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Systems Networks and Cancer

Systems Networks and Cancer. Sharmila Bapat, Ph.D National Centre for Cell Science, Pune, India sabapat@nccs.res.in. Bifx Africa-India Joint Virtual Conference 2011. Basic Life Processes. Environment. Individuals. Epigenetic Regulation. Genetic Composition. Homeostasis. Tissues.

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Systems Networks and Cancer

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  1. Systems Networks and Cancer Sharmila Bapat, Ph.D National Centre for Cell Science, Pune, India sabapat@nccs.res.in Bifx Africa-India Joint Virtual Conference 2011

  2. Basic Life Processes Environment Individuals Epigenetic Regulation Genetic Composition Homeostasis Tissues Cells Molecular Pathways & Networks Bifx Africa-India Joint Virtual Conference 2011

  3. Digitalization of the human genome Chromatin Chromosomes Genes (DNA) RNA Proteins Networks Cells Tissues Individuals Environment Bifx Africa-India Joint Virtual Conference 2011

  4. Imbalance in such profiles that constitute networks leads to aberrant states such as cancer in which cell, tissue and functioning are compromised. Bifx Africa-India Joint Virtual Conference 2011

  5. It thus becomes pertinent to question cancer as an aberrant state defined by disrupted networks in the transformed cell / tissue i.e. Resolve the transformed state as a function of systems networks rather than address it on a reductionist level as being driven by a single gene Bifx Africa-India Joint Virtual Conference 2011

  6. Applying the principles of systems biology Systems Biology Hypothesis-driven Global data acquisition Integrate data from different platforms/diff types of data Delineate biological network dynamics Formulate models that can be validated Bifx Africa-India Joint Virtual Conference 2011

  7. An example in point studied recently in our lab is that of the identification of a gene signature associated with serous ovarian adenocarcinoma Bifx Africa-India Joint Virtual Conference 2011

  8. Serous ovarian adenocarcinoma is one of the most aggressive • Gynecological cancers and is associated with a • very poor prognosis for most patients • It is often termed as “Silent Killer” because of lack of early symptoms of • disease and rapid progression to a metastatic stage Bifx Africa-India Joint Virtual Conference 2011

  9. Integrative Approaches for the identification and assessment of Cancer Biomarkers Poor correlation in biomarkers identified between previous studies - Limited sample size - Limited normal tissue – primary Vs.metastases - Discrepancy across microarray platforms - Limited validation Bifx Africa-India Joint Virtual Conference 2011

  10. Identification of a “SeOvCa gene signature” based on transcriptional profiling Differential Gene Expression Databases for serous ovarian carcinomas 1.A4 in vitro model transformation system 2.IST - In Silico Transcriptomics 3.TCGA -The Cancer Genome Atlas Bifx Africa-India Joint Virtual Conference 2011

  11. SMARCA2 SGK1 RNASE4 A4 IST PTGIS Affymetrix PROS1 Agilent PAPSS2 LRRC17 LHFP KLF2 HNMT GNB5 OVERLAPS FBN1 EFEMP1 DIXDC1 DAB2 TNNT1 TM7SF2 SYNCRIP SOX17 SLC39A4 RRM2 MMP9 MEST MCM2 MAL LAMA5 EXO1 CDCA4 BCAT1 ATAD2 -4 -2 0 2 4 Fold Change Analytical Pipeline Upregulated genes (15) Downregulated genes (15)

  12. ? ? ? ? Ovarian Cancer ? Cancer ? Unknown function in cancer ? ? “SeOvCa gene signature” Downregulated genes TNNT1 SMARCA2 TM7SF2 SGK1 SYNCRIP RNASE4 SOX17 PTGIS SLC39A4 PROS1 RRM2 PAPSS2 MMP9 LRRC17 MEST LHFP MCM2 KLF2 MAL HNMT LAMA5 GNB5 FBN1 EXO1 CDCA4 EFEMP1 BCAT1 DIXDC1 DAB2 ATAD2 Bifx Africa-India Joint Virtual Conference 2011

  13. In silico validation with the database – Prognoscan -1 +2 Bifx Africa-India Joint Virtual Conference 2011

  14. Systems Networks Analysis Applying ARACNe Algorithm for the Reconstruction of Accurate Cellular Networks • 30 SeOvCa signature genes were considered as nodes around which • interactions were predicted as individual or interacting hubs. • The MI (Mututal Information) was fixed at 0.20 • DPI (Data Processing Inequality) was set at 0.10. Bifx Africa-India Joint Virtual Conference 2011

  15. Lost Nodes • MAL • GNB5 • TM7SF2 Positive Correlation Negative Correlation Upregulated Node Stable expression Downregulated Node Interacting Gene Node - Interacting Genes Network (27-781) Social Nodes Stand-alone Nodes Bifx Africa-India Joint Virtual Conference 2011

  16. Node - Linker - Node Interactions (23 nodes – 95 linkers) Bifx Africa-India Joint Virtual Conference 2011

  17. Drug targets Components of the Interface Network (20 nodes – 9 linkers) Bifx Africa-India Joint Virtual Conference 2011

  18. 8q22.1-24.3 20q13.33 8q24.3 ATAD2 19q13.1-13.423 6q12-16.1 LAMA5 19p13.2-13.3 16q22.1-24.2 SYNCRIP SLC39A4 11p13-15.5 11q13-21 22q13.1-13.33 6p21.3 8q22.3-24.3 3q21-22.1 RRM2 14q32.33 6q21 2p22-25.3 CDCA4 MCM2 1q31-44 SOX17 14q22-22.3 EXO1 TNNT1 14q11.1 DAB2 RNASE4 EFEMP1 3q11.2 PROS1 DIXDC1 2q31-37 9q21-22.31 21q21.1-22.3 FBN1 LHFP 10q22.3-26.3 KLF2 PTGIS Chromosomal location analyses of the network Node-gene same chromosome location

  19. Secondary Network Genes Primary Network Genes 120 100 80 % Genes In Region 60 40 20 0 6q12-14 6q21 8q22.1 -24.13 8q24.3 20q13.3 Predictions of copy no. amplifications 6q12 - 6q16.1 SYNCRIP LAMA5 ATAD2 SLC39A4 6q21 8q24.3 20q11.2-20q13.3 Bifx Africa-India Joint Virtual Conference 2011

  20. Validation - FISH 6q21 /8q24.3 20q Bifx Africa-India Joint Virtual Conference 2011

  21. 73 -256 -4480 -212 -499 -455 -18 -4530 -4419 -3717 -4369 -3567 -3517 -3667 UP -2183 -754 -701 -618 -798 -2124 -946 -662 -902 -751 Epigenetic Regulation of some SeOvCa genes TSS K27 K4-K9 K4 Hypomethylation K4-K27 K9-K27 No change in methylation K4-K9-K27 A2 A1 MAL MAL 413 -4592 455 499 -4673 -3473 -3523 -4542 -4723 MEST 369 PAPSS2 A2 A1 MEST PTGIS -122 -3003 -159 -77 -203 -2951 EFEMP1 FBN1 PAPSS2 A1 L E L E L E L L E E -63 -349 -866 -405 -251 -171 90 -930 134 -107 H3K4 Ab CTRL INPUT H3K9 H3K27 A1 PTGIS DOWN EFEMP1 A1 -1077 -1200 -1122 -233 -189 -1478 -1244 -1522 FBN1 A1 Bifx Africa-India Joint Virtual Conference 2011

  22. Protein Interaction Networks (PINA) Bifx Africa-India Joint Virtual Conference 2011

  23. CDC7 MCM3 CDC2 Up MCM4 MCM7 MCM2 RRM2 PRMT8 SYNCRIP TNNT1 TNNI3 PINA ARACNe DCN FBN1 DAB2 TGFBR2 Down Overlap of ARACNe and PINA Networks Bifx Africa-India Joint Virtual Conference 2011

  24. Gene and Protein Networks c-MYC p53 Rb1 Bifx Africa-India Joint Virtual Conference 2011

  25. 20q copy no. changes Rb Inactivation p53 - DNA Damage Repair LAMA5 HDAC SMARCA2 pRb p53 p53 Targets DNA mismatch repair CDCA4 DNA Damage MDM2 ATR KLF2 EXO1 E2F1 E2 ATM CXCR4 CBP ERα Cell Cycle Progression (G1/S) Invasion ATAD2 MYCN NF-kB RRM2 MMP9 MCM2 SGK1 Angio- genesis RANKL c-Myc Targets ATAD2 MYC VEGF BCAT1 c-MYC mRNA stabilization 8q copy no. changes Cell Cycle Progression EFEMP1 Cancer Progression Myc Activation SYNCRIP 6q copy no. changes LRRC17 Identification of functional modules in serous ovarian adenocarcinoma Bifx Africa-India Joint Virtual Conference 2011

  26. References for this presentation cited in the following publication – Sharmila A. Bapat, Anagha Krishnan, Avinash D. Ghanate, Anjali P. Kusumbe and Rajkumar S. Kalra - Gene Expression: Protein Interaction Systems Network Modeling Identifies Transformation-Associated Molecules and Pathways in Ovarian Cancer Cancer Research 70(12); 4809–19 2010

  27. Acknowledgements Research group@NCCS Mr. Avinash Mali Ms. Anjali Kusumbe Mr. Rajkumar Kalra Mr. Brijesh Kumar Mr. Anand Kamal Singh Ms. Rutika Naik Ms. Rohini Kshirsagar Ms. Tejaswini Deshpande Mr. Swapnil Kamble Mr. Nilesh Garde Funding Department of Biotechnology, Ministry of Science & Technology, Govt. of India Alumni Dr. Nicholas Berry Dr. Nawneet Kurrey Dr. Neeti Sharma Ms. Swati Jalgaonkar Ms. Shweta Jadhav Ms. Nimisha Singh Mr. Avinash Ghanate Ms. Anagha Krishnan Mr. Prasad Chaskar Mr. Mihir Metkar Ms. Divya Ramalingam Mr. Alok Joglekar Collaborators Dr. C.B.Koppiker, Jehangir Hospital, Pune Dr. Matthias Nees, VTT, Turku, Finland Dr. Renu Wadhwa, AIST Japan Dr. Sunil Kaul, AIST Japan

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