A Predictor of Pathological Complete Response to Neoadjuvant Chemotherapy Stratifies Triple Negative Breast Cancer Patients with High Risk of Recurrence

Abstract

We developed a test to predict which patients will achieve pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and which will have residual disease (RD). Gene expression data from pretreatment biopsies of patients with all breast cancer subtypes were combined into a 519-patient cohort containing 177 TNBC patients. Two RNA classifiers of 16 genes each were sequentially applied to the total cohort, classifying patients into 3 distinct classes. The test performance was further validated in an independent 304-patient cohort. The test accurately identified 70.5% (79/112) of pCR and 83.5% (340/407) of RD patients in the total population, and 75.0% (45/60) of pCR and 75.2% (88/117) of RD patients in the TNBC subset. For the independent cohort, the test identified 91.5% RD patients in the total population and 86.2% RD patients in the TNBC subset. However, the identification of pCR in both total and TNBC population are as low as 21.1% and 30%, respectively. The TNBC RD patients were subdivided by our classifiers, with one class showing significantly higher levels of Ki67 expression and having significantly poorer survival rates than the other classes. This stratification of patients may allow predicted residual disease classes to be assigned an alternative therapy.

Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer

“…However, most of them basically focused on the characteristics from imaging or laboratory indexes. From molecular perspective, Hamy et al focused on the predictive values of immunological infiltration for the response to NCT in TNBC, while Ocana and colleagues investigated the immunologic phenotypes of TNBC and the potential associations with the clinical outcomes, which was inconsistent with the findings curated from the study conducted by Fournier et al [31][32][33]. Although the heterogenous profiles of TNBC have been extensively discussed, these predictive techniques were primarily centered on the molecular characteristics with few clinical features adopted.…”

RNA expression classifiers from a model of breast epithelial cell organization to predict pathological complete response in triple negative breast cancer

“…We used a previously described methodology to develop and apply RNA expression classifiers sequentially to the patient cohort to stratify patients based on clinical outcomes [14]. We combined bias-reduction generalized linear model (brglm) and bootstrapping of samples and genes to identify genes that have the most predictive values.…”

Effects of Cancer Stem Cells in Triple-Negative Breast Cancer and Brain Metastasis: Challenges and Solutions

“…In addition, the development of systemic chemotherapy regimens in TNBC patients is challenging because of the limitation of disease classification that provides clinical considerations and the lack of proven oncogenic drivers that target vast disease heterogeneity [ 16 , 54 ]. In current treatment practices for TNBC primaries, there are over one-half of the patients receiving chemotherapy who do not achieve pathologic complete response (pCR) [ 55 ]. Thus, the selection of chemotherapy regimens to treat BM-TNBC remains largely debatable.…”

“…Notably, at present, none of the studies has reached the level of guiding systemic treatment efficacy for various TNBC tumors. For over one-half of patients receiving chemotherapy who failed to achieve pCR, predictors are required to be identified for selecting suitable chemotherapy candidates [ 55 ]. Nevertheless, given the vast heterogeneity from patient-to-patient and from primary-to-metastatic site in TNBC, there is almost always a discrepancy between preclinical or clinical trials and real-world practice.…”