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Aims: To evaluate the accuracy of Chat Generative Pre-trained Transformer (ChatGPT) powered by GPT-4 in histopathological image detection and classification of colorectal adenomas using the diagnostic consensus provided by pathologists as a reference standard., Methods: A study was conducted with 100 colorectal polyp photomicrographs, comprising an equal number of adenomas and non-adenomas, classified by two pathologists. These images were analysed by classic GPT-4 for 1 time in October 2023 and custom GPT-4 for 20 times in December 2023. GPT-4's responses were compared against the reference standard through statistical measures to evaluate its proficiency in histopathological diagnosis, with the pathologists further assessing the model's descriptive accuracy., Results: GPT-4 demonstrated a median sensitivity of 74% and specificity of 36% for adenoma detection. The median accuracy of polyp classification varied, ranging from 16% for non-specific changes to 36% for tubular adenomas. Its diagnostic consistency, indicated by low kappa values ranging from 0.06 to 0.11, suggested only poor to slight agreement. All of the microscopic descriptions corresponded with their diagnoses. GPT-4 also commented about the limitations in its diagnoses (eg, slide diagnosis best done by pathologists, the inadequacy of single-image diagnostic conclusions, the need for clinical data and a higher magnification view)., Conclusions: GPT-4 showed high sensitivity but low specificity in detecting adenomas and varied accuracy for polyp classification. However, its diagnostic consistency was low. This artificial intelligence tool acknowledged its diagnostic limitations, emphasising the need for a pathologist's expertise and additional clinical context., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background and Objective: Treatment approaches for colorectal cancer (CRC) are highly dependent on the molecular subtype, as immunotherapy has shown efficacy in cases with microsatellite instability (MSI) but is ineffective for the microsatellite stable (MSS) subtype. There is promising potential in utilizing deep neural networks (DNNs) to automate the differentiation of CRC subtypes by analyzing hematoxylin and eosin (H&E) stained whole-slide images (WSIs). Due to the extensive size of WSIs, multiple instance learning (MIL) techniques are typically explored. However, existing MIL methods focus on identifying the most representative image patches for classification, which may result in the loss of critical information. Additionally, these methods often overlook clinically relevant information, like the tendency for MSI class tumors to predominantly occur on the proximal (right side) colon., Methods: We introduce 'CIMIL-CRC', a DNN framework that: (1) solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches, and (2) integrates clinical priors, particularly the tumor location within the colon, into the model to enhance patient-level classification accuracy. We assessed our CIMIL-CRC method using the average area under the receiver operating characteristic curve (AUROC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort, contrasting it with a baseline patch-level classification, a MIL-only approach, and a clinically-informed patch-level classification approach., Results: Our CIMIL-CRC outperformed all methods (AUROC: 0.92±0.002 (95% CI 0.91-0.92), vs. 0.79±0.02 (95% CI 0.76-0.82), 0.86±0.01 (95% CI 0.85-0.88), and 0.87±0.01 (95% CI 0.86-0.88), respectively). The improvement was statistically significant. To the best of our knowledge, this is the best result achieved for MSI/MSS classification on this dataset., Conclusion: Our CIMIL-CRC method holds promise for offering insights into the key representations of histopathological images and suggests a straightforward implementation., Competing Interests: Declaration of competing interest We affirm that there exist no financial or personal affiliations with individuals or organizations that could unjustly sway our research. Further, we have no professional or personal interests, of any nature, in any products, services, and/or companies that could be interpreted as influencing the stance taken in, or the review of, the manuscript under consideration., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background & Aims: The identification of colorectal cancer (CRC) molecular subtypes has prognostic and potentially diagnostic value for patients, yet reliable subtyping remains unavailable in the clinic. The current consensus molecular subtype (CMS) classification in CRCs is based on complex RNA expression patterns quantified at the gene level. The clinical application of these methods, however, is challenging due to high uncertainty of single-sample classification and associated costs. Alternative splicing, which strongly contributes to transcriptome diversity, has rarely been used for tissue type classification. Here, we present an AS-based CRC subtyping framework sensitive to differential exon use that can be adapted for clinical application., Methods: Unsupervised clustering was used to measure the strength of association between different categories of alternative splicing and CMSs. To build a classifier, the ground truth for CMS labels was derived from expression data quantified at the gene level. Feature selection was achieved through bootstrapping and L1-penalized estimation. The resulting feature space was used to construct a subtype prediction framework applicable to single and multiple samples. The performance of the models was evaluated on unseen CRCs from 2 independent sources (Indivumed, n = 129; The Cancer Genome Atlas, n = 99)., Results: We developed a CRC subtype identifier based on 29 exon-skipping events that accurately classifies unseen tumors and enables more precise differentiation of subtypes characterized by distinct biological and prognostic features as compared to classifiers based on gene expression., Conclusions: Here, we demonstrate that a small number of exon-skipping events can reliably classify CRC subtypes using individual patient specimens in a manner suitable to clinical application., (Copyright © 2024 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Opinion Statement: The biological heterogeneity of colorectal cancer makes its molecular characteristics essential for therapeutic decision-making and prognostic evaluation. Recent advancements in consensus molecular subtyping, based on gene expression profiling, have provided deeper insights into the heterogeneity of CRC. CMS1, known as the immune subtype, is characterized by robust immune activity and microsatellite instability. CMS2, the canonical subtype, exhibits significant activation of the WNT and MYC signaling pathways. CMS3, the metabolic subtype, features unique metabolic dysregulations. CMS4, the mesenchymal subtype, is recognized for its stromal invasion and angiogenesis, which are associated with a poorer prognosis. This review delivers a thorough analysis of the biological and clinical responses of each CMS subtype in colorectal cancer, highlighting their therapeutic vulnerabilities. It integrates data and clinical trial results to suggest potential new therapies for each subtype. The goal is to improve therapeutic efficacy, minimize treatment disparities, and offer CRC patients more precise treatment options., Competing Interests: Declarations. Approval of the research protocol by an Institutional Reviewer Board: N/A. Informed Consent: N/A. Registry and the Registration No. of the study/trial: N/A. Animal Studies: N/A. Competing interests: The authors declare no competing interests. Human and Animal Rights and Informed Consent: This article does not contain any studies with human or animal subjects performed by any of the authors., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Objective: Colorectal cancer (CRC) represents a heterogeneous malignancy that has concerned global burden of incidence and mortality. The traditional tumor-node-metastasis staging system has exhibited certain limitations. With the advancement of omics technologies, researchers are directing their focus on developing a more precise multi-omics molecular classification. Therefore, the utilization of unsupervised multi-omics integrative clustering methods in CRC, advocating for the establishment of a comprehensive benchmark with practical guidelines., Methods: In this study, we obtained CRC multi-omics data, encompassing DNA methylation, gene expression, and protein expression from the cancer genome atlas (TCGA)database. We then generated interrelated CRC multi-omics data with various structures based on realistic multi-omics correlations, and performed a comprehensive evaluation of eight representative methods categorized as early integration, intermediate integration, and late integration using complementary benchmarks for subtype classification accuracy. Lastly, we employed these methods to integrate real-world CRC multi-omics data, survival and differential analysis were used to highlight differences among newly identified multi-omics subtypes., Results: Through in-depth comparisons, we observed that similarity network fusion (SNF) exhibited exceptional performance in integrating multi-omics data derived from simulations. Additionally, SNF effectively distinguished CRC patients into five subgroups with the highest classification accuracy. Moreover, we found significant survival differences and molecular distinctions among SNF subtypes., Conclusions: The findings consistently demonstrate that SNF outperforms other methods in CRC multi-omics integrative clustering. The significant survival differences and molecular distinctions among SNF subtypes provide novel insights into the multi-omics perspective on CRC heterogeneity with potential clinical treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier B.V.)

Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its early stages can help stop its growth by providing the necessary treatments, thereby saving many people's lives. There are various tests that doctors can perform to diagnose CRC; however, biopsy using histopathological images is considered the "gold standard" for CRC diagnosis. Deep learning techniques can now be leveraged to build computer-aided diagnosis (CAD) systems that can affirm if an input sample shows any symptoms of cancer and determine its stage and location with an acceptable degree of confidence. In this research, we utilize deep learning to study the CRC classification problem using weakly annotated histopathological whole slide images (WSIs). We relax the constraints of the multiple instance learning (MIL) algorithm and primarily propose WSI-label prediction functions to be integrated with MIL, which significantly enhances the performance of WSI-level classification. We also applied efficient preprocessing techniques that output a computationally power-efficient dataset representation and performed multiple experiments to compose the most efficient CAD system. Our study introduces a notable improvement over the results obtained by the baseline research where we achieved an accuracy of 93.05% compared to 84.17%. Furthermore, our results using only the weakly annotated WSIs outperformed the baseline results that are based on performing initial pre-training using a strongly annotated part of the dataset., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

The acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant challenges for nuclei segmentation and classification. In addition, the imbalance of various nuclei classes exacerbates the difficulty of nuclei classification and segmentation using deep learning models. To address these challenges, we present a novel attention-enhanced residual refinement network (AER-Net), which consists of one encoder and three decoder branches that have same network structure. In addition to the nuclei instance segmentation branch and nuclei classification branch, one branch is used to predict the vertical and horizontal distance from each pixel to its nuclear center, which is combined with output by the segmentation branch to improve the final segmentation results. The AER-Net utilizes an attention-enhanced encoder module to focus on more valuable features. To further refine predictions and achieve more accurate results, an attention-enhancing residual refinement module is employed at the end of each encoder branch. Moreover, the coarse predictions and refined predictions are combined by using a loss function that employs cross-entropy loss and generalized dice loss to efficiently tackle the challenge of class imbalance among nuclei in histology images. Compared with other state-of-the-art methods on two colorectal cancer datasets and a pan-cancer dataset, AER-Net demonstrates outstanding performance, validating its effectiveness in nuclear segmentation and classification.

Background: Mutations in the TP53 tumor suppressor gene are well-established drivers of colorectal cancer (CRC) development. However, data on the prevalence of TP53 variants and their association with consensus molecular subtype (CMS) classification in patients with CRC from Rwanda are currently lacking. This study addressed this knowledge gap by investigating TP53 mutation status concerning CMS classification in a CRC cohort from Rwanda., Methods: Formalin-fixed paraffin-embedded (FFPE) tissue blocks were obtained from 51 patients with CRC at the University Teaching Hospital of Kigali, Rwanda. Exons 4 to 11 and their flanking intron-exon boundaries in the TP53 gene were sequenced using Sanger sequencing to identify potential variants. The recently established immunohistochemistry-based classifier was employed to determine the CMS of each tumor., Results: Sequencing analysis of cancerous tissue DNA revealed TP53 pathogenic variants in 23 of 51 (45.1%) patients from Rwanda. These variants were predominantly missense types (18/23, 78.3%). The most frequent were c.455dup (p.P153Afs*28), c.524G > A (p.R175H), and c.733G > A (p.G245S), each identified in three tumors. Trinucleotide sequence context analysis of the 23 mutations (20 of which were single-base substitutions) revealed a predominance of the [C > N] pattern among single-base substitutions (SBSs) (18/20; 90.0%), with C[C > T]G being the most frequent mutation (5/18, 27.8%). Furthermore, pyrimidine bases (C and T) were preferentially found at the 5' flanking position of the mutated cytosine (13/18; 72.2%). Analysis of CMS subtypes revealed the following distribution: CMS1 (microsatellite instability-immune) (6/51, 11.8%), CMS2 (canonical) (28/51, 54.9%), CMS3 (metabolic) (9/51, 17.6%), and CMS4 (mesenchymal) (8/51, 15.7%). Interestingly, the majority of TP53 variants were in the CMS2 subgroup (14/23; 60.1%)., Conclusion: Our findings indicate a high frequency of TP53 variants in CRC patients from Rwanda. Importantly, these variants are enriched in the CMS2 subtype. This study, representing the second investigation into molecular alterations in patients with CRC from Rwanda and the first to explore TP53 mutations and CMS classification, provides valuable insights into the molecular landscape of CRC in this understudied population., (© 2024. The Author(s).)

Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classification technique is applied to identify colorectal cancer. But visually categorizing polyps is difficult since different polyps have different lighting conditions. Different from previous works, this article presents Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), a polyp classification technique that combines Convolutional Neural Network (CNN) and Scattering Wavelet Transform (SWT) to improve polyp classification performance. This method concatenates simultaneously learnable image filters and wavelet filters on each input channel. The scattering wavelet filters can extract common spectral features with various scales and orientations, while the learnable filters can capture image spatial features that wavelet filters may miss. A network architecture for ESWCNN is designed based on these principles and trained and tested using colonoscopy datasets (two public datasets and one private dataset). An n-fold cross-validation experiment was conducted for three classes (adenoma, hyperplastic, serrated) achieving a classification accuracy of 96.4%, and 94.8% accuracy in two-class polyp classification (positive and negative). In the three-class classification, correct classification rates of 96.2% for adenomas, 98.71% for hyperplastic polyps, and 97.9% for serrated polyps were achieved. The proposed method in the two-class experiment reached an average sensitivity of 96.7% with 93.1% specificity. Furthermore, we compare the performance of our model with the state-of-the-art general classification models and commonly used CNNs. Six end-to-end models based on CNNs were trained using 2 dataset of video sequences. The experimental results demonstrate that the proposed ESWCNN method can effectively classify polyps with higher accuracy and efficacy compared to the state-of-the-art CNN models. These findings can provide guidance for future research in polyp classification., Competing Interests: There are no conflicts of interest associated with the submission of this manuscript, and all authors have approved it for publication., (Copyright: © 2024 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images., (© 2024. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation., (© The Author(s) 2024. Published by Oxford University Press.)

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824±0.02 vs. 0.761±0.04, p<0.05, AP: 0.652±0.06 vs. 0.58±0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834±0.01 vs. 0.787±0.03, p<0.05, AP: 0.687±0.02 vs. 0.64±0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847±0.01 vs. 0.787±0.03, p = 0.01, AP: 0.68±0.02 vs. 0.64±0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hezi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Colorectal carcinoma (CRC) is a common cause of mortality 1 , but a comprehensive description of its genomic landscape is lacking 2-9 . Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia coli pks+ colibactin in rectal cancers 10 and the importance of the SBS93 signature 11-13 , which suggests that diet or smoking is a risk factor. Immune-escape driver mutations 14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care., (© 2024. The Author(s).)

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways 1 . Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy., (© 2024. The Author(s).)

The JNET classification, combined with magnified narrowband imaging (NBI), is essential for predicting the histology of colorectal polyps and guiding personalized treatment strategies. Despite its recognized utility, the diagnostic efficacy of JNET classification using NBI with dual focus (DF) magnification requires exploration in the Vietnamese context. This study aimed to investigate the diagnostic performance of the JNET classification with the NBI-DF mode in predicting the histology of colorectal polyps in Vietnam. A cross-sectional study was conducted at the University Medical Center in Ho Chi Minh City, Vietnam. During real-time endoscopy, endoscopists evaluated the lesion characteristics and recorded optical diagnoses using the dual focus mode magnification according to the JNET classification. En bloc lesion resection (endoscopic or surgical) provided the final pathology, serving as the reference standard for optical diagnoses. A total of 739 patients with 1353 lesions were recruited between October 2021 and March 2023. The overall concordance with the JNET classification was 86.9%. Specificities and positive predictive values for JNET types were: type 1 (95.7%, 88.3%); type 2A (81.4%, 90%); type 2B (96.6%, 54.7%); and type 3 (99.9%, 93.3%). The sensitivity and negative predictive value for differentiating neoplastic from non-neoplastic lesions were 97.8% and 88.3%, respectively. However, the sensitivity for distinguishing malignant from benign neoplasia was lower at 64.1%, despite a specificity of 95.9%. Notably, the specificity and positive predictive value for identifying deep submucosal cancer were high at 99.8% and 93.3%. In Vietnam, applying the JNET classification with NBI-DF demonstrates significant value in predicting the histology of colorectal polyps. This classification guides treatment decisions and prevents unnecessary surgeries., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Over the past decade, our understanding of the diversity of colorectal cancer has expanded significantly, raising hopes of tailoring treatments more precisely for individual patients. A key achievement in this direction was the establishment of the consensus molecular classification, particularly identifying the challenging consensus molecular subtype (CMS) CMS4 associated with poor prognosis. Because of its aggressive nature, extensive research is dedicated to the CMS4 subgroup. Recent years have unveiled molecular and microenvironmental features at the tissue level specific to CMS4 colorectal cancer. This has paved the way for mechanistic studies and the development of preclinical models. Simultaneously, efforts have been made to easily identify patients with CMS4 colorectal cancer. Reassessing clinical trial results through the CMS classification lens has improved our understanding of the therapeutic challenges linked to this subtype. Exploration of the biology of CMS4 colorectal cancer is yielding potential biomarkers and novel treatment approaches. This overview aims to provide insights into the clinico-biological characteristics of the CMS4 subgroup, the molecular pathways driving this subtype, and available diagnostic options. We also emphasize the therapeutic challenges associated with this subtype, offering potential explanations. Finally, we summarize the current tailored treatments for CMS4 colorectal cancer emerging from fundamental and preclinical studies., (©2024 The Authors; Published by the American Association for Cancer Research.)

Background: Recently, consensus molecular subtypes (CMSs) have been proposed as a robust transcriptome-based classification system for colorectal cancer (CRC). Tetraspanins (TSPANs) are transmembrane proteins. They have been associated with the development of numerous malignancies, including CRC, through their role as "master organizers" for multi-molecular membrane complexes. No previous study has investigated the correlation between TSPANs and CMS classification. Herein, we investigated the expression of TSPANs in patient-derived primary CRC tissues and their CMS classifications., Methods: RNA samples were derived from primary CRC tissues (n = 100 patients diagnosed with colorectal adenocarcinoma) and subjected to RNA sequencing for transcriptome-based CMS classification and TSPAN-relevant analyses. Immunohistochemistry (IHC) and immunofluorescence (IF) stains were conducted to observe the protein expression level. To evaluate the relative biological pathways, gene-set enrichment analysis was performed., Results: Of the highly expressed TSPAN genes in CRC tissues (TSPAN8, TSPAN29, and TSPAN30), TSPAN8 was notably overexpressed in CMS3-classified primary tissues. The overexpression of TSPAN8 protein in CMS3 CRC was also observed by IHC and IF staining. As a result of gene-set enrichment analysis, TSPAN8 may potentially play a role in organizing signaling complexes for kinase-based metabolic deregulation in CMS3 CRC., Conclusions: The present study reports the overexpression of TSPAN8 in CMS3 CRC. This study proposes TSPAN8 as a subtype-specific biomarker for CMS3 CRC. This finding provides a foundation for future CMS-based studies of CRC, a complex disease and the second leading cause of cancer mortality worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Objectives: The Japan NBI Expert Team (JNET) classification has good diagnostic potential for colorectal diseases. We aimed to explore the diagnostic value of the JNET classification type 2B (JNET2B) criteria for colorectal laterally spreading tumors (LSTs) based on magnifying endoscopy with blue laser imaging (ME-BLI) examination., Methods: Between January 2017 and June 2023, 218 patients who were diagnosed as having JNET2B-type LSTs using ME-BLI were included retrospectively. Endoscopic images were reinterpreted to categorize the LSTs as JNET2B-low (n = 178) and JNET2B-high (n = 53) LSTs. The JNET2B-low and JNET2B-high LSTs were compared based on their histopathological and morphological classifications., Results: Among the 178 JNET2B-low LSTs, 86 (48.3%) were histopathologically classified as low-grade intraepithelial neoplasia, 54 (30.3%) as high-grade intraepithelial neoplasia (HGIN), 37 (20.8%) as intramucosal carcinoma (IMC), and one (0.6%) as superficial invasive submucosal carcinoma (SMC1). Among the 53 JNET2B-high LSTs, five (9.4%) were classified as HGIN, 28 (52.9%) as IMC, 15 (28.3%) as SMC1, and 5 (9.4%) as deep invasive submucosal carcinoma. There were significant differences in this histopathological classification between the two groups (P < 0.001). However, there was no significant difference between JNET2B-low and JNET2B-high LSTs based on their morphological classification (granular vs nongranular) or size (<20 mm vs ≥20 mm). Besides, the κ value for JNET2B subtyping was 0.698 (95% confidence interval 0.592-0.804) between the two endoscopists who reassessed the endoscopic images., Conclusion: The JNET2B subtyping of LSTs has a diagnostic potential in the preoperative setting, and may be valuable for treatment decision-making., (© 2024 Chinese Medical Association Shanghai Branch, Chinese Society of Gastroenterology, Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24-2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10-27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples., (© 2024 de Back et al.)

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes., (© 2024. The Author(s).)

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists., (© 2024. The Author(s).)

Background: Colorectal cancer (CRC) exhibits molecular and morphological diversity, involving genetic, epigenetic alterations, and disruptions in signaling pathways. This necessitates a comprehensive review synthesizing recent advancements in molecular mechanisms, established biomarkers, as well as emerging ones like CDX2 for enhanced CRC assessment. Material and Methods: This review analyzes the last decade's literature and current guidelines to study CRC's molecular intricacies. It extends the analysis beyond traditional biomarkers to include emerging ones like CDX2, examining their interaction with carcinogenic mechanisms and molecular pathways, alongside reviewing current testing methodologies. Results: A multi-biomarker strategy, incorporating both traditional and emerging biomarkers like CDX2, is crucial for optimizing CRC management. This strategy elucidates the complex interaction between biomarkers and the tumor's molecular pathways, significantly influencing prognostic evaluations, therapeutic decision-making, and paving the way for personalized medicine in CRC. Conclusions: This review proposes CDX2 as an emerging prognostic biomarker and emphasizes the necessity of thorough molecular profiling for individualized treatment strategies. By enhancing CRC treatment approaches and prognostic evaluation, this effort marks a step forward in precision oncology, leveraging an enriched understanding of tumor behavior., (Celsius.)

Background: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and poorer survival. The authors investigated diffuse reflectance spectroscopy (DRS) to distinguish tumour and non-tumour tissue in ex-vivo colorectal specimens, to aid margin assessment and provide augmented visual maps to the surgeon in real-time., Methods: Patients undergoing elective colorectal cancer resection surgery at a London-based hospital were prospectively recruited. A hand-held DRS probe was used on the surface of freshly resected ex-vivo colorectal tissue. Spectral data were acquired for tumour and non-tumour tissue. Binary classification was achieved using conventional machine learning classifiers and a convolutional neural network (CNN), which were evaluated in terms of sensitivity, specificity, accuracy and the area under the curve., Results: A total of 7692 mean spectra were obtained for tumour and non-tumour colorectal tissue. The CNN-based classifier was the best performing machine learning algorithm, when compared to contrastive approaches, for differentiating tumour and non-tumour colorectal tissue, with an overall diagnostic accuracy of 90.8% and area under the curve of 96.8%. Live on-screen classification of tissue type was achieved using a graduated colourmap., Conclusion: A high diagnostic accuracy for a DRS probe and tracking system to differentiate ex-vivo tumour and non-tumour colorectal tissue in real-time with on-screen visual feedback was highlighted by this study. Further in-vivo studies are needed to ensure integration into a surgical workflow., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, the diagnosis by pathologists is highly subjective and prone to misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and MCCBAM, called HCCANet. In this study, 630 histopathology images processed with Gaussian filtering denoising were included and gradient-weighted class activation map (Grad-CAM) was used to visualize regions of interest in HCCANet to improve its interpretability. The experimental results show that the proposed HCCANet model outperforms four advanced deep learning (ResNet50, MobileNetV2, Xception, and DenseNet121) and four classical machine learning (KNN, NB, RF, and SVM) techniques, achieved 90.2%, 85%, and 86.7% classification accuracy for colorectal cancers with high, medium, and low differentiation levels, respectively, with an overall accuracy of 87.3% and an average AUC value of 0.9.In addition, the MCCBAM constructed in this study outperforms several commonly used attention mechanisms SAM, SENet, SKNet, Non&#95;Local, CBAM, and BAM on the backbone network. In conclusion, the HCCANet model proposed in this study is feasible for postoperative adjuvant diagnosis and grading of colorectal cancer., (© 2022. The Author(s).)

Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive's unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists' annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into 'low risk' (benign, inflammation) and 'high risk' (dysplasia, malignancy) categories. We further trained the composite AI-model's performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands., (© 2022. The Author(s).)

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC., (© 2022. The Author(s).)

Background: Emerging studies have proved that colonic inflammation caused by refractory inflammatory bowel disease (IBD) can initiate the colitis-associated cancer (CAC), but the transition from inflammation to carcinoma is still largely unknown., Methods: In this study, mouse colitis and CAC models were established, and the RNA-seq by circRNA microarray was employed to identify the differentially expressed circRNAs and mRNAs in different comparisons (DSS vs. NC and AOM/DSS vs. DSS). The bioinformatics analyses were used to search the common characteristics in mouse colitis and CAC., Results: The K-means clustering algorithm packaged these differential expressed circRNAs into subgroup analysis, and the data strongly implied that mmu&#95;circ&#95;0001109 closely correlated to the pro-inflammatory signals, while mmu&#95;circ&#95;0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu&#95;circ&#95;0001109 could exacerbate the colitis by up-regulating the Jak-STAT3 and NF-kappa B signalling pathways, and mmu&#95;circ&#95;0001845 promoted the CAC transformation through the Wnt signalling pathway. By RNA blasting between mice and humans, the human RTEL1- and PRKAR2A-derived circRNAs, which might be considered as homeotic circRNAs of mmu&#95;circ&#95;0001109 and mmu&#95;circ&#95;0001845, respectively, were identified. The clinical data revealed that RTEL1-derived circRNAs had no clinical significance in human IBD and CAC. However, three PRKAR2A-derived circRNAs, which had the high RNA similarities to mmu&#95;circ&#95;0001845, were remarkably up-regulated in CAC tissue samples and promoted the transition from colitis to CAC., Conclusions: Our results suggested that these human PRKAR2A-derived circRNAs could be novel candidates for distinguishing CAC patients and predicted the prognosis of CAC., (© 2022 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Introduction: Optical diagnosis is necessary when selecting the resection modality for large superficial colorectal lesions. The COlorectal NEoplasia Endoscopic Classification to Choose the Treatment (CONECCT) encompasses overt (irregular pit or vascular pattern) and covert (macroscopic features) signs of carcinoma in an all-in-one classification using validated criteria. The CONECCT IIC subtype corresponds to adenomas with a high risk of superficial carcinoma that should be resected en bloc with free margins., Methods: This prospective multicentre study investigated the diagnostic accuracy of the CONECCT classification for predicting submucosal invasion in colorectal lesions >20 mm. Optical diagnosis before en bloc resection by endoscopic submucosal dissection (ESD) was compared with the final histological diagnosis. Diagnostic accuracy for the CONECCT IIC subtype was compared with literature-validated features of concern considered to be risk factors for submucosal invasion (non-granular large spreading tumour [NG LST], macronodule >1 cm, SANO IIIA area, and Paris 0-IIC area)., Results: Six hundred 63 lesions removed by ESD were assessed. The en bloc, R0, and curative resection rates were respectively 96%, 85%, and 81%. The CONECCT classification had a sensitivity (Se) of 100%, specificity (Sp) of 26.2%, positive predictive value of 11.6%, and negative predictive value (NPV) of 100% for predicting at least submucosal adenocarcinoma. The sensitivity of CONECCT IIC (100%) to predict submucosal cancer was superior to all other criteria evaluated. COlorectal NEoplasia Endoscopic Classification to Choose the Treatment IIC lesions constituted 11.5% of all submucosal carcinomas., Conclusion: The CONECCT classification, which combines covert and overt signs of carcinoma, identifies with very perfect sensitivity (Se 100%, NPV 100%) the 30% of low-risk adenomas in large laterally spreading lesions treatable by piecemeal endoscopic mucosal resection or ESD according to expertise without undertreatment. However, the low specificity of CONECCT leads to a large number of potentially not indicated ESDs for suspected high-risk lesions., (© 2022 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

Colorectal cancers (CRCs) exhibit differences in incidence, pathogenesis, molecular pathways, and outcome depending on the location of the tumor. The transcriptomes of 27,927 single human CRC cells from 3 left-sided and 3 right-sided CRC patients were profiled by single-cell RNA-Seq (scRNA-Seq). Right-sided CRC harbors a significant proportion of exhausted CD8+ T cells of a highly migratory nature. One cluster of cells from left-sided CRC exhibiting states preceding exhaustion and a high ratio of preexhausted/exhausted T cells were favorable prognostic markers. Notably, we identified a potentially novel RBP4+NTS+ subpopulation of cancer cells that exclusively expands in left-sided CRC. Tregs from left-sided CRC showed higher levels of immunotherapy-related genes than those from right-sided CRC, indicating that left-sided CRC may have increased responsiveness to immunotherapy. Antibody-dependent cellular phagocytosis (ADCP) and antibody-dependent cellular cytotoxicity (ADCC) induced by M2-like macrophages were more pronounced in left-sided CRC and correlated with a good prognosis in CRC.

Colorectal cancer (CRC) is a leading cause of cancer death in the United States. Standard treatment for advanced-stage CRC for decades has included 5-fluorouracil-based chemotherapy. More recently, targeted therapies for metastatic CRC are being used based on the individual cancer's molecular profile. In the past few years, several different molecular subtype schemes for human CRC have been developed. The molecular subtypes can be distinguished by gene expression signatures and have the potential to be used to guide treatment decisions. However, many subtyping classification methods were developed using mRNA expression levels of hundreds to thousands of genes, making them impractical for clinical use. In this study, we assessed whether an immunohistochemical approach could be used for molecular subtyping of CRCs. We validated two previously published, independent sets of immunohistochemistry classifiers and modified the published methods to improve the accuracy of the scoring methods. In addition, we evaluated whether protein and genetic signatures identified originally in the mouse were linked to clinical outcomes of patients with CRC. We found that low DDAH1 or low GAL3ST2 protein levels in human CRCs correlate with poor patient outcomes. The results of this study have the potential to impact methods for determining the prognosis and therapy selection for patients with CRC., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Colorectal cancers (CRCs) with microsatellite instability-high (MSI-H) are hypermutated tumors and are generally regarded as immunogenic. However, their heterogeneous immune responses and underlying molecular characteristics remain largely unexplained., Methods: We conducted a retrospective analysis of 73 primary MSI-H CRC tissues to characterize heterogeneous immune subgroups. Based on combined tumor-infiltrating lymphocyte (TIL) immunoscore and tertiary lymphoid structure (TLS) activity, MSI-H CRCs were classified into immune-high, immune-intermediate, and immune-low subgroups. Of these, the immune-high and immune-low subgroups were further analyzed using whole-exome and transcriptome sequencing., Results: We found considerable variations in immune parameters between MSI-H CRCs, and immune subgrouping of MSI-H CRCs was performed accordingly. The TIL densities and TLS activities of immune-low MSI-H CRCs were comparable to those of an immune-low or immune-intermediate subgroup of microsatellite-stable CRCs. There were remarkable differences between immune-high and immune-low MSI-H CRCs, including their pathological features (medullary vs mucinous), genomic alterations (tyrosine kinase fusions vs KRAS mutations), and activated signaling pathways (immune-related vs Wnt and Notch signaling), whereas no significant differences were found in tumor mutational burden (TMB) and neoantigen load. The immune-low MSI-H CRCs were subdivided by the consensus molecular subtype (CMS1 vs CMS3) with different gene expression signatures (mesenchymal/stem-like vs epithelial/goblet-like), suggesting distinct immune evasion mechanisms. Angiogenesis and CD200 were identified as potential therapeutic targets in immune-low CMS1 and CMS3 MSI-H CRCs, respectively., Conclusions: MSI-H CRCs are immunologically heterogeneous, regardless of TMB. The unusual immune-low MSI-H CRCs are characterized by mucinous histology, KRAS mutations, and Wnt/Notch activation, and can be further divided into distinct gene expression subtypes, including CMS4-like CMS1 and CMS3. Our data provide novel insights into precise immunotherapeutic strategies for subtypes of MSI-H tumors., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Tumor budding scoring guidelines from the International Tumor Budding Consensus Conference (ITBCC) for colorectal cancer propose three groups: BD1 (0-4 buds/0.785 mm 2 ), BD2 (5-9 buds/0.785 mm 2 ), and BD3 (10 or more buds/0.785 mm 2 ). Here, we investigate whether a fourth scoring category, namely zero buds, may have additional clinical relevance. The number of tumor buds/0.785 mm 2 was scored in 959 cases. Those with zero tumor buds were considered BD0, while a new BD1 category of 1-4 buds was proposed. Associations of both scoring approaches with clinicopathological features were analyzed. Conventional ITBCC scoring showed expected associations with unfavorable histopathological prognostic factors. In total, 111/959 (11.6%) were BD0. A significant difference was found when BD0 was compared statistically to BD1 (1-4 buds) for pT, TNM, tumor grade, and lymphatic, venous, and perineural invasion (p < 0.01, all). Tumors with BD0 occur relatively frequently and contribute additional information on tumor behavior. BD0 should be considered for subsequent ITBCC guidelines., (© 2021. The Author(s).)

Opinion Statement: The heterogenous nature of colorectal cancer (CRC) renders it a major clinical challenge. Increasing genomic understanding of CRC has improved our knowledge of this heterogeneity and the main cancer drivers, with significant improvements in clinical outcomes. Comprehensive molecular characterization has allowed clinicians a more precise range of treatment options based on biomarker selection. Furthermore, this deep molecular understanding likely extends therapeutic options to a larger number of patients. The biological associations of consensus molecular subtypes (CMS) with clinical outcomes in localized CRC have been validated in retrospective clinical trials. The prognostic role of CMS has also been confirmed in the metastatic setting, with CMS2 having the best prognosis, whereas CMS1 tumors are associated with a higher risk of progression and death after chemotherapy. Similarly, according to mesenchymal features and immunosuppressive molecules, CMS1 responds to immunotherapy, whereas CMS4 has a poorer prognosis, suggesting that a CMS1 signature could identify patients who may benefit from immune checkpoint inhibitors regardless of microsatellite instability (MSI) status. The main goal of these comprehensive analyses is to switch from "one marker-one drug" to "multi-marker drug combinations" allowing oncologists to give "the right drug to the right patient." Despite the revealing data from transcriptomic analyses, the high rate of intra-tumoral heterogeneity across the different CMS subgroups limits its incorporation as a predictive biomarker. In clinical practice, when feasible, comprehensive genomic tests should be performed to identify potentially targetable alterations, particularly in RAS/BRAF wild-type, MSI, and right-sided tumors. Furthermore, CMS has not only been associated with clinical outcomes and specific tumor and patient phenotypes but also with specific microbiome patterns. Future steps will include the integration of clinical features, genomics, transcriptomics, and microbiota to select the most accurate biomarkers to identify optimal treatments, improving individual clinical outcomes. In summary, CMS is context specific, identifies a level of heterogeneity beyond standard genomic biomarkers, and offers a means of maximizing personalized therapy., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice., (© 2021. The Author(s).)

Purpose: Regorafenib (REG) is approved for the treatment of metastatic colorectal cancer, but has modest survival benefit and associated toxicities. Robust predictive/early response biomarkers to aid patient stratification are outstanding. We have exploited biological pathway analyses in a patient-derived xenograft (PDX) trial to study REG response mechanisms and elucidate putative biomarkers., Experimental Design: Molecularly subtyped PDXs were annotated for REG response. Subtyping was based on gene expression (CMS, consensus molecular subtype) and copy-number alteration (CNA). Baseline tumor vascularization, apoptosis, and proliferation signatures were studied to identify predictive biomarkers within subtypes. Phospho-proteomic analysis was used to identify novel classifiers. Supervised RNA sequencing analysis was performed on PDXs that progressed, or did not progress, following REG treatment., Results: Improved REG response was observed in CMS4, although intra-subtype response was variable. Tumor vascularity did not correlate with outcome. In CMS4 tumors, reduced proliferation and higher sensitivity to apoptosis at baseline correlated with response. Reverse phase protein array (RPPA) analysis revealed 4 phospho-proteomic clusters, one of which was enriched with non-progressor models. A classification decision tree trained on RPPA- and CMS-based assignments discriminated non-progressors from progressors with 92% overall accuracy (97% sensitivity, 67% specificity). Supervised RNA sequencing revealed that higher basal EPHA2 expression is associated with REG resistance., Conclusions: Subtype classification systems represent canonical " termini a quo " (starting points) to support REG biomarker identification, and provide a platform to identify resistance mechanisms and novel contexts of vulnerability. Incorporating functional characterization of biological systems may optimize the biomarker identification process for multitargeted kinase inhibitors., (©2021 American Association for Cancer Research.)

Purpose: The clinical behavior of ampullary adenocarcinoma varies widely. Targeted tumor sequencing may better define biologically distinct subtypes to improve diagnosis and management., Experimental Design: The hidden-genome algorithm, a multilevel meta-feature regression model, was trained on a prospectively sequenced cohort of 3,411 patients (1,001 pancreatic adenocarcinoma, 165 distal bile-duct adenocarcinoma, 2,245 colorectal adenocarcinoma) and subsequently applied to targeted panel DNA-sequencing data from ampullary adenocarcinomas. Genomic classification (i.e., colorectal vs. pancreatic) was correlated with standard histologic classification [i.e., intestinal (INT) vs. pancreatobiliary (PB)] and clinical outcome., Results: Colorectal genomic subtype prediction was primarily influenced by mutations in APC and PIK3CA , tumor mutational burden, and DNA mismatch repair (MMR)-deficiency signature. Pancreatic genomic-subtype prediction was dictated by KRAS gene alterations, particularly KRAS G12D, KRAS G12R, and KRAS G12V. Distal bile-duct adenocarcinoma genomic subtype was most influenced by copy-number gains in the MDM2 gene. Despite high (73%) concordance between immunomorphologic subtype and genomic category, there was significant genomic heterogeneity within both histologic subtypes. Genomic scores with higher colorectal probability were associated with greater survival compared with those with a higher pancreatic probability., Conclusions: The genomic classifier provides insight into the heterogeneity of ampullary adenocarcinoma and improves stratification, which is dictated by the proportion of colorectal and pancreatic genomic alterations. This approach is reproducible with available molecular testing and obviates subjective histologic interpretation., (©2021 American Association for Cancer Research.)

Importance: Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists' classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy., Objective: To compare standard microscopic assessment with an artificial intelligence (AI)-augmented digital system that annotates regions of interest within digitized polyp tissue and predicts polyp type using a deep learning model to assist pathologists in colorectal polyp classification., Design, Setting, and Participants: In this diagnostic study conducted at a tertiary academic medical center and a community hospital in New Hampshire, 100 slides with colorectal polyp samples were read by 15 pathologists using a microscope and an AI-augmented digital system, with a washout period of at least 12 weeks between use of each modality. The study was conducted from February 10 to July 10, 2020., Main Outcomes and Measures: Accuracy and time of evaluation were used to compare pathologists' performance when a microscope was used with their performance when the AI-augmented digital system was used. Outcomes were compared using paired t tests and mixed-effects models., Results: In assessments of 100 slides with colorectal polyp specimens, use of the AI-augmented digital system significantly improved pathologists' classification accuracy compared with microscopic assessment from 73.9% (95% CI, 71.7%-76.2%) to 80.8% (95% CI, 78.8%-82.8%) (P < .001). The overall difference in the evaluation time per slide between the digital system (mean, 21.7 seconds; 95% CI, 20.8-22.7 seconds) and microscopic examination (mean, 13.0 seconds; 95% CI, 12.4-13.5 seconds) was -8.8 seconds (95% CI, -9.8 to -7.7 seconds), but this difference decreased as pathologists became more familiar and experienced with the digital system; the difference between the time of evaluation on the last set of 20 slides for all pathologists when using the microscope and the digital system was 4.8 seconds (95% CI, 3.0-6.5 seconds)., Conclusions and Relevance: In this diagnostic study, an AI-augmented digital system significantly improved the accuracy of pathologic interpretation of colorectal polyps compared with microscopic assessment. If applied broadly to clinical practice, this tool may be associated with decreases in subsequent overuse and underuse of colonoscopy and thus with improved patient outcomes and reduced health care costs.

Background: Patient selection for addition of anti-EGFR therapy to chemotherapy for patients with RAS and BRAF wildtype metastatic colorectal cancer can still be optimised. Here we investigate the effect of anti-EGFR therapy on survival in different consensus molecular subtypes (CMSs) and stratified by primary tumour location., Methods: Retrospective analyses, using the immunohistochemistry-based CMS classifier, were performed in the COIN (first-line oxaliplatin backbone with or without cetuximab) and PICCOLO trial (second-line irinotecan with or without panitumumab). Tumour tissue was available for 323 patients (20%) and 349 (41%), respectively., Results: When using an irinotecan backbone, anti-EGFR therapy is effective in both CMS2/3 and CMS4 in left-sided primary tumours (progression-free survival (PFS): HR 0.44, 95% CI 0.26-0.75, P = 0.003 and HR 0.12, 95% CI 0.04-0.36, P < 0.001, respectively) and in CMS4 right-sided tumours (PFS HR 0.17, 95% CI 0.04-0.71, P = 0.02). Efficacy using an oxaliplatin backbone was restricted to left-sided CMS2/3 tumours (HR 0.57, 95% CI 0.36-0.96, P = 0.034)., Conclusions: The subtype-specific efficacy of anti-EGFR therapy is dependent on the chemotherapy backbone. This may provide the possibility of subtype-specific treatment strategies for a more optimal use of anti-EGFR therapy., (© 2021. The Author(s).)

Background: In transitioning from the 7th edition of the tumor-node-metastasis classification (TNM-7) to the 8th edition (TNM-8), colorectal cancer with peritoneal metastasis was newly categorized as M1c. In the 9th edition of the Japanese Classification of colorectal, appendiceal, and anal carcinoma (JPC-9), M1c is further subdivided into M1c1 (without other organ involvement) and M1c2 (with other organ involvement). This study aimed to compare the model fit and discriminatory ability of the M category of these three classification systems, as no study to date has made this comparison., Methods: The study population consisted of stage IV colorectal cancer patients who were referred to the National Cancer Center Hospital from 2000 to 2017. The Akaike information criterion (AIC), Harrell's concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to compare the three classification systems. Subgroup analyses, stratified by initial treatment year, were also performed., Results: According to TNM-8, 670 (55%) patients had M1a, 273 (22%) had M1b, and 279 (23%) had M1c (87 M1c1 and 192 M1c2 using JPC-9) tumors. Among the three classification systems, JPC-9 had the lowest AIC value (JPC-9: 10546.3; TNM-7: 10555.9; TNM-8: 10585.5), highest C-index (JPC-9: 0.608; TNM-7: 0.598; TNM-8: 0.599), and superior time-dependent ROC curves throughout the observation period. Subgroup analyses were consistent with these results., Conclusions: While the revised M category definition did not improve model fit and discriminatory ability from TNM-7 to TNM-8, further subdivision of M1c in JPC-9 improved these parameters. These results support further revisions to M1 subcategories in future editions of the TNM classification system., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: Although the effect of the early detection of colorectal cancer (CRC) on medical costs needs to be clarified, there are few reports on the actual medical costs of CRC patients in Japan. We aimed to identify medical costs according to CRC stage, using health insurance claims., Methods: This observational study included CRC patients who had received specific treatment for CRC, which was defined by the procedure code and the claim computer processing system code associated with the treatment of CRC. CRC patients who underwent endoscopic or radical surgical treatment were defined as the curable group and those with palliative treatment, including palliative chemotherapy, as the non-curable group. Total medical costs and medical costs of specific treatments for CRC for 3 years were measured using the claims held by Hachioji City from May 2014 to July 2019., Results: This study included 442 patients in the curable group, including 267 patients who underwent endoscopic treatment, and 175 patients who underwent radical surgical treatment, and 161 patients in the non-curable group. The mean (standard deviation) total medical costs in the curable and non-curable groups were 2,130 (2,494) and 8,279 (5,600) thousand Japanese Yen (JPY), respectively. The mean (standard deviation) medical costs for the specific treatment of CRC in the curable and non-curable groups were 408 (352) and 3,685 (3,479) thousand JPY, respectively., Conclusions: We clarified the actual medical costs of CRC in curable and non-curable groups. These results suggest the effect of early detection of CRC in reducing medical costs., (© 2021. Japanese Society of Gastroenterology.)

Immunotherapy involving immune checkpoint inhibitors (ICIs) for enhancing immune system activation is promising for tumor management. However, the patients' responses to ICIs are different. Here, we applied a non-negative matrix factorization algorithm to establish a robust immune molecular classification system for colorectal cancer (CRC). We obtained data of 1503 CRC patients (training cohort: 488 from The Cancer Genome Atlas; validation cohort: 1015 from the Gene Expression Omnibus). In the training cohort, 42.8% of patients who exhibited significantly higher immunocyte infiltration and enrichment of immune response-associated signatures were subdivided into immune classes. Within the immune class, 53.1% of patients were associated with a worse overall prognosis and belonged to the immune-suppressed subclass, characterized by the activation of stroma-related signatures, genes, immune-suppressive cells, and signaling. The remaining immune class patients belonged to the immune-activated subclass, which was associated with a better prognosis and response to anti-PD-1 therapy. Immune-related subtypes were associated with different copy number alterations, tumor-infiltrating lymphocyte enrichment, PD-1/PD-L1 expression, mutation landscape, and cancer stemness. These results were validated in patients with microsatellite instable CRC. We described a novel immune-related class of CRC, which may be used for selecting candidate patients with CRC for immunotherapy and tailoring optimal immunotherapeutic treatment., (© 2021. The Author(s).)

Consensus molecular subtypes (CMSs) are emerging as critical factor for prognosis and treatment of colorectal cancer. Gene regulators, including chromatin regulator, RNA-binding protein and transcriptional factor, are critical modulators of cancer hallmark, yet little is known regarding the underlying functional mechanism in CMSs. Herein, we identified a core set of 235 functional gene regulators (FGRs) by integrating genome, epigenome, transcriptome and interactome of CMSs. FGRs exhibited significant multi-omics alterations and impacts on cell lines growth, as well as significantly enriched cancer driver genes and pathways. Moreover, common FGRs played different roles in the context of CMSs. In accordance with the immune characteristics of CMSs, we found that the anti-tumor immune pathways were mainly activated by FGRs (e.g. STAT1 and CREBBP) in CMS1, while inhibited by FGRs in CMS2-4. FGRs mediated aberrant expression of ligands, which bind to receptor on immune cells, and modulated tumor immune microenvironment of subtypes. Intriguingly, systematic exploration of datasets using genomic and transcriptome co-similarity reveals the coordinated manner in FGRs act in CMSs to orchestrate their pathways and patients' prognosis. Expression signatures of the FGRs revealed an optimized CMS classifier, which demonstrated 88% concordance with the gold-standard classifier, but avoiding the influence of sample composition. Overall, our integrative analysis identified FGRs to regulate core tumorigenic processes/pathways across CMSs., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learning (DL)-based classifier as a screening tool for MSI status, we built a fully automated DL-based MSI classifier using pathology whole-slide images (WSIs) of CRCs. On small image patches of The Cancer Genome Atlas (TCGA) CRC WSI dataset, tissue/non-tissue, normal/tumor and MSS/MSI-H classifiers were applied sequentially for the fully automated prediction of the MSI status. The classifiers were also tested on an independent cohort. Furthermore, to test how the expansion of the training data affects the performance of the DL-based classifier, additional classifier trained on both TCGA and external datasets was tested. The areas under the receiver operating characteristic curves were 0.892 and 0.972 for the TCGA and external datasets, respectively, by a classifier trained on both datasets. The performance of the DL-based classifier was much better than that of previously reported histomorphology-based methods. We speculated that about 40% of CRC slides could be screened for MSI status without molecular testing by the DL-based classifier. These results demonstrated that the DL-based method has potential as a screening tool to discriminate molecular alteration in tissue slides., (© 2021 Union for International Cancer Control.)

Introduction: We recently described the sulfur microbial diet, a pattern of intake associated with increased gut sulfur-metabolizing bacteria and incidence of distal colorectal cancer (CRC). We assessed whether this risk differed by CRC molecular subtypes or presence of intratumoral microbes involved in CRC pathogenesis (Fusobacterium nucleatum and Bifidobacterium spp.)., Methods: We performed Cox proportional hazards modeling to examine the association between the sulfur microbial diet and incidence of overall and distal CRC by molecular and microbial subtype in the Health Professionals Follow-Up Study (1986-2012)., Results: We documented 1,264 incident CRC cases among 48,246 men, approximately 40% of whom had available tissue data. After accounting for multiple hypothesis testing, the relationship between the sulfur microbial diet and CRC incidence did not differ by subtype. However, there was a suggestion of an association by prostaglandin synthase 2 (PTGS2) status with a multivariable adjusted hazard ratio for highest vs lowest tertile of sulfur microbial diet scores of 1.31 (95% confidence interval: 0.99-1.74, Ptrend = 0.07, Pheterogeneity = 0.04) for PTGS2-high CRC. The association of the sulfur microbial diet with distal CRC seemed to differ by the presence of intratumoral Bifidobacterium spp. with an adjusted hazard ratio for highest vs lowest tertile of sulfur microbial diet scores of 1.65 (95% confidence interval: 1.14-2.39, Ptrend = 0.01, Pheterogeneity = 0.03) for Bifidobacterium-negative distal CRC. We observed no apparent heterogeneity by other tested molecular markers., Discussion: Greater long-term adherence to the sulfur microbial diet could be associated with PTGS2-high and Bifidobacterium-negative distal CRC in men. Additional studies are needed to further characterize the role of gut microbial sulfur metabolism and CRC., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Yan Wang et al.)

Background: The histopathological discrepancy between endoscopic forceps biopsy (EFB) and post-resection specimens is considered a practical clinical problem. This retrospective study aimed to determine the current diagnostic concordance between the EFB and surgical specimens of colorectal cancer (CRC) and then investigated the useful factors in EFB diagnosis., Methods: We used the representative pathological data of 2188 CRCs. The comparison of histopathological discrepancy between EFB and the related surgical specimens was performed. Furthermore, 418 biopsy specimen slides in our hospital were reviewed to determine the classification of intratumor desmoplastic reaction (DR)., Results: Among the 2188 patients, the positive sensitivity of EFB for adenocarcinoma was 82.7%. The discrepancy rate between the EFB and surgical specimens was 10.8-40.0% corresponding to different T stages. On the basis of DR classification, 32, 131, and 255 tumors were categorized as little, moderate and extensive, respectively. The correlation between DR classification and tumor invasion based on T stage was significant (Spearman's rho= 0.112; p<0.05). The extensive DR provided better estimates for advanced tumors than the little and moderate DR (χ²= 3.977, p=0.046). Besides DR, factors including deeper cutting the slides and histological types were significantly associated with "adenocarcinoma" diagnosis in EFB of CRCs (p<0.05)., Conclusion: To the best of our knowledge, this is the first time that a DR classification specifically for EFB specimens was proposed. It might contribute to improve the accuracy of biopsy-based diagnosis of CRC.

The 2019 World Health Organization (WHO) classification of colorectal carcinoma (CRC) profoundly reclassified CRC subtypes and introduces tumor budding as a second major grading criterion, while condensing conventional grade into a 2-tiered system. So far it remains largely unexplored how these parameters interact with each other and whether they truly have an independent impact on patient prognosis. We reclassified a large single-center cohort of 1004 CRCs spanning 2 decades for adjusted WHO grade (low vs. high), tumor budding (Bd1/Bd2/Bd3), and CRC subtype (adenocarcinoma not otherwise specified, micropapillary, mucinous, serrated, medullary, adenoma-like, signet-ring cell, mixed adenoneuroendocrine carcinoma/neuroendocrine carcinoma, undifferentiated) according to the criteria of the 2019 WHO classification. We investigated the interaction of these parameters, their connection to stage/microsatellite status, and their significance for patient survival in the different subgroups. Specific subtypes other than adenocarcinoma not otherwise specified represented one third of all CRCs and were unevenly distributed throughout stage and microsatellite subgroups. Subtypes, WHO grade and tumor budding profoundly impacted all survival parameters (P<0.001 for all analyses), with CRC subtypes and tumor budding-but not WHO grade-being stage-independent prognosticators for all survival comparisons. WHO grade had very limited prognostic value in CRC subtypes, while tumor budding retained its strong prognostic impact in most scenarios. Accurate delineation of CRC subtypes introduced in the 2019 WHO classification provides strong stage-independent prognostic information, arguing that they should be considered in pathology reports and in clinical trials. Of the morphology-based grading schemes included in the 2019 WHO, tumor budding outperforms WHO grade., Competing Interests: Conflicts of Interest and Source of Funding: Supported by grants of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 360372040—SFB 1335 to W.W. and K.S. Project-ID 39535707—SFB 1371. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)