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Updated: May 12, 2026
Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
Systematic identification of combinatorial drivers and targets in cancer cell lines
Adel Tabchy1, Nevine Eltonsy, David E Housman
1Department of Systems Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA. atabchy@gmail.com
Abstract:
There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance.
Insights
This study identifies novel drug targets for combination cancer therapy by analyzing tumor cell line data. It reveals cooperating genomic events that predict drug sensitivity, offering new strategies to overcome treatment resistance.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Large-scale molecular characterization of tumors necessitates effective methods for identifying combinatorial therapeutic targets.
- Discovering biomarkers for combination therapies is crucial for advancing precision oncology.
Purpose of the Study:
- To systematically identify combinatorial biomarkers and co-actionable targets in cancer using integrated computational and experimental platforms.
- To validate genomic biomarkers predictive of response to targeted agents in diverse cancer cell lines.
Main Methods:
- Established an in silico bioinformatic platform integrated with a high-throughput screening platform.
- Evaluated 37 novel targeted agents across 669 diverse cancer cell lines.
- Validated genomic biomarkers identified in a training set within an independent test set.
Main Results:
- Identified co-occurring and mutually exclusive genomic events as potential drivers and combinatorial targets.
- Demonstrated that cooperating genomic events predict drug sensitivity irrespective of tumor lineage.
- Validated genomic biomarkers in independent cancer cell line sets.
Conclusions:
- The integration of in silico and high-throughput biological platforms enables the identification of rational combinatorial targets.
- This approach can inform synthetic lethal strategies to preempt the emergence of drug resistance in cancer treatment.
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