Protein Mutations and Pathways in Cancer Toward Modular & Combinatorial Therapy

1. Chris Sander Computational & Systems BiologyMemorial Sloan-Kettering Cancer Center, New York Protein Mutations and Pathways in CancerToward Modular & Combinatorial Therapy International Conference on BioinformaticsAsia-Pacific Bioinformatics Network Less war !! more science !!

2. Cancer Simplicity of phenotype Diversity of implementation Modular therapy! Combinatorial therapy!

3. Cancer Genomics Functional consequences of somatic mutations Molecular alterations in pathway context Toward Combinatorial Therapy Combinatorial Perturbations & Network Models Information Infrastructure Pathway Commons & Author Fact Deposition

4. Cancer GenomicsNikolaus SchultzBarry Taylor, Ethan CeramiNick Socci, John MajorSam SingerMarc Ladanyi, Cameron BrennanMatt Meyerson, Jordi Barretina & TCGA community Niki Schultz Function of Protein MutationsBoris RevaJenya Antipin Alyosha Stupalov PathwayCommons.org Emek DemirGary Bader, Toronto Ethan CeramiBen GrossRobert HoffmanKen FukudabioPAX community

5. The Cancer Genome Atlas (TCGA) Sample processingClinical annotation DNA copy number DNA methylation Gene expression - mRNA (exon-level) - miRNA DNA sequencingcurrently 1300 genes soon 6000 or all genes Genomic rearrangements Proteomics Data storage and distribution Integrative analysis Next: lung squamous, kidney, breast, colon

6. Glioblastoma Multiforme – Aggressive Disease

7. DNA copy number alterations in GBM and ovarian cancer Clinical data DNA copy number DNA methylation mRNA expression miRNA expression mutations OVA GBM More than half of the genes copy-number altered in ovarian cancer correlated with expression

8. Cancer Genomics Functional Consequences of Protein Mutations

9. Somatic mutations in cancer:What are the functional consequences ? Input allele 1 … GCC ATC CCG … ALA ILE/MET PRO allele 2 … GCC AAC CCG … mutation in coding region Public Databases protein family 3D structure 3D complex pathway Q9SD07RLHIGGL Q44861RLLIGRV Q61EI4RLLIGRV Q7PY36RLFIGKI Q4I4E0RLWMDQI Q58SJ9RLLIGRV O01811RLFIGKI P55159RLLIGRV Superfamily PDB PFAM SCOP NCBI ENSEMBL Reactome specificity & conservation correlation between interacting residues protein stability protein-protein interactions pppi psc pcr pps Output Probability(Disruptive/Non-disruptive) = f ( Psc, Pcr, Pps, Pppi )

10. Assessing the functional consequences of mutations EGFR_human

11. CEO algorithmCombinatorial Entropy Optimization Boris Reva, MSKCC

12. Defining subfamilies and specificity residues Input Output 1 2 Clustering Sub-Families 3 4 Specificity Residues ConservedResidues

13. Q: How one can achieve the most distinctive=informative separation of sequences into clusters? Minimize contrast function = difference between entropies of ordered and disordered clusters of sequences of the same size S ordered S’ disordered S-S’=-9 S-S’=0 S-S’=-3.5 S-S’=-7.5 Goal: S-S’->min

14. combinatorial entropy measure of specificity patterns is the number of sequences in cluster (subfamily) k; is the number of residues of type a in the column i of the cluster k. For each column i of the alignment one computes the combinatorial entropy and the reference entropy : The entropy difference , summed up over all columns i, is a measure of the deviation of a given sequence clustering from random. This difference is minimal when each cluster has its distinct type of residues. Optimization problem: form clusters (subfamilies) of sequences, so as to minimize the combinatorial entropy difference .

15. Rank of residue position Conserved region Specificity region Contrast entropy difference Specificity residues - high contrastGlobally conserved residues - low contrast Family of 390 protein kinases

16. Defining subfamilies and specificity residues Input Output 1 2 Clustering Sub-Families 3 4 Specificity Residues ConservedResidues

17. Assessing the functional consequences of mutations EGFR_human

18. OMA - Online Mutation Analysis www.cbio.mskcc.org/cancergenomics www.proteinfunction.org

19. Functional implications of cancer mutationsat the protein level ERBB2 mutationsL49H no alignment data available C311R strong functional impact, conserved residue N319D likely functional impact, conserved and specificity residue E321G likely functional impact, specificity residue D326G likely functional impact, specificity residue - binding site? C334S strong functional impact, conserved residue in S-S bridge V750E strong functional impact, strongly conserved residue V777A unlikely functional NF1 mutations V1308E strong functional impact, buried residue R1412S strong functional impact D1849N no alignment data available A2336T likely functional impact, specificity residue

20. likely functional impact specificity residue with conserved neighborsmay be a part of binding site Examples of mutations predicted as functional by OMA D326G in ERBB2- Tyrosine kinase-type cell surface receptor HER2 D->G

21. Examples of mutations predicted as functional by OMA C334S in ERBB2 - Tyrosine kinase-type cell surface receptor HER2 strong functional impact conserved residue mutation eliminates SS bridge C334-C338 C334 C338

22. Cancer Genomics Molecular alterations in pathway context

23. TCGA glioblastomaDNA copy number changes and mutations amplified mutated deleted

24. Glioblastoma copy number alterationsWhich events are functional, which are passengers ? RAE recurrence amplitude extent RAE: Barry Taylor, Nick Socci, Chris Sander PLoS ONE 2008

25. Analyzing genetic alterations in pathway context www.cbio.mskcc.org/cancergenomics

26. Combining molecular profiles and prior biological knowledge www.cbio.mskcc.org/cancergenomics

27. “GBM pathway” Based on:Genes Dev. 2007 Nov 1;21(21):2683-710.

28. copy number datasample 2

29. copy number datasample 3

30. copy number datasample 4

31. copy number datasample 5

32. copy number datasample 6

33. copy number datasample 7

34. copy number datasample 8

35. mRNA expr. datasample 8

36. methylation datasample 8

37. mutation datasample 8

38. mutation datasample 3

39. Mapping molecular alterations in 200 glioblastoma samples onto biological pathwaysGoal: determine oncogenic programs www.cbio.mskcc.org/cancergenomics

40. EGFR ERBB2 PDGFRA MET RTK/RAS/PI-3Ksignaling altered in 85% mutation in 7% amplification In 14% amplification in 3% mutation, amplification in 46% Cancer program by sub-networks mutation, deletion in 17% mutation, deletion in 33% NF-1 RAS PI-3KClass I PTEN mutation in 2% mutation, amplification in 24% AKT amplification in 2% FOXO Proliferation mutation in 2% P53signalingaltered in86% The CancerGenome AtlasPilot Project(2006-2008) ~200 cases ofglioblastoma m. brain tumors CDKN2A (INK4A) CDKN2B CDKN2C Activated oncogenes homozygous deletion in 51% homozygous deletion in 48% homozygous deletion in 2% CDKN2A (ARF) homozygous deletion in 49% CDK4 amplification in 17% MDM2 amplification in 13% amplification in 5% MDM4 RB1 deletion, mutation in 11% RB signalingaltered in 77% TP53 mutation, deletion in 35% G1/S progression Senescence Apoptosis www.cbio.mskcc.org/cancergenomics

41. Automate module analysis (make it objective)

42. Key: capture biological knowledge in computable form http://www.pathwaycommons.org bioPAX Facilitate creation and communication of pathway data Aggregate pathway data in the public domain Provide easy access for pathway analysis Community Process !

43. Algorithm(s) to detectaltered modules in cancer

44. glioblastoma – altered modules

45. PI3K module change in subtypes ?

46. whole proteome/genome sequencing will lead to more complete module map

47. Network pharmacology Toward Combinatorial Therapy Simple Models from Complex Data CoPIA – Nelander et al. - 2008

48. Perturbation Cell Biology – CoPIASven NelanderPeter Gennemark & Wei Qing WangBjoern Nilsson, Christine Pratilas, QingBai SheNeal Rosen Sven Nelander http://cbio.mskcc.org/copia/ Nelander, Sander et al., Molecular Systems Biology, 2008

49. Reality Abstraction / Model Application Therapy

50. Experiment: Dual drug perturbation of MCF7 cancer cell line@ MSKCC Wei Qing Wang, Sven Nelander & Rosen Lab 2007-2008