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.
Artículo
en Inglés
| 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.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Resistencia a Medicamentos
/
Línea Celular
/
Estudios de Cohortes
/
Sensibilidad y Especificidad
/
Genoma
/
Paclitaxel
/
Taxoides
/
Aprendizaje Automático
/
Aprendizaje
/
Métodos
Tipo de estudio:
Estudio diagnóstico
/
Estudio de etiología
/
Estudio de incidencia
/
Estudio observacional
/
Estudio pronóstico
/
Factores de riesgo
Límite:
Humanos
Idioma:
Inglés
Revista:
Cancer Research and Treatment
Año:
2019
Tipo del documento:
Artículo
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