AbdulJabbar, Khalid

Abstract Background: Stromal tumor infiltrating lymphocytes (sTILs) are associated with improved response to chemotherapy and better overall survival (OS) in triple-negative breast cancer (TNBC). Quantification of sTILs via hematoxylin and eosin (H&E) tumor slides offers a widely available method to define a simple, effective surrogate for host anti-tumor immunity. Digitization of H&E slides has rapidly gained acceptance for use in diagnostic pathology and research, opening the door for image-based computational approaches to enumerate sTILs, among other features. Methods: We describe QuTILs - a multilayer perceptron-based framework to detect/enumerate TILs trained on >700,000 cells via The Cancer Genome Atlas Program (TCGA) slide images, implemented in the widely used open-source QuPath digital pathology suite. QuTILs was applied to digital H&E images using standard computing in primary breast tumors from phase 3 breast cancer clinical trials - CALGB 40502 (testing), CALGB 40601 and 40603 (validation) - in total 1,729 total images from 962 unique patients. The aims of this investigation were: 1) to assess the relationship between TIL patterns with overall or event-free survival outcomes; 2) to benchmark diverse image-based characteristics of TIL abundance and spatial architecture; and 3) to evaluate the association between image-based TIL levels and RNA-seq-based immune deconvolution metrics (CIBERSORT, EPIC, quanTIseq). Results: QuTILs average processing time was 7.9 minutes for whole-slide total TIL count and area density on a standard laptop workstation. In CALGB 40502, there was positive but modest correlation between pathologist TILs and QuTILs total TILs %, higher in the TNBC subgroup of CALGB 40502 (Pearson r = 0.55). In Cox proportional hazards models, the CALGB 40502 TNBC cohort showed significant univariate association for total TILs % with increasing decile (higher total TIL%) associated with reduced hazard (hazard ratio [HR]: 0.712, 95% CI: 0.559-0.906; P = 0.006), which remained significant in multivariable model incorporating age, race, and treatment arm (HR: 0.713, 95% CI: 0.556-0.915; P = 0.008). In validation TNBC clinical trial CALGB 40603, marginal TILs % was significant in the univariable Cox model (HR: 0.786, 95% CI: 0.639-0.966). Evaluation of TILs % by QuTILs with RNAseq-based deconvolution metrics in cohort-stratified and deconvolution method-stratified multivariable models showed that decile total TILs % by QuTILs was shown to significantly predict outcome in the EPIC (HR: 0.668, 95% CI: 0.466-0.957) and quanTIseq (HR: 0.646, 95% CI: 0.446-0.936) sets, with a similar magnitude as in clinical Cox, suggesting that RNA-based deconvolution estimates may not supplement the prognostic power of image-based TIL estimates. Exploratory analyses of pre- versus post-neoadjuvant therapy H&E images in CALGB 40603 and CALGB 40601 revealed a significant decrease in QuTILs total TIL% in CALGB 40603 for all pairs (n = 118 pairs, pre-median=9.52%, post-median=6.33%; Wilcoxon signed rank P < 0.001) but no significant change in QuTILs total TIL% among all pairs in CALGB 40601 (n = 82 pairs, pre-median=15.92%, post-median=15.10%; P = 0.886). Conclusions: QuTILs provides a computationally efficient open-source workflow for sTIL identification from digital H&E images that can be deployed on a standard laptop. We present the first evaluation of digital sTIL enumeration in CALGB 40502, CALGB 40603, and CALGB 40601 phase III breast cancer clinical trials and demonstrate significant prognostic association with QuTIL total TIL % among TNBCs across studies. Citation Format: M. Vater, R. Salgado, E. Blige, H. Lefebvre, A. Strahan, M. Gatti-Mays, H. Rugo, K. Ballman, M. Watson, Y. Wen, R. Leon-Ferre, K. Niazi, W. Symmans, L. Carey, K. AbdulJabbar, Z. Li, D. Stover. Qutils: open-source image-based infiltrating immune cell detection and outcomes in calgb 40502, calgb 40603, and calgb 40601 phase iii breast cancer clinical trials [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-07-27.

Abstract Recent advances in the field of immuno‐oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high‐throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi‐marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

AbstractThe clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.