Abstract P3-08-12: An open source, automated tumor infiltrating lymphocyte algorithm for prognosis in triple-negative breast cancer
Authors: Yalai Bai, Balazs Acs, Jon Zugazagoitia, Sandra Martinez-Morilla, Fahad Shabbir Ahmed, David L. Rimm
Journal: Cancer Research (2020)

Abstract Background: The presence of high TILs (tumor infiltrating lymphocytes) have been shown to be predictive of response to chemotherapy and is also a prognostic factor associated with better outcome in breast cancer, especially in early stage triple-negative (TNBC) and HER2-positive breast cancers. TIL assessment, while now more standardized due to the efforts of Salgado and the International TIL Working Group (https://www.tilsinbreastcancer.org/), are still a subjective test with variability in evaluation that has prevented broad adoption. Given the advances in application of artificial intelligence to pathology images, we believe the next step for TILs is to make them automated and objective and to identify a standardized and meaning TIL cut-point. The aim of this study is to build an open source, H&amp;E image-based automated TIL assessment algorithm for breast cancer that allows global standardization of TILs for prognostic value. Materials and methods: Using QuPath open source software, we first built a neural network classifier for image-based, automated assessment of TILs. It distinguishes tumor cells, lymphocytes, stromal cells and other cells on hematoxylin-eosin (H&amp;E) stained sections. We then defined “eTILs%” calculated as follows for the percentage of machine defined TILs: (TILs/TILs+Tumor cells) *100. A retrospective collection of 63 TNBC cases was used as the training set (Set A) and then tested for cell classifier accuracy and the optimal “eTILs%” cut point. The validation sets were a retrospective collection of 354 TNBC patients comprised of three independent validation subsets (Set B; N=87, Set C; N=183, and Set D; N=83) in both tissue microarray (TMA) and whole tissue section (WTS) format. Results: Using an optimal cut point (30%) derived from TNBC cohort training set A, patients with high eTILs% displayed an overall survival benefit (HR 0.4, p=0.0150). This algorithm was then applied in other three TNBC validation sets (Set B: HR=0.42, p=0.0033; Set C: HR=0.42, p=0.0127; Set D in TMA format: HR=0.39, p=0.0089). For Set D, we also tested WTS format which showed HR=0.23, (p=0.0155). The validation sets were combined to assess independence from clinical status in a multi-variable analysis where eTILs% was independently associated with improved overall survival (HR=0.35, p&amp;lt;0.0001). Conclusion: We have constructed a single institutional algorithm built from open source QuPath software that is a robust and independent prognostic factor. With further validation in tissues from other institutions and larger cohorts, this algorithm could be potentially useful as a broadly applicable, standardized and globally available method for assessment of TILs in triple negative breast cancer patients. Citation Format: Yalai Bai, Balazs Acs, Jon Zugazagoitia, Sandra Martinez-Morilla, Fahad Shabbir Ahmed, David L. Rimm. An open source, automated tumor infiltrating lymphocyte algorithm for prognosis in triple-negative breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-12.