Abstract
AbstractCombination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose–response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Medicine (miscellaneous)
Reference54 articles.
1. Fowler, H. et al. Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers. BMC Cancer 20, 2 (2020).
2. Ketcher, D., Otto, A. & Reblin, M. Chronic conditions among advanced cancer patients and their spouse caregivers. J. Clin. Orthod. 37, 20–20 (2019).
3. Bayat Mokhtari, R. et al. Combination therapy in combating cancer. Oncotarget 8, 38022–38043 (2017).
4. O’Neil, J. et al. An unbiased oncology compound screen to identify novel combination strategies. Mol. Cancer Ther. 15, 1155–1162 (2016).
5. Holbeck, S. L. et al. The National Cancer Institute ALMANAC: a comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity. Cancer Res. 77, 3564–3576 (2017).
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