A mouse-specific model to detect genes under selection in tumors

Abstract

AbstractMouse is a widely used model organism in cancer research. However, no computational methods exist to identify cancer driver genes in mice due to a lack of labeled training data. To address this knowledge gap, we adapted the GUST (genes under selection in tumors) model, originally trained on human exomes, to mouse exomes using transfer learning. The resulting tool, called GUST-mouse, can estimate long-term and short-term evolutionary selection in mouse tumors, and distinguish between oncogenes, tumor suppressor genes, and passenger genes using high throughput sequencing data. We applied GUST-mouse to analyze 65 exomes of mouse primary breast cancer models, leading to the discovery of 24 driver genes. The GUST-mouse method is available as an open-source R package on github (https://github.com/liliulab/gust.mouse).