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Mapping cancer specific drug-gene interactions

Mapping cancer specific drug-gene interactions. EMBL. Wolfgang Huber Computational Biology. Thorsten Zenz Lymphoproliferative disease DKFZ, Univ. Clinic Heidelberg. Genomics of drug sensitivity: drug screens in pan-cancer cell line panels.

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Mapping cancer specific drug-gene interactions

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  1. Mapping cancer specific drug-gene interactions EMBL Wolfgang Huber Computational Biology Thorsten Zenz Lymphoproliferative disease DKFZ, Univ. Clinic Heidelberg

  2. Genomics of drug sensitivity: drug screens in pan-cancer cell line panels • Garnett et al.: Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012 • Barretina et al. 2012: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012 • Basu et al.: An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules. Cell 2013

  3. Challenges to translation • Rare diseases with diverse biology • chronic lymphoid leukemias, lymphomas: • (e.g. T-PLL, LPL, MZL, MCL, refr. CLL, B-PLL, Sezary syndrome) • Lack of reliable cell line models • Lacking standard of care • Limited understanding of biology • Limited targeted treatment

  4. Genomics of drug sensitivity: primary lymphoproliferative disease Exome-seq, RNA-seq, 450k methylation, copy number analysis (SNParray) Small molecule library Automated seeding of cells Measurement of ATP-levels etc. Thorsten Zenz, Leo Sellner, NCT

  5. Key questions • Which drugs / patient-derived tumours exhibit variable drug sensitivity? • Which somatic mutations / other molecular features are associated with these differences? • Which biological mechanisms underlie these relationships? • How can we predict sensitivities? (multivariate modelling) • Innovative therapeutic approaches (single drugs, combinations) • Serve as a model for other tumour entities

  6. Tumour specific patterns of drug response drugs primary tumour samples Leo Sellner L. Sellner

  7. Clustering of drugs reflects mechanism of action drug-drug correlation matrix Fludarabine Nutlin-3 Everolimus Deferolimus Kinase inhibitors: SaracatinibPKI−402DasatinibSelumetinibTipifarnib…

  8. Clustering of patients according to drug response Decreased sensitivity towards kinase inhibition Patients Compounds Increased sensitivity towards kinase inhibition Red: more sensitivity Blue: less sensitivity M. Oles

  9. p53 mutation in CLL T. Zenz et al., Blood (2008) Sellner, Oles, et al. unpublished

  10. Interfering with the MDM2/p53 interaction in CLL TP53-deleted CLL patient samples are less susceptible to Nutlin-3 inhibition Sellner unpublished

  11. Understanding sensitivity: p53 Why are these patients less sensitive? Clone size 3-10% No 17p-

  12. Atlas of drug sensitivity in CLL • Custom “pilot” library (67 substances) • Drugs in clinical use • Key CLL/cancer pathway inhibitors • Hits from CLL drug screens • 111 patients’ tumours screened • Prestwick Library: 1120 FDA-approved drugs • Quick clinical translation • 20 patients screened • NIH Phase I-III Library: 731 compounds • Main targets known, toxicity data • 20 patients screened • GSK Kinase Library: 367 small molecules • Well characterized targets (220 kinase assays) • 20 patients screened

  13. CLL – EMBL screen, one tumour

  14. High quality screening data(2.6k compounds, 20 patients) Screening window - positive & negative controls Z’ = 0.84 18 Plates: T-PLL

  15. New options for genotype specific treatment of CLL Compound A 10µM Compound C 8µM p<0.05 p<0.01 Sellner unpublished

  16. Membrane-bound immunoglobulin Targeting Key Signaling Pathways in CLL GA101 B-cell receptor CD20 Fostamatinib GS-9973 Lyn PI3K/Akt pathway CD20 Syk Ibrutinib CC-292 Btk 571300080995 NF-κB pathway MAPK pathway Idelalisib IPI-145 TGR-1202 Bcl-2 ABT-199 Cell survival Normal B-cell activation and proliferation Malignant B-cell initiation and progression All are small molecule inhibitors except GA101, which is a monoclonal antibody Friedman and Weinberg. The Hematologist. 2013

  17. Genetic factors modulating response to B-cell receptor inhibition → ability to pick up subtle findings Similar to result of clinical trial: Byrd et al. NEJM 2013

  18. Summary • Unique opportunity to „solve“ diseases (CLL and others) • n ~ 35 with large library • n ~ 200 with selection library (CLL n=100-150. MCL, T-PLL, FL, LPL, B-PLL, Sezary syndrome n=10-20 each) • Cover biological heterogeneity • Understand outliers • Robust modelling of subtle smaller but significant differences (e.g. BCR) • Assay response after 48h dominated by major clone - clinical outcome often depends on subclasses • No stroma interactions (yet)

  19. High-throughput imaging-based automated multivariate cellular phenotyping Laufer, Fischer et al. Nature Methods 2013 Boutros, Bras, Huber, Genome Biol. 2006 Fuchs, Pau et al. Mol. Sys. Biol. 2010 Pau, Fuchs et al. Bioinf. 2010 Neumann et al. Nature 2010 Kuttenkeuler et al. J. Innate Imm. 2010 Axelsson et al. BMC Bioinf. 2011 Horn et al. Nature Methods 2011 Laufer et al. Nature Methods 2013 Michael Boutros

  20. Aug 2013

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