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The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment (TME). Understanding immune contexture requires multiplex staining, imaging, and analysis to obtain multi-marker phenotypes of specific cells and analyze their biodistribution in the TME. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and highplex imaging of FFPE tissues. FFPE tissue is autofluorescent, which limits the utility of immunofluorescence methods. Lung and colorectal tissue (and bone, skin, etc) are highly autofluorescent, and therefore good targets for IMC. However, developments in analysis software for highplex imagery have not kept pace with imaging advances. We present a comprehensive workflow designed specifically for highplex image analysis, covering tissue segmentation, cell segmentation based on IMC DNA images, cellular phenotyping, and spatial analyses. Lung and colorectal tissue sections with a 30-marker IMC panel of structural, tumor, stroma, immune cell, and immune activation markers were imaged (Hyperion+™, Standard BioTools). Highplex image analysis (Phenoplex™, Visiopharm) was performed as a multi-step workflow in a single software package that includes: conversion of IMC images to pyramidal format; easy visualization methods for displaying different marker subsets; a paint-to-train algorithm for tissue segmentation (into tumor, stroma, blood vessels, etc.); deep-learning-based nuclear segmentation pre-trained on IMC DNA channels; cellular phenotyping based on thresholds based on visual assessment of positivity; spatial biodistribution metrics for cell populations; and a flexible set of outputs for downstream analysis. Tissue segmentation was used to divide the tissue into tumor, stromal, and tumor margin regions, and these regions were used to compare the immune contexture through a series of t-SNE images partitioned by spatial region. We demonstrate that a simple analysis workflow can be used for highplex images of different tissue types by users with no programming knowledge. Visualization templates for the marker subsets and the pre-trained IMC nuclear segmentation are reusable. A new tissue segmentation algorithm for each tissue type is required, as are new thresholds for biomarker positivity. Spatial biodistribution metrics, heatmaps and partitioned t-SNE plots were generated for each tissue type with a minimum of work. Highplex IMC imaging of lung and colorectal tumor samples is a simple and effective means of obtaining high-parameter images without interfering autofluorescence. Having a comprehensive workflow for the analysis of this complex data makes obtaining useful results from highplex images more accessible to biologists and immunologists by circumventing the requirement for expert programming for each specific application. Citation Format: Brenna O'Neill, Smriti Kala, Sam Lim, Clinton Hupple, Nina Lane, Rasmus Norre Sorensen, Rasmus A. Lyngby, Alessandro Massaro, Andreas Hussing, Jeppe Thagaard, Johan Dore-Hansen, James Robert Mansfield. A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4625.

Simple Summary Around 15% of breast cancer patients are diagnosed as triple-negative (TNBC), which have significantly lower 5-year survival rates (77%) than other types of breast cancer (93%). Our study aimed at developing an image analysis-based biomarker to assess how the immune system interacts with the tumor and investigate the potential added value of stromal tumor-infiltrating lymphocytes (sTIL) for the prognosis of overall survival compared to the manual approach. In a large retrospective cohort of 257 patients, we found that our fully automated hematoxylin and eosin (H&E) image analysis pipeline can quantify sTIL density showing both high concordance with manual scoring and association with the prognosis of patients with TNBC. It also overcomes natural limitations of manual assessment that hinder clinical adoption of the immune biomarker. We conclude that sTIL scoring by automated image analysis has prognostic potential comparable to manual scoring and should be further investigated for future use in a clinical setting. Abstract 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.

Spatial biology is the study of tissues within their own context. This has become increasingly important in immuno-oncology because of the need to understand complex tumor microenvironment architecture, especially regarding the immune contexture in and around solid tumors. The main method for understanding the spatial biology of tumors has been the use of multi-marker staining and multiplexed imaging methods in FFPE sections. This imaging data is used to segment the tissue into relevant compartments (tumor, stroma, etc.) and perform multiplex phenotyping of the cells. For highplex data (10+ markers), phenotyping has typically used an unsupervised clustering algorithm. This was a good starting point, but has many limitations, including a reliance on having a sufficient population of cells of each type, variability sample to sample, and no means of differentiating between biologically relevant or impossible phenotypes. Improving cellular phenotyping for highplex samples is a requirement for many of the new multiplexed imaging modalities that are being used. We have developed a novel method of phenotyping cells in situ in multiplexed imaging data that involves the biological concept of dividing markers into two user-defined categories: lineage markers and functional markers. Lineage markers are used to identify cellular phenotypes, while functional markers are measured afterwards for their expression levels in across each phenotype and in each individual cell. Lineage markers (e.g., CD45, α-SMA, CD8, cytokeratin, CD20, etc.) define the phenotype of the cell and are essentially binary; they are either present or not present with the correct biodistribution. Functional markers (PD-1, PD-L1, Ki67, etc.), on the other hand, are quantitatively measured in each cell. This new method first uses a supervised classification methodology using lineage markers to find cells with expected phenotypes. In a second step, the levels of functional markers are measured for each cell. Unexpected phenotypes in unclassified cells can then be explored. These data can then be used for downstream spatial/distance/hotspot analysis. During the setup of the analysis the user can explore and QC the data using an interactive data exploration tool which allows for a constant algorithm supervision and correction. Here we present data of the novel workflow for 7-plex (Vectra) and 20+-plex (Hyperion/CODEX) data and found that the new method increased the efficiency of setting up complex analysis workflows and improved analysis results. Fewer non-biological phenotypes were found, cell classifications were improved compared to auto-clustering, while still exploring unexpected phenotypes. Interactive exploration of the cell populations enabled quicker validation of results and deeper understanding of the tumor immune contexture. This new method will improve analyses of the next generation of highplex imaging datasets. Citation Format: Fabian T. Schneider, James R. Mansfield, Alessandro S. Massaro, Simon Haastrup, Rasmus A. Lyngby, Andreas Hussing, Johan Dore Hansen, Jeppe Thagaard. Novel analysis method for in situ spatial phenotyping of cell populations in multimarker imagery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1708.

Pancreatic cancer remains a deadly disease due to difficulties hindering its early diagnosis, giving way to metastasis of the tumor and resulting in poor prognosis. While there are many neoplasms of the pancreas, pancreatic invasive ductal adenocarcinoma (PDAC) is the most common, and treatment options are few, with poor overall survival. Recently, several publications have demonstrated improved outcomes with the inclusion of immunotherapy to cytotoxic drug combinations in some patients. However, optimally selecting patients as candidates for immunotherapy-chemotherapy combinations remains a critical challenge. The complexities of the tumor microenvironment (TME) have been implicated in the failure of chemotherapy, radiation therapy, and immunotherapy. The tumor microenvironment of PDAC is especially rich with multiple interactions between pancreatic epithelial/cancer cells, stromal cells, immune cells, and the extracellular matrix (ECM). PDACs are characterized by a complex ECM of desmoplastic reaction consisting of an extensive and dense fibrotic stroma that surrounds and infiltrates clusters of malignant epithelial cells, together with the loss of basement membrane integrity and an abnormal vasculature. PD-L1 is also expressed in PDACs, and its overexpression has been associated with a poor prognosis. In the present study we demonstrate a unique tissue phenotyping workflow combining complementary methods that can unravel the complexity of the tumor microenvironment. We highlight how a workflow combining multiple elements provides utility, robustness, and an ability to derive biological insights in PDAC samples. An InSituPlex® PD-L1 multiplex immunofluorescence assay (4 markers, CD8, CD68, PD-L1, Pan CK/Sox10) was used on whole slides to identify areas of high, medium, and low PD-L1 expression. Imaging Mass Cytometry™ (IMC™, 40 markers) was performed on selected regions of interest from each slide. Advanced tissue and cell segmentation followed by multiplex cellular phenotyping and spatial analyses were performed on both whole-slide InSituPlex and IMC data. These methods combine to give a detailed readout of the location and bio-distribution of specific cell phenotypes in situ in the TME of PDAC. Four-plex imaging and analysis of whole slides gives an overview of the immune status of the section, while 40-plex imaging and analysis gives a comprehensive and multiparametric exploration of cells present. These methods combine to reveal an exceptional view of the PDAC TME at the single-cell level. Citation Format: Andrew Quong, Jordan Nieto, Derek Quong, Amanda Esch, Kirsteen Maclean, Mark Rees, Devan Fleury, Gourab Chatterjee, Keith Wharton, Jeppe Thagaard, Fabian Schneider, Dan Winkowski, James Mansfield. Improved understanding of the biology and pathophysiology of the tumor microenvironment in PDAC samples revealed by InSituPlex, Imaging Mass Cytometry, and advanced image processing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1709.

Understanding the rate of tumor cell growth in breast cancer specimens may be indicative of disease aggressiveness, a tumor characteristic which can be used to make an informed treatment decision. The nuclear protein Ki-67 is increased in cells as they prepare to divide or proliferate and is therefore widely used as a proliferation marker for tumor progression. This degree of tumor cell proliferation, or the proliferative index, is commonly detailed in pathology reports shared with the patient care team. In this study, we utilized the Ki-67 [K2] immunohistochemistry (IHC) assay to stain 10 breast cancer specimens. Stained slides were imaged using the AT2 scanner (Leica Biosystems, Buffalo Grove, IL) and analyzed using the Visiopharm Image Analysis platform. Previous efforts to assess Ki-67 positivity utilizing image analysis have relied on the use of a secondary stain or manual effort by the pathologist to exclude non-invasive tumor regions. These antiquated methods are costly to the lab as they require additional materials or valuable pathologist time. Our novel image analysis approach utilizes artificial intelligence (AI) to automatically denote non-invasive verses invasive tumor regions, which can then be used to quantify the Ki-67 proliferative index. This valuable tool will allow for greater accuracy, cost-savings, and time efficiency when analyzing breast cancer samples compared to traditional methods. Citation Format: Bhavika Patel, Stephanie Allen, Sameer Talwalkar, Navi Mehra, Jeppe Thagaard, Thomas W. Ramsing, Agnete Overgaard, Jenifer Caldara. An AI-based, automated workflow for identification and scoring of invasive tumors in Ki-67 stained breast cancer specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4144.

e14576 Background: Immunotherapies targeting complex programmed death-ligand 1 (PD-L1) interactions have been groundbreaking in the treatment of non-small cell lung cancer (NSCLC), offering new strategies for patients who are likely to respond. PD-L1 binds to the checkpoint PD-1 to regulate T cells, which has an important role in boosting the immune response. Identifying patients who may benefit from PD-L1 focused care is dependent on the interpretation of the drug’s associated companion diagnostic (CDx) immunohistochemistry (IHC) assay. PD-L1 IHC assays stratify NSCLC patients using a Tumor Proportion Score (TPS), a manual scoring paradigm which must be applied by a clinical pathologist. Application of the score is complex as it requires the reader to visually exclude stromal and tumor-infiltrating immune cells across a full sample, retaining PD-L1 expression information for true tumor cells only. Methods: In this study, we utilized the PD-L1 [28-8] immunohistochemistry (IHC) assay to stain 10 NCSLC specimens. Stained slides were then imaged using the AT2 scanner (Leica Biosystems, Buffalo Grove, IL) scanner and analyzed using the Visiopharm Image Analysis platform. Results: The intricacy of TPS application is time consuming for the pathologist and introduces opportunity for inter- and intra-reader variability. Our novel image analysis approach utilizes artificial intelligence (AI) to automatically denote tumor nests and exclude tumor-infiltrating immune cells. The remaining true tumor cells may then be quantified for PD-L1 expression across the full biopsy, producing a TPS for each sample. Conclusions: This method allows for great accuracy and time-efficiency in providing a TPS than traditional methods, which may result in improved patient care.

e15108 Background: Understanding the rate of tumor cell growth in breast cancer specimens may be indicative of disease aggressiveness, a tumor characteristic which can be used to make an informed treatment decision. The nuclear protein Ki-67 is increased in cells as they prepare to divide or proliferate and is therefore widely used as a proliferation marker for tumor progression. This degree of tumor cell proliferation, or the proliferative index, is commonly detailed in pathology reports shared with the patient care team. Methods: In this study, we utilized the Ki-67 [K2] immunohistochemistry (IHC) assay to stain 10 breast cancer specimens. Stained slides were imaged using the AT2 scanner (Leica Biosystems, Buffalo Grove, IL) and analyzed using the Visiopharm Image Analysis platform. Results: Previous efforts to assess Ki-67 positivity utilizing image analysis have relied on the use of a secondary stain or manual effort by the pathologist to exclude non-invasive tumor regions. These antiquated methods are costly to the lab as they require additional materials or valuable pathologist time. Our novel image analysis approach utilizes artificial intelligence (AI) to automatically denote non-invasive verses invasive tumor regions, which can then be used to quantify the Ki-67 proliferative index. Conclusions: This valuable tool will allow for greater accuracy, cost-savings, and time efficiency when analyzing breast cancer samples compared to traditional methods.

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., info:eu-repo/semantics/published