Jul 2026· International Journal of Clinical Oncology and Cancer Research· Vol 11, pp. 43-47· 0 citations· 14 references
TL;DR
Current evidence demonstrates that proteomics has significantly improved the identification of reliable biomarkers for early cancer detection, tumor classification, prognosis, therapeutic target discovery, and treatment monitoring, and integration of proteomics with genomic data has enhanced understanding of cancer progression and individualized therapy.
Abstract
Proteomics has emerged as a transformative field in cancer research by providing comprehensive insights into protein expression, structure, function, interactions, and post-translational modifications associated with tumor development and progression. Cancer is a complex and heterogeneous disease influenced by genetic, molecular, cellular, tissue, environmental, and socioeconomic factors that continuously evolve throughout disease progression. Despite remarkable advances in cancer diagnosis and treatment, the disease remains one of the leading causes of mortality worldwide, highlighting the need for more accurate diagnostic biomarkers and personalized therapeutic approaches. This review aims to examine the role of proteomics in cancer biomarker discovery and its contribution to the development of personalized medicine for cancer detection and treatment. The review is based on published literature describing recent advances in proteomic technologies, cancer biomarker identification, proteogenomics, and precision oncology. Current evidence demonstrates that proteomics has significantly improved the identification of reliable biomarkers for early cancer detection, tumor classification, prognosis, therapeutic target discovery, and treatment monitoring. Furthermore, integration of proteomics with genomic data has enhanced understanding of cancer progression and individualized therapy. Overall, proteomics represents a powerful platform for advancing cancer diagnosis, biomarker discovery, and personalized medicine.
Breast cancer remains a leading cause of cancer‐related morbidity and mortality among women worldwide. Despite major progress in genomic classification, molecular pathology, and targeted therapy, the identification of clinically reliable proteomics‐derived biomarkers for early detection, prognostic stratification, treatment response prediction, and longitudinal monitoring remains an unresolved translational challenge. Recent studies have applied proteomic profiling to breast cancer tissues and clinically accessible biofluids, including serum, plasma, saliva, nipple aspirate fluid, urine, and extracellular vesicle‐enriched fractions, to identify protein signatures with potential diagnostic and therapeutic relevance. Contemporary platforms, including LC‐MS/MS, data‐independent acquisition mass spectrometry, multiplexed quantitative proteomics, targeted proteomics using multiple reaction monitoring and parallel reaction monitoring, spatial proteomics, and affinity‐based high‐throughput assays such as Olink and SomaScan, have expanded the analytical depth and clinical scalability of breast cancer biomarker research. However, many candidate biomarkers remain confined to discovery or early verification stages because of limited analytical sensitivity, insufficient specificity, inter‐platform variability, small and heterogeneous cohorts, incomplete external validation, and uncertainty regarding clinical utility beyond established pathological markers. This review critically evaluates current proteomic technologies and proteomics‐derived biomarker candidates in breast cancer, distinguishes discovery‐level findings from clinically validated evidence, and discusses the major methodological, regulatory, and implementation barriers that continue to limit translation into routine oncology practice. Greater emphasis on standardized workflows, targeted verification, multi‐center validation, and clinically actionable biomarker panels will be essential for integrating proteomics into precision breast cancer care.
Mahan Hassani, Iman Morshedi, K. Matmurotov et al.· Journal of biochemical and m...· 0 citations
Recent advances ranging from aberrant genetic and epigenetic alterations; dysregulation of oncogenic signaling pathways; sophisticated crosstalk within the tumor microenvironment; intricate microbiota-inflammation interactions; fundamental discoveries to potential clinical translation; and emerging therapeutic strategies for GC are summarized.
Xiya Cheng, Yunshu Ma, Jinglu Yan et al.· Oncology Research· 0 citations
Simple Summary Metastatic colorectal cancer is associated with poor prognosis, as patients respond very differently to available therapies, and the disease can change over time. Better biomarkers are needed to help clinicians choose the most effective treatment for each patient and to monitor whether therapy is working. This review summarizes the most promising biomarkers currently being investigated in metastatic colorectal cancer, including those measured in tumor tissue and those detected through blood-based tests. We describe prognostic and predictive markers related to tumor genetics, immune activity, tumor-associated cells, and interactions with the microbiome, as well as emerging metabolic and lipid-related signatures. We also highlight circulating biomarkers such as circulating tumor DNA, immune cell profiling, and circulating tumor cells. Finally, we discuss key limitations preventing clinical implementation and explore how combining multiple biomarker types with artificial intelligence may accelerate the development of reliable tools for personalized cancer treatment.
E. Benidovskaya, N. Huyghe, M. V. Giolito et al.· Cancers· 0 citations
The increasing biological complexity and heterogeneity of cancer have driven a shift in oncology drug discovery from single-target approaches toward system-level strategies capable of capturing multilayered disease regulation. Multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics, have emerged as powerful tools for elucidating cancer-driving mechanisms, identifying therapeutic targets, and enabling biomarker-guided drug development. This review examines how integrative multi-omics approaches support cancer drug discovery and therapeutic targeting, focusing on target identification, pathway elucidation, target validation, biomarker discovery, and therapeutic development. Genomics and transcriptomics facilitate the identification of driver alterations and dysregulated signaling pathways, whereas proteomics and metabolomics provide functional insights into protein activity, metabolic reprogramming, and treatment response. We further highlight the contributions of epigenomic and microbiomic profiling to biomarker discovery, therapeutic response prediction, and precision oncology. Given the complexity of multi-omics datasets, the review also explores the application of artificial intelligence (AI) and machine-learning methodologies for data integration, network modeling, biomarker discovery, and drug repurposing, including deep learning, Bayesian frameworks, graph-based models, and explainable AI approaches. Emerging computational frameworks and integration strategies that enable interpretation of heterogeneous molecular datasets and support therapeutic discovery are also discussed. Cancer-focused examples demonstrate how integrative multi-omics frameworks have enabled the identification of clinically relevant biomarkers, therapeutic targets, and rational combination therapies. Furthermore, the clinical translation of biomarker-driven precision oncology, exemplified by HER2-, EGFR-, and MSI-directed therapies, highlights the growing impact of omics-informed approaches on personalized cancer treatment. Overall, AI-enabled multi-omics approaches hold substantial promise for accelerating cancer drug discovery and precision oncology. Multi-omics approach contributes at multiple stages of the cancer drug discovery and development pipeline. Integrated omics analyses enable systematic identification of therapeutic targets and predictive biomarkers in oncology. Artificial intelligence and machine learning facilitate efficient integration and interpretation of complex multi-omics datasets, supporting target discovery and drug repurposing. Multi-omics approach contributes at multiple stages of the cancer drug discovery and development pipeline. Integrated omics analyses enable systematic identification of therapeutic targets and predictive biomarkers in oncology. Artificial intelligence and machine learning facilitate efficient integration and interpretation of complex multi-omics datasets, supporting target discovery and drug repurposing.
Deval Koshti, N. Khandale, Jignesh Shah et al.· AAPS PharmSciTech· 0 citations
Cancer remains one of the major causes of morbidity and mortality worldwide and it creates a continuous need for more effective and personalized treatment strategies. Targeted cancer therapy has changed the field of oncology by focusing on specific molecular abnormalities involved in tumor growth, survival, and progression. By selectively interfering with cancer-related molecular pathways, targeted therapies can improve treatment precision and reduce unnecessary damage to normal tissues. Precision oncology brings together molecular diagnostics, pharmacogenomics, biomarker testing, and companion diagnostics to support individualized treatment selection and improve clinical outcomes. This narrative review summarizes recent pharmacological advances in targeted cancer therapy and examines their role in precision oncology. A structured literature search was conducted using major scientific databases and authoritative sources, with emphasis on peer-reviewed articles, clinical studies, and international guidance related to targeted therapies, molecular biomarkers, pharmacogenomics, and precision medicine. The review covers the molecular basis of precision oncology, major classes of targeted anticancer agents, predictive biomarkers, companion diagnostics, and clinical applications across different cancer types. Mechanisms responsible for drug resistance and emerging therapeutic strategies are also discussed. Recent developments in antibody-drug conjugates, liquid biopsy, multi-omics technologies, and artificial intelligence are highlighted for their potential contribution to more personalized cancer care. Several challenges continue to affect the clinical use of targeted therapies like drug resistance, tumor heterogeneity, limited access to molecular testing, and the high cost of treatment. Additionally, progress in fields of molecular pharmacology, biomarker-guided treatment, and innovative therapeutic strategies may expand the clinical applications of precision oncology. Overall, targeted cancer therapy gradually strengthens personalized cancer management and remains an important area for future oncology research and clinical practice.
Nataraj Palaniyappan, Vijayakumar Murugesan, Kannan Arumugam et al.· International journal of med...· 0 citations
The present review examines the emerging role of germline human leukocyte antigen class I genotype as a prognostic and predictive biomarker, explicitly integrating it with tissue-based immune contexture and liquid biopsy readouts by proposing immunoediting as a unifying mechanistic framework that links allele-specific antigen presentation to immune infiltration.
C. N. Baxevanis, S. Stokidis, S. Fortis et al.· International Journal of Onc...· 0 citations
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