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Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
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Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop

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    Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop Presentation Transcript

    • Oncogenomics Workshop - EBI - UKMarch 14th, 2013Nuria Lopez-BigasUniversity Pompeu FabraBarcelonahttp://bg.upf.edu
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.orgacross projects - across cancer sites
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.orgacross projects - across cancer siteshttp://beta.intogen.org
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • ExpressionpatternsSomaticmutationsEpigenomicprofilesStructuralaberrationsCopy numberalterationsPatient cohortPrimary tumorsCancer Genomics Data
    • ExpressionpatternsSomaticmutationsEpigenomicprofilesStructuralaberrationsCopy numberalterationsPatient cohortPrimary tumorsCancer Genomics Data
    • tumor samplemachednormal sampleExome/Wholegenome sequencingReadsReadsAligmentAligned readsFASTQAligned readsBAMMutationcallingTumorsomaticmutationsVCFFile formats:Analysis protocolLaboratory protocolCancer genome re-sequencingTumours are heterogeneous in nature (multiclonality)Variant calling pipelines entail judgement calls
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • tumor samplemachednormal sampleExome/Wholegenome sequencingReadsReadsAligmentAligned readsFASTQAligned readsBAMMutationcallingTumorsomaticmutationsVCFFile formats:Analysis protocolLaboratory protocolCancer genome re-sequencingWhich mutations arecancer drivers?
    • How to identify cancer drivers?
    • How to identify cancer drivers?Find signs of positive selection acrosstumour re-sequenced genomes
    • Frequency based approaches to identify driversAssume that cancer drivers are mutated more frequently thanbackground in a cohort of tumourssamplesRecurrence analysisgenesgenesnot mutatedmutated driver geneMutSig (Broad Institute)MuSiC-SMG (Washington University)
    • Frequency based approaches to identify driversAssume that cancer drivers are mutated more frequently thanbackground in a cohort of tumourssamplesRecurrence analysisgenesgenesnot mutatedmutated driver geneMutSig (Broad Institute)MuSiC-SMG (Washington University)• Difficulty to correctly estimate the background mutation rates• Cannot identify lowly recurrent mutated driver genes• Need raw data (eg. BAM files) to assess sequencing coverage per region• Computationally costlyMain Challenges of frequency based approaches
    • How to identify drivers across projects in a scalable way?
    • How to identify drivers across projects in a scalable way?• Do not need large nor protected data (eg. list of tumour somatic mutations)• Are not computationally expensive• Are robust to differences in mutation callingIdeally computational methods that:
    • How to identify drivers across projects in a scalable way?• Do not need large nor protected data (eg. list of tumour somatic mutations)• Are not computationally expensive• Are robust to differences in mutation callingIdeally computational methods that:OncodriveFM OncodriveCLUSTWe have developed 2 methods with these properties:
    • Finding drivers using functional impact bias (FM bias)Gonzalez-Perez and Lopez-Bigas. NAR 2012Abel Gonzalez-PerezGene A Gene BFunctional Impact metrics:•SIFT•Mutation Assessor•Polyphen2FI scorehighlowOncodriveFM
    • 1. Compute FI scores for nsSNVs (combining MutationAssessor, SIFT, Polyphen2)2. Compute FI scores of other variants (STOP, synonymous and frameshift) using a set of rulesSIFT Polyphen2 MutationAssessorSynonymous 1 0 -2STOP-gain 0 1 3.5Frameshift 0 1 3.5STEP 1: Assess the functional impact (FI) of all variantsFI scorenot mutatedFI scorehighlowOncodriveFM method’s details
    • OncodriveFM method’s detailsSTEP 2: Compute FM bias per genesamplesgenesgenesFunctional ImpactHighLowOncodriveFMnot mutated driver gene
    • OncodriveFM method’s detailsCompute FM bias per modulenot mutatedFI scorehighlow 0.0010FM qvaluesamplesmodule1module2module 1module 2OncodriveFM
    • • It does not depend on background mutation rates• Only needs list of somatic mutations• It is computationally cheap• Can identify lowly recurrent mutated driver genesMain Advantages of FM bias approachOncodriveFM main advantages
    • One example: TCGA Glioblastoma FMbiasqvalueMutSigqvalueTP53PTENEGRFNF1RB1FKBP9ERBB2PIK3R1PIK3CAPIK3C2GIDH1ZNF708FGFR3CDKN2AALDH1A3PDGFRAFGFR1MAPK9DCNPIK3C2ACHEK2PSMD13GSTM58.5E-118.5E-118.5E-118.5E-112.5E-98.5E-111.2E-81.2E-82.3E-40.0028.5E-117.4E-103.2E-92.5E-85.2E-51.5E-62.0E-62.2E-51.5E-66.2E-5111<1.0E-8<1.0E-8<1.0E-8<1.0E-8<1.0E-82.7E-81.0E-81.0E-81.0E-86.1E-5NANS0.82NSNS0.210.65NSNSNS0.0020.010.009not mutatedMA score5-2 00.05 10FM / MutSig qvalueGonzalez-Perez and Lopez-Bigas. NAR 2012OncodriveFM Results
    • OncodriveFM ResultsPIK3R1PTENEGFRTP53IDH1RB1NF1BRAFPIK3CASPTA1KRTAP4-11GABRA6KELCDH18RPL5STAG2OR8K3OR5AR1LZTR1MYH8RPL5OncodriveFMQvalueMutSig QvalueTCGA Glioblastoma (2013)
    • TP53KDM6AFBXW7NFE2L2EP300RB1ERCC2CDKN1AARID1AOncodriveFMQvalueMutSig QvalueTCGA BLCA (2013)OncodriveFM Results
    • PIK3CA is recurrently mutated in thesame residue in breast tumoursLowly scored byfunctional impact metricsH1047LPIK3CAProtein position0 1047Proteinaffectingmutations800
    • Finding drivers using regional clustering of mutationsTamborero et al., Under reviewProteinaffectingmutationsProtein positionKRAS05000 175OncodriveCLUST12David Tamborero
    • OncodriveCLUST method’s detailsThGene A Gene B(I)(II)(III)(IV)(V)ThSgeneA= Sc1SgeneB= Sc1+ SC2(VI)0ZAZBmutationsAmino acidC1C1 C2Amino acidmutationsmutationsmutationsSgeneASgeneBTamborero et al., Under reviewbackground model obtained bycalculating the clustering score pergene of the coding-silent mutations
    • • It does not depend on background mutation rates• It is computationally cheap• Only needs list of somatic mutations• It is complementary to OncodriveFMMain Advantages of FM bias approachOncodriveCLUST main advantages
    • OncodriveCLUST ResultsCGCqOncoFMqOncoCLUSTqMutSig1389491221107655818635734348744484TP53CDH1GATA3SF3B1AKT1MLL3MAP2K4RUNX1PTENRB1MYBNF1PIK3CAGNASCBFBPIK3R1KRASFGFR2EP300HLFARID1AMLLT4JAK2BRCA1ARID2ERBB2NINBRCA LUSCCGCqOncoFMqOncoCLUSTqMutSigTP53CDKN2ANFE2L2FBXW7PIK3CAPTENNF1EP300MLL2JUNCDH11EGFRNOTCH1MLL3RB1PPP2R1AGPC3ABL2SMARCA4MYH9NSD1TSC1EBF1NCOA2ARID1AAPCBRCA1DICER189102010201118628345818245451174697967Gene significance is obtained by:3 methods2 methods1 methodonly by OncodriveCLUSTCancer gene census phenotype:dominantrecessiveCorrected p values scale:00.051Not assessable
    • Combining methods withcomplementary principles helps toobtain a more comprehensive andreliable list of cancer drivers✓ Functional Impact Bias✓ Mutation Clustering✓ Mutation Frequency
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • Catalogs oftumor somaticmutations✓ Identify consequences of mutations (Ensembl VEP)✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC)✓ Compute frequency of mutations per gene and pathway✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST)Input data Analysis Pipeline (powered by Wok) BrowserIntOGen SM-Analysis pipelineTo interpret catalogs of cancer somatic mutationsChristian Perez-LlamasWorkflow Management Sytem
    • Catalogs oftumor somaticmutations✓ Identify consequences of mutations (Ensembl VEP)✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC)✓ Compute frequency of mutations per gene and pathway✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST)Input data Analysis Pipeline (powered by Wok) BrowserIntOGen SM-Analysis pipelineTo interpret catalogs of cancer somatic mutationsChristian Perez-LlamasWorkflow Management Sytem
    • Catalogs oftumor somaticmutations✓ Identify consequences of mutations (Ensembl VEP)✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC)✓ Compute frequency of mutations per gene and pathway✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST)Input data Analysis Pipeline (powered by Wok) BrowserIntOGen SM-Analysis pipelineTo interpret catalogs of cancer somatic mutationsCurrently:27 Projects12 Cancer sites3229 tumours.orghttp://beta.intogen.orgChristian Perez-LlamasWorkflow Management Sytem
    • 27 cancer sequencing datasets analysed so farTotal = 3329CANCER SITE AUTHORS SOURCENumber ofSamplesbrain TCGA TCGA DATA PORTAL 248brain DKFZ ICGC DCC 114brain Johns Hopkins University ICGC DCC 88breast TCGA TCGA DATA PORTAL 510breast Broad Institute PubMed 102breast WTSI ICGC DCC 100breast Washington University School of Medicine PubMed 75breast University of British Columbia PubMed 65breast Johns Hopkins University ICGC DCC 41colon TCGA TCGA DATA PORTAL 105colon Johns Hopkins University ICGC DCC 34corpus uteri TCGA TCGA DATA PORTAL 247hematopoietic CLL-ICGC ICGC DCC 109hematopoietic Dana-Farber Cancer Institute PubMed 90Kidney TCGA TCGA DATA PORTAL 298liver and bile ducts IACR ICGC DCC 24lung and bronchus TCGA TCGA DATA PORTAL 177lung and bronchus Washington University School of Medicine ICGC DCC 156lung and bronchus Johns Hopkins University PubMed 43lung and bronchus Medical College of Wisconsin PubMed 31lung and bronchus University of Cologne PubMed 26oropharynx Broad Institute PubMed 74ovary TCGA TCGA DATA PORTAL 337pancreas Johns Hopkins University ICGC DCC 113pancreas Queensland Centre for Medical Genomics ICGC DCC 67pancreas Ontario Institute for Cancer Research ICGC DCC 33stomach Pfizer Worldwide Research and Development PubMed 22
    • Combining results across projects0.05 1p-value0project1samplesgenesFunctional Impactproject 1HighLowNo mutationOncodriveFMgenes
    • Combining results across projects0.05 1p-value0project1samplesgenesFunctional Impactproject 1HighLowNo mutationOncodriveFMgenes+project2project3project4CancersiteA...combineCancer site A
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • Jordi Deu-PonsPowered byOnexus creates IntOGen web discovery toolWeb discovery toolTabulated Fileswww.onexus.org
    • http://beta.intogen.org
    • http://beta.intogen.org
    • KRASTP53SMAD4CDKN2ASMARCA4Frequency
    • http://beta.intogen.org/analysis
    • Tumor Somatic Mutations in one tumorUsers’s Data User’s private browserSMpipelineTumor Somatic Mutations per sampleUsers’s Data User’s private browserSMpipelineUse case 1: Cohort analysisUse case 2: Single sample analysisView matrix of mutated genes per sampleSee predicted impact of mutationsFind cancer driver genesFind FMbiased pathwaysExplore the results in the context of accummulated knownledge in IntOGenSee predicted impact of mutationsFind recurrent mutations found in IntOGenFind mutations in candidate driver genes found in IntOGen
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.org
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResults.orgPanCancer project
    • The Mechanisms of tumorigenesisDataComputationalmethodsAnalysisResultsPanCancer project
    • Visualization and analysis of genomicdata using Interactive Heatmapshttp://www.gitools.org Perez-Llamas and Lopez-Bigas. PLoS ONE 2011Christian Perez-Llamas
    • Muldimesional heatmapsMichael P. SchroederSort by mutually exclusive alterationsSchroeder MP, Gonzalez-Perez A and Lopez-Bigas N. Visualizing multidimensional cancer genomics data.Genome Medicine. 2013, 5:9
    • Summary• OncodriveFM and OncodriveCLUST are complementary methodsto identify cancer drivers• Oncodrive methods are scalable and robust• IntOGen contains results of analysing more than 3000 tumours toidentify cancer drivers across sites• IntOGenSM pipeline is available to run your own projects• TCGA PanCancer analysis on the way• Gitools - interactive heatmaps - useful to explore multidimesionalcancer genomics data
    • Biomedical Genomics Lab@bbglab@nlbigasGunes GundemChristian Perez-LlamasJordi Deu-PonsMichael SchroederAlba Jené-SanzNuria Lopez-Bigas David Tamborero Abel Gonzalez-PerezAlberto Santoshttp://bg.upf.edu/blog