Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression

SIMPLE SUMMARY: The assessment of tumor-infiltrating lymphocytes (TILs) is gaining acceptance as a robust biomarker to help predict prognosis and treatment response. We evaluated TILs in whole-slide images (WSIs) of breast cancer tissue specimens stained with hematoxylin and eosin (H&E) from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). Our approach utilized computational pathology to characterize the abundance and spatial distribution of TIL infiltrates in breast cancer WSIs. This work (1) examines the relationship between the global abundance and spatial features of TIL infiltrates with clinical outcomes in order to (2) evaluate their significance as prognostic biomarkers in a multifactorial analysis of progression-free interval in the TCGA BRCA and UNC CBCS datasets. Our findings present a paradigm for pathologists to assess the risk of recurrence in breast cancer by using computational pathology to spatially map, quantify, and interpret TILs in the tumor microenvironment. ABSTRACT: Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.