TL-iGeneCombo: Transfer Learning for Individual Sample Specific Gene Combination Effect Prediction and Selection in Cancer Cells.

Abstract

Motivation: Identifying effective gene combinations is critical for developing combinatorial cancer therapies. Synthetic lethality (SL) has been widely used to characterize genetic interactions based on cell viability, but predictive models are often limited by the availability of combinatorial double-knockout (CDKO) data for individual cancer cells. How to borrow information from CDKO experiments in other cancer cells to improve prediction in a targeted cancer cell remains an important challenge.

Results: We investigated the CDKO effects of paired genes using both SL as an indirect measure and gene combination effect as a direct measure. We developed TL-iGeneCombo, a transfer learning approach, to borrow data from CDKO experiments in other cancer cells for the prediction of SL and gene combination effect in a targeted cancer cell. Applying TL-iGeneCombo improved the accuracy of SL classification by 19% and increased the Spearman correlation of gene combination effect scores by 29%. Using data and models built from the first CDKO experiment in a prostate cancer cell, 22RV1, we further designed gene pairs for a second CDKO experiment in the same cell. It validated the superior prediction of gene combination effect, but not SL prediction.

Availability: The software is freely available at https://github.com/BirkanGokbag/TL-iGeneCombo under the GNU General Public License v3.0.

Supplementary information: Supplementary data are available at Bioinformatics online.