Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining.

Abstract

To generate a model for predicting nodal response to neoadjuvant systemic treatment (NAST) in biopsy-proven node-positive breast cancer patients (cN+) that incorporates tumor microenvironment (TME) characteristics and could be used for planning the axillary surgical staging procedure. Clinical and pathologic features were retrospectively collected for 437 patients. Core biopsy (CB) samples were reviewed for stromal content and tumor-infiltrating lymphocytes (TIL). Orange Datamining Toolbox was used for model generation and assessment. 151/437 (34.6%) patients achieved nodal pCR (ypN0). The following 5 variables were included in the prediction model: ER, Her-2, grade, stroma content and TILs. After stratified tenfold cross-validation, the logistic regression algorithm achieved and area under the ROC curve (AUC) of 0.86 and F1 score of 0.72. Nomogram was used for visualization. We developed a clinical tool to predict nodal pCR for cN+ patients after NAST that includes biomarkers of TME and achieves an AUC of 0.86 after tenfold cross-validation.