Breast cancer risk stratification based on combined analysis of proliferation and apoptosis.

Abstract

Abstract Background: Accurate risk stratification of breast cancer (BC) patients is critical for predicting behaviour and guiding management decision making. Despite the well-established prognostic value of proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study we hypothesised that the combined proliferation and apoptosis index will provide a more accurate in vivo growth rate measure and a precise prognostic indicator in the era of digital pathology and artificial intelligence. Methods and Results: Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 early-stage BC cases derived from two well defined BC cohorts. Mitotic and apoptotic figures were counted in defined areas visually using the published criteria. This showed significant correlation between apoptotic and mitotic scores. The morphological scoring technique was shown to be reliable since there was a significant positive correlation between apoptosis score and cleaved caspase-3 expression. High apoptotic counts were associated with features of aggressive behaviour including high grade, high pleomorphism score, and hormonal receptor negativity. Although apoptotic index (AI) was an independent prognostic indicator in multivariate analysis, the prognostic value increased when combined with the mitotic index (MI). BC patients with high MI and high AI (HM/HA) had the shortest survival in terms of BC specific survival (BCSS), distant metastasis (DMFS) and recurrence (RFS) free survival. Differential gene expression analysis (DGE) of the cases in TCGA cohort showed several genes associated with HM/HA subgroup with transcription factor Dp-1 (TFDP1) was the top gene significantly up regulated in this subgroup.Conclusions: Apoptotic cells counted in histological BC sections provides additional prognostic value in BC when combined with mitotic counts. This can be considered when using artificial intelligence algorithms to assess proliferation in BC as a prognostic indicator.