Computational Molecular Biology 2025, Vol.15, No.3, 131-140 http://bioscipublisher.com/index.php/cmb 131 Research Insight Open Access Genomic Biomarker Discovery for Drug Sensitivity Using Omics Data Jiayi Wu, Keyan Fang Traditional Chinese Medicine Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China Corresponding author: keyan.fang@cuixi.org 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 Copyright © 2025 Wu and Fang, 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.6 Preferred citation for this article: 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) Abstract 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. Keywords Drug sensitivity; Genomic markers; Omics integration; Individualized treatment; Bioinformatics 1 Introduction The same anti-cancer drug, when applied to different people, often yields vastly different results. Some people show remarkable therapeutic effects, while others have almost no reaction. The statistics are quite striking - even for drugs approved by the FDA, the average effective rate is only around 40%, which means that more than half of the patients may have suffered in vain. It's no wonder. Tumors, as a disease, are inherently complex, involving genetic differences, tissue heterogeneity, metabolic status... Either of them can affect the efficacy of the medicine (Creighton et al., 2013). Drug sensitivity, in essence, refers to whether a drug can exert its expected effect on a specific person or cell. The problem is that such differences cannot be discernment based on experience; answers have to be sought at the molecular level (Shaffer, 2022). If the key molecules that determine the drug response can really be identified, doctors can tailor the prescription to the patient's condition before prescribing drugs, which not only improves the therapeutic effect but also reduces the patient's suffering. This is precisely the core issue that precision medicine aims to address. The development speed of omics technology in recent years has almost kept people at a loss. In the past, when studying drug responses, it was often done by checking each gene one by one. Nowadays, it is possible to observe the changes of tens of thousands of genes at once, and even the expression patterns can be analyzed simultaneously. Once the field of vision is broadened, there will naturally be more things to discover. Many new markers have been unearthed under this kind of "panoramic scanning" (Guang et al., 2018). The more data there is, the more complex the problem becomes. However, this has instead given rise to various algorithms - machine learning and network analysis, all of which have come into play to dig out connections and find patterns from the vast amount of data (Jung et al., 2021). Genome, transcriptome, proteome, metabolome... When these levels of information are put together, we can see how drugs truly work and even identify the root causes behind drug resistance (Sun et al., 2021). It can be said that current drug sensitivity research is almost inseparable from the
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