Spatial interplay of lymphocytes and fibroblasts in estrogen receptor-positive HER2-negative breast cancer

Abstract

In estrogen-receptor-positive, HER2-negative (ER+HER2−) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent cells in breast cancer and harbor immunomodulatory capabilities. Here, we evaluate the molecular and clinical impact of the spatial patterns of TILs and fibroblast in ER+HER2− breast cancer. We used a deep neural network to locate and identify tumor, TILs, and fibroblasts on hematoxylin and eosin-stained slides from 179 ER+HER2− breast tumors (ICGC cohort) together with a new density estimation analysis to measure the spatial patterns. We clustered tumors based on their spatial patterns and gene set enrichment analysis was performed to study their molecular characteristics. We independently assessed the spatial patterns in a second cohort of ER+HER2− breast cancer (N = 630, METABRIC) and studied their prognostic value. The spatial integration of fibroblasts, TILs, and tumor cells leads to a new reproducible spatial classification of ER+HER2− breast cancer and is linked to inflammation, fibroblast meddling, or immunosuppression. ER+HER2− patients with high TIL did not have a significant improved overall survival (HR = 0.76, P = 0.212), except when they had received chemotherapy (HR = 0.447). A poorer survival was observed for patients with high fibroblasts that did not show a high level of TILs (HR = 1.661, P = 0.0303). Especially spatial mixing of fibroblasts and TILs was associated with a good prognosis (HR = 0.464, P = 0.013). Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER+HER2− breast cancer and suggest that this spatial interplay holds a decisive role in their cancer-immune interactions.

Deep Learning Allows Assessment of Risk of Metastatic Relapse from Invasive Breast Cancer Histological Slides

“…4A) were not only located in tumor stroma (P < 0.0001), but also in the adjacent normal tissue (P < 0.0001). Moreover, the model was also able to capture complex cell interactions, as spatial mixing of fibroblasts and tumor infiltrating lymphocytes (TILs) were also retrieved in low risk tiles (P = 2.5E-04), a feature of good prognosis highlighted in a recent study (35). These results showed that our model was able to capture well-established prognostic factors as well as complex and novel patterns to predict patient outcomes.…”

Tumor-infiltrating lymphocytes in HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2-blockade

“…We recommend that such trials always involve at least two pathologists for TIL scoring, and rereview by a third pathologist in case of discordance. In addition, the evolving research related to spatial infiltration patterns may impact interpretation of TIL scores in the future [47][48][49] . In our study we did not assess spatial distribution of TILs or interactions with other cells in the tumor microenvironment.…”

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma

“…TIME is primarily affected by the interactions between tumor-infiltrating lymphocytes (TILs) and tumor cells. At present, many research fields [ [8] , [9] , [10] , [11] ] have demonstrated that the interaction between TILs and tumor cells is associated with the prognosis of patients. Therefore, it is anticipated that the interactions between TILs and tumor cells will be characterized in order to identify a novel biomarker for prognosis prediction and risk stratification in LUAD.…”