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CBIO243: Principles of Cancer Systems Biology

CBIO243: Principles of Cancer Systems Biology. 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.

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CBIO243: Principles of Cancer Systems Biology

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  1. CBIO243: Principles of Cancer Systems Biology Sylvia Plevritis, PhD Course Director Melissa Ko Teaching Assistant FuadNijim CCSB Program Manager March 31, 2014

  2. Goals of CBIO243 • Introduce 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. Computational • Sciences: • Bioinformatics, • Engineering, • Computer Science, • Physics, • Statistics, • etc. • Biological • Sciences: • Cancer Biology, • Hematology, • Immunology, • Genetics, • etc. CSB

  4. Approach: Integrative Analysis Components of Cancer Systems Biology • Computational Sciences: • Statistical Regression • Machine Learning • Bayesian Analysis • Boolean Analysis • ODE/PDE • Network Reconstruction • Pathway Analysis • Other _____ • ________ • Experimental • Sciences: • Sequencing • Methylation • Gene Expression • CNV • TMA • Proteomics • Single Cell Analysis • LCM, Sorted Cells • Drug Screening • Other ______ • _______ • Cancer Research Goal: • Drug Targets • Drug Resistance • Combination Therapies • Tumor Evolution • Cancer Drivers • Metastasis • Tumor Heterogeneity • Cancer Stem Cells • EMT • Personalized Medicine • Biomarkers • Other ______ Functional Validation

  5. Topics Covered • Basic principles of molecular biology of cancer • Experimental high-throughput technologies • Design of perturbation studies, including drug screening. • Overview of publically available datasets, including GEO, TCGA, CCLE, and ENCODE • Online biocomputationaltools, including selected accessible tools from the NCI Center for Bioinformatics • Network reconstruction from genomic data • Application of systems biology to identifying drug targets • Application of systems biology to personalized medicine

  6. Grading • Weekly 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.

  7. 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.

  8. First Reading Assignment • Chuang, 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.

  9. Background Material • Overview of Cancer • Hannahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation, Cell 14(5), 2011. • Overview of Molecular Biology • Kimball’s Biology Pages • http://home.comcast.net/~john.kimball1/BiologyPages

  10. Background Material • Visualization of Genomic Data • Schroeder MP, et al, Visualizing multidimensional cancer genomics data, Genome Medicine, 5:9, 2013 • Overview of Programming • R/Bioconductor • http://www.r-project.org/ • www.cyclismo.org/tutorial/R/ • Python • http://www.python.org/ • https://developers.google.com/edu/python/

  11. Center for Cancer Systems Biology(ccsb.stanford.edu) • Monthly Seminar Series • GENOMIC BIOMAKERS OF CANCER PREVENTION AND TREATMENT • Friday April 11th at 11 am (Alway Building, Room M114)
Andrea Bild, Department of Pharmacology and Toxicology, University of Utah • Annual Symposium (Friday October 17, 2014) • R25T Training Grant • Two year postdoctoral training fellowship

  12. Cancer as a Complex System Pienta 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.

  13. Multiscale View of Cancer • Genes and proteins • Complex signaling and regulatory networks • Multiple cellular processes • Micro-environment • Host systems • Environmental factors • Population dynamics Initiation Progression Metastasis Recurrence Time - Progression

  14. Hallmarks of Cancer Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646–674.

  15. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646–674.

  16. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646–674.

  17. http://www.cell.com/image/S0092-8674(11)00127-9?imageId=gr2&imageType=hiReshttp://www.cell.com/image/S0092-8674(11)00127-9?imageId=gr2&imageType=hiRes

  18. Network types • Protein-protein • Protein-DNA • miRNA-RNA • Transcriptional (expression) networks • Signaling networks • Sachs et al. http://www.sciencemag.org/content/308/5721/523.full

  19. The MultiscaleChallenge • Many components and interactions of the “cancer system” are known • Linkages between global dynamics and phenotypic properties from local interactions are not well known

  20. http://circ.ahajournals.org/content/123/18/1996/F5.expansion.htmlhttp://circ.ahajournals.org/content/123/18/1996/F5.expansion.html

  21. Goals of Cancer Systems Biology Research • To 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 progression • Identify potential therapeutic targets and mechanisms of action

  22. Principles in Cancer Systems Biology Research • Cancer networks are dynamic and response to genetic variants, epigenetics and the microenvironment • Tumors may not be a random collection of malignant cells but cells that may be related through processes of developmental biology

  23. Cancer Systems Biology The Past Experimentation Computation

  24. Cancer Systems Biology The Present Computation Experimentation

  25. Cancer Systems Biology The Future Experimentation Computation

  26. Objective: Identify genes and networks differentially expressed in lymphoma transformation FL DLBCL • Glas et al. “Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment.” Blood 2005 • 24 paired samples (12 FL/12 DLBCL) • 88 FL/DLBCL arrays • 30 DLBCL • 40 FL-transforming (FL_t) • 18 FL-non-transforming (FL_nt)

  27. Identify differentially expressed genes • Average Fold Change (AFC) • Pro: Easy • Con: Does not account for variance • p-value, based on t-test statistic • Pro: Easy, accounts for variance • Con: Does not account for the problem of multiple hypothesis testing -Log10(p-value) Log2(Average Fold Change)

  28. Statistical Analysis of Microarrays (SAM) Address 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 False Discovery Rate, based on permutation testing. observed expected http://www-stat.stanford.edu/~tibs/

  29. GOminer • Identify enrichment in Gene Ontology (GO) terms based a hierarchy describing biological process; cellular component; molecular function Genes 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 movement http://discover.nci.nih.gov/gominer/

  30. Gene set enrichment analysis (GSEA) • Evaluate enrichment of curated gene sets, such as • Pathways • Genes that share a motif • Genes at a similar chromosomal location • Computationally predicted gene sets • Your own favorite list of genes • Evaluating related genes together adds statistical power • http://broad.mit.edu/gsea

  31. Legend UP DOWN GSEA on Lymphoma Data • Myc targets up-regulated, in agreement with Myc up-regulation found by SAM • GSEA detects ~200 sets of differentially expressed genes at low FDR • Many metabolic pathways up-regulated in DLBCL • Myc target genes significant • In general, GSEA produces many “generic” gene sets • many metabolic • many a consequence of aggressive phenotype • no graphical view of pathways DLBCL FL

  32. Overlap expression levels on canonical pathways IPA, Ingenuity Pathway Analysis (www.ingenuity.com)

  33. Cellular assembly & organization network

  34. Cellular assembly & organization network • Expand network using interactions from the literature • Visualization using cellular localization

  35. IPA links to literature

  36. Protein-protein Interaction Networks Protein-protein interaction networks http://string-db.org

  37. String-db.org - example • DNA repair genes

  38. Inferring Gene Regulatory Networks Useful non-technical review: “Computational methods for discovering gene networks from expression data” Lee & Tzou

  39. individuals gene A induced repressed Single gene focus is limiting FL DLBCL

  40. individuals gene A gene B induced repressed Gene interaction is more powerful AUP BDOWN FL DLBCL FL

  41. X UP Y DOWN Module X individuals Module Y induced repressed Interaction of gene clusters DLBCL FL FL

  42. Module1 Module2 Module3 gene1 samples gene2 geneN Inferring Gene Regulation

  43. Mod1 Mod3 samples Mod6 Mod8 Average expression of each module Inferring Gene Regulation

  44. Key Idea of Regulatory Module Networks Look for a set of regulatory factors that, in combination, predict a gene’s expression level Regulatory factors can include: mRNA level of regulatory proteins Genotypic 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 regulators Transcription factors, signal transduction proteins, mRNA binding proteins, chromatin modification factors, … Segal et al., Nature Genetics 2003

  45. Computational Derived Regulatory Module Group of co-expressed genes are driven by a computationally derived transcriptional regulatory program, derived from a candidate list of regulators. Gene A Off On Regulatory program Gene B Off On Module genes Segal E et al, Nature Genetics 2003.

  46. Core module network of FL transformation Gentles A et al, Blood 2009

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