Xiaowen Cao, Li Xing, Hao Ding, He Li, Yushan Hu, Yao Dong, Hua He, Junhua Gu, Xuekui Zhang
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引用次数: 0
Abstract
Background and objective: Timely treatment is crucial for cancer patients, so it's important to administer the appropriate treatment as soon as possible. Because individuals can respond differently to a given drug due to their unique genomic profiles, we aim to use their genomic information to predict how various drugs will affect them and determine the best course of treatment.
Methods: We present Kernelized Residual Stacking (KRS), a new multi-task learning approach, and use it to predict the responses to anti-cancer drugs based on genomic data. We demonstrate the superior predictive performance of KRS, outperforming popular competitors, by utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) study and the Cancer Cell Line Encyclopedia (CCLE) study. Downstream analysis of feature genes selected by KRS is conducted to discover novel therapies.
Results: We used two genomic studies to show that KRS outperforms a few popular competitors in predicting drugs' susceptibilities. Through downstream analysis of feature genes selected by KRS, we found that the PI3K-Akt pathway could alter drugs' susceptibilities, and its expression correlated positively with the hub gene ERBB2. We discovered eight novel small molecules based on these feature genes, which could be developed into novel combination therapies with anti-cancer drugs.
Conclusions: KRS outperforms competitors in prediction performance and selects feature genes highly correlated with drugs' susceptibilities. Novel biological results are found by investigating KRS's feature genes.
期刊介绍：
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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