimi Connie Chen Carolyn Guo Julie Buring Dan Chasman MEC C Haiman B Henderson L Kolonel F Schumacher ACS Michael Thun Heather Feigelson Ryan Diver Vickie Stevens EPIC Elio Riboli Afshan Siddiq Rudolf Kaaks Federico Canzian Daniela Campa

27 Acknowledgements DRIVE Discovery, Biology, and Risk of Inherited Variants in Breast Cancer Cancer Research UK HSPH University it of Utah Jack Cuzick Peter Kraft David Goldgar Connie Chen Dana Farber Cancer Institute Sara Lindstroem Vanderbilt University John Quackenbush Amit Joshi Wei Zheng Matthew Freedman Jirong Long Andrew Beck Alejandro Quiroz-Zarate Mayo Clinic University Hawaii Judy Garber Fergus Couch Loic Le Marchand Alexander Miron National Institutes of Health NCI Program Office Fred Hutchinson CRI Stephen Chanock Daniela Seminara Paul Auer Ross Prentice German Cancer Research Center (DKFZ) Federico Canzian Rudolf Kaaks Daniele Campa BWH Rulla Tamimi Sue Hankinson Aditi Hazra Imperial College Elio Riboli Stanford University Alice Whittemore University of Cambridge Douglas Easton University of Chicago Brandon Pierce Habibul Ahsan University of Southern California Brian Henderson Chris Haiman Fred Schumacher

Value of environmental information and how to gather it

Value of environmental information and how to gather it David Hunter Nuffield Department of Population Health University of Oxford Harvard TH Chan School of Public Health Channing Laboratory, Brigham and