Precision medicine in oncology: integrative multi-omics approaches for personalized cancer therapy
multi-omics integration; precision oncology; computational oncology; biomarker discovery; cancer subtyping; machine learning; systems biology; translational bioinformatics
AuthorsAbstract:Cancer remains a leading cause of global morbidity and mortality, with more than 19 million new cases diagnosed annually and an increasing burden in both developed and developing regions. The extraordinary molecular heterogeneity of tumors, coupled with dynamic genomic instability and diverse microenvironmental interactions, challenges the efficacy of conventional therapeutic paradigms. Traditional approaches relying on histopathological and single-gene profiling inadequately capture the multidimensional complexity of cancer biology. Consequently, there is a pressing need for integrative frameworks that can unravel the interplay among multiple molecular layers to guide precision medicine. This study explores the application of integrative multi-omics analysis— encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics—to develop robust predictive models for personalized cancer therapy. Using cross-platform datasets from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), data integration was achieved through advanced machine learning pipelines combining latent variable modeling and ensemble classifiers. The integrative model achieved superior predictive performance compared with singleomics methods, revealing clinically actionable biomarkers and pathwaylevel interactions governing therapeutic sensitivity. Multi-layer clustering identified distinct molecular subtypes associated with DNA repair deficiency, immune activation, and metabolic dysregulation, each of which corresponded to specific therapeutic vulnerabilities. Empirical findings underscore the translational value of multi-omics–driven strategies in improving patient stratification, optimizing drug selection, and facilitating mechanism-based therapeutic interventions. By bridging diverse molecular landscapes into coherent predictive frameworks, integrative multi-omics approaches offer a transformative path toward data-informed, individualized oncology care. This study demonstrates how precision oncology can evolve from descriptive molecular profiling to truly predictive and personalized clinical management.
IntroductionCancer persists as one of the leading causes of mortality worldwide, responsible for approximately 10 million deaths annually, with its incidence projected to rise as global populations age and lifestyle-associated risk factors increase [World Health Organization, 2025]. The disease’s molecular complexity and heterogeneity represent major obstacles to effective treatment and long-term disease control. Even within a single tumor type, patients frequently demonstrate divergent therapeutic responses, relapse patterns, and clinical outcomes. This variability arises from the interplay of genomic instability, epigenetic remodeling, cellular signaling dynamics, tumor–microenvironmental interactions, and immune modulation [Hanahan, 2022]. Such heterogeneity underscores the fundamental inadequacy of conventional •••••••••••••••••••••••••••••••• ejprd.org- Published by Riset Publishing Services LLC.
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