Mutual exclusivity: drivers, pathways, and beyond
Teresa Przytycka NIH / NLM / NCBI Cancer drivers, passengers, supporting actors, witnesses
• Driver mutations /alterations– mutations contributing to cancer progression
• Passenger mutations – neutral mutations accumulating during cancer progression
• Challenges in detecting driver mutations: – Heterogeneity - phenotypically similar cancer cases might be caused by different sets of driver mutations – Rare drivers
• Best supporting actors (Igor’s talk)
• Witnesses (this talk) Cancer driving pathways examples of BRCA mutated genes in their pathway context
RAS 1.5% or more PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 Mutual exclusivity of cancer drivers Thomas et al 2007 Ciriello, et al., 2012; Vandin, et al., 2012; Szczuret et.al , 2014, 2015 Leiserson, et al., Vadin et al. 2013,2014,2015; Kim et al. 2015 Constantinescu et al. 2015 patients mutations in gene 1 Mutations in gene 2 Explanations
• Two drivers dysregulating the same pathway
• Each of the drivers corresponds to of a unique cancer type or subtype
• Negative genetic interactions between drivers 4 Mutually exclusive pairs often share pathways
RAS
PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 5 Fabio Vandin et al. Genome Res. 2012;22:375-385 Mutually exclusive pairs often share pathways
RAS
PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 6 Mutually exclusive pairs often share pathways
RAS
PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 7 Fabio Vandin et al. Genome Res. 2012;22:375-385 Mutually exclusive pairs often share pathways
RAS
PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 8 Fabio Vandin et al. Genome Res. 2012;22:375-385 Mutually exclusive pairs often share pathways
RAS
PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 9 Fabio Vandin et al. Genome Res. 2012;22:375-385 Mutual exclusivity relation with a gebe “outside” the pathway
Genes exclusive with TP53 RAS
PIK3CR1 PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation NCOR1 10 Fabio Vandin et al. Genome Res. 2012;22:375-385 Kim et al. 2016 Introducing a classification of mutual exclusivity
Motivation – distinguishing ME between drivers that:
• Result in a similar phenotype (WITHIN_ME)
• Occur across multiple cancer types (ACROSS_ME)
• Between type specific drivers (BETWEEN_ME)
Kim et al. ISMB /Bioinformatics 2015 Mutual exclusivity classes in PanCancer context
• Within tissue exclusivity WITHIN_ME
Traditional permutation test
• Across tissues exclusivity ACROSS_ME
Type-restricted permutation test
• Between tissues exclusivity BETWEEN_ME
12 Traditional, type-oblivious permutation test Kim et al. ISMB /Bioinformatics 2015 WITHIN and ACRPSS ME is biased towards pathway edges
13 Kim et al. ISMB /Bioinformatics 2015 Finding cross-cancer dysregulated modules by combining interaction and ACROSS_ME
• Within tissue exclusivity WITHIN_ME
Traditional permutation test
• Across tissues exclusivity ACROSS_ME
Type-restricted permutation test
• Between tissues exclusivity BETWEEN_ME
Type-oblivious permutation test 14 Finding PanCancer dysregulated pathway - ME Module Cover Approach
Optimization problem: Find smallest cost set of modules so that each disease case is covered at least k times
Cost is a function of:
distance in the network of genes in same module
Mutual exclusivity
Score of covering edge Optimization problem: unit cost per module Gene cover
Kim et al. PSB 2013, Bioinformatics 2015 Does putting together ACROSS_ME and interaction data actually helps ?
MEMCover we find more cancer drivers
Compared to Module Cover Compared to HotNet2
16 Robust mutual exclusivity within some modules
17 Kim et al. ISMB /Bioinformatics 2015 Hub-like ME within some modules
18 Splicing
19 Kim et al. ISMB /Bioinformatics 2015 No ME within some modules
20 Kim et al. ISMB /Bioinformatics 2015 Mutual Exclusivity Hubs
21 Kim et al. ISMB /Bioinformatics 2015 Beyond cancer drivers A. B.
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted TTN – presumed passenger - no known role in cancer
A. B.
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted Presumed to be passenger mutations gene has a role in cancer
A. B.
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted FBXW7 – tumor suppressor but can harbor passenger mutations
A. B.
FBXW7
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted If TTN is a passenger that what is the train it is ridding on?
A. B.
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted TTN carries APOBEC signature in BRCA and Pol ε signature in UCEC
From Alexandrov et al, Nature 2013
Consistent with TTN spectrum in BRCA
APOBEC cytidine deaminase mutational spectrum
Consistent with TTN spectrum in UCEC
Pol II ε mutation mutational spectrum 27 TTN and TP53 have common neighbors in BRACE
A. B.
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted Co-occurrences - a causal relation or same underlying cause?
A. B.
FBXW7
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted Can APOBEC cause TP53 mutations? Burns et al.
TP53, TTN concurrence (p-value < 0.0002, hypergeometric test).
30 Can APOBEC cause TP53 mutations? Burns et al.
TP53, TTN concurrence (p-value < 0.0002, hypergeometric test).
TP53, TTN concurrence after correcting for patients mutation frequency p-value > 0.29
31 Can APOBEC cause TP53 mutations? Burns et al.
TP53, TTN concurrence Immune response (p-value < 0.0002, hypergeometric test).
TP53, TTN concurrence after correcting for APOBEC patients mutation frequency p-value > 0.29 TP53 TTN
32 True for all TP53 mutations in BRCA?
NO
Immune response
APOBEC
TP53 TTN
33 Co-occurrences - a causal relation or same underlying cause?
A. B.
FBXW7
BRCA (FDR 0.0125) UCEC (FDR 0.0025) (computed with our new method WeSME; width p-value; color shade FDR)
Kim et al. submitted TTN and TP53 share exclusivity partners
TP53 RAS TTN
PTEN
PIK3CA
CTCF
AKT1 MAPK signaling
FOXA1 MAP3K1
MAP3K4 SWI/SNF
MAP2K4 ARID1A
mTOR NCOA3 EP300 CEBPA Mediator complex co –activator /co-repressor MED23 co –activation
NCOR1 Genes ME with TTN are predictors of better survival
100% Cases with Alteration(s) in Query Gene(s) Cases without Alteration(s) in Query Gene(s) Cases with Alteration(s) in Query Gene(s) Logrank Test P-Value: 100%0.0283 Cases without Alteration(s) in Query Gene(s) 90% Logrank Test P-Value: 0.0425 90% 80%
80%
70% 70%
60% 60% Surviving 50% Disease Free 50%
40% 40%
30% 30%
20% 20%
10% 10%
0% 0%
0 20 40 60 80 100 120 140 160 180 200 220 240 0 20 40 60 80 100 120 140 160 180 200 220 240
Months Disease Free Months Survival 36 Summary
• Introduction of mutual exclusivity classes and their relation to interaction network
• Combining ME with interaction network improves identification of PanCancer dysregulated modules
• Mutual exclusivity and co-occurrence of passenger mutations can provide important insights into mutagenesis of cancer
37 AlgoCSB Algorithmic Methods in Computational and Systems Biology
Przytycka’s group
Phung Dao Jan Hoinka YooAh Kim Damian Wojtowicz Yijie Wang
DongYeon Cho Sanna Madan (alumnae) Poolesville HS