protein mutations and pathways in cancer toward modular & combinatorial therapy

Protein Mutations and Pathways in CancerToward Modular & Combinatorial Therapy

Less war !! more science !!

Chris SanderComputational & Systems Biology

Memorial Sloan-Kettering Cancer Center, New York

International Conference on BioinformaticsAsia-Pacific Bioinformatics Network

CancerCancerSimplicity of phenotypeSimplicity of phenotype

Diversity of implementationDiversity of implementationModular therapy!Modular therapy!

Combinatorial therapy!Combinatorial therapy!

Cancer GenomicsCancer GenomicsFunctional consequences of somatic Functional consequences of somatic

mutationsmutationsMolecular alterations in pathway contextMolecular alterations in pathway context

Toward Combinatorial TherapyToward Combinatorial TherapyCombinatorial Perturbations & Network Combinatorial Perturbations & Network

Models Models

Information InfrastructureInformation InfrastructurePathway Commons & Author Fact DepositionPathway Commons & Author Fact Deposition

Function of Protein Mutations

Boris RevaJenya AntipinAlyosha Stupalov

Cancer Genomics

Nikolaus SchultzBarry Taylor, Ethan Cerami

Nick Socci, John MajorSam Singer

Marc Ladanyi, Cameron BrennanMatt Meyerson, Jordi Barretina

& TCGA community Niki Schultz

PathwayCommons.org Emek Demir

Gary Bader, Toronto Ethan Cerami

Ben GrossRobert Hoffman

Ken FukudabioPAX community

The Cancer Genome Atlas (TCGA)

DNA copy number

Gene expression - mRNA (exon-level) - miRNA

DNA sequencingcurrently 1300 genes

soon 6000 or all genes

DNA methylation

Genomic rearrangements

Proteomics

Sample processingClinical annotation

Data storage and distribution

Integrative analysis

Next: lung squamous, kidney, breast, colon

Glioblastoma Multiforme – Aggressive Disease

DNA copy number DNA methylation mRNA expression miRNA expression mutationsClinical data

DNA copy number alterations in GBM and ovarian cancerO

VAGB

M

More than half of the genes copy-number altered in ovarian cancer correlated with expression

Cancer GenomicsCancer Genomics

Functional Consequences of Protein Functional Consequences of Protein MutationsMutations

Q9SD07 RLHIGGLQ44861 RLLIGRVQ61EI4 RLLIGRVQ7PY36 RLFIGKIQ4I4E0 RLWMDQIQ58SJ9 RLLIGRVO01811 RLFIGKIP55159 RLLIGRV

Public Databases

protein stability

psc pcr ppspppi

correlation betweeninteracting residues

specificity &conservation

Probability(Disruptive/Non-disruptive) = f ( Psc, Pcr, Pps, Pppi )Output

protein family

Input mutationin coding region

protein-protein interactions

SuperfamilyPDBPFAMSCOPNCBIENSEMBLReactome

allele 1 … GCC ATC CCG … ALA ILE/MET PROallele 2 … GCC AAC CCG …

3D structure 3D complex pathway

Somatic mutations in cancer:What are the functional consequences ?

Variant Annotation Top Spec/Cons Probability (%)G719S (in lung cancer; somatic mutation) Yes 99G724S (in lung cancer) Yes 100E734K (in lung cancer) 74L747F (in lung cancer) 98R748P (in lung cancer) 99Q787R (in lung cancer) Yes 73T790M (in lung cancer) Yes 98L833V (in lung cancer) Yes 96V834L (in lung cancer) 98L858R (in lung cancer; somatic mutation) 100L861Q (in lung cancer) 99G873E (in lung cancer) Yes 78R962G (in dbSNP:17337451) 100D761Y (in lung cancer, MSKCC) 96

Assessing the functional consequences of mutations

EGFR_human

CEO algorithmCombinatorial Entropy Optimization

Boris Reva, MSKCC

G E K Q E S S S S Y E P K E E F A Q C V L LG E S L E E A S V N G P F Q Y F Y T V E C LG E S S E V A A Q N V P M L W F Y Q R H V MG E Q V E S S E S Q E P H E E F Y Q I R T LW E S K E E N A V N V P H Q K F F T V L T MK E T N E V P W F K K P M R E F Y S AW G LE E Q S E S A E S Q Q P E E P F Y Q I L E LG E K N E V E A F K L P F R E F Y S V Q R VH E R V E S A A S N V P M E T F Y Q I A E LW E E K E E F A V Y I P L Q P F L T F G R LR E C H E V K A Q Y V P M L E F Y Q V K P WG E T N E E E A F N V P R R V F F S V S N LG E S P E E N F V N V P H Q Y F Y T V E P MT E N P E V E L F K V P F R V F F S L S H YS G W K E E L A V N Q P V Q E F E T F E I EG E A S E V E H Q N V P H L K F Y Q E G P PR E A Q E S Q A S N V P M E T F Y Q V R T L

S G W K E E L A V N Q P V Q E F E T F E I EW E E K E E F A V Y I P L Q P F L T F G R LG E S P E E N F V N V P H Q Y F Y T V E P MG E S L E E A S V N G P F Q Y F Y T V E C LW E S K E E N A V N V P H Q K F F T V L T MT E N P E E E L F K V P F R V F F S L S H YK E T N E E P W F K K P M R E F Y S AW G LG E T N E E E A F N V P R R V F F S V S N LG E K N E E E A F K L P F R E F Y S V Q R VE E Q S E S A E S Q Q P E E P F Y Q I L E LG E Q V E S S E S Q E P H E E F Y Q I R T LG E K Q E S S S S Y E P K E E F A Q C V L LR E A Q E S Q A S N V P M E T F Y Q V R T LH E R V E S A A S N V P M E T F Y Q I A E LR E C H E V K A Q Y V P M L E F Y Q V K P WG E S S E V A A Q N V P M L W F Y Q R H V MG E A S E V E H Q N V P H L K F Y Q E G P P

Defining subfamilies and specificity residues

Input Output

Sub-Fam

ilies

1

2

3

4

Specificity Residues

Clustering

Conserved Residues

Minimize contrast function = difference between entropies of ordered and disordered clusters of sequences of the same size

S

S’

S-S’=0 S-S’=-9S-S’=-3.5 S-S’=-7.5

ordered

disordered

Q: How one can achieve the most distinctive=informative separation of sequences into clusters?

Goal: S-S’->min

∑ ∏=

=k ki

ki N

NS

20,...,1,, !

!ln

αα

∑∏=

=k ki

ki

N

NS

20,...,1,,

~

~

!

!ln

αα

ikki PNN ,,,

~

αα =

∑ ∑=k k

kkii NNP /,,, αα

)(~

0 ∑ −=Δi

ii SSS

Optimization problem: form clusters (subfamilies) of sequences, so as to minimize the combinatorial entropy difference .

For each column i of the alignment one computes the combinatorial entropy

and the reference entropy :iS

is the number of sequences in cluster (subfamily) k;

is the number of residues of type α in the column i of the cluster k.

kN

kiN ,,α

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.

ii SS~

−

combinatorial entropy measure of specificity patterns

iS~

Specificity residues - high contrastGlobally conserved residues - low contrast

-400

-350

-300

-250

-200

-150

-100

-50

0

0 30 60 90 120 150 180 210 240 270

Specificity region

Conserved region

Rank of residue position

Contrast entropy difference Family of 390 protein kinases

G E K Q E S S S S Y E P K E E F A Q C V L LG E S L E E A S V N G P F Q Y F Y T V E C LG E S S E V A A Q N V P M L W F Y Q R H V MG E Q V E S S E S Q E P H E E F Y Q I R T LW E S K E E N A V N V P H Q K F F T V L T MK E T N E V P W F K K P M R E F Y S AW G LE E Q S E S A E S Q Q P E E P F Y Q I L E LG E K N E V E A F K L P F R E F Y S V Q R VH E R V E S A A S N V P M E T F Y Q I A E LW E E K E E F A V Y I P L Q P F L T F G R LR E C H E V K A Q Y V P M L E F Y Q V K P WG E T N E E E A F N V P R R V F F S V S N LG E S P E E N F V N V P H Q Y F Y T V E P MT E N P E V E L F K V P F R V F F S L S H YS G W K E E L A V N Q P V Q E F E T F E I EG E A S E V E H Q N V P H L K F Y Q E G P PR E A Q E S Q A S N V P M E T F Y Q V R T L

S G W K E E L A V N Q P V Q E F E T F E I EW E E K E E F A V Y I P L Q P F L T F G R LG E S P E E N F V N V P H Q Y F Y T V E P MG E S L E E A S V N G P F Q Y F Y T V E C LW E S K E E N A V N V P H Q K F F T V L T MT E N P E E E L F K V P F R V F F S L S H YK E T N E E P W F K K P M R E F Y S AW G LG E T N E E E A F N V P R R V F F S V S N LG E K N E E E A F K L P F R E F Y S V Q R VE E Q S E S A E S Q Q P E E P F Y Q I L E LG E Q V E S S E S Q E P H E E F Y Q I R T LG E K Q E S S S S Y E P K E E F A Q C V L LR E A Q E S Q A S N V P M E T F Y Q V R T LH E R V E S A A S N V P M E T F Y Q I A E LR E C H E V K A Q Y V P M L E F Y Q V K P WG E S S E V A A Q N V P M L W F Y Q R H V MG E A S E V E H Q N V P H L K F Y Q E G P P

Defining subfamilies and specificity residues

Input Output

Sub-Fam

ilies

1

2

3

4

Specificity Residues

Clustering

Conserved Residues

Variant Annotation Top Spec/Cons Probability (%)G719S (in lung cancer; somatic mutation) Yes 99G724S (in lung cancer) Yes 100E734K (in lung cancer) 74L747F (in lung cancer) 98R748P (in lung cancer) 99Q787R (in lung cancer) Yes 73T790M (in lung cancer) Yes 98L833V (in lung cancer) Yes 96V834L (in lung cancer) 98L858R (in lung cancer; somatic mutation) 100L861Q (in lung cancer) 99G873E (in lung cancer) Yes 78R962G (in dbSNP:17337451) 100D761Y (in lung cancer, MSKCC) 96

Assessing the functional consequences of mutations

EGFR_human

OMA - Online Mutation Analysis

www.cbio.mskcc.org/cancergenomics

www.proteinfunction.org

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

V750Estrong functional impact, strongly conserved residue

V777Aunlikely functional

NF1 mutationsV1308E strong functional impact, buried residue

R1412S strong functional impact

D1849N no alignment data available

A2336T likely functional impact, specificity residue

D326G in ERBB2- Tyrosine kinase-type cell surface receptor HER2

Examples of mutations predicted as functional by OMA

likely functional impact

specificity residue with conserved neighborsmay be a part of binding site

D->G

C334S in ERBB2 - Tyrosine kinase-type cell surface receptor HER2

Examples of mutations predicted as functional by OMA

strong functional impact

conserved residue

mutation eliminates SS bridge C334-C338

C334C338


Molecular alterations in pathway contextMolecular alterations in pathway context

TCGA glioblastomaDNA copy number changes and mutations

mutated

deleted

amplified

Glioblastoma copy number alterationsWhich events are functional, which are passengers ?

RAE: Barry Taylor, Nick Socci, Chris Sander PLoS ONE 2008

RAErecurrenceamplitudeextent


Analyzing genetic alterations in pathway context

Combining molecular profiles and prior biological knowledge


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

copy number datasample 2

mRNA expr. datasample 8

methylation datasample 8

mutation datasample 8

mutation datasample 3

Mapping molecular alterations in 200 glioblastoma samples

onto biological pathways

Goal: determine oncogenic programs


EGFR ERBB2

PI-3KClass I

PDGFRA MET

mutation, amplificationin 46%

mutationin 7%

amplificationIn 14%

amplificationin 3%

RASNF-1

AKT

FOXO

PTEN

Proliferation

Activated oncogenes

MDM4

TP53

MDM2

CDKN2A(ARF)

RB1

RTK/RAS/PI-3Ksignaling

altered in

85%

P53signaling

altered in

86%

Senescence Apoptosis

CDK4

CDKN2A(INK4A)

CDKN2B CDKN2C

G1/S progression

homozygousdeletion in 51%

RB signaling

altered in

77%



homozygous deletion in 49%

amplification in 13%

amplification in 5%

mutation,deletion in 35%

amplification in 17%

deletion,mutation in 11%

mutation in 2%

amplification in 2%

mutation in 2% mutation,amplification in 24%



Cancerprogram bysub-networks

The CancerGenome AtlasPilot Project(2006-2008)

~200 cases ofglioblastoma m.brain tumors


Automate module analysis (make it objective)

Key: capture biological knowledge in computable form

Facilitate creation and communication of pathway dataAggregate pathway data in the public domainProvide easy access for pathway analysis

http://www.pathwaycommons.org

Community Process !

bioPAX

Algorithm(s) to detectaltered modules in cancer

glioblastoma – altered modules

PI3K module change in subtypes ?

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

Network pharmacologyNetwork pharmacologyToward Combinatorial TherapyToward Combinatorial Therapy

Simple Models from Complex DataSimple Models from Complex Data

CoPIA – Nelander et al. - 2008CoPIA – Nelander et al. - 2008

Perturbation Cell Biology – CoPIA

Sven NelanderPeter Gennemark & Wei Qing Wang

Bjoern Nilsson, Christine Pratilas, QingBai She

Neal Rosen

Sven Nelander

http://cbio.mskcc.org/copia/

Nelander, Sander et al., Molecular Systems Biology, 2008

http://cbio.mskcc.org/copia/

Reality Abstraction / Model

Application

Therapy

Experiment: Dual drug perturbation of MCF7 cancer cell line@ MSKCC

Wei Qing Wang, Sven Nelander & Rosen Lab 2007-2008

Mathematical Model System Simulation by Bounded ODEs

like Hopfield Network

€

dx idt

= ( W ijx jj∑ ) −α ix i + Pi

€

dx idt

= β i f ( W ij x jj∑ + Pi) −α ix i

Mean Field Model for Combinatorial Perturbation

linear

non-linear

€

dx idt


transfer function

f(…)

A simple but effective non-linear deviceto capture cooperative effects(epistatis, synergy, antagonism)

Optimize the network model

Minimize the discrepancy between prediction and experimentwhile keeping the model simple !

Sum of squares pred-expt error

Structural complexity€

E = ESSQ + ESTRUCT

€

dx idt


Optimization algorithm – examplesexplore alternative network structures

movie by Evan Molinelli

Best network model deduced from dual drug perturbation

in MCF7 cancer cell lines

Dual drug perturbation in MCF7 cancer

cell line@ MSKCC

Does the model work ?Leave out one drug combo at a time, compute best model, predict & compare with experiment

€

dx idt


Set of Best Network Model(s)

Network structure deduced from dual drug perturbation

in MCF7 cancer cell lines

€

dx idt


Power of CoPIA network modelsCoPIA = Combinatorial Perturbation Analysis

Capture …

multiple perturbation

epistasis (synergy/antagonism)

feedback loops

time-dependent processes

modification of prior knowledge

CoPIA Network Models - Applications

design combination

therapy

refine pathway models

identify drug

targetspredict

outcomes

Extend & complete existing network models !

Cancer GenomicsCancer GenomicsFunctional consequences of somatic Functional consequences of somatic

mutationsmutationsMolecular alterations in pathway contextMolecular alterations in pathway context

Toward Combinatorial TherapyToward Combinatorial TherapyCombinatorial Perturbations & Network Combinatorial Perturbations & Network

Models Models

Information InfrastructureInformation InfrastructurePathway Commons & Author Fact DepositionPathway Commons & Author Fact Deposition

Integrate Pathway Information

Facilitate creation and communication of pathway dataAggregate pathway data in the public domainProvide easy access for pathway analysis

http://www.pathwaycommons.org

Community Process !

bioPAX

http://iHOP-net.orgGenes & compounds & interactions from millions of abstracts - instantly

Robert Hoffmann, Benjamin Gross, Chris Sander iHop-net.org version 2 released 6 Dec 2006

Factoidsdigital abstracts to databases

As authors submit a paper they deposit structured facts

to a public database

How to get rich biological knowledge into a computable form

Postdocs wantedSander Group – Computational & Systems Biology @ MSKCC in NYC

Upper East Side Tri-I Campus: Sloan Kettering, Cornell Weill, Rockefeller

Cancer genomics (dry)Network pharmacology (wet)

We pause for station identification…

Toward Combinatorial TherapyToward Combinatorial TherapyUse multiple perturbation experiments to Use multiple perturbation experiments to

build predictive network modelsbuild predictive network models


The active sub-pathway model of cancer The active sub-pathway model of cancer biologybiology

Pathway CommonsPathway CommonsOne-stop-shop access to pathway informationOne-stop-shop access to pathway information

using the bioPAX common languageusing the bioPAX common language

SummarySummary

Cytoscape, bioPAX & Pathway Commons

Emek DemirEthan Cerami

Ben GrossRobert Hoffman

Ken FukudabioPAX community

Gary Bader

Perturbational cell biologySven Nelander

Wei Qing Wang Peter Gennemark

Neal Rosen, Christine Pratilas

Small RNAsDoron Betel

Rob SheridanChristina Leslie, Debora Marks

Tom Tuschl, Eric Kandel

Protein Families & Combinatorial Entropy

Boris Reva, Jenya Antipin

Cancer GenomicsNikolaus Schultz

Barry TaylorBoris Reva,

J Antipin, A StukalovJohn Major

Nick Socci, Sam SingerMarc Ladanyi

Matt Meyerson, Jordi Barretina

tools >

TGFα • 6000 gene RNAi screenNikolaus SchultzDina Marenstein

Joan Massague, Hakim DjaballahSupport: Bioinformatics Core in the

Computational Biology Center at MSKCC

Optimization algorithm 1outer loop - explore alternative network structures

€

dx idt

= β i f ( W ijx jj∑ + Pi) −α ix imodel

occasoinally climb uphill in Monte Carlo fashion

error

Optimization algorithm 2inner loop – optimize parameters along a trajectory in solution space

€

dx idt


adjust weights

explicit

errormodel

result: optimal model parameters Wij

€

dx idt


protein mutations and pathways in cancer toward modular & combinatorial therapy

Documents

17337451100d761yin lung

lung squamous

gcc aac ccg

ethan cerami nick socci

implementationmodular

ovarian cancerovagbmmore

cameron brennan matt

emek demir gary bader