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High-Complexity shRNA Libraries and PI3 Kinase Inhibition in Cancer: High-Fidelity Synthetic Lethality Predictions
Author(s) -
Marsilius Mues,
Laila Karra,
Damià Romero–Moya,
Anica M. Wandler,
Matthew J. Hangauer,
Olga Ksionda,
Yvonne J. Thus,
Marthe F. Lindenbergh,
Kevin Shan,
Michael T. McManus,
Jeroen P. Roose
Publication year - 2019
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2019.03.045
Subject(s) - synthetic lethality , small hairpin rna , cancer research , kinase , signal transduction , biology , cancer cell , cancer , bioinformatics , computational biology , microbiology and biotechnology , cell culture , genetics , gene , dna repair , gene knockdown
Deregulated signal transduction is a cancer hallmark, and its complexity and interconnectivity imply that combination therapy should be considered, but large data volumes that cover the complexity are required in user-friendly ways. Here, we present a searchable database resource of synthetic lethality with a PI3 kinase signal transduction inhibitor by performing a saturation screen with an ultra-complex shRNA library containing 30 independent shRNAs per gene target. We focus on Ras-PI3 kinase signaling with T cell leukemia as a screening platform for multiple clinical and experimental reasons. Our resource predicts multiple combination-based therapies with high fidelity, ten of which we confirmed with small molecule inhibitors. Included are biochemical assays, as well as the IPI145 (duvelisib) inhibitor. We uncover the mechanism of synergy between the PI3 kinase inhibitor GDC0941 (pictilisib) and the tubulin inhibitor vincristine and demonstrate broad synergy in 28 cell lines of 5 cancer types and efficacy in preclinical leukemia mouse trials.

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