Spatially-resolved tumour infiltrating immune cells and prognosis in breast cancer

Abstract

Background The immune response in breast tumors has an important role in prognosis, but the role of spatial localization of immune cells and of interaction between subtypes is not well characterized. We evaluated the association between spatially resolved tissue infiltrating immune cells (TIICs) and breast cancer specific survival (BCSS) in a large multicenter study. Patients and methods Tissue microarrays with tumor cores from 17,265 breast cancer patients of European descent were stained for CD8, FOXP3, CD20, and CD163. We developed a machine learning based tissue segmentation and immune cell detection algorithm using Halo to score each image for the percentage of marker positive cells by compartment (overall, stroma, or tumor). We assessed the association between log transformed TIIC scores and BCSS using Cox regression. Results Total CD8+ and CD20+ TIICs (stromal and intra-tumoral) were associated with better BCSS in women with ER-negative (HR per standard deviation = 0.91 [95% CI 0.85 - 0.98] and 0.89 [0.84 - 0.94] respectively) and ER-positive disease (HR = 0.92 [95% CI 0.87 - 0.98] and 0.93 [0.86 - 0.99] respectively) in multi-marker models. In contrast, CD163+ macrophages were associated with better BCSS in ER-negative disease (0.94 [0.87 - 1.00]) and a poorer BCSS in ER-positive disease 1.04 [0.99 - 1.10]. There was no association between FOXP3 and BCSS. The observed associations tended to be stronger for intra-tumoral than stromal compartments for all markers. However, the TIIC markers account for only 7.6 percent of the variation in BCSS explained by the multi-marker fully-adjusted model for ER-negative cases and 3.0 percent for ER-positive cases. Conclusions The presence of intra-tumoral and stromal TIICs is associated with better BCSS in both ER-negative and ER-positive breast cancer. This may have implications for the use of immunotherapy. However, the addition of TIICs to existing prognostic models would only result in a small improvement in model performance.