Journal
BIOINFORMATICS
Volume 35, Issue 4, Pages 701-702Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty673
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Funding
- Intramural Research Program of the National Institutes of Health, National Cancer Institute
- NATIONAL CANCER INSTITUTE [ZIASC010372] Funding Source: NIH RePORTER
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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 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/underexpression in the synthetic lethal gene is evaluated using Kaplan-Meier analysis. The method helps to identify new therapeutic approaches by exploiting the concept of synthetic lethality.
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