Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines

Abstract

SummaryOne approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.

Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels

“…Positive and negative controls are included on each plate-typically non-targeting siRNA as a negative control and an siRNA pool targeting PLK1 as a positive control. The full experimental protocol for this screen has been described elsewhere [4,5]. Briefly, following siRNA transfection, the cells were cultured for 5 days, after which a luminescence assay measuring cellular ATP was used to estimate cell viability.…”

“…Toward this end, a number of laboratories have used loss-of-function screening to generate resources describing the genetic requirements of panels of tumor cell lines [4][5][6][7][8][9][10][11]. The majority of these resources use either genome-scale shRNA screens carried out in a pooled format [6,7,10] or siRNA screens carried out in an arrayed format [4,5,11] to identify genetic dependencies. In the near future CRISPR-based approaches will likely be used for similar purposes, although to date the number of cell lines profiled by genome-wide CRISPR libraries remains small (e.g., five cell lines in [8]).…”

“…The interpretation of the resulting associations remains challenging-the statistical tests provide information on which genes are required in the presence of specific driver genes, but not the mechanistic explanation as to why these dependencies exist. Inspired by approaches initially developed for the interpretation of genetic interactions in yeast [16], we have recently used the integration of functional interaction networks to aid the interpretation of dependencies identified in loss-of-function screens in cancer cell lines [5]. For instance in ERBB2-amplified cell lines we see an increased dependency upon ERBB2 itself and also the ERBB2 proteininteraction partners ERBB3 and PIK3CA [5].…”

“…Inspired by approaches initially developed for the interpretation of genetic interactions in yeast [16], we have recently used the integration of functional interaction networks to aid the interpretation of dependencies identified in loss-of-function screens in cancer cell lines [5]. For instance in ERBB2-amplified cell lines we see an increased dependency upon ERBB2 itself and also the ERBB2 proteininteraction partners ERBB3 and PIK3CA [5]. This suggests that ERBB2 amplified cell lines are frequently "addicted" to the functionality of ERBB2, the binding of ERBB2 to its interaction partner ERBB3, and the function of the downstream effector PIK3CA.…”

“…We use as example data a recent kinome-wide siRNA screen performed in a panel of osteosarcoma cell lines [5]. Our analysis protocol involves three main steps:…”