1. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer Max Leiserson *, Hsin-Ta Wu *, Fabio Vandin, Benjamin Raphael Genome Biology 16, 160 (2015) doi:10.1186/s13059-015-0700-7doi:10.1186/s13059-015-0700-7 * Equal contribution February 2 nd, 2016

2. Driver mutations target pathways 2 Vogelstein, et al. (Science, 2013) Significance Score 10-35 genes N=250-500 samples A B interacts with TCGA (Nature 2012) “long tail” A B Colorectal study Can we discover the pathways?

3. Driver mutations in pathways are often mutually exclusive 3 Few driver mutations distributed across multiple pathways ➔ Approximately one driver mutation per pathway per patient [Thomas, et al. 2007]Patients Genes/Loci  Dendrix [Vandin, et al. RECOMB 2011]  Muex [Szczurek, et al. RECOMB 2014]  Dendrix++ [TCGA, NEJM 2013]  MEMCover [Kim, et al. ISMB 2015]  Mutex [Babur, et al. Genome Biology 2015]  Others…

4. Combinations of mutually exclusive alterations (CoMEt) algorithm 1.Statistical score that is less biased towards most frequently mutated genes. 2.MCMC algorithm identifies multiple exclusive modules by examining distribution of solutions 3.Outperforms prior methods on simulated and real data. 4.Identifies combinations overlapping cancer pathways in multiple tumor types. 4 [Leiserson*, Wu*, et al. Genome Biology 2015. Also RECOMB 2015.] Hsin-Ta Wu Gene 1 Gene 2 Gene 3

5. Motivation for statistical score 5 TCGA Glioblastoma (GBM) (Nature, 2008) Most frequently mutated genes can dominate the mutual exclusivity signal. exclusiveco-occurring EGFR(A) (127) TRHDE (8) MAST2 (6) PTEN (76) PTEN(D) (41) IDH1 (14) (A) = amplification; (D) = deletion Not MutatedMutated Not mutated72 Mutated41 Gene 1 Gene 2 Surprise of mutual exclusivity conditioned on the mutation’s frequency. Exclusivity Hypergeometric probability One-sided Fisher’s exact test for independence

6. 6 Gene 1 Gene 2 Gene 3 Not MutatedMutated Not mutated50 Mutated00 Gene 1 Gene 2 exclusive co-occurring Not MutatedMutated Not mutated22 Mutated41 Gene 1 Gene 2 Gene 3 mutated not mutated Exclusivity Hypergeometric probability

7. 7 Gene 1 Gene 2 Gene 3 … Gene 1 Gene 2 … … Not MutatedMutated Not mutated72 Mutated41 Not MutatedMutated Not mutated22 Mutated41 Not MutatedMutated Not mutated50 Mutated00 not mutatedmutated

8. Computing exact test can be expensive 8 Sample size [Zelterman, et al. 1995] Compute tail probability by enumerating contingency tables with fixed margins. Exclusivity Hypergeometric prob. more exclusiveless exclusive Only enumerate the more exclusive tables.

9. Only enumerate “more exclusive” tables Gene 1 Gene 2 Gene 3 more exclusive less exclusive Hypergeometric prob. Sum of exclusive cells Gene 1 Gene 2 Gene 3 exclusiveco-occurring No tables to enumerate! Hypergeometric prob. Sum of exclusive cells Hypergeometric prob. Sum of exclusive cells Enumerate more exclusive tables. Binomial/Permutational approximation. Gene 1 Gene 2 Gene 3 Perfectly exclusive Approximately exclusiveLess exclusive

10. Combinations of mutually exclusive alterations (CoMEt) algorithm 1.Statistical score that is less biased towards most frequently mutated genes. 2.MCMC algorithm identifies multiple exclusive modules by examining distribution of solutions 3.Outperforms prior methods on simulated and real data. 4.Identifies combinations overlapping cancer pathways in multiple tumor types. 10 [Leiserson*, Wu*, et al. Genome Biology 2015. Also RECOMB 2015.] Hsin-Ta Wu Gene 1 Gene 2 Gene 3

11. Patients have alterations in multiple pathways 11 Combinations of genes Sampling frequency G1,G2,G3; G4,G5,G6 2505000 G1,G2,G3; G4,G5,G7 2453000 G1,G2,G3; G4, G5, G8 2402000 Marginal probability graphMultiple suboptimal solutions G2 1.0 0.20 0.50 1.0 = CoMEt module = other Gene legend Need to search for multiple sets of alterations simultaneously [Multi-Dendrix; Leiserson, et al. 2013] But we do not know the number or size of pathways a priori and there are often many suboptimal solutions... [Vogelstein, et al. 2013] Compute marginal probability of pairs with exclusive alterations to identify modules

12. Results 1.Comparison to Multi-Dendrix, muex, and mutex on simulated data. 2.Application to TCGA glioblastoma, breast cancer, leukemia, and stomach cancer datasets.  Combinations of mutations in cancer pathways  Overlapping pathways  Subtype-specific mutations 12

13. CoMEt results on TCGA Glioblastoma (GBM) 13 398 mutated genes in 261 tumor samples [TCGA, Nature 2008] Figure 5 [TCGA, Nature 2008] CDKN2A (D) CDK4 (A) RB1 MSL3 TP53 MDM2(A) MDM4 (A) (A) = amplification (D) = deletion exclusive co-occurring Rb signaling NPAS(D) p53 signaling

14. CoMEt results on TCGA Glioblastoma (GBM) 14 398 mutated genes in 261 tumor samples [TCGA, Nature 2008] Rb signaling Exclusive modules p53 signaling Figure 5 [TCGA, Nature 2008] Different variants of CDKN2A are in Rb and p53 signaling p53 signaling PI(3)K signaling Rb signaling Co-occurence gene-gene module

15. Patients CoMEt analysis of subtype-specific mutations 15 Subtype Predefined subtypes CoMEt Subtype Simultaneous analysis of exclusive and subtype-specific alterations Genes exclusiveco-occurring ERBB2 Luminal B HER2-enriched Luminal A Basal Normal-like TP53 PIK3CA (TCGA 2012) Breast cancer expression subtypes (Sørlie et al. 2003)

16. CoMEt results on TCGA breast cancer (BRCA) 16 375 mutated genes and 4 subtypes in 507 tumor samples [TCGA, Nature 2012] p53 signalingPI(3)K signalingRTK/Ras signalingsubtype CoMEt simultaneously uncovers mutually exclusive and subtype-specific alterations.

17. Acknowledgements 17 Funding & Data Research Group Benjamin J. Raphael (advisor) Fabio Vandin Hsin-Ta Wu Mohammed El-Kebir Dora Erdos Matthew Reyna Ashley Conard Cyrus Cousins Rebecca Elyanow Gryte Satas CoMEt  Software (R and Python packages) https://github.com/raphael-group/comet https://github.com/raphael-group/comet  Interactive results http://compbio-research.cs.brown.edu/comet http://compbio-research.cs.brown.edu/comet