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DiscoverSL: An R package for Multi-omic data driven prediction of synthetic lethality in cancers

Friday, September 14, 2018 — Poster Session V

12:00 p.m. – 1:30 p.m.
FAES Terrace
NCI
COMPBIO-3

Authors

  • S Das
  • X Deng
  • K Camphausen
  • U Shankavaram

Abstract

Summary: Synthetic lethality is a state when simultaneous loss of two genes is lethal to a cancer cell, while the loss of the individual genes is not. We developed an R package DiscoverSL to predict and visualize synthetic lethality in cancers using multi-omic cancer data. Mutation, copy number alteration, and gene expression data from The Cancer Genome Atlas (TCGA) project were combined to develop a multi-parametric Random Forest classifier. The effects of selectively targeting the predicted synthetic lethal genes is tested in silico using shRNA and drug screening data from cancer cell line databases. The clinical outcome in patients with mutation in primary gene and over/under-expression in the syn-thetic lethal gene is evaluated using Kaplan Meier analysis. The method helps to identify new therapeu-tic approaches by exploiting the concept of synthetic lethality. Availability: DiscoverSL package with usage instructions user manual and sample workflow is available for download from github url: https://github.com/shaoli86/DiscoverSL/releases/tag/V1.0 under GNU GPL-3 Contact: uma@mail.nih.gov, shaoli.das@nih.gov

Category: Computational Biology