Rimm, David L.

Importance Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin–stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. Objective To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. Design, Setting, and Participants This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. Main Outcomes and Measures Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. Results There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin–stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W , 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). Conclusions and Relevance In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

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: Disease relapse is observed in a significant proportion of HER2-positive breast cancer (BC) patients with residual disease (RD) after neoadjuvant treatment, as well as in a subgroup of those achieving pathological complete response (pCR). As the host immune response plays a key role in modulating the activity of anti-HER2 agents, we investigated the association of T- and B-cell receptor (TCR and BCR) repertoires with pCR and event-free survival (EFS) in the NeoALTTO phase 3 trial. Methods: RNA sequencing (RNAseq) data from baseline tumor biopsies were available for 254 patients out of the 455 enrolled in the study. Among those, 166 did not achieve a pCR defined as ypT0/is. Matched RNAseq data from RD samples were available for 43 cases. TCR/BCR repertoires were extracted from RNAseq data using the MiXCR software. TCR and BCR read counts, number of clones, evenness, Shannon entropy, Gini index, length of the complementarity determining region 3, top and second top clone proportion were evaluated. Survival analysis was performed using univariate and multivariate (adjusted for tumor size, nodal status, grade, estrogen receptor [ER] status, age and treatment arm) Cox proportional hazard models, while logistic regressions were used for pCR. False discovery rate (FDR) was obtained using Benjamini & Hochberg method. Results: Baseline TCR top (odds ratio [OR]=0.63 [95% CI 0.46-0.87], FDR=0.021) and second top (OR=0.57 [0.41-0.79], FDR=0.004) clone proportion were significantly associated with a lower probability of achieving pCR in the multivariate analysis. BCR evenness (hazard ratio [HR]=1.5 [1.2-2], FDR=0.015) and Gini index (HR=0.66 [0.52-0.85], FDR=0.015) were significantly associated with EFS in the multivariate analysis. In residual disease, BCR evenness and Gini index showed a similar trend in the EFS univariate analysis, while TCR read counts, number of clones, and entropy were associated with lower HR (P<0.05), although FDR were borderline significant (0.05<FDR<0.1). A model to predict EFS including baseline BCR evenness, ER status, stromal tumor-infiltrating lymphocyte level, and pCR was able to identify 3 groups with good, intermediate and poor prognosis (Kaplan-Meier 5-year EFS rates of 95%, 80% and 58%, respectively). Of note, about 63%, 45% and 12% of the patients achieved a pCR in the three prognostic groups, respectively. Conclusions: In the NeoALTTO trial, the presence of an evenly distributed BCR repertoire was associated with worse EFS. A model integrating baseline immune-related and clinical features was able to identify patients with excellent prognosis despite RD or, conversely, with poor prognosis after pCR. We envision that our model has the potential to allow the personalization of post-operative treatment strategies after both pCR and RD in HER2-positive BC. Further validation of our findings is warranted. Citation Format: Mattia Rediti, David Venet, Françoise Rothé, Tao Qing, Marion Maetens, Ian Bradbury, Miguel A. Izquierdo, Serena Di Cosimo, Florentine Hilbers, Mohammed Bajji, Nadia Harbeck, Michael Untch, David L. Rimm, Stephen Chia, Minetta C. Liu, Cristina Saura, Jens Huober, Paolo Nuciforo, Roberto Salgado, Sherene Loi, Lajos Pusztai, Christos Sotiriou. Predictive and prognostic role of T- and B-cell receptor repertoire in HER2-positive breast cancer: An analysis of the NeoALTTO clinical trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1998.

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.

AbstractImmune checkpoint inhibitor therapies targeting PD‐1/PD‐L1 are now the standard of care in oncology across several hematologic and solid tumor types, including triple negative breast cancer (TNBC). Patients with metastatic or locally advanced TNBC with PD‐L1 expression on immune cells occupying ≥1% of tumor area demonstrated survival benefit with the addition of atezolizumab to nab‐paclitaxel. However, concerns regarding variability between immunohistochemical PD‐L1 assay performance and inter‐reader reproducibility have been raised. High tumor‐infiltrating lymphocytes (TILs) have also been associated with response to PD‐1/PD‐L1 inhibitors in patients with breast cancer (BC). TILs can be easily assessed on hematoxylin and eosin–stained slides and have shown reliable inter‐reader reproducibility. As an established prognostic factor in early stage TNBC, TILs are soon anticipated to be reported in daily practice in many pathology laboratories worldwide. Because TILs and PD‐L1 are parts of an immunological spectrum in BC, we propose the systematic implementation of combined PD‐L1 and TIL analyses as a more comprehensive immuno‐oncological biomarker for patient selection for PD‐1/PD‐L1 inhibition‐based therapy in patients with BC. Although practical and regulatory considerations differ by jurisdiction, the pathology community has the responsibility to patients to implement assays that lead to optimal patient selection. We propose herewith a risk‐management framework that may help mitigate the risks of suboptimal patient selection for immuno‐therapeutic approaches in clinical trials and daily practice based on combined TILs/PD‐L1 assessment in BC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

9517 Background: Although PD-1/L1 axis directed therapies induce durable responses in some mm patients (pts), biomarkers of response remain elusive. We hypothesized that quantifying key immune suppression mechanisms within the tumor microenvironment would provide superior predictors of response to anti-PD-1 compared with single marker assessment. Methods: Pre-treatment tumor biopsies from 124 mm pts treated with anti-PD-1 at 7 academic centers were fluorescently stained with multiple immune markers in discovery (n = 24) and validation (n = 100) cohorts. Selected biomarker signatures, PD-1/PD-L1 interaction score (proportion of PD-1+ cells co-localized with PD-L1) and IDO1/HLA-DR co-expression were evaluated for anti-PD-1 treatment response and survival. Slides were imaged using Vectra; biomarker positive cells and their co-localization were objectively quantified in pathologist-selected regions using novel Automated Quantitative Analysis (AQUA) algorithms. Results: In the discovery cohort, high levels of PD-1/PD-L1 interaction score and/or IDO1/HLA-DR coexpression was strongly positively associated with response to anti-PD-1 (p = 0.0005). In contrast, other individual biomarkers (PD-1, PD-L1, CD8) were not associated with response or survival (p > 0.10). This finding was replicated in the validation cohort: pts with high PD-1/PD-L1 and/or IDO1/HLA-DR were more likely to respond to treatment (p = 0.009). These pts also experienced a three-fold increase in progression free survival (hazards ratio (HR) = 0.33; p = 0.003) and overall survival (HR = 0.34; p = 0.004). Multivariate analyses revealed that these findings were independent of BRAF mutation, stage, LDH and prior therapy. In the combined cohort (n = 124), 80% of responding pts had higher levels of PD-1/PD-L1 interaction scores and/or IDO1/HLA-DR. In contrast, PD-L1 expression alone (≥1% or ≥50%) was not predictive of PFS or OS (p > 0.1). Conclusions: This novel multiplexed method profiling key tumor-immune suppression pathways identified mm pts likely to respond to anti-PD-1 therapy. This method could help stratify patients for PD-1 monotherapy and be useful in guiding future clinical trials.

Abstract Purpose: PD-1/L1 axis–directed therapies produce clinical responses in a subset of patients; therefore, biomarkers of response are needed. We hypothesized that quantifying key immunosuppression mechanisms within the tumor microenvironment by multiparameter algorithms would identify strong predictors of anti–PD-1 response. Experimental Design: Pretreatment tumor biopsies from 166 patients treated with anti–PD-1 across 10 academic cancer centers were fluorescently stained with multiple markers in discovery (n = 24) and validation (n = 142) cohorts. Biomarker-positive cells and their colocalization were spatially profiled in pathologist-selected tumor regions using novel Automated Quantitative Analysis algorithms. Selected biomarker signatures, PD-1/PD-L1 interaction score, and IDO-1/HLA-DR coexpression were evaluated for anti–PD-1 treatment outcomes. Results: In the discovery cohort, PD-1/PD-L1 interaction score and/or IDO-1/HLA-DR coexpression was strongly associated with anti–PD-1 response (P = 0.0005). In contrast, individual biomarkers (PD-1, PD-L1, IDO-1, HLA-DR) were not associated with response or survival. This finding was replicated in an independent validation cohort: patients with high PD-1/PD-L1 and/or IDO-1/HLA-DR were more likely to respond (P = 0.0096). These patients also experienced significantly improved progression-free survival (HR = 0.36; P = 0.0004) and overall survival (HR = 0.39; P = 0.0011). In the combined cohort, 80% of patients exhibiting higher levels of PD-1/PD-L1 interaction scores and IDO-1/HLA-DR responded to PD-1 blockers (P = 0.000004). In contrast, PD-L1 expression was not predictive of survival. Conclusions: Quantitative spatial profiling of key tumor-immune suppression pathways by novel digital pathology algorithms could help more reliably select melanoma patients for PD-1 monotherapy. Clin Cancer Res; 24(21); 5250–60. ©2018 AACR.

Abstract Background: Pembrolizumab (Pembro), an anti-PD-1 immune checkpoint inhibitor, has been approved for the treatment of a variety of cancers including melanoma, non-small cell lung cancer, head and neck squamous cell carcinoma, and urothelial carcinoma. Pembro was recently evaluated in HER2- breast cancer patients in the neoadjuvant I-SPY 2 TRIAL and graduated in the triple negative (TN), HR+HER2-, and HER2- signatures. HER2- patients were randomized to receive Pembro+paclitaxel followed by doxorubicin/cyclophosphamide (P+T -> AC) vs. T -> AC. We and others have shown that TN breast cancers tend to have high numbers of immune infiltrates, including T cells and tumor associated macrophages (TAMs). We evaluated expression signatures representing 14 immune cell types (TILs, T cells, CD8 T cells, exhausted T cells, Th1, Tregs, cytotoxic cells, NK, NK CD56dim, dendritic cells, mast cells, B cells, macrophages, and neutrophils) as specific predictors of response to Pembro. Methods: Data from 248 patients (Pembro: 69; controls: 179) were available. Pre-treatment biopsies were assayed using Agilent gene expression arrays. Signature scores are calculated by averaging cell type specific genes. All I-SPY 2 qualifying biomarker analyses follow a pre-specified analysis plan. We used logistic modeling to assess biomarker performance. A biomarker is considered a specific predictor of Pembro response if it associates with response in the Pembro arm but not the control arm, and if the biomarker x treatment interaction is significant (likelihood ratio test, p<0.05). This analysis is also performed adjusting for HR status as covariates, and within receptor subsets. For successful biomarkers, we use Bayesian modeling to estimate the pCR rates of 'predicted sensitive' patients in each arm. Our statistics are descriptive rather than inferential and do not adjust for multiplicities of other biomarkers outside this study. Results: 10 out of the 14 cell-type signatures tested are associated with response in the Pembro arm. Higher expression levels of 9 of these cell-type signatures are associated with higher pCR rates (T cells, exhausted T cells, Th1, cytotoxic cells, NK, NK CD56dim, dendritic cells, B cells, and macrophages), whereas higher mast cell signature expression is associated with non-pCR. Interestingly, many of these same signatures also associate or trend towards association with response in the control arm; and in a model adjusting for HR status, only 3 of these signatures (Th1, B cells and dendritic cells) show significant interaction with treatment. Within the whole population and the TN subtype, the dendritic cell signature is the strongest predictor of specific response to Pembro (OR/1SD: 4.04 and 4.4, LR p < 0.001 overall and in TN). Although other immune signatures (T cells, exhausted T cells, NK, and macrophages) also associate with response in the Pembro arm in the TN subtype, only the dendritic cell and Th1 signatures have a significant interaction with treatment. In contrast, in the HR+HER2- subtype, only 3 signatures (Th1, B cells, and mast cells) associate with response to Pembro; but none of these signatures have significant interaction with treatment. Of note, in both the Pembro and control arms, HR+HER2- patients with higher average mast cell marker expression have lower pCR rates (OR/1SD: 0.33 and 0.51, LRp: 0.006 and 0.04 in Pembro and control arm). Conclusion: As expected, multiple immune cell expression signatures are predictive of response in the Pembro arm; but only dendritic cells and Th1 cells are specific to Pembro in both the population as a whole and the TN subtype. Interestingly, the presence of mast cells may impede response, especially in HR+HER2- patients. Correlation of these signatures with multiplex-IF immune markers is pending. Citation Format: Campbell M, Yau C, Borowsky A, Vandenberg S, Wolf D, Rimm D, Nanda R, Liu M, Brown-Swigart L, Hirst G, Asare S, van't Veer L, Yee D, DeMichele A, Berry D, Esserman L. Analysis of immune infiltrates (assessed via multiplex fluorescence immunohistochemistry) and immune gene expression signatures as predictors of response to the checkpoint inhibitor pembrolizumab in the neoadjuvant I-SPY 2 trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD6-08.