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KMID : 0939920190510020672
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대한암학회지
2019 Volume.51 No. 2 p.672 ~ p.684
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Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method
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Kim Young-Rae
Kim Dong-Ha
Kim Sung-Young
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Abstract
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Purpose: This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR).
Materials and Methods: Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the learning parameter via robust cross-study validation.
Results: For internal cross-study validation, the ATR model produced a perfect performance with an overall area under the receiver operating curve (AUROC) of 1.000 with an area under the precision-recall curve (AUPRC) of 1.000, a Brier score of 0.007, a sensitivity and a specificity of 100%. The model showed an excellent performance on two independent blind ATR cohorts (overall AUROC of 0.940, AUPRC of 0.940, a Brier score of 0.127). When we applied our algorithm to two large-scale pharmacogenomic resources for ITR, the Cancer Genome Project (CGP) and the Cancer Cell Line Encyclopedia (CCLE), an overall ITR cross-study AUROC was 0.70, which is a far better accuracy than an almost random level reported by previous studies. Furthermore, this model had a high transferability on blind ATR cohorts with an AUROC of 0.69, suggesting that general predictive features may be at work across both ITR and ATR.
Conclusion: We successfully constructed a multi-study?derived personalized prediction model for ATR with excellent accuracy, generalizability, and transferability.
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KEYWORD
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Taxoids, Paclitaxel, Docetaxel, Drug resistance, Molecular diagnosis, Machine learning
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