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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Analysis of individual protein regions provides novel insights on cancer pharmacogenomics
Eduard Porta Pardo1, Adam Godzik1
1Program on Bioinformatics and Systems Biology, Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America.
Abstract:
The promise of personalized cancer medicine cannot be fulfilled until we gain better understanding of the connections between the genomic makeup of a patient's tumor and its response to anticancer drugs. Several datasets that include both pharmacologic profiles of cancer cell lines as well as their genomic alterations have been recently developed and extensively analyzed. However, most analyses of these datasets assume that mutations in a gene will have the same consequences regardless of their location. While this assumption might be correct in some cases, such analyses may miss subtler, yet still relevant, effects mediated by mutations in specific protein regions. Here we study such perturbations by separating effects of mutations in different protein functional regions (PFRs), including protein domains and intrinsically disordered regions. Using this approach, we have been able to identify 171 novel associations between mutations in specific PFRs and changes in the activity of 24 drugs that couldn't be recovered by traditional gene-centric analyses. Our results demonstrate how focusing on individual protein regions can provide novel insights into the mechanisms underlying the drug sensitivity of cancer cell lines. Moreover, while these new correlations are identified using only data from cancer cell lines, we have been able to validate some of our predictions using data from actual cancer patients. Our findings highlight how gene-centric experiments (such as systematic knock-out or silencing of individual genes) are missing relevant effects mediated by perturbations of specific protein regions. All the associations described here are available from http://www.cancer3d.org.
Insights
Analyzing mutations within specific protein functional regions (PFRs) reveals novel drug response associations in cancer cell lines. This approach uncovers insights missed by traditional gene-focused analyses, improving personalized cancer medicine understanding.
Area of Science:
- Genomics
- Pharmacology
- Computational Biology
Background:
- Personalized cancer medicine requires understanding tumor genomics and drug response.
- Current analyses often overlook mutation location, assuming uniform gene effects.
- This limitation may obscure crucial genotype-phenotype relationships.
Purpose of the Study:
- To investigate the impact of mutations in specific protein functional regions (PFRs) on anticancer drug response.
- To identify novel drug-gene associations missed by traditional gene-centric analyses.
- To enhance understanding of cancer cell line drug sensitivity mechanisms.
Main Methods:
- Analysis of pharmacogenomic datasets correlating cancer cell line genomic alterations with drug sensitivity.
- Stratification of mutations based on their location within protein functional regions (PFRs), including domains and intrinsically disordered regions.
- Comparison of PFR-centric analysis with traditional gene-centric approaches.
Main Results:
- Identification of 171 novel associations between mutations in specific PFRs and altered activity of 24 drugs.
- Demonstration that PFR-focused analysis recovers significant correlations missed by gene-level analyses.
- Validation of some identified PFR-drug associations using data from cancer patients.
Conclusions:
- Focusing on specific protein functional regions provides deeper insights into cancer drug sensitivity.
- Gene-centric analyses may miss critical genotype-drug response relationships.
- This PFR-based approach holds promise for advancing personalized cancer medicine.
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