RT Journal Article
SR Electronic
T1 Using protein interaction networks to identify cancer dependencies from tumor genome data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.27.270520
DO 10.1101/2020.08.27.270520
A1 Heiko Horn
A1 Christian Fagre
A1 Anika Gupta
A1 Kalliopi Tsafou
A1 Nadine Fornelos
A1 James T Neal
A1 Kasper Lage
YR 2020
UL http://biorxiv.org/content/early/2020/08/28/2020.08.27.270520.abstract
AB Genes required for tumor proliferation and survival (dependencies) are challenging to predict from cancer genome data, but are of high therapeutic value. We developed an algorithm (network purifying selection [NPS]) that aggregates weak signals of purifying selection across a gene’s first order protein-protein interaction network. We applied NPS to 4,742 tumor genomes to show that a gene’s NPS score is predictive of whether it is a dependency and validated 58 NPS-predicted dependencies in six cancer cell lines. Importantly, we demonstrate that leveraging NPS predictions to execute targeted CRISPR screens is a powerful, highly cost-efficient approach for identifying and validating dependencies quickly, because it eliminates the substantial experimental overhead required for whole-genome screening.Competing Interest StatementThe authors have declared no competing interest.