Jiayi Wu , Keyan Fang

Traditional Chinese Medicine Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 3   doi: 10.5376/cmb.2025.15.0013
Received: 18 Mar., 2025    Accepted: 29 Apr., 2025    Published: 19 May, 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wu J.Y., and Fang K.Y., 2025, Genomic biomarker discovery for drug sensitivity using omics data, Computational Molecular Biology, 15(3): 131-140 (doi: 10.5376/cmb.2025.15.0013)
 

Drug sensitivity refers to the differences in the degree of response of different individuals or cells to drugs. Revealing its molecular mechanism is crucial for achieving individualized and precise treatment. However, the average efficacy rate of the anti-cancer drugs approved by the FDA among patients is only about 40%, indicating that the traditional "one-size-fits-all" treatment model is difficult to meet the diverse needs of patients. The development of omics technology has made it possible to conduct a global analysis of biomarkers related to drug responses. By integrating multi-level data such as genomics, transcriptomics, and proteomics, genomic markers closely related to drug sensitivity can be systematically screened out, thereby predicting patients' responses to specific drugs and guiding clinical medication. This study starts from the basic concepts and molecular mechanisms of drug sensitivity, reviews the application of omics data in drug response research, methods and algorithms for genomic marker screening, as well as common data resources, and conducts a case analysis of multi-omics marker screening taking the anti-cancer drug EGFR inhibitor as an example, discussing the current challenges and limitations. Finally, the development direction of precise drug response prediction driven by artificial intelligence is prospected. This study aims to provide a reference for mining drug sensitivity biomarkers using omics data, promoting precision medicine and new drug development.

Drug sensitivity; Genomic markers; Omics integration; Individualized treatment; Bioinformatics