AI Article Synopsis
- Synthetic lethality involves the combined inhibition of two genes, which is lethal, while inhibiting each one alone is not, making it a powerful tool for targeted cancer treatment.
- A computational pipeline has been developed to identify synthetic lethal interactions across the genome by analyzing extensive cancer genomic data, effectively capturing known interactions.
- The study successfully constructs a network of SL interactions, predicts gene essentiality, and enhances clinical prognosis, providing insights into drug efficacy and cancer-specific vulnerabilities.
Article Abstract
Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.
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| http://dx.doi.org/10.1016/j.cell.2014.07.027 | DOI Listing |
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