Proteomics and cancer research

DEFINITION: The term proteome was coined in 1994 to describe the study of all the protein forms expressed within an organism, tissue, or group of cells as a function of time, age, state, and external factors. Many technological advances have led to the emergence of proteomics, which is widely employed in the cancer field.

Background: Proteomics technologies allow scientists to identify new cancer biomarkers—specific protein species in the body that are helpful for disease prediction or treatment—and to understand the underlying mechanisms associated with cancer onset and progression. These technologies are also helpful in drug development and identifying patients who may benefit from targeted therapies. Proteomics integrates key fundamental technologies such as high-throughput protein separation and profiling, mass spectrometry, large databases, and bioinformatics tools to analyze and extract information from these databases.

Scientific rationale: After the human genome was sequenced, it consisted of fewer genes than scientists had expectedapproximately thirty thousand, about twice as many genes as a worm or fly. This raised the question of how human complexity can be explained by a genome with such a relatively small number of genes. Scientists realized that the economical use of genes partly achieves such complexity. The effective number of distinct protein species present in a cell is significantly increased through various ways proteins can be modified after being synthesized, collectively called posttranslational modifications. These modifications range from the addition of biochemical functional groups such as phosphate groups (resulting in phosphorylation) and carbohydrate groups (resulting in glycosylation), the addition of other proteins or peptides, chemical changes to the amino acids of the protein, and structural changes to the protein such as formation of disulfide bridges or proteolytic cleavage of the protein.

Besides their ability to undergo modifications, proteins have a dynamic state. They can constantly move around and bind to other proteins or cellular components. Proteins can respond quickly to a changing cellular environment and play an important role in the elaborate communication pathways within and between cells. A cell’s complement of proteins in their specific posttranslationally modified forms has increasingly been recognized as playing an important role in many cellular processes and states, including cancer.

Cancer develops through a multistep process involving the accumulation of genetic alterations that lead to altered gene expression patterns, protein structures, and functions. Because the transformation of a normal cell to a cancerous one involves changes in the proteins present in the cell, proteomics in cancer research aims to monitor these changes and use the information to provide valuable insight that may aid diagnosis, prognosis, and monitoring response to therapy.

One goal of proteomics in cancer research is to define the expression patterns of proteins expressed at different levels in cells in various physiological states, such as cancer cells compared with noncancerous cells or late-stage cancer cells compared with early-stage cancer cells. Another goal is the identification of tumor markers—proteins associated with particular types of cancer. Ideally, such markers would be sensitive, selective, and measurable by a noninvasive procedure (such as by analysis of blood or urine). In addition, the emergence of proteomics offered the promise of helping elucidate the complex molecular events involved in cancer and those that control clinically important tumor behaviors such as metastases, invasion, and resistance to therapy. Biomarkers may also help devise optimal treatment plans for different patient subsets and monitor the treatment's effect.

Technologies: Proteomics consists of sample preparation, separation, and identification. Since the late 1990s, mass spectrometry has increasingly become the method of choice for analyses of complex protein samples. Mass spectrometry is a technique that generates electrically charged fragments from the proteins present in the mixture and measures particular properties of those fragments. The end product is a spectrum or chart with a series of peaks. The size of the peaks and the distance between them provide a “fingerprint” of the sample. Mass spectrometry offers the ability to rapidly and inexpensively measure thousands of proteins in a few drops of blood. The entire process, from collecting a few drops of blood to analyzing the “fingerprint,” can take less than one minute. Hundreds of samples can be analyzed sequentially, and very small amounts of protein can be detected.

The spectrum obtained from mass spectrometry experiments is difficult to analyze manually, but computers can analyze such patterns and distinguish slight differences in patterns between patients. Bioinformatics tools are computer-based algorithms that convert raw proteomics data into a useful form that can be analyzed, compared, interpreted, and stored in large databases.

High-throughput technologies capable of simultaneous and rapid analysis of multiple samples are particularly interesting in proteomics research. One example is microarray technology—an automated technique for simultaneously analyzing thousands of samples affixed to a thumbnail-sized “chip” of glass or silicon. Microarray technology allows doctors to classify cancer types based on the cancer cells' gene activity.

Examples of tumor markers: Patients at high risk for primary liver cancer are screened by the tumor marker alpha-fetoprotein in combination with ultrasonography. This regimen has resulted in earlier detection, more effective treatment, and more prolonged survival for patients with this type of cancer. Available screening tests for ovarian cancer include the biomarker cancer antigen 125 (CA 125), which has also proved to be a useful marker for monitoring response to chemotherapy. A rapid fall in the CA 125 level during chemotherapy predicts a favorable prognosis. In another example for ovarian cancer, a blood test for the levels of four proteins (leptin, prolactin, osteopontin, and insulin-like growth factor II) has been shown to discriminate with high accuracy between patients with early ovarian cancer and those who were disease-free.

Challenges: Identifying large numbers of proteins from complex biological samples remained a continuing challenge in this field. Techniques to address this challenge were evolving quickly. The biological variability among patient samples and the large concentration range over which proteins can be present also present challenges to deducing diagnostic patterns unique to specific cancer types. The fast pace of technological innovation in this area should result in identifying new biomarkers for different cancers and their use to improve diagnosis and treatment.

Standardization of proteomics techniques is needed to ensure the reproducibility required for medical applications. A large knowledge base is also needed to allow for the accurate interpretation of proteomics data. Large databases, such as the publicly available Human Protein Atlas, are helpful. Frozen tissue banks will be useful for preserving tissue samples for future analysis and comparing samples taken at different times.

The main bottlenecks for proteomics research are the rapid accumulation of data and the need for suitable computational tools for analysis. Data has typically been collected faster than researchers can validate, interpret, and integrate with other known data. Software tools are needed in all areas of data analysis, including data collection, searching, evaluation, archiving, and retrieval.

Prospects: With the ongoing rapid technological developments in this field, the prospects for identifying highly sensitive and specific biomarkers for different cancers and using them to significantly contribute to understanding, diagnosing, and treating cancer remained hopeful.

Bibliography

Al-Amrani, Safa, et al. “Proteomics: Concepts and Applications in Human Medicine.” World Journal of Biological Chemistry, vol. 12, no. 5, 2021, pp. 57-69, doi:10.4331/wjbc.v12.i5.57. Accessed 12 July 2024.

Beeton-Kempen, Natasha. "Proteomics: Principles, Techniques and Applications." Technology Networks, 5 July 2024, www.technologynetworks.com/proteomics/articles/proteomics-principles-techniques-and-applications-343804. Accessed 12 July 2024.

Kwon, Yang Woo, et al. "Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery." Frontiers, vol. 8, 2021, doi.org/10.3389/fmed.2021.747333. Accessed 12 July 2024.

Liang, S.-L., and D. W. Chan. “Enzymes and Related Proteins as Cancer Biomarkers: A Proteomic Approach.” Clinica Chimica Acta, vol. 381, 2007, pp. 93–97.

Santamaria, Salvatore. Proteases and Cancer: Methods and Protocols. Humana Press, 2024. 

Sobti, R. C., et al. Genomics, Proteomics and Biotechnology. CRC Press, 2023.

Vlahou, Antonia, and Manousos Makridakis. Clinical Proteomics: Methods and Protocols. 2nd ed. Humana, 2014.

Wajapeyee, Narendra. Cancer Genomics and Proteomics: Methods and Protocols. 2nd ed. Humana, 2014.