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ONCOMINE: A Bioinformatics Infrastructure for Cancer Genomics

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Presentation on theme: "ONCOMINE: A Bioinformatics Infrastructure for Cancer Genomics"— Presentation transcript:

1 ONCOMINE: A Bioinformatics Infrastructure for Cancer Genomics
Dan Rhodes Chinnaiyan Laboratory Bioinformatics Program Cancer Biology Training Program Medical Scientist Training Program University of Michigan Medical School

2 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

3 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

4 The Cancer Transcriptome

5 The Cancer Transcriptome

6 The Cancer Transcriptome

7 The Cancer Transcriptome

8 The Cancer Transcriptome

9 The Cancer Transcriptome

10 The Cancer Transcriptome

11 The Cancer Transcriptome

12 The Cancer Transcriptome

13 The Cancer Transcriptome

14 The Cancer Transcriptome
180+ studies profiling human cancer Each profiling 5 – 100+ samples We estimate > 10,000 microarrays 10k chips measuring 20k genes = 200+ million data points

15 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

16 Oncomine oncology + data-mining = oncomine
105 independent datasets (90 analyzed) 7,292 cancer microarrays 79 million gene expression measurements 382 distinct cancer signatures > 5 million tests of differential expression > 5 million tests of gene set enrichment > 5 billion pairwise correlations

17 Oncomine Database – relational, Oracle 9.2
Statistical computing – R, Perl, Java Front End – Java Server Pages Server – Apache/Tomcat Graphics – Scalable Vector Graphics (SVG)

18 Data Collection Monthly Pubmed searches (cancer + microarray + transcriptome + tumor + gene expression profiling) Gene Expression Repositories Gene Expression Omnibus (GEO) ( ArrayExpress ( Stanford Microarray Database ( Whitehead Cancer Genomics (

19 Data Normalization Global normalization – same scaling factors applied to all microarray features – mean and variance normalization Affymetrix - Quantile normalization Spotted cDNA - Loess normalization normalize an M vs. A plot

20 Data Storage Generic data structures to accommodate a variety of data
Samples Microarray Features / Genes Normalized Data Statistical Tests Gene Sets

21 Samples

22 Samples

23 Microarray Features / Genes

24 Normalized Data

25 Gene Sets

26 Statistical Tests

27 Statistical Tests

28 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & schema Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

29 Differential Expression Analysis
Two-sided t-test for each gene: False discovery rate correction for multiple hypothesis testing

30 R, Oracle, RODBC

31 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

32 Oncomine Tutorial part I
Gene Differential Expression Gene Co-Expression Study Differential Expression SHORTCOURSE PASSWORD: MCBI

33 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

34 Therapeutic Targets / Biomarkers
Gene Ontology Consortium Biological Process (apoptosis, cell cycle) Cellular Component (cytoplasmic membrane, extracellular) Molecular Function (kinase, phosphatase, protease, etc.) Known Therapeutic Targets NCI Clinical Trials Database Therapeutic Target Database

35 Therapeutic Target Database
338 proteins with Literature-documented Inhibitor, antagonist, Blocker, etc.

36 Known Drug Targets Expressed in Bladder Cancer

37 Secreted proteins highly expressed in Ovarian Cancer

38

39 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

40 Metabolic & Signaling Pathways
KEGG Kyoto Encyclopedia of Genes & Genomes 87 metabolic pathways, 1700 gene assignments Biocarta Signaling pathways reviewed and entered by ‘expert’ biologists 215 signaling pathways, 3700 gene assignments

41 Pathway enrichment analysis
Identify pathways and functional groups of genes deregulated in particular cancer types Enrichment Analysis using Kolmogrov-Smirnov Scanning (Lamb et al)

42 Kolmogrov-Smirnov Scanning (Lamb et al)
1 2 * 3 4 * 5 6 * 7 * 8 9 10 11 12 13 14 15 16 17 18 * 19 20 (1,2,3,4…,19,20) Vs. (2,4,6,7,18)

43 Pathway Enrichment Liver vs. other Normal tissues

44 Pathway Enrichment cont

45 Pathway enrichment analysis
A search for the Biocarta pathways most enriched in a medulloblastoma signature (C2) uncovered involvement of the Ras/Rho pathway

46 Pathway enrichment analysis cont.
A direct link to the Biocarta pathway provides the details (Medulloblastoma genes with red boxes)

47 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

48 Known Protein-Protein Interactions
HPRD Human Protein Reference Database Manually curated 20,000+ papers, 15,000+ distinct interactions PKDB Protein Kinase Database Natural Language Processing 60,000+ abstracts suggest interaciton, 16,000 distinct interactions Error prone Co-RIF Locus Link Reference into Function 12,000+ co-RIFs

49 Human Interactome Map (www.himap.org)

50 INTERACT

51 Outline Background ONCOMINE ONCOMINE Data Integration
DNA Microarrays and the Cancer Transcriptome ONCOMINE Data collection, normalization & storage Statistical Analysis Visualization of Data and Analysis ONCOMINE Data Integration Therapeutic Targets / Biomarkers Metabolic and Signaling Pathways Known protein-protein Interactions ONCOMINE tutorial

52 Oncomine Tutorial Part II
Gene set filtering to identify therapeutic targets and biomarkers Enrichment Analysis to identify pathways and processes deregulated in cancer Pathway and protein interaction networks deregulated in cancer

53 Acknowledgements Chinnaiyan Lab Pandey Lab IOB
Radhika, Terry, Vasu, Jianjun, Scott, Soory Pandey Lab IOB Shanker, Nandan

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