Integration of Cancer Genome into GECCO- Genetics and Epidemiology of Colorectal Cancer Consortium

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1 Integration of Cancer Genome into GECCO- Genetics and Epidemiology of Colorectal Cancer Consortium Ulrike Peters Fred Hutchinson Cancer Research Center University of Washington U01-CA , PI: Peters

2 Genetics and Epidemiology of Colorectal Cancer Consortium -GECCO GWAS, Sequencing Epi, Clinical Biomarker Novel Statistical Methods Functional Annotation GECCO 50+ papers Risk Prediction Tumor Genome Survival Clinical Trials Growing Resource 30+ cohort & case-control studies 75,000 GWAS, basic clinical, epidemiologic & lifestyle data 21,000 ExomeChip data 3,000 with whole genome sequencing data 8,000+ with extended clinical and survival data 12,000 with tumor characteristics data 4,200 for targeted candidate gene tumor sequencing U01-CA137088, U01-CA164930, U01-CA185094, X01-HG006662, X01HG007585, X01 HG006196,R01-CA176272, R01-CA059045

3 The Two Genomes of Cancer Inherited Mutations/Variation ~15 high-penetrance genes leading to colorectal cancer syndromes, such as FAP or Lynch Syndrome discovered ~45 low-penetrance variants discovered primarily through GWAS Somatic Mutation Cancer arises as clone of somatically acquired genetic and epigenetic changes Develop over time Heterogeneous Large scale studies like The Cancer Genome Atlas (TCGA) or the Interactional Cancer Genome Consortium (ICGC) have allowed for substantial progress

4 Passenger vs. Driver Somatic Mutations Somatic mutations randomly distributed throughout genome and most mutations are passenger mutations In cancer clones, a subset of driver mutations fall in key cancer genes, leading to growth advantage Adapted from Campbell et. al., Nature 2010

5 Rationale: Etiologic Risk Factors in Relation to Somatic Mutational Profile Germline mutations impact cancer susceptibility in multiple ways DNA repair capacity, growth of cancer clone, metabolism of carcinogens etc. Environmental and lifestyle factors effect rate and type of acquired somatic mutations Smoking and UV already demonstrate clear signatures Examples from existing tumor characteristics CIMP+/BRAF- and smoking in CRC, Limsui et al., JNCI 2010 BRAF- and aspirin in CRC, Nishihara et al.,, JAMA 2013 PIK3CA+ and aspirin in CRC progression, Liao et al., N Engl J Med 2012 MSS and obesity in CRC, Slattery et al JNCI 2000, Champbell et al. Hoffmeister et al., CEBP 2013 Improve understanding of underlying etiologic pathways More homogenously defined subtypes will improve ability to identify novel genetic and environmental risk factors

6 Effect of Aspirin Differs by Tumor PIK3CA Mutation Status Data from NHS & HPFS Probability of Death PIK3CA mutant Log-rank P < PIK3CA wild-type Log-rank P = 0.76 Survival Time (years) Survival Time (years) Non-user Aspirin user Progression, death Aspirin use Liao et al. N Engl J Med 2012

7 Integration of the Cancer Genome Existing tumor characteristics and new tumor sequencing 7 Aim 1: harmonize and study existing tumor characteristics Aim 2: Targeted sequencing of ~100 genes in tumor DNA Integrated analysis with germline genetics and environmental risk factors CCFR NHS HPFS PMH- CCFR NFCCR WHI GEC CO Coo ting rdin Cent atin er g Cent er GECCO OFCCR VITAL DACHS EPIC PLCO Expanding Aims to integrate survival outcomes PHS MEC Colo2& 3 DALS ASTERIS K CPS-II CORSA *CCFR is collaborating with GECCO N 11,900 cases with tumor characteristics *N 4,200 tumors will be sequenced

8 Tumor Characteristics & Tissue by Study Study Epi* BRAF CIMP MSI KRAS Tissue GWAS / Exome Epi* GWAS / Exome Epi* GWAS / Exome Epi* GWAS / Exome Epi* GWAS / Exome Available CPS-II # 1,019 1,019 1,019 1,019 1,019 1,019 1,019 1,019 DACHS 2,556 2,556 2,556 2,556 2,556 2,556 2,556 2,556 2,556 2,556 DALS 2, , , , HPFS NFCCR NHS OFCCR 1,734 1,834 1,179 1,183 1,734 1,183 1,734 1,183 PLCO-I PMH-CCFR 2, , , , , Process of Collection PLCO-II WHI Ω TOTAL 11,541 9,496 6,551 5,840 10,618 9,449 10,797 8,956 8,853 7,260 Plan for Collection (Pending Funding for EPIC and Tissue Availability in CORSA and NCGCCS) EPIC To Be Determined ~500 ~500 CORSA N/A 1,250 1,250 NCS To Be Determined ~3,000 ~3,000 NCGCCS N/A *Epi: Epidemiologic data; Numbers based on MSI gene panel testing, though IHC will be used in concert; # About 450 samples will undergo MSI testing at Mayo and ~600 normal surround samples will undergo GWAS; 513 cases from NHS and 590 cases from HPFS have already whole exome sequencing data for Aim 2 and up to ~1,000 normal surround samples in NHS & HPFS will undergo GWAS; About 498 samples will undergo MSI testing at Mayo; Ω All WHI samples will undergo MSI testing at Mayo.

9 Aim 2 Newly Defined Subtypes Identify driver mutation to define tumor subtypes Identify significantly mutated driver genes based on whole exome sequencing data from: ~700 NHS/HPFS tumor-normal pairs ~600 TCGA tumor-normal pairs Define subtypes based on mutated pathways using mutational profiles from deep targeted tumor sequencing Deep sequencing of ~200 genes in ~4,200 tumors Tissue evaluation & DNA extraction (S Thibodeau at Mayo ) Targeted sequencing at >500-1,000x coverage (T Hudson, H Zaidi, J McPherson at OICR)

10 Composition of Targeted Sequencing Panel Primary: Mutation analysis (MutSigCV) and copy number in nonhypermutated samples (TCGA and NHS/HPFS) Secondary: Mutation analysis (MutSigCV) in hypermutated samples (manual inspection) Microsatellite in hypermutated samples Literature Pathway analysis Cluster analysis Pan-cancer analysis Nomination by investigators and senior advisors Bacteria genes Loss of heterozygosity (LOH) analysis

11 Final Targeted Sequencing Panel to sequence CRC tumor samples 205 candidate genes 190 somatically mutated genes 15 high penetrance germline genes 55 copy number alterations 24 losses 31 gains 25 microsatellite instability marker 213 homopolymer repeat regions 1 Fuso bacterium 3 gender probe

12 Points to Consider Several genetic and epigenetic mutations are relevant that require different laboratory methods Single substitutions, indels, CNVs, amplifications, deletions, rearrangements, translocations, methylation, ImmunoSequencing, gene expression Cost Targeted sequencing vs. whole exome/genome sequencing To be efficient, we will sequence tumor and use GWAS/Exomechip to exclude most germline mutations, with targeted follow up to confirm somatic mutations With samples size remains relative small Tumor heterogeneity Accounting for in statistical analysis by considering allele frequency by read depth Infrequently mutated genes Grouping/pathway analysis Statistical power: subtype analyses reduce # of cases, although this is counterbalanced Subtypes are less heterogeneous Common subtypes across cancer sites in Pan-Cancer analyses

13 Opportunities For CRC, whole exome sequencing studies are underway (~1,300 tumor-normal pairs) Allows for identification of large fraction of significantly mutated cancer genes Consistent approach to profiling tumors across multiple studies and thousands of cases Increased power due to large population size Reduced publication bias due to large #s Improving technologies for formalin-fixed paraffin embedded (FFPE) tissue samples

14 GECCO Annual Meeting 2015

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16 ADDITIONAL SLIDES

17 Passenger vs. Driver Somatic Mutations Somatic mutations randomly distributed throughout genome and most mutations are passenger mutations In cancer clones, a subset of driver mutations fall in key cancer genes, leading to growth advantage Adapted from Campbell et. al., Nature 2010

18 ONTARIO INSTITUTE FOR CANCER RESEARCH Pan-Cancer Analysis: 127 Significantly Mutated Genes can be Grouped into 20 Cellular Processes in Cancer Selecting genes in this list if at least some evidence for mutations in CRC samples N=3,281 Kandoth et al. Nature (2013)

19 Mutated Pathways Analyses will be based on mutated pathways (to improve power) No significant annotation EGFR, ERBB, Ras, mtor signaling We will create networks of mutated genes and identify gene clusters, which will be annotated to create CRC-specific pathways gene module maps TGFβ, Wnt, SMAD signaling p53 signaling ERBB, MAPK signaling Yung and Stein, Preliminary data based on TCGA analysis 2013 using Reactome, see Wu et al Genome Biology 2010

20 TCGA Colorectal Cancer CRC has an additional challenge due to very different mutation profiles between hypermutated and nonhypermutated tumors Colorectal Cancer (n=276) TCGA Network, Nature 2012