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Integative Genomic Approaches to Personalized Cancer Therapy

Explore the intricate network structures and pathway signatures impacting cancer outcomes using genomic data. Discover how tightly controlled genes play a crucial role in tumor behavior and clinical prognosis. This conference delves into the significance of gene regulation in cancer and its implications for therapeutic strategies.

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Integative Genomic Approaches to Personalized Cancer Therapy

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  1. Integative Genomic Approaches to Personalized Cancer Therapy Patrick Tan, MD PhD International Conference on Bioinformatics Singapore, Sept 09 2009

  2. Disease Genes Clinical Biomarkers Cancer Pathways Genomic Oncology in Singapore : Translating Information into Knowledge

  3. Basic Science to Translation 1) Metastasis Genes - Network Structures 2) Cancer Classification - Pathway Signatures 3) Lung Cancer Outcome - Integrative Genomics

  4. Hub Gene Edge Gene Can we infer ‘hub-like’ genes in cancer? Yu Kun Biological Networks – Robust Yet Fragile Ultrasensitive Low Variation Tolerant Wide Variation

  5. Lung Thyroid Liver Esophagus Breast 270 Tumors Large Variation Identifying Precisely Controlled Genes in Cancer RestrictedVariation

  6. Cancer Non-malignant 48 Precisely Controlled Genes in Cancers Restricted Variation Only in Cancers

  7. Significance 0 1 2 3 4 5 6 7 8 9 10 11 12 13 The PGC is Precisely Controlled in Many Solid Tumors Tumor Gastric, NPC (99) Breast (286) Lung (118) Ovarian (146) Breast (189) Glioma (77) Colon (100)

  8. Significance 0 1 2 3 4 5 6 7 8 9 10 11 12 13 The PGC is NOT Precisely Controlled in Normal Tissues Normal Novartis (158) Ge et al (36)

  9. PGC Genes are Enriched in the Integrin Signaling Pathway Growth Factor Regulation RAS/MAPK Signaling PI3K Signaling JNK/SAPK Signaling Cytoskeletal Interactions Cell Motility

  10. Implications of Precise PGC Regulation Dedicated Cellular Mechanisms to Ensure Accurate Expression A Functional Requirement for Tight PGC Control in Tumors? Are Tumors Ultrasensitive to PGC Activity?

  11. P=0.01 Invasive Non-invasive PGC PGC Expression in Breast Cancer Cell Lines 30 Breast Cancer Cell Lines

  12. HCT116 Tumor Cells Splenic Injection Liver Metastases Adapted from Clark et al (2000) PGC Expression in Experimental Metastasis

  13. P=0.022 Reduced PGC Expression Correlates with Metastatic Potential

  14. p53CSV siRNA qRT-PCR siRNA Knockdown of PGC Genes Enhances Metastasis

  15. PGC Expression in Primary Tumors

  16. Reduced PGC Expression Predicts Clinical Prognosis Elevated PGC Decreased PGC

  17. mRNA variance overlaid on a protein-protein network Black nodes = missing data. A: proteasome regulatory lid B: mediator complex C: SAGA complex D: SWR1 complex Goel and Wilkins, unpublished. Slide Courtesy of Marc Wilkins Are Low-Variance Genes True Hubs? (Lessons from Yeast)

  18. Take Home Messages • A General Strategy for Identifying Tightly Regulated • Genes - A Precisely Regulated Expression Cassette in Cancer • Fine-scale alterations potently modulate tumor behaviour • and clinical outcome • Not discernible by conventional microarray analysis • methods Yu et al (2008) PLOS Genetics

  19. Basic Science to Translation 1) Metastasis Genes - Network Structures 2) Cancer Classification - Pathway Sigantures 3) Lung Cancer Outcome - Integrative Genomics

  20. High Prevalence of Gastric Cancer in Asia Global Cancer Mortality Lung (1.3 million deaths/year) Stomach (1 million deaths/year) Liver (662,000 deaths/year) Colon (655,000 deaths/year) Breast (502,000 deaths/year) - WHO, 2005 From The Scientist, Sep 22, 2003

  21. Gastric Cancer “Many Diseases” Imatinib 5-FU 100% Response 20% Response Tumor Heterogeneity Impacts Response CML “One Disease”

  22. Subtype E Subtype F Subtype A Subtype B Subtype C Subtype D Rx 1 Rx 2 Rx 3 Rx 4 Rx 5 Rx 6 Pre-Selecting Patients for Optimal Therapy Gastric Cancer

  23. Genes A B Expression Signatures as Cancer Phenotypes Tumor Type B (“State B”) Tumor Type A (“State A”)

  24. Expression Signatures Capture Heterogeneity Tay et al., Cancer Research (2003)

  25. Experimental System Pathway A Chia Huey Ooi Tumor Profiles Pathway A Using Pathway Signatures to Guide Targeted Therapies

  26. Pathway A Pathway B Pathway D Pathway E Pathway C Tumor Profiles A B C D Mapping Pathway Signatures to Tumor Profiles

  27. P21 E2F (a) E2F (b) Stem cell (a) Stem cell (b) Myc (a) Stem cell (c) Myc (b) Oncogenic Pathways NF-kB (a) Wnt NF-kB (b) p53 (a) HDAC b-catenin Src Ras BRCA1 HDAC p53 (b) BRCA1 Activation score Predominant Oncogenic Pathways in Gastric Cancer 200 primary gastric tumors Proliferation/stem cell pathways activated b-catenin pathway activation p53 pathway activation

  28. NFKB Proliferation Wnt Validating Oncogenic Pathway Predictions Pathways GC cell lines

  29. High Proliferation Scores are Associated with Rapid Growth Proliferative capacity Summarized activation score of the proliferation/stem cell cluster

  30. High Wnt Scores are Associated with Wnt Activity

  31. p=4.549106 NFKB Proliferative capacity Cell Death Assay Wnt % apoptotic cells Neg siRNA b-catenin siRNA b-catenin (WB) Actin (WB) Control shRNA p65 shRNA Neg siRNA b-catenin siRNA GC cell lines Oncogenic Pathways in Gastric Cancer are Functionally Significant Cell Lines Pathways

  32. Pathway Combinations NFKB + Prolif. Wnt + Prolif. Pathway Interactions Influence Survival Single Pathways NFKB Proliferation Wnt

  33. Australia (90) Clinical Validation of Pathway Combinations Singapore (200) Proliferation and NKFB Proliferation and Wnt

  34. Potential Therapies P21 HLM006474 E2F (a) E2F (b) Stem cell (a) Stem cell (b) CX-3543 Myc (a) Stem cell (c) Myc (b) RTA-402 Oncogenic Pathways NF-kB (a) Wnt NF-kB (b) p53 (a) PXD-101 HDAC b-catenin KX2-391 Src Salirasib Ras BRCA1 HDAC pifithrin-a p53 (b) BRCA1 Activation score Oncogenic Pathways in Gastric Cancer May Guide Therapy 200 primary gastric tumors

  35. Take Home Messages • A framework for mapping defined pathway signatures into complex tumor profiles • Signatures are transportable (in vitro to in vivo) • Gastric cancers can be subdivided by pathway activity into biologically and clinically relevant subgroups • “High-throughput pathway profiling” highlights the role of oncogenic pathway combinations in clinical behavior Ooi et al (2009) PLOS Genetics

  36. Basic Science to Translation 1) Metastasis Genes - Network Structures 2) Cancer Classification - Pathway Biology 3) Lung Cancer Outcome - Integrative Genomics

  37. Genomic Classification of Early Stage Lung Cancer Philippe and Sophine Broet INSERM U472, Faculté de Médecine Paris-Sud Lance Miller Wake Forest University, USA Broet et al., (2009) Cancer Research

  38. Observation (Watch and Wait) Chemotherapy? Adjuvant Chemotherapy in Early-Stage NSCLC Surgery 40-50% 5-yr Survival Stage I, II

  39. Study Questions Can we use genomics to discriminate between low risk (pseudo-stage I) & high risk (pseudo-stage II) groups? Clinical questions • Previous studies on NSCLC prognosis have been transcriptome centered, not incorporating genomic alterations

  40. Array-CGH Recurrent Amplifications And Deletions Gene Expression Profiling Highly Regulated Genes An Integrated Genomic Strategy to Identify “Poor Prognosis” NSCLC Cases Stage IB NSLCLCs (Training Set)

  41. 11q13 8q24 1q31 5p13 Recurrent Genomic Alterations in NSCLC CyclinD1 WWOX

  42. Survival associations – “Survival CNAs” Genomic Regions Associated with Outcome

  43. Copy Number Driven Expression Gene Expression Gene Expression Associated with Survival-CNAs 203342_at 205564_at 201699_at 202988_at 204322_at 201698_at 203301_at 2113458_at 203343_at 201408_at Survival CNAs

  44. Integrated Signature (Chr. 7, 16, 20, 22) Predicting Prognosis in Stage IB NSCLC 103 genes Good Prognosis P=0.002 Poor Prognosis Training Cohort

  45. Validation of the Integrated Signature Michigan Series: 73 Stage I A&B NSCLCs Good Prognosis P=0.025 Poor Prognosis

  46. Candidates for Chemotherapy? Another Validation of the Integrated Signature Duke Series: 31 Stage I A&B NSCLCs Good Prognosis P=0.003 Poor Prognosis

  47. Stage II NSCLC Implications for Chemotherapy Selection Poor Prognosis Stage IB Poor Prognosis Ib Patients Are Comparable to Stage II Patients

  48. Good Prognosis (“Stage Ia-like”) Observation Genomic Predictor Adjuvant Chemotherapy Poor Prognosis (“Stage II-like”) A Genomic Approach to Guide Chemotherapy in Early-Stage NSCLC Stage Ib NSCLC Surgery

  49. Acknowledgements Kun Yu Kumaresan Ganesan Ooi Chia Huey Tatiana Ivanova Shenli Zhang Wu Yonghui Lai Ling Cheng Veena Gopalakrishnan Jun Hao Koo Julian Lee Ming Hui Lee Iain Tan Angie Tan Jiong Tao Jeanie Wu Yansong Zhu Philippe Broet (Paris) Sophine Broet (Paris) Lance Miller (GIS) Elaine Lim (NUH) Wei Chia Lin (GIS) Hooi Shing Chuan (NUS) Alex Boussioutas (Peter Mac, AU) David Bowtell (Peter Mac, AU) Sun Yong Rha (S. Korea) Heike Grabsch (Leeds) Support : French-Singapore MERLION program Singapore Cancer Syndicate Biomedical Research Council National Medical Research Council

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