Abstract
Loss-of-function screening using RNA interference or CRISPR approaches can be used to identify genes that specific tumor cell lines depend upon for survival. By integrating the results from screens in multiple cell lines with molecular profiling data, it is possible to associate the dependence upon specific genes with particular molecular features (e.g., the mutation of a cancer driver gene, or transcriptional or proteomic signature). Here, using a panel of kinome-wide siRNA screens in osteosarcoma cell lines as an example, we describe a computational protocol for analyzing loss-of-function screens to identify genetic dependencies associated with particular molecular features. We describe the steps required to process the siRNA screen data, integrate the results with genotypic information to identify genetic dependencies, and finally the integration of protein-protein interaction data to interpret these dependencies.
Highlights
Recent large-scale sequencing projects and decades of small-scale studies have led to the identification of hundreds of “driver” genes in cancer—genes whose alteration through genetic or epigenetic means provides a growth or survival advantage for tumor cells [1, 2]
A key remaining challenge is to understand how these driver mutations alter cellular states to promote tumor progression and how this altered state may be exploited for the development of targeted therapeutics [3]
Identifying the set of genes that are required for growth in a given tumor cell line provides both an insight into the cellular state and suggests genes whose products may be targeted therapeutically
Summary
Recent large-scale sequencing projects and decades of small-scale studies have led to the identification of hundreds of “driver” genes in cancer—genes whose alteration through genetic or epigenetic means provides a growth or survival advantage for tumor cells [1, 2]. Identifying the set of genes that are required for growth in a given tumor cell line provides both an insight into the cellular state and suggests genes whose products may be targeted therapeutically 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 [4–11]. Examples of non-oncogene addictions identified from loss-of-function screens include a dependence of ARID1A mutant cell lines upon the ARID1A paralog ARID1B [14], an increased sensitivity of PTEN mutant breast cancer cell lines to inhibition of the mitotic kinase TTK [4], and an increased sensitivity of MYC amplified breast cancer cell lines to inhibition of multiple spliceosome component coding genes [15] Both oncogene addictions and synthetic lethalities identified in these screens may be exploited for the development of novel targeted therapeutics in cancer [13].
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