UTD Press Journals
Precision Health and Clinical Innovation

Multi-Omics Stratification in Precision Oncology

Read & download PDF
Abstract

This review examines multi-omics stratification in precision oncology. The organizing question is which integration and validation steps convert molecular patterns into reproducible treatment-relevant strata. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to deriving clinically precise claims from high-dimensional exploratory clusters. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in precision oncology and biomarker-guided care.

Keywords
multi-omicsprecision oncologybiomarkersstratificationvalidation
References
  1. Acharya, D., & Mukhopadhyay, A. (2024). A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Briefings in Functional Genomics, 23(5), 549-560. https://doi.org/10.1093/bfgp/elae013 DOI
  2. Anda-Jáuregui, G. D., & Hernández-Lemus, E. (2020). Computational Oncology in the Multi-Omics Era: State of the Art. Frontiers in Oncology, 10, 423. https://doi.org/10.3389/fonc.2020.00423 DOI
  3. Arjmand, B., Hamidpour, S. K., Tayanloo-Beik, A., Goodarzi, P., Aghayan, H. R., Adibi, H., & Larijani, B. (2022). Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer. Frontiers in Genetics, 13, 824451. https://doi.org/10.3389/fgene.2022.824451 DOI
  4. Biswas, N., & Chakrabarti, S. (2020). Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer. Frontiers in Oncology, 10, 588221. https://doi.org/10.3389/fonc.2020.588221 DOI
  5. Finotello, F., & Eduati, F. (2018). Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology. Frontiers in Oncology, 8, 430. https://doi.org/10.3389/fonc.2018.00430 DOI
  6. Hsieh, W. C., Budiarto, B. R., Wang, Y. F., Lin, C. Y., Gwo, M. C., So, D. K., Tzeng, Y. S., & Chen, S. Y. (2022). Spatial multi-omics analyses of the tumor immune microenvironment. Journal of Biomedical Science, 29(1), 96. https://doi.org/10.1186/s12929-022-00879-y DOI
  7. Hsu, C. Y., Askar, S., Alshkarchy, S. S., Nayak, P. P., Attabi, K. A. L., Khan, M. A., Mayan, J. A., Sharma, M. K., Islomov, S., & Samarkhazan, H. S. (2025). AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions. Clinical and Experimental Medicine, 26(1), 29. https://doi.org/10.1007/s10238-025-01965-9 DOI
  8. Jiang, Z., Zhang, H., Gao, Y., & Sun, Y. (2025). Multi-omics strategies for biomarker discovery and application in personalized oncology. Molecular Biomedicine, 6(1), 115. https://doi.org/10.1186/s43556-025-00340-0 DOI
  9. Nicora, G., Vitali, F., Dagliati, A., Geifman, N., & Bellazzi, R. (2020). Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools. Frontiers in Oncology, 10, 1030. https://doi.org/10.3389/fonc.2020.01030 DOI
  10. Wei, L., Niraula, D., Gates, E. D. H., Fu, J., Luo, Y., Nyflot, M. J., Bowen, S. R., Naqa, I. M. E., & Cui, S. (2023). Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. British Journal of Radiology, 96(1150), 20230211. https://doi.org/10.1259/bjr.20230211 DOI
Publication details
Journal
Precision Health and Clinical Innovation
Volume
1 (2026)
Article number
phci20260003
License
CC BY 4.0