AI‐based intra‐tumor heterogeneity score of Ki67 expression as a prognostic marker for early‐stage ER+/HER2− breast cancer

Abstract

Abstract Early‐stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2−) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra‐tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well‐characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2− BC patients with high significance in terms of BC‐specific survival (p < 0.00001) and distant metastasis‐free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Keywords

• AI based prognostic biomarkers
• computational pathology
• Ki67 expression
• luminal breast cancer