Identifying mismatch repair-deficient colon cancer: near-perfect concordance between immunohistochemistry and microsatellite instability testing in a large, population-based series.

AIMS: Establishing the mismatch repair (MMR) status of colorectal cancers is important to enable the detection of underlying Lynch syndrome and inform prognosis and therapy. Current testing typically involves either polymerase chain reaction (PCR)-based microsatellite instability (MSI) testing or MMR protein immunohistochemistry (IHC). The aim of this study was to compare these two approaches in a large, population-based cohort of stage 2 and 3 colon cancer cases in Northern Ireland. METHODS AND RESULTS: The study used the Promega pentaplex assay to determine MSI status and a four-antibody MMR IHC panel. IHC was applied to tumour tissue microarrays with triplicate tumour sampling, and assessed manually. Of 593 cases with available MSI and MMR IHC results, 136 (22.9%) were MSI-high (MSI-H) and 135 (22.8%) showed abnormal MMR IHC. Concordance was extremely high, with 97.1% of MSI-H cases showing abnormal MMR IHC, and 97.8% of cases with abnormal IHC showing MSI-H status. Under-representation of tumour epithelial cells in samples from heavily inflamed tumours resulted in misclassification of several cases with abnormal MMR IHC as microsatellite-stable. MMR IHC revealed rare cases with unusual patterns of MMR protein expression, unusual combinations of expression loss, or secondary clonal loss of expression, as further illustrated by repeat immunostaining on whole tissue sections. CONCLUSIONS: MSI PCR testing and MMR IHC can be considered to be equally proficient tests for establishing MMR/MSI status, when there is awareness of the potential pitfalls of either method. The choice of methodology may depend on available services and expertise.

Tissue-based next generation sequencing: application in a universal healthcare system.

In the context of solid tumours, the evolution of cancer therapies to more targeted and nuanced approaches has led to the impetus for personalised medicine. The targets for these therapies are largely based on the driving genetic mutations of the tumours. To track these multiple driving mutations the use of next generation sequencing (NGS) coupled with a morphomolecular approach to tumours, has the potential to deliver on the promises of personalised medicine. A review of NGS and its application in a universal healthcare (UHC) setting is undertaken as the technology has a wide appeal and utility in diagnostic, clinical trial and research paradigms. Furthermore, we suggest that these can be accommodated with a unified integromic approach. Challenges remain in bringing NGS to routine clinical use and these include validation, handling of the large amounts of information flow and production of a clinically useful report. These challenges are particularly acute in the setting of UHC where tests are not reimbursed and there are finite resources available. It is our opinion that the challenges faced in applying NGS in a UHC setting are surmountable and we outline our approach for its routine application in diagnostic, clinical trial and research paradigms.

Molecular profiling of signet ring cell colorectal cancer provides a strong rationale for genomic targeted and immune checkpoint inhibitor therapies.

BACKGROUND: Signet ring cell colorectal cancer (SRCCa) has a bleak prognosis. Employing molecular pathology techniques we investigated the potential of precision medicine in this disease. METHODS: Using test (n=26) and validation (n=18) cohorts, analysis of mutations, DNA methylation and transcriptome was carried out. Microsatellite instability (MSI) status was established and immunohistochemistry (IHC) was used to test for adaptive immunity (CD3) and the immune checkpoint PDL1. RESULTS: DNA methylation data split the cohorts into hypermethylated (n=18, 41%) and hypomethylated groups (n=26, 59%). The hypermethylated group predominant in the proximal colon was enriched for CpG island methylator phenotype (CIMP), BRAF V600E mutation and MSI (P<0.001). These cases also had a high CD3+ immune infiltrate (P<0.001) and expressed PDL1 (P=0.03 in intra-tumoural lymphoid cells). The hypomethylated group predominant in the distal colon did not show any characteristic molecular features. We also detected a common targetable KIT mutation (c.1621A>C) across both groups. No statistically significant difference in outcome was observed between the two groups. CONCLUSIONS: Our data show that SRCCa phenotype comprises two distinct genotypes. The MSI+/CIMP+/BRAF V600E+/CD3+/PDL1+ hypermethylated genotype is an ideal candidate for immune checkpoint inhibitor therapy. In addition, one fourth of SRCCa cases can potentially be targeted by KIT inhibitors.

Novel antibodies directed against the human erythropoietin receptor: creating a basis for clinical implementation.

Recombinant human erythropoietin (rHuEPO) is an effective treatment for anaemia but concerns that it causes disease progression in cancer patients by activation of EPO receptors (EPOR) in tumour tissue have been controversial and have restricted its clinical use. Initial clinical studies were flawed because they used polyclonal antibodies, later shown to lack specificity for EPOR. Moreover, multiple isoforms of EPOR caused by differential splicing have been reported in cancer cell lines at the mRNA level but investigations of these variants and their potential impact on tumour progression, have been hampered by lack of suitable antibodies. The EpoCan consortium seeks to promote improved pathological testing of EPOR, leading to safer clinical use of rHuEPO, by producing well characterized EPOR antibodies. Using novel genetic and traditional peptide immunization protocols, we have produced mouse and rat monoclonal antibodies, and show that several of these specifically recognize EPOR by Western blot, immunoprecipitation, immunofluorescence, flow cytometry and immunohistochemistry in cell lines and clinical material. Widespread availability of these antibodies should enable the research community to gain a better understanding of the role of EPOR in cancer, and eventually to distinguish patients who can be treated safely by rHuEPO from those at increased risk from treatment.

Epidermal growth factor receptor (EGFR), HER2 and insulin-like growth factor-1 receptor (IGF-1R) status in ovarian adult granulosa cell tumours.

AIMS: Adult granulosa cell tumours (AGCTs) are uncommon ovarian sex cord-stromal tumours which recur following surgical removal in up to 50% of patients. Treatment options for recurrent and advanced stage AGCTs are limited, with poor response to chemotherapy and radiotherapy. We aimed to assess epidermal growth factor receptor (EGFR), HER2 and insulin-like growth factor-1 receptor (IGF-1R) status in AGCTs with a view to investigating whether or not these receptors might be potential therapeutic targets in these neoplasms. METHODS AND RESULTS: Immunohistochemical staining for EGFR, HER2 and IGF-1R was undertaken in 31 AGCTs. Tumour DNA was also analysed for mutations in the tyrosine kinase domain of EGFR (exons 18-21) by Cobas mutation RT-PCR. Twenty-three of 31 (74%) AGCTs showed some degree of EGFR expression, generally with cytoplasmic or mixed membranous and cytoplasmic staining of variable intensity. Eleven of 27 (41%) cases exhibited strong membranous and cytoplasmic expression of IGF-1R. HER2 expression was not seen. No mutations were found in exons 18-21 of the EGFR gene in hot-spots of therapeutic relevance. CONCLUSIONS: This study raises the possibility that anti-EGFR and/or anti-IGF-1R therapies may be of potential benefit in ovarian AGCTs, and this requires further study. Lack of known mutations within the tyrosine kinase domain of EGFR suggests that EGFR-related tyrosine kinase inhibitors may not be useful therapeutically.

FOXP3+ regulatory T-cell counts correlate with histological response in Crohn's colitis treated with infliximab.

Crohn's disease is a chronic inflammatory bowel disease of unknown aetiology. Mucosal inflammatory dysregulation is likely important, with increased production of pro-inflammatory cytokines, including tumour necrosis factor alpha (TNFα). The chimeric monoclonal antibody, infliximab, inhibits TNFα and promotes intestinal mucosal healing. Despite this, many patients still require surgical intervention. Patients who have undergone colonic resection post-infliximab therapy, show markedly variable morphological response to treatment. FOXP3+ CD4+ regulatory T-cells have been shown to have a protective role in autoimmune/inflammatory diseases and their sequestration to the bowel is found in those treated with infliximab. We examined the immunohistochemical profile of lymphoid aggregates in tissue sections from post-infliximab Crohn's colitis resection specimens, classified as morphological responders or non-responders, defined in relation to the absence/presence of mucosal ulceration and active inflammation, and a control group. Results indicated no significant diffences in CD68-positive cell counts but increased FOXP3-positive (P = 0.02) and CD4-positive (P = 0.05) cell counts in responders versus non-responders. Untreated control scores were similar to non-responders. Although based on small study numbers, our results suggest an association between upregulation of FOXP3+/CD4+ regulatory T-cells and morphological response to infliximab therapy. This represents a possible quantitative methodology for monitoring therapeutic response to infliximab therapy, based on immunohistochemical evaluation of endoscopic biopsy specimens.

True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images.

The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.

KRASFormer: a fully vision transformer-based framework for predicting KRAS gene mutations in histopathological images of colorectal cancer.

Detecting the Kirsten Rat Sarcoma Virus (KRAS) gene mutation is significant for colorectal cancer (CRC) patients. The KRAS gene encodes a protein involved in the epidermal growth factor receptor (EGFR) signaling pathway, and mutations in this gene can negatively impact the use of monoclonal antibodies in antiEGFR therapy and affect treatment decisions. Currently, commonly used methods like next-generation sequencing (NGS) identify KRAS mutations but are expensive, time-consuming, and may not be suitable for every cancer patient sample. To address these challenges, we have developed KRASFormer, a novel framework that predicts KRAS gene mutations from Haematoxylin and Eosin (H&E) stained WSIs that are widely available for most CRC patients. KRASFormer consists of two stages: the first stage filters out non-tumor regions and selects only tumour cells using a quality screening mechanism, and the second stage predicts the KRAS gene either 'wildtype' or 'mutant' using a Vision Transformer-based XCiT method. The XCiT employs cross-covariance attention to capture clinically meaningful long-range representations of textural patterns in tumour tissue and KRAS mutant cells. We evaluated the performance of the first stage using an independent CRC-5000 dataset, and the second stage included both The Cancer Genome Atlas colon and rectal cancer (TCGA-CRCDX) and in-house cohorts. The results of our experiments showed that the XCiT outperformed existing state-of-the-art methods, achieving AUCs for ROC curves of 0.691 and 0.653 on TCGA-CRC-DX and in-house datasets, respectively. Our findings emphasize three key consequences: the potential of using H&E-stained tissue slide images for predicting KRAS gene mutations as a cost-effective and time-efficient means for guiding treatment choice with CRC patients; the increase in performance metrics of a Transformer-based model; and the value of the collaboration between pathologists and data scientists in deriving a morphologically meaningful model.

BRCA1 immunohistochemical staining as a prognostic indicator in uterine serous carcinoma.

OBJECTIVES: The objective of this study was to investigate the relationship between BRCA1 protein expression, as determined by immunohistochemistry, and clinical outcome in uterine serous carcinoma (USC). METHODS: A tissue microarray containing duplicate cores of 73 cases of USC was immunohistochemically stained with mouse anti-BRCA1 (Ab-1) mouse monoclonal (MS110) antibody. The cores were scored in a semiquantitative manner evaluating both the distribution and intensity of nuclear staining. BRCA1 protein expression was correlated with progression-free survival. RESULTS: Seventy-two of 73 cases were assessable, and there was a statistically significant decreased progression-free survival for those cases exhibiting tumor cell nuclei staining of 76% or greater (P = 0.0023). CONCLUSIONS: Our study illustrates that a low level of BRCA1 protein expression is a favorable prognostic indicator in USC, similar to what is observed in high-grade serous ovarian carcinoma. Further studies should focus on the BRCA1 status of USCs at a molecular level and also investigate whether BRCA1 protein expression is associated with response to chemotherapy in USC.

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.

Molecular profiling of aromatase inhibitor sensitive and resistant ER+HER2- postmenopausal breast cancers.

Aromatase inhibitors (AIs) reduce recurrences and mortality in postmenopausal patients with oestrogen receptor positive (ER+) breast cancer (BC), but >20% of patients will eventually relapse. Given the limited understanding of intrinsic resistance in these tumours, here we conduct a large-scale molecular analysis to identify features that impact on the response of ER + HER2- BC to AI. We compare the 15% of poorest responders (PRs, n = 177) as measured by proportional Ki67 changes after 2 weeks of neoadjuvant AI to good responders (GRs, n = 190) selected from the top 50% responders in the POETIC trial and matched for baseline Ki67 categories. In this work, low ESR1 levels are associated with poor response, high proliferation, high expression of growth factor pathways and non-luminal subtypes. PRs having high ESR1 expression have similar proportions of luminal subtypes to GRs but lower plasma estradiol levels, lower expression of estrogen response genes, higher levels of tumor infiltrating lymphocytes and immune markers, and more TP53 mutations.

Technical note on the exploration of COVID-19 in autopsy material.

Interrogation of immune response in autopsy material from patients with SARS-CoV-2 is potentially significant. We aim to describe a validated protocol for the exploration of the molecular physiopathology of SARS-CoV-2 pulmonary disease using multiplex immunofluorescence (mIF).The application of validated assays for the detection of SARS-CoV-2 in tissues, originally developed in our laboratory in the context of oncology, was used to map the topography and complexity of the adaptive immune response at protein and mRNA levels.SARS-CoV-2 is detectable in situ by protein or mRNA, with a sensitivity that could be in part related to disease stage. In formalin-fixed, paraffin-embedded pneumonia material, multiplex immunofluorescent panels are robust, reliable and quantifiable and can detect topographic variations in inflammation related to pathological processes.Clinical autopsies have relevance in understanding diseases of unknown/complex pathophysiology. In particular, autopsy materials are suitable for the detection of SARS-CoV-2 and for the topographic description of the complex tissue-based immune response using mIF.

General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology.

Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist's task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.

ICOSeg: Real-Time ICOS Protein Expression Segmentation from Immunohistochemistry Slides Using a Lightweight Conv-Transformer Network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters.

Autocrine/paracrine erythropoietin signalling promotes JAK/STAT-dependent proliferation of human cervical cancer cells.

Erythropoietin (Epo) regulates erythropoiesis by binding to its receptor (EpoR) and promoting cell proliferation, differentiation and inhibition of apoptosis. Epo is widely used to treat cervical cancer-related anaemia. However, there are data suggesting that administration of Epo is associated with an increment in recurrence rate, and decreased disease-free and overall survival. In the present study, we investigated the expression of Epo and EpoR on cervical cancer cell lines. We observed that both EpoR and extracellular Epo are constitutively expressed in cervical cancer cells. Inhibition of either Epo or EpoR expression with siRNA attenuated cell proliferation, whereas addition of exogenous Epo led to a significant increase in cell growth, both in vitro and in vivo. Epo-induced proliferation was associated with the activation of JAK2, JAK3, STAT3 and STAT5 but not JAK1 and STAT1. Our results are consistent with the existence of a functional, endogenous Epo/EpoR system in cervical cancer with the capacity to activate the transduction of signals resulting in an increased proliferation potential.

BRCA1 is both a prognostic and predictive biomarker of response to chemotherapy in sporadic epithelial ovarian cancer.

OBJECTIVES: We investigated the relationship between BRCA1 protein expression by immunohistochemistry (IHC) and clinical outcome following platinum and platinum/taxane chemotherapy in sporadic epithelial ovarian cancer (EOC). METHODS: BRCA1 IHC was performed on a cohort of 292 ovarian tumours from two UK oncology centres. BRCA1 protein expression levels were correlated with overall survival (OS), progression free survival (PFS) and clinical response to chemotherapy by multivariate analysis. RESULTS: EOC patients with absent/low BRCA1 protein expression (41%) had a better chance of clinical response following chemotherapy as compared to patients with high BRCA1 expression (odds ratio 2.47: 95%CI 1.10-5.55, p=0.029). Patients with absent/low BRCA1 had a higher probability of clinical response following single agent platinum compared to high BRCA1 expressing patients (68.5% vs. 46.8%), while addition of a taxane increased response rates independent of BRCA1. Overall, patients with absent/low BRCA1 had a better clinical outcome compared to patients with high BRCA1 protein expression in terms of both OS (HR=0.65: 95%CI 0.48-0.88, p=0.006) and PFS (HR=0.74, 95%CI 0.55-0.98, p=0.040). CONCLUSIONS: We confirm that absent/low BRCA1 protein expression is a favourable prognostic marker. However, we also provide the first evidence that absent/low BRCA1 protein expression in sporadic EOC patients predicts for an improved clinical response to chemotherapy.

A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer.

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.

Hyperacetylation in prostate cancer induces cell cycle aberrations, chromatin reorganization and altered gene expression profiles.

Histone acetylation is a fundamental mechanism in the regulation of local chromatin conformation and gene expression. Research has focused on the impact of altered epigenetic environments on the expression of specific genes and their pathways. However, changes in histone acetylation also have a global impact on the cell. In this study we used digital texture analysis to assess global chromatin patterns following treatment with trichostatin A (TSA) and have observed significant alterations in the condensation and distribution of higher-order chromatin, which were associated with altered gene expression profiles in both immortalised normal PNT1A prostate cell line and androgen-dependent prostate cancer cell line LNCaP. Furthermore, the extent of TSA-induced disruption was both cell cycle and cell line dependent. This was illustrated by the identification of sub-populations of prostate cancer cells expressing high levels of H3K9 acetylation in the G(2)/M phase of the cell cycle that were absent in normal cell populations. In addition, the analysis of enriched populations of G(1) cells showed a global decondensation of chromatin exclusively in normal cells.