Sylvia Plevritis PhD Course Director Melissa Ko Teaching Assistant Fuad Nijim CCSB Program Manager March 31 2014 Goals of CBIO243 Introduce major principles of cancer systems biology that integrate experimental and computational biology ID: 1015707
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1. CBIO243: Principles of Cancer Systems BiologySylvia Plevritis, PhDCourse DirectorMelissa KoTeaching AssistantFuad NijimCCSB Program ManagerMarch 31, 2014
2. Goals of CBIO243Introduce major principles of cancer systems biology that integrate experimental and computational biology.Gain familiarity with methods to analyze high-dimensional and highly-multiplexed data in order to synthesize biologically and clinically relevant insights and generate hypotheses for functional testing.
3. Biological Sciences:Cancer Biology,Hematology,Immunology,Genetics,etc.Computational Sciences:Bioinformatics,Engineering,Computer Science,Physics,Statistics,etc.CSB
4. Approach: Integrative AnalysisCancer Research Goal:Drug TargetsDrug ResistanceCombination TherapiesTumor EvolutionCancer DriversMetastasisTumor HeterogeneityCancer Stem CellsEMTPersonalized MedicineBiomarkers Other ______Experimental Sciences:SequencingMethylationGene ExpressionCNVTMAProteomicsSingle Cell AnalysisLCM, Sorted CellsDrug ScreeningOther _____________Computational Sciences:Statistical RegressionMachine LearningBayesian AnalysisBoolean AnalysisODE/PDENetwork ReconstructionPathway AnalysisOther _____________Functional ValidationComponents of Cancer Systems Biology
5. Topics CoveredBasic principles of molecular biology of cancerExperimental high-throughput technologiesDesign of perturbation studies, including drug screening.Overview of publically available datasets, including GEO, TCGA, CCLE, and ENCODEOnline biocomputational tools, including selected accessible tools from the NCI Center for BioinformaticsNetwork reconstruction from genomic dataApplication of systems biology to identifying drug targetsApplication of systems biology to personalized medicine
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7. GradingWeekly paper review/class participation (30%)Project Presentations (20%)Final Project Report (50%): 6-7 page written report and oral presentation demonstrating the understanding of key concepts in cancer systems biology research.
8. Weekly Reading Review Summarize objective/hypothesis, the data, the controls, results and the published interpretations. Discuss whether the authors' conclusions were justified, and suggest improved analyses and/or future research.Describe relevance to cancer systems biology, and any gaps in training to fully understand paper.
9. First Reading AssignmentChuang, H.-Y., Lee, E., Liu, Y.-T., Lee, D., & Ideker, T. (2007). Network-based classification of breast cancer metastasis. Molecular Systems Biology.Akavia, U. D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H. C., Pochanard, P., et al. (2010). An Integrated Approach to Uncover Drivers of Cancer. Cell, 143(6), 1005–1017.
10. Background MaterialOverview of CancerHannahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation, Cell 14(5), 2011. Overview of Molecular BiologyKimball’s Biology Pageshttp://home.comcast.net/~john.kimball1/BiologyPages
11. Background MaterialVisualization of Genomic DataSchroeder MP, et al, Visualizing multidimensional cancer genomics data, Genome Medicine, 5:9, 2013Overview of ProgrammingR/Bioconductorhttp://www.r-project.org/www.cyclismo.org/tutorial/R/Pythonhttp://www.python.org/https://developers.google.com/edu/python/
12. Center for Cancer Systems Biology(ccsb.stanford.edu)Monthly Seminar SeriesGENOMIC BIOMAKERS OF CANCER PREVENTION AND TREATMENTFriday April 11th at 11 am (Alway Building, Room M114) Andrea Bild, Department of Pharmacology and Toxicology, University of UtahAnnual Symposium (Friday October 17, 2014)R25T Training GrantTwo year postdoctoral training fellowship
13. Cancer as a Complex SystemPienta et al, Ecological Therapy for Cancer: Defining Tumors Using an Ecosystem Paradigm Suggests New Opportunities for Nove Cancer Treatments, Translational Oncology, 2008, 1(4):158-164.
14. Multiscale View of CancerGenes and proteinsComplex signaling and regulatory networksMultiple cellular processesMicro-environmentHost systemsEnvironmental factorsPopulation dynamics InitiationProgressionMetastasisRecurrenceTime - Progression
15. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646–674. Hallmarks of Cancer
16. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646–674.
17. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646–674.
18. http://www.cell.com/image/S0092-8674(11)00127-9?imageId=gr2&imageType=hiRes
19. Network typesProtein-proteinProtein-DNAmiRNA-RNATranscriptional (expression) networksSignaling networks Sachs et al. http://www.sciencemag.org/content/308/5721/523.full
20. 20The Multiscale ChallengeMany components and interactions of the “cancer system” are known Linkages between global dynamics and phenotypic properties from local interactions are not well known
21. http://circ.ahajournals.org/content/123/18/1996/F5.expansion.html
22. Goals of Cancer Systems Biology ResearchTo derive a comprehensive understanding of cancer’s complexity by integrating diverse information to:Identify cellular networks and cell-cell interactions that drive cancer initiation and progressionIdentify potential therapeutic targets and mechanisms of action
23. Principles in Cancer Systems Biology ResearchCancer networks are dynamic and response to genetic variants, epigenetics and the microenvironmentTumors may not be a random collection of malignant cells but cells that may be related through processes of developmental biology
24. Cancer Systems Biology The PastExperimentationComputation
25. Cancer Systems Biology The PresentExperimentationComputation
26. Cancer Systems Biology The FutureExperimentationComputation
27. Objective: Identify genes and networks differentially expressed in lymphoma transformation Glas et al. “Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment.” Blood 200524 paired samples (12 FL/12 DLBCL)88 FL/DLBCL arrays 30 DLBCL40 FL-transforming (FL_t)18 FL-non-transforming (FL_nt)FLDLBCL
28. Identify differentially expressed genesAverage Fold Change (AFC)Pro: EasyCon: Does not account for variancep-value, based on t-test statisticPro: Easy, accounts for varianceCon: Does not account for the problem of multiple hypothesis testingLog2(Average Fold Change)-Log10(p-value)
29. Statistical Analysis of Microarrays (SAM)http://www-stat.stanford.edu/~tibs/expectedobservedAddress the problem of Multiple Hypothesis Testing:Suppose measure 10,000 genes and nothing changes.At the %1 significance level, 100 genes could be selected as differentially expressed but all would be false positives. SAM corrects for this by computing the FalseDiscovery Rate, based onpermutation testing.
30. GOminerIdentify enrichment in Gene Ontology (GO) terms based a hierarchy describing biological process; cellular component; molecular functionGenes significantly differentially expressed in compact vs. non-compact tumors are related to cell death, Cell-to-cell signaling and interaction, cellular assembly and organization, DNA replication and Cellular movementhttp://discover.nci.nih.gov/gominer/
31. Gene set enrichment analysis (GSEA)Evaluate enrichment of curated gene sets, such asPathwaysGenes that share a motifGenes at a similar chromosomal locationComputationally predicted gene setsYour own favorite list of genesEvaluating related genes together adds statistical power http://broad.mit.edu/gsea
32. GSEA on Lymphoma DataMyc targets up-regulated, in agreement with Myc up-regulation found by SAMGSEA detects ~200 sets of differentially expressed genes at low FDRMany metabolic pathways up-regulated in DLBCLMyc target genes significantIn general, GSEA produces many “generic” gene setsmany metabolicmany a consequence of aggressive phenotypeno graphical view of pathwaysFLDLBCLLegendUPDOWN
33. Overlap expression levels on canonical pathwaysIPA, Ingenuity Pathway Analysis (www.ingenuity.com)
34. Cellular assembly & organization network
35. Cellular assembly & organization network Expand network using interactions from the literature Visualization using cellular localization
36. IPA links to literature
37. Protein-protein Interaction NetworksProtein-protein interaction networkshttp://string-db.org
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39. String-db.org - exampleDNA repair genesBARD1FANCLPOLD3TOPBP1BLMFEN1POLETREX1BRCA1GMNNPOLE2UNGBRIP1ING2PRIM2AUSP1DCLRE1AMLH3RAD51ADCLRE1BMSH2RAD54BDDX11MSH5RECQL4DNA2LMSH6RFC3EXO1PARP2RFC4FANCGPCNARPA2
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42. Inferring Gene Regulatory NetworksUseful non-technical review:“Computational methods for discovering gene networks from expression data” Lee & Tzou
43. Single gene focus is limitinginducedrepressedgene AFL DLBCLindividuals
44. Gene interaction is more powerfulinducedrepressedDLBCLindividualsgene Agene BFL FL A UPB DOWN
45. Interaction of gene clustersinducedrepressedDLBCLFL FL individualsModule XModule YX UPY DOWN
46. Module1Module2Module3samplesgene1gene2geneNInferring Gene Regulation
47. Inferring Gene RegulationMod1Mod8Mod3Mod6samplesAverage expression of each module
48. Key Idea of Regulatory Module NetworksLook for a set of regulatory factors that, in combination, predict a gene’s expression levelRegulatory factors can include:mRNA level of regulatory proteinsGenotypic factors (SNPs, CNVs)Epigenetic factors (methylation status)TF binding (measured by ChIP-seq)…Factors that robustly predict a target’s expression across different experiments are inferred to be its regulatorsSegal et al., Nature Genetics 2003Transcription factors, signal transduction proteins, mRNA binding proteins, chromatin modification factors, …
49. Computational Derived Regulatory ModuleGroup of co-expressed genes are driven by a computationally derived transcriptional regulatory program, derived from a candidate list of regulators. Gene AGene BOnOffOnOffModule genesRegulatory program Segal E et al, Nature Genetics 2003.
50. Core module network of FL transformationGentles A et al, Blood 2009
51. Integration with survival dataModule A is single most predictive of survival data by Cox regression (bad prognosis in FL)Define a linear predictor of survival:LPS=1.14*ModuleA + 0.72*GFL3027 – 1.35*GFL2738Bad Part: ESC like expressionGood Part: TGFB signalingGentles A et al, Blood 2009
52. Survival based on LPSGentles A et al, Blood 2009
53. DATABASESTCGACCLEENCODE
54. The Cancer Genome Atlas (TCGA)Phase I: Initiated in 2005 by the National Cancer Institute and National Human Genome Research Institute to catalog genetic mutations causing cancer, using genome sequencing; focused on GBM, lung and ovarian cancerPhase II: Expanded to 20-25 different cancer types, complement genome sequencing with genomic characterization, including gene expression profiling, copy number variation, DNA methylation, miRNA
55. TCGA:Cancer measured at multiple scalesmRNA & miRNA expressionCopy numberDNA MethylationMutation (NGS)Pathology imagesMedical ImagesTreatmentSurvival Outcome
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57. TCGA OrganizationTSS:Tissue Source Sites BCR: Biospecimen Core Resources DCC: Data Coordinating Center GCC: Genome Characterization Centers GSC: Genome Sequencing Center CGSub: Cancer Genomics HubGDACS: Genome Data Analysis Centers
58. Major TCGA PublicationsComprehensive molecular characterization of human colon and rectal cancer. Nature. 487 (7407):330-337, 2012. Mutations in ARlD1A, SOX9, FAM123B/WTX;, IGF2; mutations in WNT pathwayComprehensive genomic characterization of squamous cell lung cancers. Nature. 489 (7417):519:525, 2012. Comprehensive molecular portraits of human breast tumors. Nature. 490 (7418):61-70, 2012.- Mutations in ESR1, GATA3, FOXA1, XBP1, and cMYB.Integrated genomic analyses of ovarian carcinoma. Nature. 474 (7353):609-615, 2011.Mutations in TP53 occurred in 96% of the cases studied; mutations in BRCA1 and BRCA2 occurred in 21% of the casesAn integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1. Cancer Cell. 17 (1):98-110, 2010. Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma. Cancer Cell. 17 (5):510-522 , 2010.Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 455 (7216):1061-1068, 2008.Mutations in NF1, ERBB2, TP53, PlK3R1
59. UCSC Cancer Browser – Chromosome Viewhttps://genome-cancer.ucsc.edu
60. UCSC Cancer Browser Gene View
61. Cancer Browser – Survival Analysis
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63. Cancer Cell Line Encyclopedia (CCLE)The Cancer Cell Line Encyclopedia (CCLE) project is a collaboration between the Broad Institute, and Novartis to conduct a genetic and pharmacologic characterization of a large panel of human cancer cell linesLink distinct drug response to genomic patterns and to translate cell line integrative genomics into cancer patient stratification. Public access analysis and visualization of DNA copy number, mRNA expression and mutation data for about 1000 cell lines.
64. http://www.broadinstitute.org/ccle/home
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67. Cellular Information Processing
68. ENCODE http://genome.ucsc.edu/ENCODE/index.html
69. ENCODE
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71. SummaryBasic principles of molecular biology of cancerExperimental high-throughput technologiesDesign of perturbation studies, including drug screening.Overview of publically available datasets, including GEO, TCGA, CCLE, and ENCODEOnline biocomputational tools, including selected accessible tools from the NCI Center for BioinformaticsNetwork reconstruction from genomic dataApplication of systems biology to identifying drug targetsApplication of systems biology to personalized medicine