Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Abstract

Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.

Association between CD8+ Tumor Infiltrating Lymphocytes and the Clinical Outcome of Patients with Operable Breast Cancer Treated with Adjuvant Dose-Dense Chemotherapy—A 10 Year Follow-Up Report of a Hellenic Cooperative Oncology Group Observational Study

“…These were predominantly tumor size, nodal status, and histological grade, likely following the reported knowledge that the more advanced and aggressive tumors offer a biological background for neoantigen production. This, in turn, enhances a tumor’s immunogenicity, and intratumoral/stromal lymphocytic infiltration is able to predict better responses [ 52 , 53 ]. The role of tumor size and nodal size in prognosis is a well-known issue, and its importance to the high expression of TILs has previously been emphasized in other reports [ 13 , 54 ].…”

Multi-modality artificial intelligence in digital pathology

“…In more complicated immune infiltration-related studies, it is a strong prognostic biomarker that relies on manual scoring [ 114–116 ]. With the aid of a computer, we can clearly detect immune cells in the tumor area and derive evaluation indicators of prognostic value from the number and distribution of cells (such as stromal TILs density statistics) [ 96 , 117 , 118 ]. This is the necessary research for clinical application of image features to drive the digital pathology assistance system to the ground.…”

“…Manual analysis of immune tissue environmental characteristics requires cytokeratin and IHC to identify each tissue and cell. Especially in complex tumor models, there is an insurmountable difficulty for humans to accurately calculate the number of each type of cells in the matrix [ 96 ]. Martino et al .…”

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer

“…Anticipating the influx of artificial intelligence and machine learning algorithms (AI/ML) to assess TILs [17][18][19][20][21][22], we began the High Throughput Truthing (HTT) project in collaboration with an international team of pathologists, clinical scientists, and leadership from the Working Group [23]. Our goal is to create a dataset of digital slide data with pathologist annotations for the validation of computational pathology models (e.g., AI/ML) for stromal TILs (sTILs) assessment that will be fit for a regulatory purpose as a medical device development tool [24].…”