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. 2021 Aug;4(4):1050-1072.
doi: 10.1039/d1cb00039j. Epub 2021 May 19.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Hannah N Miles #  1 , Daniel G Delafield #  2 , Lingjun Li  1   2
Affiliations

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Hannah N Miles et al. RSC Chem Biol. 2021 Aug.

Abstract

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

Keywords: Quantitative proteomics; biomarker discovery; cancer research; isotopic labeling; mass spectrometry; systems biology.

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Conflict of interest statement

CONFLICTS OF INTEREST There are no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1. Complete workflow utilized by Zhou et al. detailing the quantitative approach to investigate site-specific fucosylation and glycoproteins associated with aggressive prostate cancer phenotypes. The optimized enrichment strategy used to identify glycopeptides contributing to prostate cancer aggressiveness shows promise for application in a variety of cancer glycosylation studies but should also be applied to other prostate cancer models to determine its utility across sample types. Reprinted with permission.
Fig. 2
Fig. 2. Workflow described by An et al. for the quantitative analysis of chemotherapy patient exosomes through iTRAQ labeling and quantitative mass spectrometry. This example of a facile isobaric labeling proteomics experiment provides deep proteomic profiling of multiple complex samples with lower spectral complexity than isotopic labeling methods. Reprinted with permission.
Fig. 3
Fig. 3. Workflow implemented by Nigjeh et al. Quantitative workflows utilizing isobaric labels present the greatest propensity for deep proteome profiling. However, these workflows are limited by their instrument acquisition speed and cycle time required to select and fragment top precursors. For this reason, implementation of DIA strategies presents the ability to sequence a greater number of peptides in the same amount of time. Though the data processing methods are significantly more involved, DIA workflows are sure to be of critical importance to proteome profiling in the coming years. Reprinted with permission.
Fig. 4
Fig. 4. Representative workflow established by Going et al. As quantitative proteomics is critical for discovering and validated biomolecules of interest during periods of disease and treatment, this workflow represents an example of how treatment strategies may be controlled and systematically evaluated. While SILAC methods would be useful in situations where cell growth is monitored, isotopic labeling methods may be considered inherently lower throughput due to the increases in spectral complexity they may provide. Reprinted with permission.
Fig. 5
Fig. 5. Analysis by Coscia et al. to determine proteomic differences in ovarian cancer tissue samples either resistant or sensitive to platinum-based chemotherapeutics. This strategy identified CT45 as a chemosensitivity modulator and demonstrates the ability of quantitative methods to identify factors that play a role in therapeutic resistance. Reprinted with permission.
None
Hannah N. Miles
None
Daniel G. Delafield
None
Lingjun Li
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