Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

August 28, 2014

Livnat Jerby-Arnon, Nadja Pfetzer, Yedael Y. Waldman, Lynn McGarry, Daniel James, Emma Shanks,  Brinton Seashore-Ludlow, Adam Weinstock, Tamar Geiger, Paul A. Clemons, Eyal Gottlieb, Eytan Ruppin.

Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in humans. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation of the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervised training technique in SL prediction.

Read the full publication in Cell