Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method / Journal of the Korean Cancer Association, 대한암학회지
Cancer Research and Treatment
;
: 672-684, 2019.
Article
in English
| WPRIM
| ID: wpr-763128
ABSTRACT
PURPOSE:
This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR). MATERIALS ANDMETHODS:
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Drug Resistance
/
Cell Line
/
Cohort Studies
/
Sensitivity and Specificity
/
Genome
/
Paclitaxel
/
Taxoids
/
Machine Learning
/
Learning
/
Methods
Type of study:
Diagnostic study
/
Etiology study
/
Incidence study
/
Observational study
/
Prognostic study
/
Risk factors
Limits:
Humans
Language:
English
Journal:
Cancer Research and Treatment
Year:
2019
Type:
Article
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