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

Abstract

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.

Keywords: biomarker; mass spectrometry; ovarian cancer; proteomics.

Figure 1

Principles of data-dependent acquisition (DDA) and data-independent acquisition (DIA) in untargeted quantitative proteomics. (A) In DDA, precursor ions are stochastically selected on the basis of their signal intensity in Q1 followed by fragmentation of the selected precursor ions in a collision cell. All fragmented ions are separated and detected by a mass analyzer such as an Orbitrap or time-of-flight (TOF) analyzer. (B) In DIA, all MS1 precursor ions within pre-defined mass windows are selected in Q1 followed by fragmentation of all precursor ions from each window in a collision cell. The resultant MS2 spectra are comprised of fragment ions from all of the precursor ions in the selected Q1 window.

Figure 2

Schematic overview of targeted quantitative proteomics methods. (A) In multiple reaction monitoring (MRM), the precursor ion of a pre-defined specific peptide is selected and fragmented in Q1 and Q2, respectively. Pre-defined fragmented ions are selected and detected in Q3. (B) Unlike MRM, parallel reaction monitoring (PRM) can detect all fragmented ions generated from precursor ions in parallel using a high resolution accurate mass (HRAM) mass analyzer such as an Orbitrap.