The analysis of tumor genome and transcriptome has now become a tool for discovering new biomarkers, but changes in proteome expression are more likely to reflect changes in tumor pathophysiology. In the past, clinical diagnosis relied on antibody-based detection strategies, but these methods have certain limitations. Mass spectrometry (MS) is a powerful method that enables people to understand the changes in the proteome more and more comprehensively, thereby promoting the development of personalized medicine.
Researchers from the Princess Margaret Cancer Center of the Health Network of the University of Toronto in Canada published in the《Clinical Proteomics》magazine the progress of MS-based clinical proteomics research, with special attention to cancer research. The researchers gave a detailed overview of clinical sample types, sample preparation techniques, mass spectrometry configuration, protein quantification strategies, and research progress in cancer tissue/body fluid proteomics.
Clinical Proteomics Methods
The researchers described in detail the types of clinical samples, protein purification methods, mass spectrometry configuration, and protein quantification strategies.
Types of clinical samples: 1) Direct proteomic studies of clinical tissues are becoming more common. Methods for preserving tissue samples include fresh freezing (FF), formalin-fixed paraffin embedding (FFPE), optimal cutting temperature embedding, etc. From the perspective of proteomic coverage, FF is the preferred preservation method, but FFP tissue has been stored for decades, providing extensive clinical follow-up and valuable resources for clinical proteomics. Tissue samples can be visualized by laser capture Micro-cutting (LCM) is further prepared to increase spatial resolution elements. 2) Clinical samples obtained by non-invasive or minimally invasive methods (liquid biopsy) are ideal samples for the research of new biomarkers. The most commonly analyzed biological fluids are blood (plasma/serum) and urine. Other body fluids include prostate secretions, saliva, tears, cerebrospinal fluid (CSF), and ascites.
Clinical sample preparation: There is no uniform plan for proteomics sample preparation, but the selected strategy should be optimized or selected according to the complexity of the proteome, the available amount of samples, and the research goals. The first step of MS sample preparation includes Separate and extract proteins from clinical samples. This includes extraction reagents such as different organic solvents and detergents. Then there are tissue destruction techniques such as freeze-thaw cycles, ultrasound, or mechanical destruction to maximize protein extraction and dissolution.
Mass spectrometry configuration and scanning model: ESI relies on mature reversed-phase nano-LC technology or combined with capillary electrophoresis, which is more practical for discovery-based experiments. Orthogonal peptide separation technology is more and more widely used in clinical proteomics research. In order to improve the coverage of the proteome, the peptide pool can be separated by alkaline reversed-phase liquid chromatography or strong cation exchange chromatography.
Most discovery-based clinical proteomics studies still use data-dependent acquisition mode (DDA) to identify potential biomarkers or obtain biological insights. At present, the field of clinical proteomics is shifting to data-independent acquisition (DIA).
Protein quantification strategies: Generally speaking, relative quantification strategies are used for clinical proteomics based on discovery. Relative quantification usually relies on the use of stable isotope labels to produce covalently derived peptides, such as SILAC. Newer strategies include TMT and iTRAQ. Once the target protein has been identified, it requires further validation, and while antibody-based technologies (such as ELISA) perform this role, targeted assays based on MS are well suited for validation, especially when appropriate antibodies are not available. The most commonly used method is multiple reaction monitoring (MRM) mass spectrometry, and the newer technology is parallel reaction monitoring (PRM) mass spectrometry.
Application Of Clinical Proteomics In Cancer Research
Tissue Sample
“Discovery” basic research: Routine proteomic analysis of patient tumor tissues has become an important means for discovering biomarkers, biological pathways, and integration with existing genome/transcriptomes. Tissue-based proteomics strategies have been applied to the study of many cancer types, including prostate cancer, breast cancer, melanoma, lung cancer, ovarian cancer, and oropharyngeal cancer.
The general research idea is to compare cancer tissue samples with “healthy” adjacent control groups from the same patient to find potential diagnostic biomarkers. At the same time, compare the prognostic information of cancer patients at different stages. Once a small number of candidate proteins are identified, pathway analysis can provide insight into how these proteins are related to tumorigenesis, proliferation, metastasis, and other cancer-driving processes. Subsequently, antibody-based technology is usually used to supplement/verify the differential expression results in a larger independent cohort.
Target/multi-site verification: So far, tissue proteomics research has mainly focused on discovery-based basic research, which has revealed many potential cancer markers. In the future, it is necessary to develop targeted MS detection methods in the direction of routine tumor analysis. For example, PRM of tissue lysates will become a more mainstream direction.
Focus on spatial resolution in proteomics: A factor that needs to be considered when quantifying changes in tissue proteome is tumor specificity. Researchers are working to distinguish between changes in protein expression that stem from disease progression, and those stemming from tissue heterogeneity and changes in secondary biological pathways. The development of laser capture microdissection (LCM) is a specific way to isolate tumor cross-sections. Provides a powerful tool. Mass spectrometry imaging (MSI) technology also provides spatial information and shows its clinical potential. In addition, the heterogeneity of tumors makes proteomics require single-cell resolution, and an MS-based “mass cytometry” allows the monitoring of dozens of protein markers in a single cell by coupling antibody probes to unique stable heavy metal isotopes.
Multi-omics analysis: Although the genomes and transcriptomes of many cancers have been well elucidated, the cancer proteome and its relationship with upstream genome changes are rarely documented. In recent years. More and more studies are beginning to integrate all levels of omics data to describe the comprehensive multi-component assessment of tumors. In the future, the integration of proteomic data sets and genome-level data will become more and more common in future oncology and personalized medicine research.
Body Fluid Sample
In clinical laboratory tests, blood is the most widely used body fluid for disease diagnosis, prognosis, and treatment results. The Human Plasma Proteome Project (HPPP), launched in 2002, aims to generate an open-source database of human plasma and serum proteomes through MS.
Urine is another common body fluid sampled because it is produced in large quantities and can be easily collected in a non-invasive manner. 70% of urine protein comes from the kidneys and urethra, which is a valuable resource for urinary tract testing. The remaining 30% of urine protein comes from the filtration of blood by the glomerulus, which indicates that urine can 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 human body fluids that may be used to find biomarkers. For example, cerebrospinal fluid as a valuable source of biomarkers cannot be ignored because it has recently been shown to contain more than 300 total proteins, and is enriched in brain-specific proteins. These alternative body fluid proteomes will be described in further detail as part of the search for powerful non-invasive cancer biomarkers.
Conclusion
Due to standardized, high-throughput sample preparation technology, the field of clinical proteomics may rapidly expand in the clinical cohort. This will minimize the frequency of research due to insufficient statistics and increase the number of candidate biomarkers and drugs. The efficiency of target conversion into clinical application. Proteomics will increasingly become an important part of cancer systems biology. It integrates multi-omics data from genomics, epigenomics, transcriptomics, and PTMs. This will require higher computing power to process and analyze increasingly large data. The further increase in the sensitivity and speed of the mass spectrometer will make the depth coverage of the proteome more regular, especially without the need for a large amount of pre-separation.
The improvement of detection/quantification level will also enable clinical proteomics to develop towards the smallest input sample size and single-cell proteomics. Finally, the optimization of the data analysis process will provide more diagnostic and predictive accuracy with relatively single markers. These advances are all necessary for MS-based clinical proteomics to fully realize its potential to translate research findings into clinical practice improvements.