Analysis of the tumor gene portfolio transcriptome is now a tool for novel biomarkers, but changes in proteome expression are more likely to reflect changes in the physiology of the tumor case. In the past. Clinical diagnosis relies on antibody – based detection strategies, but these methods have some limitations. Mass spectrometry is a powerful way to provide an increasingly comprehensive understanding of proteomic changes that will drive the development of personalized medicine.
Researchers from the Princess Margaret Cancer Centre, University of Toronto Health Network, Canada, published advances in MS-BASED Clinical Proteomics research in the journal Clinical Proteomics, with a special focus on cancer research. We provide a detailed overview of clinical sample types, sample preparation techniques, mass spectrometry profiles, protein quantitative strategies, and cancer tissue/body fluid proteomics.
Application Of Clinical Proteomics In Cancer Research
Tissue samples
“Discovery-type” basic research: Routine proteomic analysis of patient tumor tissue has become an important tool for the discovery of biomarkers, biological pathways, and integration studies with the existing genome/transcriptome. Tissue-based proteomics strategies have been used to study many cancer types, including prostate, breast, melanoma, lung, ovarian, and oropharyngeal cancers.
Target/multi-site validation: To date, tissue proteomics research has focused on foundational discovery-based studies that have revealed many potential cancer markers. In the future, the development of targeted MS detection methods, such as PRM for tissue lysates, will become more mainstream in the direction of routine analysis of tumors.
Focus on spatial resolution in proteomics: One factor that needs to be considered when quantifying tissue proteome changes is tumor heterogeneity. In an effort to distinguish between changes in protein expression that arise from disease progression and those that arise from tissue heterogeneity and secondary biological pathways, the development of laser capture microdissection (LCM) provides a powerful tool to isolate specific areas of tumor cross sections. Mass spectrometry (MSI) techniques also provided spatial information and demonstrated their clinical potential.
Multiomics analysis. Although the genomes and transcriptomes of many cancers have been well elucidated, the cancer proteome and its relationship to upstream genomic changes have been poorly documented. In recent years, more and more studies have begun to integrate all levels of omics data to describe comprehensive multicomponent evaluations of tumors. In the future, the integration of proteome data sets with genome-level data will become more and more common in future oncology and personalized medicine research.
Body fluid samples
In clinical laboratory tests, blood is the most widely used body fluid for disease diagnosis, prognosis, and treatment outcomes. The Human Plasma Proteome Project (HPPP), launched in 2002, aims to generate an open-source database of human plasma and serum proteome through MS.
Urine is another common sample of body fluids because it is produced in large quantities and can be easily collected in a non-invasive manner. 70% of urinary protein comes from the kidneys and urethra, which is a valuable resource for the urinary tract monitoring. The remaining 30 percent of urine protein comes from the filtration of blood by the glomeruli, suggesting that urine could also help us understand cancer in distant organs. At the same time, urine is an important source of prostate cancer biomarkers.
In addition to blood and urine, there are a variety of alternative body fluids that could potentially be used to find biomarkers. Cerebrospinal fluid, for example, as a valuable source of biomarkers cannot be ignored, as it has recently been shown to contain more than 3,300 total proteins and is enriched in brain-specific proteins. These replacement humoral proteomes will then be further detailed as part of the search for robust noninvasive cancer biomarkers.
Conclusion
The field of clinical proteomics is likely to expand rapidly in clinical cohort size due to standardized, high-throughput sample preparation techniques. This will minimize the frequency of studies due to statistical deficiencies and improve the efficiency of translating candidate biomarkers and drug targets into clinical applications. Proteomics will increasingly become an important component of cancer systems biology, integrating multi-omics data from genomics, epigenomics, transcriptomics, and PTMs. This will require more computing power to process and analyze ever-increasing amounts of data. Further improvements in the sensitivity and speed of mass spectrometers will allow for more regular deep coverage of the proteome, especially without the need for extensive pre-separation. Improved detection/quantification levels will also move clinical proteomics towards minimum input sample sizes and single-cell proteomics. Finally, the data analysis process optimization will provide more diagnostic and predictive accuracy relative to a single marker. These advances are necessary for MS-BASED clinical proteomics to fully realize its potential for translating research findings into improvements in clinical practice.
Reference
Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research[J]. Clinical Proteomics, 2020, 17: 1-25.