4.6 Review

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Journal

MOLECULES
Volume 26, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/molecules26092674

Keywords

ovarian cancer; biomarker; proteomics; mass spectrometry

Funding

  1. National Institutes of Health's National Center for Advancing Translational Sciences [UL1TR002494]
  2. University of Minnesota Department of Laboratory Medicine and Pathology

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Ovarian cancer is a deadly gynecologic malignancy with the need for new and more effective treatment strategies, while early detection and protein biomarkers play crucial roles. Targeted proteomics methods, including mass spectrometry, offer robust solutions for the development of ovarian cancer biomarkers.
Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70-80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

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