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Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) remains a leading cause of cancer mortality. Here, we define the colonic epithelial expression of cathelicidin (LL‐37) in CRC. Cathelicidin exerts pleotropic effects including anti‐microbial and immunoregulatory functions. Genetic knockout of cathelicidin led to increased size and number of colorectal tumours in the azoxymethane‐induced murine model of CRC. We aimed to translate this to human disease. The expression of LL‐37 in a large (n = 650) fully characterised cohort of treatment‐naïve primary human colorectal tumours and 50 matched normal mucosa samples with associated clinical and pathological data (patient age, gender, tumour site, tumour stage [UICC], presence or absence of extra‐mural vascular invasion, tumour differentiation, mismatch repair protein status, and survival to 18 years) was assessed by immunohistochemistry. The biological consequences of LL‐37 expression on the epithelial barrier and immune cell phenotype were assessed using targeted quantitative PCR gene expression of epithelial permeability (CLDN2, CLDN4, OCLN, CDH1, and TJP1) and cytokine (IL‐1β, IL‐18, IL‐33, IL‐10, IL‐22, and IL‐27) genes in a human colon organoid model, and CD3+, CD4+, and CD8+ lymphocyte phenotyping by immunohistochemistry, respectively. Our data reveal that loss of cathelicidin is associated with human CRC progression, with a switch in expression intensity an early feature of CRC. LL‐37 expression intensity is associated with CD8+ T cell infiltrate, influenced by tumour characteristics including mismatch repair protein status. There was no effect on epithelial barrier gene expression. These data offer novel insights into the contribution of LL‐37 to the pathogenesis of CRC and as a therapeutic molecule. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we propose a general artificial intelligence (AI) based workflow for applying deep learning to the problem of cell identification in immunohistochemistry-stained slides as a basis for quantifying nuclear staining biomarkers. Our approach consists of two main parts: a simplified but robust annotation process, and the application of cell identification models. This results in an optimised process with a new user-friendly tool that can interact with other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell identification to quantify and analyse the trade-offs between different deep learning architectures, providing a more accurate and less time-consuming tool than using traditional methods. This approach can identify the best tool to deliver AI tools for clinical utility. Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pathology, including the quantification of biomarkers. In this study, we propose a general AI-based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user-friendly tool that can interact with1 other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell-based segmentation/detection to quantify and analyse the trade-offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) remains a leading cause of cancer mortality. Here, we define the colonic epithelial expression of cathelicidin (LL-37) in CRC. Cathelicidin exerts pleotropic effects including anti-microbial and immunoregulatory functions. Genetic knockout of cathelicidin led to increased size and number of colorectal tumours in the azoxymethane-induced murine model of CRC. We aimed to translate this to human disease. The expression of LL-37 in a large (n = 650) fully characterised cohort of treatment-naïve primary human colorectal tumours and 50 matched normal mucosa samples with associated clinical and pathological data (patient age, gender, tumour site, tumour stage [UICC], presence or absence of extra-mural vascular invasion, tumour differentiation, mismatch repair protein status, and survival to 18 years) was assessed by immunohistochemistry. The biological consequences of LL-37 expression on the epithelial barrier and immune cell phenotype were assessed using targeted quantitative PCR gene expression of epithelial permeability (CLDN2, CLDN4, OCLN, CDH1, and TJP1) and cytokine (IL-1β, IL-18, IL-33, IL-10, IL-22, and IL-27) genes in a human colon organoid model, and CD3 + , CD4 + , and CD8 + lymphocyte phenotyping by immunohistochemistry, respectively. Our data reveal that loss of cathelicidin is associated with human CRC progression, with a switch in expression intensity an early feature of CRC. LL-37 expression intensity is associated with CD8 + T cell infiltrate, influenced by tumour characteristics including mismatch repair protein status. There was no effect on epithelial barrier gene expression. These data offer novel insights into the contribution of LL-37 to the pathogenesis of CRC and as a therapeutic molecule., (© 2021 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland & John Wiley & Sons, Ltd.)

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. This generally requires consultation of documentation for multiple coding libraries, as well as trial and error to ensure that the techniques used on the images are appropriate. Herein we introduce HistoClean; a user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. HistoClean is an application that aims to help bridge the knowledge gap between pathologists, biomedical scientists and computer scientists by providing transparent image augmentation and pre-processing techniques which can be applied without prior coding knowledge. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. This study demonstrates how HistoClean can be used to improve a standard deep learning workflow via classical image augmentation and pre-processing techniques, even with a relatively simple convolutional neural network architecture. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean., Competing Interests: Dr. M.S.T has recently received honoraria for advisory work in relation to the following companies: Incyte, MindPeak, QuanPathDerivatives and MSD. He is part of academia-industry consortia supported by the UK government (Innovate UK). Dr J.J. is also involved in an academic-industry research programme funded by IUK. These declarations of interest are all unrelated with the submitted publication. All other authors declare no competing interests., (© 2021 The Authors.)