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Published on: November 15, 2017
Onco-proteogenomics: Multi-omics level data integration for accurate phenotype prediction
Lampros Dimitrakopoulos1,2,3, Ioannis Prassas2, Eleftherios P Diamandis1,2,3,4
1a Department of Laboratory Medicine and Pathobiology , University of Toronto , Toronto , ON , Canada.
Abstract:
The overall goal of translational oncology is to identify molecular alterations indicative of cancer or of responsiveness to specific therapeutic regimens. While next-generation sequencing has played a pioneering role in this quest, the latest advances in proteomic technologies promise to provide a holistic approach to the further elucidation of tumor biology. Genetic information may be written in DNA and flow from DNA to RNA to protein, according to the central dogma of molecular biology, but the observed phenotype is dictated predominantly by the DNA protein coding region-derived proteotype. Proteomics holds the potential to bridge the gap between genotype and phenotype, because the powerful analytical tool of mass spectrometry has reached a point of maturity to serve this purpose effectively. This integration of "omics" data has given birth to the novel field of onco-proteogenomics, which has much to offer to precision medicine and personalized patient management. Here, we review briefly how each "omics" technology has individually contributed to cancer research, discuss technological and computational advances that have contributed to the realization of onco-proteogenomics, and summarize current and future translational applications.
Insights
Onco-proteogenomics integrates genomics and proteomics to understand cancer biology. This approach bridges the gap between genetic information and observable traits, advancing precision medicine and patient care.
Area of Science:
- Oncology
- Proteomics
- Genomics
Background:
- Translational oncology aims to find molecular markers for cancer detection and treatment response.
- Next-generation sequencing revolutionized cancer research, but proteomics offers a holistic view of tumor biology.
- The proteotype, derived from DNA coding regions, largely dictates the observed cancer phenotype.
Purpose of the Study:
- To review the individual contributions of omics technologies to cancer research.
- To discuss technological and computational advancements enabling onco-proteogenomics.
- To summarize current and future translational applications of onco-proteogenomics in precision medicine.
Main Methods:
- Review of existing literature on genomics, proteomics, and mass spectrometry in cancer research.
- Discussion of technological advancements in omics data generation and analysis.
- Exploration of computational strategies for integrating multi-omics data.
Main Results:
- Genomics and proteomics provide complementary insights into tumor biology.
- Mass spectrometry has matured as a key tool for proteomic analysis.
- The integration of omics data has led to the development of onco-proteogenomics.
Conclusions:
- Onco-proteogenomics holds significant promise for advancing precision medicine.
- This integrated approach can enhance personalized patient management in oncology.
- Future research will focus on refining onco-proteogenomic strategies for clinical application.
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