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<p>Multi-omics is the coordinated acquisition, integration, and interpretation of multiple datasets generated from diverse molecular layers of a biological system, intending to capture a comprehensive understanding of interactions between molecular hierarchies and reveal the complex regulatory architecture underlying cellular states, physiological processes, and disease phenotypes. Multi-omics integration signifies a transformative approach in cancer research, facilitating a systems-level comprehension of tumor biology that goes beyond the analysis of individual data layers. Through the combined analysis of data from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, this method reveals the intricate molecular networks that influence tumorigenesis and its variability. High-throughput technologies play a crucial role in this context, enabling the identification of new biomarkers, the detection of actionable therapeutic targets, and the classification of unique cancer subtypes. Integrative omics is essentially transforming precision oncology by enhancing patient risk assessment and forecasting treatment responses, thus guiding personalized diagnosis and therapy approaches. The effectiveness of this method increases when molecular data are integrated with clinical and imaging information, resulting in stronger predictive models for personalized patient treatment. Nonetheless, considerable obstacles remain, such as the integration of diverse data, the adjustment of batch effects, and the clinical understandability of intricate computational models. Tackling these challenges requires sophisticated machine learning methods, uniform data processing workflows, and ongoing cross-disciplinary teamwork. With the advancement of these methodologies, multi-omics integration will act as the essential link between large-scale data and precision medicine, providing unmatched chances to unravel the intricacies of cancer and produce effective, tailored treatments. This review highlights the latest advancements, ongoing challenges, and future pathways that are influencing the next wave of cancer research and clinical applications.</p>

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Integrating multi-omics data for next-generation cancer research and precision medicine

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Soham Biswas,
  • Sovon Acharya,
  • Anjali Prajapat,
  • Poonam Sen,
  • Shailesh Desai,
  • Shatrunjai Giri,
  • Rune Matthiesen,
  • Gyaneshwer Chaubey,
  • Prashanth Suravajhala

摘要

Multi-omics is the coordinated acquisition, integration, and interpretation of multiple datasets generated from diverse molecular layers of a biological system, intending to capture a comprehensive understanding of interactions between molecular hierarchies and reveal the complex regulatory architecture underlying cellular states, physiological processes, and disease phenotypes. Multi-omics integration signifies a transformative approach in cancer research, facilitating a systems-level comprehension of tumor biology that goes beyond the analysis of individual data layers. Through the combined analysis of data from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, this method reveals the intricate molecular networks that influence tumorigenesis and its variability. High-throughput technologies play a crucial role in this context, enabling the identification of new biomarkers, the detection of actionable therapeutic targets, and the classification of unique cancer subtypes. Integrative omics is essentially transforming precision oncology by enhancing patient risk assessment and forecasting treatment responses, thus guiding personalized diagnosis and therapy approaches. The effectiveness of this method increases when molecular data are integrated with clinical and imaging information, resulting in stronger predictive models for personalized patient treatment. Nonetheless, considerable obstacles remain, such as the integration of diverse data, the adjustment of batch effects, and the clinical understandability of intricate computational models. Tackling these challenges requires sophisticated machine learning methods, uniform data processing workflows, and ongoing cross-disciplinary teamwork. With the advancement of these methodologies, multi-omics integration will act as the essential link between large-scale data and precision medicine, providing unmatched chances to unravel the intricacies of cancer and produce effective, tailored treatments. This review highlights the latest advancements, ongoing challenges, and future pathways that are influencing the next wave of cancer research and clinical applications.