Abstract
Simple SummaryOnly 20–50% of patients with triple negative breast cancer achieve a pathological complete response from neoadjuvant chemotherapy, a strong indicator of patient survival. Therefore, there is an urgent need for a reliable predictive model of the patient’s pathological complete response prior to actual treatment. The purpose of this study was to develop such a model based on random forest recursive feature elimination and to benchmark the performance of the proposed model against existing predictive models. Our study suggests that an 86-gene-based random forest model associated to DNA repair and cell cycle mechanisms can provide reliable predictions of neoadjuvant chemotherapy response in patients with triple negative breast cancer.Neoadjuvant chemotherapy (NAC) response is an important indicator of patient survival in triple negative breast cancer (TNBC), but predicting chemosensitivity remains a challenge in clinical practice. We developed an 86-gene-based random forest (RF) classifier capable of predicting neoadjuvant chemotherapy response (pathological Complete Response (pCR) or Residual Disease (RD)) in TNBC patients. The performance of pCR classification of the proposed model was evaluated by Receiver Operating Characteristic (ROC) curve and Precision Recall (PR) curve. The AUROC and AUPRC of the proposed model on the test set were 0.891 and 0.829, respectively. At a predefined specificity (>90%), the proposed model shows a superior sensitivity compared to the best performing reported NAC response prediction model (69.2% vs. 36.9%). Moreover, the predicted pCR status by the model well explains the distance recurrence free survival (DRFS) of TNBC patients. In addition, the pCR probabilities of the proposed model using the expression profiles of the CCLE TNBC cell lines show a high Spearman rank correlation with cyclophosphamide sensitivity in the TNBC cell lines (SRCC , p-value ). Associations between the 86 genes and DNA repair/cell cycle mechanisms were provided through function enrichment analysis. Our study suggests that the random forest-based prediction model provides a reliable prediction of the clinical response to neoadjuvant chemotherapy and may explain chemosensitivity in TNBC.
Highlights
To address the aforementioned problems, we propose a novel Neoadjuvant chemotherapy (NAC) prediction model based on random forest (RF)
Because GSE25066 had the highest number of triple negative breast cancer (TNBC) patients, we considered this dataset as the development dataset and the three other datasets as the independent validation datasets (GSE20271, GSE20194, and GSE32646)
Because the proposed model is associated with DNA Repair mechanisms, the high performance of the proposed model in MPS2 subtype characterized by upregulation of carbohydrate and nucleotide metabolic pathways can be considered as consistent results with the report of this study shows the utility of the proposed RF model, there are some limitations
Summary
Triple negative breast cancer (TNBC) is a difficult form of breast cancer to treat because of its rapid growth and high recurrence rate [1]. Compared to the successful application of targeted therapies for other types of breast cancer, targeted therapy for TNBC is difficult due to the lack of major receptors in breast cancer such as ER/PR/HER2 receptors [3,4]. Several targeted therapies for TNBC patients have recently been introduced [5–7], but due to the limited applicability of these therapies [8], cytotoxic chemotherapy remains the mainstay of treatment [9]
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