Wen, Hannah Y.

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.

Abstract Background: The IMpassion130 study demonstrated prolonged overall survival when atezolizumab, a PD-L1 inhibitor, was added to nab-paclitaxel in PD-L1 positive patients with advanced triple negative breast cancer (TNBC). Approximately 40% of tumors were PD-L1 positive, utilizing a cutoff of ≥ 1% immune cell (IC) staining with the Ventana SP142 immunohistochemical (IHC) assay. The Food and Drug Administration (FDA) approved this assay as a companion test to determine patient eligibility for atezolizumab therapy. However, literature in lung cancer shows that pathologists have a high discordance in assessing PD-L1 on IC. The FDA companion test documentation for SP142 shows high inter-laboratory reproducibility with nearly 95% overall percent agreement (OPA) between 2 readers for 2 category scoring of IC (positive versus negative) in TNBC in a central laboratory. PD-L1 testing for breast cancer is now becoming widespread in pathology laboratories. The goal of this study was to assess concordance in PD-L1 scoring between 19 pathologists from 14 institutions examining the same 86 TNBC tissues stained with the SP142 and SP263 antibodies. We also present a new method to determine the minimum number for evaluators needed to estimate concordance between large numbers of readers as occurs in real life setting. Methods: Full tissue sections were stained exactly as prescribed on the label on the Ventana Benchmark with the SP142 and SP263 PD-L1 assays. Sixty-eight and 76 cases were evaluable respectively. Stained slides were scanned and digital files distributed to pathologists who independently scored cases as either negative (< 1% IC staining) or positive (≥ 1% IC staining) and estimated the % of IC staining for positive cases. We applied a method that we call Observers Needed to Evaluate Subjective Tests (ONEST). For any combination of pathologists, we can quantify the degree of overall agreement using the proportion of tissue samples upon which all selected pathologists agreed, which can lead to a plot of non-increasing trajectory of agreement as the number of pathologists increases. We computed and plotted the empirical estimate of the overall agreement with the 95% confidence interval (CI) from 1000 randomly selected permutations of the pathologists. Results: PD-L1 was interpreted as positive with SP263 in an average of 78% of cases compared to 58% with the SP142 assay (p<.0001). IC positive continuous scores ranged from 1-95% (mean=20%) and 1-90% (mean=10%) for SP263 and SP142. Complete 2 category (positive vs negative) scoring concordance across all observers was achieved in 38 cases (50%) with SP263 and 26 cases (38%) with SP142. The intraclass correlation coefficient (ICC) for 2 category scoring of SP263 and SP142 was 0.513 and 0.560 respectively. ONEST plots made for each assay for 19 pathologists showed a decrease in OPA as number of observers increased, reaching a low plateau of approximately 0.46 at 10 observers for SP263 and 0.42 at 8 observers for SP142. Conclusions: Utilizing the FDA approved SP142 assay, we found a higher prevalence of PD-L1 positive tumors than published in the IMpassion130 trial but lower prevalence than seen with the SP263 assay. The different proportion of positive cases seen with two different assays highlights the need to establish cutoffs for each assay to correspond with clinical benefit. The ICC of scoring immune cells with both PD-L1 assays showed poor reproducibility across multiple pathologists. The ONEST analysis finds a plateau with 10 pathologists, only reaching an OPA of 0.4 for immune cell assessment. This suggests that more than half of the pathologists in real world situations may mis-assign patient IC scores. This could lead to a high percentage of patients either receiving atezolizumab, when they are unlikely to benefit or not receiving atezolizumab when they may benefit. Citation Format: Emily Suzanne Reisenbichler, Gang Han, Vasiliki Pelekanou, Vesal Yaghoobi, Fahad S Ahmed, Andrew Bellizzi, Veerle Bossuyt, Jane Brock, Kimberly Cole, Oluwole Fadare, Omar Hameed, Krisztina Hanley, Beth T Harrison, M G Kuba, Amy Ly, Dylan Miller, Mirna Podoll, Anja C Roden, Mary A Sanders, Kamaljeet Singh, Shi Wei, Hannah Wen, Lajos Pusztai, David L Rimm. Prospective multi-institutional evaluation of pathologist assessment of PD-L1 assays 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 PD5-01.