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A genetic interaction map of drug responses in cancer cell lines Felix Klein (Huber group)

A genetic interaction map of drug responses in cancer cell lines Felix Klein (Huber group). Marco Breinig. Synthetic genetic interactions. Gene A. Gene A. Gene A. Gene A. Gene B. Gene B. Gene B. Gene B. viable. viable. viable. lethal. Relevance to cancer. Gene A. Gene A. Gene A.

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A genetic interaction map of drug responses in cancer cell lines Felix Klein (Huber group)

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  1. A genetic interaction map of drug responses in cancer cell linesFelix Klein (Huber group) Marco Breinig

  2. Synthetic genetic interactions Gene A Gene A Gene A Gene A Gene B Gene B Gene B Gene B viable viable viable lethal

  3. Relevance to cancer Gene A Gene A Gene A Gene B Gene B Gene B (cancer mutation) normal cells normal cells cancer cells viable lethal viable

  4. Relevance to cancer Kornelia Polyak & Judy Garber nature medicine 2011

  5. Isogenic HCT-116 cell lines

  6. Screen setup

  7. Correlation of features • 385 extracted features

  8. Feature selection 20 non-redundant features selected signal-to-noise (cor. of residuals) ratio of positive correlations

  9. How to measure synthetic genetic interactions? Luo, J. et al., Cell (2009). Sandmann, T. & Boutros, M. Current Opinion in Genetics & Development (2012)

  10. How to measure synthetic genetic interactions? fit a linear model log(cell number) ~ drug + cell line + drug * cell line moderated t-test on the interaction terms interaction term

  11. Quality control

  12. Quality control cell number negative controls

  13. Significant interactions cell number FDR < 0.1 FDR < 0.01

  14. Interaction map cell number 1 AKT1-/-&AKT2-/- 2 MEK2-/- 3 AKT1-/- 4 CTNNB1 mt-/wt+ 5 PAR007 6 P53-/- 7 PTEN-/- 8 PI3KCA mt-/wt+ 9 KRAS mt-/wt+ 10 BAX-/- 11 MEK1-/- 12 PAR001 drugs cell lines

  15. Interaction map cell number FDR < 0.01 1 AKT1-/-&AKT2-/- 2 MEK2-/- 3 AKT1-/- 4 CTNNB1 mt-/wt+ 5 PAR007 6 P53-/- 7 PTEN-/- 8 PI3KCA mt-/wt+ 9 KRAS mt-/wt+ 10 BAX-/- 11 MEK1-/- 12 PAR001 drugs cell lines

  16. Interaction map cell eccentricity FDR < 0.1 1 AKT1-/-&AKT2-/- 2 MEK2-/- 3 AKT1-/- 4 CTNNB1 mt-/wt+ 5 PARENTAL007 6 P53 -/+ 7 P53-/- 8 PTEN-/- 9 PI3KCA mt-/wt+ 10 KRAS mt-/wt+ 11 BAX-/- 12 MEK1-/- 13 PARENTAL001 drugs cell lines

  17. Specific interaction of BIX 01294 BIX 01294 CTNNB1 mt-/wt+ negative control (DMSO) CTNNB1 mt-/wt+ BIX 01294 other cell lines AKT1-/- & AKT2 -/- MEK1 -/- Par001 KRAS mt-/wt+

  18. Network analysis on complete interaction profile

  19. Clustering of interaction profiles: Guilt by association Cluster 1: microtubule function inhibitors Cluster 2: Topoisomerase II DNA Metabolism CDK2 Cluster 3: STAT3 inhibitor Ikb-alpha inhibitor Cluster 4: PP2A and other phosphatases drugs drugs

  20. Improvement of clustering Distribution of correlation values for compound, factored by annotated selectivity

  21. Current follow ups Drug synergy (cell viability assay) MEK and PI3K inhibitors with Disulfiram PD98059 (MEK2), U0126 (MEK1/2), wortmannin (PI3K) AKT inhibitors with Bendamustine MK2206 (Akt 2(1/3), AktVIII (Akt1/2/3)

  22. Current follow ups Mode of action STAT3 and IkB-alpha inhibitors show similar interaction profiles STAT3 and NF-kb reporter assay Drug treatment Pathway activation at day 4

  23. Summary & Conclusions we established a method to detect synthetic genetic interactions using isogenic cell lines we are able to detect specific interactions similar drug interaction profiles can be linked to similar biological processes

  24. Acknowledgements Wolfgang Huber Simon Anders Joseph Barry Bernd Fischer Julian Gehring Aleksandra Pekowska Paul Theodor Pyl Alejandro Reyes Maria Secrier Tibor Pakozdi Andrzej Oles Malgorzata Oles Collaborators at DKFZ: Michael Boutros Marco Breinig

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