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Published on: September 19, 2018
Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines
James Campbell1, Colm J Ryan2, Rachel Brough1
1The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK.
Abstract:
One 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.
Insights
This study identifies kinase genetic dependencies in 117 cancer cell lines using siRNA screens. Integrating this data with molecular profiles reveals cancer vulnerabilities linked to specific driver gene mutations, aiding therapeutic target discovery.
Area of Science:
- Genomics
- Cancer Biology
- Pharmacogenomics
Background:
- Identifying cancer-specific vulnerabilities is crucial for developing targeted therapies.
- Genetic dependencies in cancer cell lines offer a platform for discovering these vulnerabilities.
Purpose of the Study:
- To profile kinase genetic dependencies across diverse cancer cell lines.
- To integrate genetic dependency data with molecular profiles to identify cancer driver gene-associated vulnerabilities.
- To demonstrate the utility of this resource for predicting drug sensitivity.
Main Methods:
- Conducted siRNA screens to identify kinase genetic dependencies in 117 cancer cell lines from ten cancer types.
- Integrated siRNA screen data with exome sequencing and protein-protein interaction data.
- Analyzed functional interaction networks to understand gene dependency connections.
Main Results:
- Identified specific kinase genetic dependencies across various cancer types.
- Linked genetic dependencies to mutations in cancer driver genes.
- Demonstrated that dependencies form dense networks with cancer driver genes.
- Predicted drug sensitivity in specific tumor subsets, including FGFR inhibitor sensitivity in osteosarcoma and mitotic inhibitor sensitivity in SMAD4 mutant cells.
Conclusions:
- The integrated dataset provides a valuable resource for identifying cancer-specific vulnerabilities and therapeutic targets.
- Understanding genetic dependencies linked to driver mutations can guide precision medicine approaches.
- This approach can predict drug responses in genetically defined cancer populations.
