Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge

The prognostic significance of tumor-infiltrating lymphocytes (TILs) in breast cancer has been recognized for over a decade. Although histology-based scoring recommendations exist to standardize visual TILs assessment, interobserver agreement and reproducibility are hampered by heterogeneous infiltration patterns, highlighting the importance of computational approaches. Despite advances to automate TILs quantification, adoption of computational models has been hindered by lack of consensus on scoring methods and lack of large-scale benchmarks. To address these limitations, we launched the international TIGER challenge, a public competition to build open-source computational TILs (cTILs) models in digital pathology. Here, we present the largest comprehensive multi-centric validation of multiple cTILs methods on surgical resections and biopsies using 3,708 Triple Negative Breast Cancer (TNBC) and human epidermal growth factor receptor 2 positive (HER2+) breast cancers from clinical practice and phase 3 clinical trials. We report benchmarks on image analysis performance of each method and show the strong agreement of cTILs with panels of pathologists. We show the positive association of cTILS with response after neoadjuvant therapy in HER2-positive, superior to visually scored TILs. We also show that cTILs add independent information to clinical variables in surgically resected TNBC but not in HER2-positive disease and breast biopsies.