Abstract
Several gene signatures have been identified to build predictors of chemosensitivity for breast cancer. It is crucial to understand how each gene in a signature contributes to the prediction, i.e., to make the prediction model interpretable instead of using it as a black box. We utilized Random Forests (RFs) to build two interpretable predictors of pathologic complete response (pCR) based on two gene signatures. One signature consisted of the top 31 probe sets (27 genes) differentially expressed between pCR and residual disease (RD) chosen from a previous study, and the other consisted of the genes involved in Notch singling pathway (113 genes). Both predictors had a higher accuracy (82% v 76% & 79% v 76%), a higher specificity (91% v 71% & 98% v 71%), and a higher positive predictive value (PPV) (68% v 52% & 73% v 52%)) than the predictor in the previous study. Furthermore, Random Forests were employed to calculate the importance of each gene in the two signatures. Findings of our functional annotation suggested that the important genes identified by the feature selection scheme of Random Forests are of biological significance.
Highlights
Breast cancer is a clinically heterogeneous disease that demonstrates a wide variation in its clinical courses and response to chemotherapy
In a significant proportion of breast cancer patients, chemotherapy does not result in response, but can induce significant side effects and financial costs
We sought to explore the utility of Random Forests were utilized to construct two predictors based on two signatures, the top 31 probe sets and the Notch signature, and take advantage of the feature selection capability of Random Forests to measure the importance of each gene in these signatures
Summary
Breast cancer is a clinically heterogeneous disease that demonstrates a wide variation in its clinical courses and response to chemotherapy. A 30-probe set Diagonal Linear Discriminant Analysis (DLDA-30) classifier was selected for independent validation. It showed a significantly higher sensitivity (92% v 61%) than a clinical predictor including age, grade, and estrogen receptor status. This 30-probe set pharmacogenomic predictor correctly identified all but one of the patients who achieved pCR (12 of 13 patients) and all but one of those who were predicted to have residual disease had residual cancer (27 of 28 patients). We sought to explore the utility of Random Forests were utilized to construct two predictors based on two signatures, the top 31 probe sets and the Notch signature, and take advantage of the feature selection capability of Random Forests to measure the importance of each gene in these signatures
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