Harnessing synthetic lethality to predict the response to cancer treatment

@inproceedings{Lee2018HarnessingSL,
  title={Harnessing synthetic lethality to predict the response to cancer treatment},
  author={Joo Sang Lee and Avinash Das and Livnat Jerby-Arnon and Rand Arafeh and Noam Auslander and Matthew Davidson and Lynn McGarry and Daniel James and Arnaud Amzallag and Seung Gu Park and Kuoyuan Cheng and Welles Robinson and Dikla Atias and Chani Stossel and Ella Buzhor and Gidi Stein and Joshua J Waterfall and Paul S. Meltzer and Talia Golan and Sridhar Hannenhalli and Eyal Gottlieb and Cyril H Benes and Yardena Samuels and Emma J Shanks and Eytan Ruppin},
  booktitle={Nature Communications},
  year={2018}
}
While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and… CONTINUE READING

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