A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues.

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.

Molecular Deciphering of Colorectal Cancer: Exploring Molecular Classifications and Analyzing the Interplay among Molecular Biomarkers MMR/MSI, KRAS, NRAS, BRAF and CDX2 - A Comprehensive Literature Review.

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.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma.

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.

Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer.

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.

Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.

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.

HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism.

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_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.

PRKAR2A-derived circular RNAs promote the malignant transformation of colitis and distinguish patients with colitis-associated colorectal cancer.

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_circ_0001109 closely correlated to the pro-inflammatory signals, while mmu_circ_0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu_circ_0001109 could exacerbate the colitis by up-regulating the Jak-STAT3 and NF-kappa B signalling pathways, and mmu_circ_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_circ_0001109 and mmu_circ_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_circ_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.

A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.

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.

The COlorectal NEoplasia Endoscopic Classification to Choose the Treatment classification for identification of large laterally spreading lesions lacking submucosal carcinomas: A prospective study of 663 lesions.

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.

Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts.

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.

Validation of genetic classifiers derived from mouse and human tumors to identify molecular subtypes of colorectal cancer.

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.

Resolving the difference between left-sided and right-sided colorectal cancer by single-cell sequencing.

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.

Genomic and transcriptomic characterization of heterogeneous immune subgroups of microsatellite instability-high colorectal cancers.

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.

Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps.

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.

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.

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.

The Evolving Role of Consensus Molecular Subtypes: a Step Beyond Inpatient Selection for Treatment of Colorectal Cancer.

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.

Identification of immune-related subtypes of colorectal cancer to improve antitumor immunotherapy.

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.

Comparison of model fit and discriminatory ability of M category as defined by the 7th and 8th editions of the tumor-node-metastasis classification of colorectal cancer and the 9th edition of the Japanese classification.

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.