Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

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.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The 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.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

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.

ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations

Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would be sufficient for the reliable estimation of inter-observer agreement of TIL categorisation. This has not been explored previously in relation to TILs. ONEST analyses, using an open-source software developed by the first author, were performed on TIL quantification in breast cancers taken from two previous studies. These were one reproducibility study involving 49 breast cancers, 23 in the first circulation and 14 pathologists in the second circulation, and one study involving 100 cases and 9 pathologists. In addition to the estimates of the number of observers required, other factors influencing the results of ONEST were examined. The analyses reveal that between six and nine observers (range 2-11) are most commonly needed to give a robust estimate of reproducibility. In addition, the number and experience of observers, the distribution of values around or away from the extremes, and outliers in the classification also influence the results. Due to the simplicity and the potentially relevant information it may give, we propose ONEST to be a part of new reproducibility analyses.

Neither Tumor-Infiltrating Lymphocytes nor Cytotoxic T Cells Predict Enhanced Benefit from Chemotherapy in the DBCG77B Phase III Clinical Trial

Simple Summary

Apart from the direct killing of cancer cells, cyclophosphamide-based chemotherapy has been shown to induce an antitumor immune response, and is being used in combination with immunotherapies in cancer care. We assessed the interaction of chemotherapy with immune biomarkers expressed on primary tumor tissue from a randomized phase III clinical trial, and confirmed that the presence of tumor-infiltrating lymphocytes is linked to improved survival in premenopausal women with high-risk breast cancer, regardless of their treatment allocation. However, immune biomarkers including tumor-infiltrating lymphocytes do not predict extra benefit from cyclophosphamide chemotherapy. This finding applies across the major molecular subgroups, including non-luminal and basal breast cancers that tend to be more immunogenic, and are often considered the most suitable subsets for receiving immunotherapy.

Abstract

Recent studies have shown that immune infiltrates in the tumor microenvironment play a role in response to therapy, with some suggesting that patients with immunogenic tumors may receive increased benefit from chemotherapies. We evaluated this hypothesis in early breast cancer by testing the interaction between immune biomarkers and chemotherapy using materials from DBCG77B, a phase III clinical trial where high-risk premenopausal women were randomized to receive chemotherapy or no chemotherapy. Tissue microarrays were evaluated for tumor-infiltrating lymphocytes (TILs) assessed morphologically on hematoxylin and eosin-stained slides, and by immunohistochemistry for CD8, FOXP3, LAG-3, PD-1 and PD-L1. Following REMARK reporting guidelines, data analyses were performed according to a prespecified statistical plan, using 10-year invasive disease-free survival as the endpoint. Differences in survival probabilities between biomarker groups were evaluated by Kaplan–Meier and Cox proportional hazard ratio analyses and prediction for treatment benefit by an interaction test. Our results showed that stromal TILs were associated with an improved prognosis (HR = 0.93; p-value = 0.03), consistent with previous studies. However, none of the immune biomarkers predicted benefit from chemotherapy in the full study set nor within major breast cancer subtypes. Our study indicates that primary tumors with higher immune infiltration do not derive extra benefit from cyclophosphamide-based cytotoxic chemotherapy.

Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer

Stromal tumour infiltrating lymphocytes (sTILs) are a strong prognostic marker in triple negative breast cancer (TNBC). Consistency scoring sTILs is good and was excellent when an internet-based scoring aid developed by the TIL-WG was used to score cases in a reproducibility study. This study aimed to evaluate the reproducibility of sTILs assessment using this scoring aid in cases from routine practice and to explore the potential of the tool to overcome variability in scoring. Twenty-three breast pathologists scored sTILs in digitized slides of 49 TNBC biopsies using the scoring aid. Subsequently, fields of view (FOV) from each case were selected by one pathologist and scored by the group using the tool. Inter-observer agreement was good for absolute sTILs (ICC 0.634, 95% CI 0.539-0.735, p < 0.001) but was poor to fair using binary cutpoints. sTILs heterogeneity was the main contributor to disagreement. When pathologists scored the same FOV from each case, inter-observer agreement was excellent for absolute sTILs (ICC 0.798, 95% CI 0.727-0.864, p < 0.001) and good for the 20% (ICC 0.657, 95% CI 0.561-0.756, p < 0.001) and 40% (ICC 0.644, 95% CI 0.546-0.745, p < 0.001) cutpoints. However, there was a wide range of scores for many cases. Reproducibility scoring sTILs is good when the scoring aid is used. Heterogeneity is the main contributor to variance and will need to be overcome for analytic validity to be achieved.

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

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.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

PD-L1 diagnostics in the neoadjuvant setting: implications of intratumoral heterogeneity of PD-L1 expression in triple negative breast cancer for assessment in small biopsies

PURPOSE

PD-L1 expression is a predictive biomarker for anti-PD-L1 immunotherapy in triple negative breast cancer (TNBC). In the neoadjuvant setting, immunohistochemical (IHC) evaluation of PD-L1 expression can only be performed on small tissue biopsies. In our study we investigated heterogeneity of PD-L1 expression in TNBC, and how reliably PD-L1 expression in small tissue samples reflects PD-L1 expression in larger tumor sections in TNBC.

METHODS

Tissue microarrays (TMAs) were constructed from surgical specimens of 110 patients with TNBC. TMAs contained 4 cores (1 mm in diameter) per patient. To evaluate PD-L1 expression, TMAs were stained with PD-L1 IHC 22C3 PharmDx. Single-core PD-L1 expression was compared to overall PD-L1 expression of each patient's tumor, to ascertain how often small samples of tumor tissue show the same PD-L1 expression as larger tumor samples.

RESULTS

Our study found substantial heterogeneity of PD-L1 expression between different TMA cores from the same patient. Heterogeneity was greater in immune cells (ICs) than in tumor cells, in large part due to the uneven distribution of ICs in the tumor. For IC PD-L1 expression, we found that sensitivity can be as low as 0.81 for detecting PD-L1 expression at the 1% threshold most commonly used in breast cancer. Negative predictive value for ICs was 0.7.

CONCLUSIONS

There is substantial heterogeneity of PD-L1 expression between small tissue samples from the same TNBC tumor, especially for IC expression. This poses challenges for evaluation of PD-L1 expression in the neoadjuvant setting. Negative biopsies should prompt further investigation, and multiple biopsies might be necessary.

Triple negative breast cancer - prognostic role of immune-related factors: a systematic review

PURPOSE

Treatment of breast cancer has been increasingly successful in recent years with the advent of HER2-receptor targeted treatment and endocrine treatment. However, the triple negative subgroup of breast cancer (TNBC) (estrogen-, progesterone- and HER2-receptor negative) still lacks targeted treatment options. TNBC is a type of breast cancer that often affects younger women, and generally has a worse prognosis than other types of breast cancer. Recently, the complex role of the immune system in cancer growth, elimination and metastasis has been the object of increased attention. There is hope that a more detailed understanding of the intricate roles of the constituents of the immune system, will hold potential both as prognostic or predictive markers of cancer progression, but also as treatment targets for a wide range of tumors, including TNBC. The aim of this review is to provide an overview of the cellular immune microenvironment in TNBC, and to highlight areas in which TNBC may differ from other types of breast cancer.

MATERIAL AND METHODS

A search of PubMed was made using the terms 'triple negative breast cancer' and 'tumor infiltrating lymphocytes', 'CD8', 'CD4', 'B cells', 'natural killer cells', 'macrophages', myeloid derived suppressor cells', 'dendritic cells', 'immune check point inhibitor', 'CTLA-4' and 'PD-L1'.

RESULTS

We find that whilst factors such as TILs and certain subgroups of TILs (e.g., CD8 + and regulator T-cells) have been extensively researched, none of these markers are currently applicable to routine clinical practice. Also, TNBC differs from other types of breast cancer with regards to cellular composition of the immune infiltrate and PD-L1 expression, and the prognostic significance of these.

CONCLUSIONS

Immune-related factors have the potential as both prognostic and predictive biomarkers for new treatments targeting the immune system in breast cancer. However, multivariate analyses, taking other well-known factors into account, are required to determine the true value of these biomarkers. Also, differences between TNBC and other types of breast cancer may have implications for treatment and use of immune-related factors as biomarkers.