Prognostic Implications of the Residual Tumor Microenvironment after Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Patients without Pathological Complete Response

Abstract

With a high risk of relapse and death, and a poor or absent response to therapeutics, the triple-negative breast cancer (TNBC) subtype is particularly challenging, especially in patients who cannot achieve a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Although the tumor microenvironment (TME) is known to influence disease progression and the effectiveness of therapeutics, its predictive and prognostic potential remains uncertain. This work aimed to define the residual TME profile after NAC of a retrospective cohort with 96 TNBC patients by immunohistochemical staining (cell markers) and chromogenic in situ hybridization (genetic markers). Kaplan-Meier curves were used to estimate the influence of the selected TME markers on five-year overall survival (OS) and relapse-free survival (RFS) probabilities. The risks of each variable being associated with relapse and death were determined through univariate and multivariate Cox analyses. We describe a unique tumor-infiltrating immune profile with high levels of lymphocytes (CD4, FOXP3) and dendritic cells (CD21, CD1a and CD83) that are valuable prognostic factors in post-NAC TNBC patients. Our study also demonstrates the value of considering not only cellular but also genetic TME markers such as MUC-1 and CXCL13 in routine clinical diagnosis to refine prognosis modelling.

Keywords: genetic markers; immune markers; neoadjuvant therapy; relapse; survival; triple-negative breast cancer; tumor microenvironment.

Figure 1

Kaplan–Meier analysis of five-year survival probability with respect to significant immunohistochemistry (IHC) and mRNA marker expression. (A) Kaplan–Meier analysis of overall survival (OS) among all patients. (B) Superimposed Kaplan–Meier curves of survival probability stratified by nodal status at diagnosis, and (C) depending on the type of response to neoadjuvant chemotherapy. In the plots in B and C, the continuous lines show the OS of patients with IHC immune marker concentrations greater than the median of all patients or when mRNA markers were expressed, and the dashed lines show the OS when IHC immune marker concentrations were less than the median of all patients or when the mRNA markers were not expressed. Significance of the log-rank test is indicated in the figures, with significant differences between variables indicated by probabilities highlighted in bold.

Figure 2

Kaplan–Meier analysis of five-year relapse-free survival (FRS) with respect to significant immunohistochemistry (IHC) and mRNA markers expression. (A) Kaplan–Meier analysis of RFS among all patients. (B) Superimposed Kaplan–Meier curves of RFS stratified by nodal status at diagnosis, and (C) depending on the type of response after NAC. In the plots in B and C, the continuous lines show the RFS of patients with IHC immune marker concentrations greater than the median of all patients or when mRNA markers were expressed, and the dashed lines show the RFS when IHC immune markers concentrations were less than the median of all patients or when mRNA markers were not expressed. Significance of the log-rank test is indicated in the figures, with significant differences between variables indicated by probabilities highlighted in bold.

Figure 3

Kaplan–Meyer analysis of five-year survival probability with respect to the effect of the combinations having elevated content of 0 to 5 of the selected immune markers (CD4, FOXP3, CD1a, CD21, CD83) on (A) overall survival (OS) and (B) relapse-free survival (RFS) probabilities.

Figure 4

Receiver operating characteristic (ROC) curves evaluating the capacity of the final multivariate models to predict: (A) overall survival (OS), (B) OS after bootstrapping, (C) relapse-free survival (RFS), and (D) RFS after bootstrapping.