oncomine: a bioinformatics infrastructure for cancer genomics dan rhodes chinnaiyan laboratory...

ONCOMINE: A ONCOMINE: A Bioinformatics Bioinformatics Infrastructure for Cancer Infrastructure for Cancer GenomicsGenomics

Dan RhodesDan RhodesChinnaiyan LaboratoryChinnaiyan LaboratoryBioinformatics ProgramBioinformatics ProgramCancer Biology Training ProgramCancer Biology Training ProgramMedical Scientist Training ProgramMedical Scientist Training ProgramUniversity of Michigan Medical SchoolUniversity of Michigan Medical School

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BackgroundBackground– DNA Microarrays and the Cancer TranscriptomeDNA Microarrays and the Cancer Transcriptome

ONCOMINEONCOMINE– Data collection, normalization & storageData collection, normalization & storage– Statistical AnalysisStatistical Analysis– Visualization of Data and AnalysisVisualization of Data and Analysis

ONCOMINE Data IntegrationONCOMINE Data Integration– Therapeutic Targets / BiomarkersTherapeutic Targets / Biomarkers– Metabolic and Signaling PathwaysMetabolic and Signaling Pathways– Known protein-protein InteractionsKnown protein-protein Interactions

ONCOMINE tutorialONCOMINE tutorial

The Cancer The Cancer TranscriptomeTranscriptome

The Cancer The Cancer TranscriptomeTranscriptome 180+ studies profiling human 180+ studies profiling human

cancercancer Each profiling 5 – 100+ samplesEach profiling 5 – 100+ samples We estimate > 10,000 We estimate > 10,000

microarraysmicroarrays 10k chips measuring 20k genes10k chips measuring 20k genes = 200+ million data points= 200+ million data points

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OncomineOncomineoncology + data-mining = oncomineoncology + data-mining = oncomine

105 independent datasets (90 analyzed)105 independent datasets (90 analyzed) 7,292 cancer microarrays7,292 cancer microarrays 79 million gene expression measurements79 million gene expression measurements 382 distinct cancer signatures382 distinct cancer signatures > 5 million tests of differential expression> 5 million tests of differential expression > 5 million tests of gene set enrichment> 5 million tests of gene set enrichment > 5 billion pairwise correlations> 5 billion pairwise correlations

Database – relational, Oracle 9.2Database – relational, Oracle 9.2 Statistical computing – R, Perl, Statistical computing – R, Perl,

JavaJava Front End – Java Server PagesFront End – Java Server Pages Server – Apache/TomcatServer – Apache/Tomcat Graphics – Scalable Vector Graphics – Scalable Vector

Graphics (SVG)Graphics (SVG)

OncomineOncomine

Data CollectionData Collection

Monthly Pubmed searches (cancer + Monthly Pubmed searches (cancer + microarray + transcriptome + tumor + gene microarray + transcriptome + tumor + gene expression profiling)expression profiling)

Gene Expression RepositoriesGene Expression Repositories– Gene Expression Omnibus (GEO) (Gene Expression Omnibus (GEO) (

http://www.ncbi.nlm.nih.gov/geo/http://www.ncbi.nlm.nih.gov/geo/))– ArrayExpress (ArrayExpress (http://www.ebi.ac.uk/arrayexpress/http://www.ebi.ac.uk/arrayexpress/))– Stanford Microarray Database (Stanford Microarray Database (

http://genome-www5.stanford.edu/http://genome-www5.stanford.edu/))– Whitehead Cancer Genomics (Whitehead Cancer Genomics (http://http://

www.broad.mit.eduwww.broad.mit.edu/cancer//cancer/))

Data NormalizationData Normalization

Global normalization – same scaling Global normalization – same scaling factors applied to all microarray factors applied to all microarray features – mean and variance features – mean and variance normalizationnormalization

Affymetrix - Quantile normalizationAffymetrix - Quantile normalization Spotted cDNA - Loess normalizationSpotted cDNA - Loess normalization

– normalize an M vs. A plotnormalize an M vs. A plot

Data StorageData Storage

Generic data structures to Generic data structures to accommodate a variety of dataaccommodate a variety of data

SamplesSamples Microarray Features / GenesMicroarray Features / Genes Normalized DataNormalized Data Statistical TestsStatistical Tests Gene SetsGene Sets

SamplesSamples

Microarray Features / Microarray Features / GenesGenes

Normalized DataNormalized Data

Gene SetsGene Sets

Statistical TestsStatistical Tests

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ONCOMINEONCOMINE– Data collection, normalization & schemaData collection, normalization & schema– Statistical AnalysisStatistical Analysis– Visualization of Data and AnalysisVisualization of Data and Analysis



Differential Expression Differential Expression AnalysisAnalysis

Two-sided t-test for each gene:Two-sided t-test for each gene:

False discovery rate correction for multiple False discovery rate correction for multiple hypothesis testinghypothesis testing

R, Oracle, RODBCR, Oracle, RODBC

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Oncomine Tutorial part Oncomine Tutorial part II

WWW.ONCOMINE.ORGEMAIL: SHORTCOURSEPASSWORD: MCBI

• Gene Differential Expression

• Gene Co-Expression

• Study Differential Expression

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Therapeutic Targets / Therapeutic Targets / BiomarkersBiomarkers Gene Ontology ConsortiumGene Ontology Consortium

– Biological Process (apoptosis, cell cycle)Biological Process (apoptosis, cell cycle)– Cellular Component (cytoplasmic Cellular Component (cytoplasmic

membrane, membrane, extracellularextracellular))– Molecular Function (Molecular Function (kinasekinase, phosphatase, , phosphatase,

protease, etc.)protease, etc.)

Known Therapeutic TargetsKnown Therapeutic Targets– NCI Clinical Trials DatabaseNCI Clinical Trials Database– Therapeutic Target DatabaseTherapeutic Target Database

Therapeutic Target Therapeutic Target DatabaseDatabase

http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp

338 proteins withLiterature-documentedInhibitor, antagonist, Blocker, etc.

Known Drug Targets Known Drug Targets Expressed in Bladder Expressed in Bladder CancerCancer

Secreted proteins highly Secreted proteins highly expressed in Ovarian expressed in Ovarian CancerCancer

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Metabolic & Signaling Metabolic & Signaling PathwaysPathways KEGGKEGG

– Kyoto Encyclopedia of Genes & GenomesKyoto Encyclopedia of Genes & Genomes– 87 metabolic pathways, 1700 gene assignments87 metabolic pathways, 1700 gene assignments

BiocartaBiocarta– Signaling pathways reviewed and entered by Signaling pathways reviewed and entered by

‘expert’ biologists‘expert’ biologists– 215 signaling pathways, 3700 gene assignments215 signaling pathways, 3700 gene assignments

Pathway enrichment Pathway enrichment analysisanalysis Identify pathways and functional Identify pathways and functional

groups of genes deregulated in groups of genes deregulated in particular cancer typesparticular cancer types

Enrichment Analysis using Enrichment Analysis using Kolmogrov-Smirnov Scanning Kolmogrov-Smirnov Scanning (Lamb et al)(Lamb et al)

Kolmogrov-Smirnov Kolmogrov-Smirnov Scanning Scanning (Lamb et al)(Lamb et al)

1122 **3344 **5566 **77 **8899101011111212131314141515161617171818 **19192020

(1,2,3,4…,19,20)(1,2,3,4…,19,20)

Vs.Vs.

(2,4,6,7,18)(2,4,6,7,18)

Pathway Enrichment Pathway Enrichment

Liver vs. otherNormal tissues

Pathway Enrichment Pathway Enrichment contcont

Pathway enrichment Pathway enrichment analysisanalysis

A search for the Biocartapathways most enriched ina medulloblastoma signature (C2)uncovered involvement ofthe Ras/Rho pathway

Pathway enrichment Pathway enrichment analysis cont.analysis cont.

A direct link to the Biocarta pathway provides the details(Medulloblastoma genes with red boxes)

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Known Protein-Protein Known Protein-Protein InteractionsInteractions HPRD HPRD

– Human Protein Reference DatabaseHuman Protein Reference Database– Manually curatedManually curated– 20,000+ papers, 15,000+ distinct interactions20,000+ papers, 15,000+ distinct interactions

PKDBPKDB– Protein Kinase DatabaseProtein Kinase Database– Natural Language Processing Natural Language Processing – 60,000+ abstracts suggest interaciton, 16,000 distinct 60,000+ abstracts suggest interaciton, 16,000 distinct

interactionsinteractions– Error proneError prone

Co-RIFCo-RIF– Locus Link Reference into FunctionLocus Link Reference into Function– 12,000+ co-RIFs12,000+ co-RIFs

Human Interactome Human Interactome Map (www.himap.org)Map (www.himap.org)

INTERACTINTERACT

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Oncomine Tutorial Part Oncomine Tutorial Part IIII Gene set filtering to identify therapeutic Gene set filtering to identify therapeutic

targets and biomarkerstargets and biomarkers

Enrichment Analysis to identify Enrichment Analysis to identify pathways and processes deregulated in pathways and processes deregulated in cancercancer

Pathway and protein interaction Pathway and protein interaction networks deregulated in cancernetworks deregulated in cancer

AcknowledgementsAcknowledgements

Chinnaiyan LabChinnaiyan Lab– Radhika, Terry, Vasu, Jianjun, Scott, Radhika, Terry, Vasu, Jianjun, Scott,

SoorySoory

Pandey LabPandey Lab

IOBIOB– Shanker, NandanShanker, Nandan

oncomine: a bioinformatics infrastructure for cancer genomics dan rhodes chinnaiyan laboratory...

Documents

cancer transcriptome

cancer microarrays

data points

human cancer

gene expression measurements

distinct cancer signatures

tests of gene set enrichment

arrayexpress http