In a recent narrative review published in Proteomes, researchers reviewed the application of proteomics-driven biomarkers for early detection, diagnosis, and treatment of pancreatic cancer.
Additionally, the study discussed proteomics-driven methodologies, their diagnostic accuracies, and which one is most apt for a particular sample type, as well as proteomics signatures associated with cancer treatment response.
Study: Proteomics-Driven Biomarkers in Pancreatic Cancer. Image Credit: vchal/Shutterstock.com
Background
Pancreatic ductal adenocarcinoma (PDA), a type of pancreatic cancer, is the seventh leading cause of mortality worldwide. Patient-specific tumoral genomic signatures characterize PDA and set it apart from other cancers, which is also the major hindrance when identifying patient phenotypes predictive of better or poor prognosis.
The United States Food and Drug Administration (FDA) has approved carbohydrate antigen (CA) 19-9, a biomarker that helps track treatment response rather than detect PDA.
Moreover, it lacks sensitivity and specificity and has low predictability of resectability. Radical resection or pancreaticoduodenectomy is the only curative treatment for PDA. It is associated with up to 60% morbidity, and up to 80% of patients experience disease relapse.
In the evolving arena of pancreatic cancer research, proteomics has emerged as a powerful tool for identifying biomarkers and therapeutic targets for PDA, which, in turn, would help formulate new healthcare policies and bring down the cost associated with pancreatic cancer screening, diagnosis, and treatment using advanced methodologies.
Proteomics for pancreatic cancer research: recent updates
With only one biomarker (CA19-9) linked to pancreatic cancer, the landscape of laboratory tests linked to pancreatic cancer is barren. Currently, clinicians rely on imaging methods like magnetic resonance imaging (MRI) and positron emission tomography (PET) scans for pancreatic cancer diagnosis, with diagnostic sensitivities of 79% and 90%, respectively.
Proteomics holds the potential to refine the diagnostic precision of existing methods. However, the challenge is to boost the sensitivity and specificity of proteomics-based tests and devise standardized protocols for sample preparation and data analysis.
Aberrant changes in cellular behavior, e.g., altered protein activity and expression, characterize all cancers, including pancreatic cancer. Proteomics helps directly quantify protein expression/activity, inter-protein interactions, signaling, and post-translational modifications. Thus, it could help derive insights into pancreatic cancer biology. Furthermore, it could help identify new therapeutic targets, ascertain the effectiveness and toxicity of already used drugs, and study the protein-drug interactions under physiological conditions.
In other words, proteomics could unveil an individual's proteome to help devise tailored treatments based on their specific needs and tumor phenotype, thus, increasing the likelihood of success of a given therapy, especially when traditional treatments have failed.
The challenge lies in dealing with protein diversity and the complexity of their interactions. A biofluid from a single cancer patient could have multiple proteins expressing in varying isoforms and forming complex networks to regulate cellular processes, and cancer could even disrupt these interactions. Furthermore, protein molecules are highly dynamic, thus, undergo continuous changes in their expression and activity.
It raises the need to apply the 'right' proteomic technique depending on the research goals and experimental design. The key is to integrate several proteomic approaches for a more comprehensive analysis.
In pancreatic cancer research, researchers could use techniques like 2D gel electrophoresis, Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS), and isobaric tags for relative and absolute quantitation (iTRAQ).
LC-MS/MS is particularly useful in proteomics due to its potential to identify differentially expressed proteins in samples from cancer patients, their interactions, post-translational alterations, and changes in expression levels. Enzyme-Linked Immunosorbent Assay (ELISA) could help validate MS-based proteomic data.
In 2020, Jia et al. used iTRAQ-based analysis to identify serum proteins, viz., double-strand break repair protein (RAD50), transforming growth factor-beta (TGF-β1), and apoptotic protease activating factor 1 (APAF1), all of which could serve as diagnostic biomarkers of PDA.
Likewise, Wu et al. used iTRAQ with MS to identify three other biomarkers for PDA, Protein Z (PROZ), TNF Receptor Superfamily Member 6b (TNFRSF6B), which together provided an area under the curve (AUC) of 0.932 for early-stage pancreatic cancer.
The role of tumor tissue samples is crucial in pancreatic cancer research as they help study the molecular changes in the cancerous cells, e.g., genetic mutations.
Other biological samples for pancreatic cancer research are peripheral blood mononuclear cells (PBMCs), serum and plasma samples, and pancreatic juice. PBMCs provide valuable information about the systemic changes in response to cancer, e.g., cytokine expression level and transcriptome changes.
Compared to other diagnostic methods, pancreatic juice is a non-invasive mode of studying the pancreas using proteomics. It could help get valuable data on the early stages of pancreatic cancer, which is often asymptomatic during early stages and tedious to detect, and monitor cancer progression and treatment efficacy.
Furthermore, extracellular vesicles (EVs) contribute to the initialization of cellular transformation in pancreatic cancerous cells, as assessed by the proteomic analysis of EVs from cancerous and healthy pancreatic organoids.
In a 2021 study, researchers used iTRAQ to identify ALG-2 interacting protein X (ALIX) as a new diagnostic biomarker for pancreatic cancer.
Likewise, researchers found epidermal growth factor receptor pathway substrate 8 (EPS8) and G protein-coupled receptor class C group 5 member C (GPRC5C) in EVs from the serum of PDAC patients, which could serve as early detection and recurrence biomarkers.
Kafita et al. performed proteogenomic analyses of pancreatic cancer subtypes and found subtype-1 and subtype-2 tumors. They also identified protein kinases and immune checkpoint molecules as therapeutic targets through their work.
Subtype-1 tumors showed increased expression levels of mammalian targets of rapamycin (mTOR), E-Cadherin, and Raf-1 phosphorylation on serine 338. Conversely, subtype-2 tumors showed increased expression of Stathmin, meiotic recombination 11 (Mre11), and mitogen-activated protein kinase kinase 1 (MAP2K1).
Silwal-Pandit et al. used LC-MS to highlight the importance of the extracellular matrix (ECM) as a prognostic biomarker in pancreatic cancer patients.
Conclusions
Continuous investments and innovation in the multi-faceted domain of proteomics-driven oncology research would unleash its full potential in the future.
Thus, there is a need for multicenter studies to accumulate data from a vast number of patients and capture data from cancer patients across varied demographics, countries, and healthcare systems. Most importantly, the larger the study size, the greater its statistical reliability.
Overall, cooperation and shared learning among researchers could accelerate the pace of innovations in the fast-growing area of pancreatic oncology research via proteomics and the comprehension of pancreatic cancer biology.
International team releases recommendations for AI use in neuro-oncology