An in-silico approach to predict and exploit synthetic lethality in cancer metabolism.

ABSTRACT: Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly availabl

SUBMITTER: Apaolaza I 

PROVIDER: S-EPMC5587678 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature