Genetic Susceptibility to Cancer the GWAS era David Hunter - - PowerPoint PPT Presentation

Genetic Susceptibility to Cancer – the GWAS era David Hunter P i G ti E id i l d St ti ti l Program in Genetic Epidemiology and Statistical Genetics Harvard School of Public Health Harvard School of Public Health Channing Laboratory, Brigham and Women’s g y, g Hospital Broad Institute of MIT and Harvard

Common variants and breast cancer – April 2007

CASP8 CASP8

Genome-wide CGEMS Scan – Breast Cancer

FGFR2

P<0.00001 P<0.01 Hunter et al, Nat Gen 2007

Common variants and breast cancer – May 2007

FGFR2 2q35 TOX3 TOX3 MAP3K1 8q24 LSP1 S nGWAS ~ 1600

The case of the missing heritability The case of the missing heritability

NATURE|Vol 456|6 November 2008

Holtzman and Marteau, Will genetics revolutionize medicine? NEJM 2000

Common variants and breast cancer – Sept 2010

FGFR2 2q35 TOX3 TOX3 MAP3K1 8q24 LSP1 S 5p12 16q12 1p11.2 RAD51L1 RAD51L1 3p24 17q23.2 CASP8 9p21 10p14 10q21 11q13 11q13 ESR1 5p12 nGWAS ~ 5000

Common variants and breast cancer – April 2012

FGFR2 12p11 2q35 12q24 TOX3 21 21 TOX3 21q21 MAP3K1 9q31 8q24 LSP1 S 5p12 16q12 1p11.2 RAD51L1 RAD51L1 3p24 17q23.2 CASP8 9p21 10p14 10q21 11q13 11q13 ESR1 5p12 nGWAS ~ 7000

Common variants and breast cancer – April 2013

FGFR2 12p11 C19orf61- 2q35 12q24 MLLT10-DNAJC1 TOX3 21 21 DNAJC1 TOX3 21q21 DNAJC1 MAP3K1 9q31 TCF7L2 8q24 PEX14 EMID1- LSP1 MKL1 PTPN22- S 5p12 HNF4G METAP1D- 16q12 CDCA7 ARHGEF5-NOBOX 1p11.2 DIRC3 DKFZp761E198- RAD51L1 ITPR1 EGOT NTN4 RAD51L1 ITPR1-EGOT NTN4 3p24 TGFBR2 BRCA2- 17q23.2 TET2 PAX9-SLC25A21 CASP8 ADAM29 RAD51L1 9p21 RAB3C CCDC88C 10p14 PDE4D MIR1972-2-FTO 10q21 EBF1 CDYL2 11q13 FOXQ1 CHST9 11q13 FOXQ1 CHST9 ESR1 RANBP9 SSBP4- 5p12 MIR1208 Chr 2, 8, 8, 9, 10, 11, 12, 18 nGWAS ~ 10000

1000 genomes imputation - Manhattan plots 17 further hits (>100 in total) from 1000 genomes imputation+meta-analysis

Combine GWAS+iCOGS Excluding known regions

g p y

(62,890 cases and 61,872 controls, GWAS+iCOGS)

Combine GWAS+iCOGS Excluding known regions Kyriaki Michailidou et a

Genetic Variants and Breast Cancer Risk Jan 2014

Easton, D in press Unexplained: 54%* p ~28 new hits from BRCA1 fine-mapping (7%) BRCA1 BRCA2 CHEK2 ATM TP53 45 iCOGS SNPs ~19 new SNPs post iCOGS (2%) ATM PALB2 TP53 PTEN LKB1 27 pre-iCOGS SNPs (9%) (5%)

* For overall breast cancer in Europeans Lower for ER- disease, early onset disease, and breast cancer in non-Europeans

GAME-ON OncoArray

Published Genome-Wide Associations through 12/2012 Published GWA at p≤5X10-8 for 17 trait categories NHGRI GWA Catalog www.genome.gov/GWAStudies

73 variants: OR in EAs vs AAs

1.3

3 a a s O s s s

1 2 1.25 EAs 1.15 1.2 OR in E 1.05 1.1 O 1 1.05 0 8 1 1 2 1 4 0.8 1 1.2 1.4 OR in AAs

Haiman et al.

Value of tumor-subset GWAS Value of tumor subset GWAS

Kraft and Haiman, Nat Gen, Oct 2010

Lessons from the GWAS Small Relative Risks (RR 1 05 1 1) can be

• Small Relative Risks (RR 1.05-1.1) can be

discovered and reproduced

• Very large sample sizes are necessary to

maximize power and reproducibility

• False positives can be minimized with very large

l i l d d l d d sample sizes pooled and analyzed de novo

• Beware results reported from small studies
• Beware results reported from small studies

Hunter DJ. Epidemiology. 2012 23(3):363-7.

GENE-ENVIRONMENT INTERACTIONS

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

GENE-ENVIRONMENT INTERACTIONS

Good examples of supramultiplicative (synergistic) interactions between strong exogenous environmental risk factors (e.g. smoking, alcohol) and genetic 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 factors and GWAS-associated SNPs.

GENE-ENVIRONMENT INTERACTIONS

Good examples of supramultiplicative (synergistic) interactions between strong exogenous environmental risk factors (e.g. smoking, alcohol) and genetic 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 factors and GWAS-associated SNPs.

GENE-ENVIRONMENT INTERACTIONS

Good examples of supramultiplicative (synergistic) interactions between strong exogenous environmental risk factors (e.g. smoking, alcohol) and genetic 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 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

• f what we know from established risk factors

Absence of G-E interaction simplifies our public health messages on E

RISK PREDICTION RISK PREDICTION

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

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

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

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

g q 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

Acknowledgements

CGEMS & DCEG

Stephen Chanock Gilles Thomas

HSPH-BWH

Peter Kraft David Cox

ACS

Michael Thun Gilles Thomas Robert Hoover Kevin Jacobs Meredith Yeager Richard Hayes J h F i David Cox Sue Hankinson Sara Lindstrom Rulla Tamimi Connie Chen Michael Thun Heather Feigelson Ryan Diver Vickie Stevens Joseph Fraumeni Daniela Gerhard Patricia Hartge Demetrius Albanes Sholom Wacholder Carolyn Guo Julie Buring Dan Chasman

EPIC

Elio Riboli Afshan Siddiq Rudolf Kaaks Federico Canzian Nilanjan Chatterjee Zhaoming Wang Kai Yu Margaret Tucker Jesus Gonzalez Bosquet

MEC

C Haiman B Henderson L Kolonel Federico Canzian Daniela Campa Jesus Gonzalez Bosquet Montse Garcia-Closas Charles Chung Julia Ciampi L Kolonel F Schumacher

Acknowledgements

DRIVE Discovery, Biology, and Risk of Inherited Variants in Breast Cancer

C R h UK HSPH U i it f Ut h Cancer Research UK HSPH University 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 Alexander Miron National Institutes of Health NCI Program Office Fred Hutchinson CRI Stephen Chanock Daniela Seminara Paul Auer Ross Prentice Stanford University Alice Whittemore German Cancer Research Center (DKFZ) Federico Canzian University of Cambridge Rudolf Kaaks Douglas Easton Daniele Campa Daniele Campa University of Chicago BWH Brandon Pierce Rulla Tamimi Habibul Ahsan Sue Hankinson Aditi H Aditi Hazra University of Southern California Imperial College Brian Henderson Elio Riboli Chris Haiman Fred Schumacher