Predicting breast cancer prognosis using PR and PIK3CA biomarkers: a comparative analysis of diagnostic groups.

Abstract

Purpose: To evaluate the prognostic significance of progesterone receptor (PR) expression and the PIK3CA mutation status in HR+/HER2 - breast cancer patients, with the goal of screening patients who may derive the greatest benefit from PI3K-targeted therapy.

Methods: A retrospective analysis was conducted on 152 HR+/HER2 - breast cancer patients stratified by PR expression levels and PIK3CA mutation status. The study population was divided into groups on the basis of a median PR threshold of 50% and further subdivided by PIK3CA mutation status. To evaluate the variability of clinicopathologic features among these groups, t tests and ANOVA were employed. The influence of these variables on survival was analyzed via Cox regression. Additionally, a risk prediction model was developed using the PR expression level and PIK3CA mutation status. The prognostic utility of this model was examined via both Kaplan‒Meier (KM) survival curves and receiver operating characteristic (ROC) analyses. These methods have also been utilized to explore the associations between clinicopathologic parameters and clinical outcomes with respect to survival prediction and prognosis.

Results: Significant differences in age, ER expression, and Ki67, HER2, and PIK3CA mutation status were detected between the groups (P < 0.05). Specifically, elevated PR expression was correlated with lower levels of Ki67 and low HER2 expression. The presence of a PIK3CA mutation was significantly linked to survival outcomes according to both univariate and multivariate Cox regression analyses. Moreover, ROC analysis revealed that models incorporating both PR expression and PIK3CA mutation status achieved the highest level of diagnostic precision (AUC = 0.82).

Conclusion: PR expression and PIK3CA mutation status are significant prognostic markers in HR+/HER2 - breast cancer patients. Assessing these biomarkers in combination can enhance prognostic stratification, potentially guiding more informed clinical decision-making.