Classification Of Colorectal Cancer Research Articles

Abstract LB177: Tumor sideomics: spatial characteristics and functional deregulation in colorectal cancer

Abstract Clinical evidence suggests that right-sided colorectal cancer (CRC) tends to have a worse prognosis compared to left-sided CRC. The distinct functional mechanisms underlying this “sidedness” remain cryptic and poorly understood. Thus, molecular classification of CRC is of paramount in targeting specific subtypes and personalizing treatment for patients. To identify targetable molecular features to improve treatment regimens, we performed multi-faceted network analyses of hundreds of CRC patients. In our network model, epigenetic features, differential expression of transcript abundance, and mutational frequency were used to identify widespread gene dysregulation and perturbed pathways to reveal disrupted networks distinct to right or left sided colorectal cancer. We discovered histone related genes and distinct epigenetic signals are at play in right-sided CRC. Additionally, our studies reveal “at risk” patient characteristics suggestive of survival differences, to improve diagnostic predictors based on CRC sidedness. Overall, our integrative approach serves to link molecular profiles distinct between left and right sided colorectal cancer, while improving upon previous models of CRC and paving the way towards identifying more effective treatment alternatives from a previously overlooked, readily characterized subtype of colorectal cancer. Citation Format: Brandon Burgman, Xingxin Pan, S. Stephen Yi. Tumor sideomics: spatial characteristics and functional deregulation in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB177.

A Novel Molecular Analysis Approach in Colorectal Cancer Suggests New Treatment Opportunities.

Colorectal cancer (CRC) is a molecular and clinically heterogeneous disease. In 2015, the Colorectal Cancer Subtyping Consortium classified CRC into four consensus molecular subtypes (CMS), but these CMS have had little impact on clinical practice. The purpose of this study is to deepen the molecular characterization of CRC. A novel approach, based on probabilistic graphical models (PGM) and sparse k-means-consensus cluster layer analyses, was applied in order to functionally characterize CRC tumors. First, PGM was used to functionally characterize CRC, and then sparse k-means-consensus cluster was used to explore layers of biological information and establish classifications. To this aim, gene expression and clinical data of 805 CRC samples from three databases were analyzed. Three different layers based on biological features were identified: adhesion, immune, and molecular. The adhesion layer divided patients into high and low adhesion groups, with prognostic value. The immune layer divided patients into immune-high and immune-low groups, according to the expression of immune-related genes. The molecular layer established four molecular groups related to stem cells, metabolism, the Wnt signaling pathway, and extracellular functions. Immune-high patients, with higher expression of immune-related genes and genes involved in the viral mimicry response, may benefit from immunotherapy and viral mimicry-related therapies. Additionally, several possible therapeutic targets have been identified in each molecular group. Therefore, this improved CRC classification could be useful in searching for new therapeutic targets and specific therapeutic strategies in CRC disease.

Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data

<abstract> <p>Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from the pathology laboratory, integrated with described intra- and inter-variabilities through the calculation of biomarkers, has prompted the quest for robust machine-based approaches in combination with routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential for supporting the analysis and forecasting of medically appropriate molecular phenotypes and microsatellite instability. Considering this background, the current research work presents a metaheuristics technique with deep convolutional neural network-based colorectal cancer classification based on histopathological imaging data (MDCNN-C3HI). The presented MDCNN-C3HI technique majorly examines the histopathological images for the classification of colorectal cancer (CRC). At the initial stage, the MDCNN-C3HI technique applies a bilateral filtering approach to get rid of the noise. Then, the proposed MDCNN-C3HI technique uses an enhanced capsule network with the Adam optimizer for the extraction of feature vectors. For CRC classification, the MDCNN-C3HI technique uses a DL modified neural network classifier, whereas the tunicate swarm algorithm is used to fine-tune its hyperparameters. To demonstrate the enhanced performance of the proposed MDCNN-C3HI technique on CRC classification, a wide range of experiments was conducted. The outcomes from the extensive experimentation procedure confirmed the superior performance of the proposed MDCNN-C3HI technique over other existing techniques, achieving a maximum accuracy of 99.45%, a sensitivity of 99.45% and a specificity of 99.45%.</p> </abstract>

Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform.

A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening. This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control. The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913). The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.

Galactic swarm optimization with deep transfer learning driven colorectal cancer classification for image guided intervention

In this era of “precision” medicine, image-guided intervention (IGI) enables real-time customized and accurate treatment using imaging phenotype-based approaches. Colorectal cancer (CC) is the third most commonly occurring cancer, resulting in nearly 10% of cases over the globe. Colorectal cancer classification (CCC) of histopathological images by artificial intelligence (AI) approaches not only enhances the accuracy and classifier results but also allows physicians to make prompt decisions. In this view, this article introduces a novel Galactic Swarm Optimization with Deep Transfer Learning Driven Colorectal Cancer Classification (GSODTL-C3M) model for IGI. The primary aim of the GSODTL-C3M model is to appropriately categorize the test images into the existence of CC. To accomplish this, the presented GSODTL-C3M model employs image pre-processing using the bilateral filtering (BF) technique to remove noise. Besides, Adam optimizer with the MobileNet model is applied as a feature extractor. Finally, the GSO methodology with long short-term memory (LSTM) is employed to recognize and classify CC. An extensive range of simulations was taken place, and the results stated the advanced performance of the current state of art classification methodologies.

Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment.

The incidence and mortality of colorectal cancer (CRC) are increasing year by year. The accurate classification of CRC can realize the purpose of personalized and precise treatment for patients. The tumor microenvironment (TME) plays an important role in the malignant progression and immunotherapy of CRC. An in-depth understanding of the clusters based on the TME is of great significance for the discovery of new therapeutic targets for CRC. We extracted data on CRC, including gene expression profile, DNA methylation array, somatic mutations, clinicopathological information, and copy number variation (CNV), from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) (four datasets-GSE14333, GSE17538, GSE38832, and GSE39582), cBioPortal, and FireBrowse. The MCPcounter was utilized to quantify the abundance of 10 TME cells for CRC samples. Cluster repetitive analysis was based on the Hcluster function of the Pheatmap package in R. The ESTIMATE package was applied to compute immune and stromal scores for CRC patients. PCA analysis was used to remove batch effects among different datasets and transform genome-wide DNA methylation profiling into methylation of tumor-infiltrating lymphocyte (MeTIL). We evaluated the mutation differences of the clusters using MOVICS, DeconstructSigs, and GISTIC packages. As for therapy, TIDE and SubMap analyses were carried out to forecast the immunotherapy response of the clusters, and chemotherapeutic sensibility was estimated based on the pRRophetic package. All results were verified in the TCGA and GEO data. Four immune clusters (ImmClust-CS1, ImmClust-CS2, ImmClust-CS3, and ImmClust-CS4) were identified for CRC. The four ImmClusts exhibited distinct TME compositions, cancer-associated fibroblasts (CAFs), functional orientation, and immune checkpoints. The highest immune, stromal, and MeTIL scores were observed in CS2, in contrast to the lowest scores in CS4. CS1 may respond to immunotherapy, while CS2 may respond to immunotherapy after anti-CAFs. Among the four ImmClusts, the top 15 markers with the highest mutation frequency were acquired, and CS1 had significantly lower CNA on the focal level than other subtypes. In addition, CS1 and CS2 patients had more stable chromosomes than CS3 and CS4. The most sensitive chemotherapeutic agents in these four ImmClusts were also found. IHC results revealed that CD29 stained significantly darker in the cancer samples, indicating that their CD29 was highly expressed in colon cancer. This work revealed the novel clusters based on TME for CRC, which would guide in predicting the prognosis, biological features, and appropriate treatment for patients with CRC.

Nanostructured Silicon Enabled HR-MS for the Label-Free Detection of Biomarkers in Colorectal Cancer Plasma Small Extracellular Vesicles

Despite improvements in treatment options for advanced colorectal cancer (CRC), survival outcomes are still best for patients with non-metastasised disease. Diagnostic tools to identify blood-based biomarkers and assist in CRC subtype classification could afford a means to track CRC progression and treatment response. Cancer cell-derived small extracellular vesicles (EVs) circulating in blood carry an elevated cargo of lipids and proteins that could be used as a signature of tumour suppressor/promoting events or stages leading up to and including metastasis. Here, we used pre-characterised biobanked plasma samples from surgical units, typically with a low volume (~100 µL), to generate and discover signatures of CRC-derived EVs. We employed nanostructured porous silicon (pSi) surface assisted-laser desorption/ionisation (SALDI) coupled with high-resolution mass spectrometry (HR-MS), to allow sensitive detection of low abundant analytes in plasma EVs. When applied to CRC samples, SALDI-HR-MS enabled the detection of the peptide mass fingerprint of cancer suppressor proteins, including serine/threonine phosphatases and activating-transcription factor 3. SALDI-HR-MS also allowed the detection of a spectrum of glycerophospholipids and sphingolipid signatures in metastatic CRC. We observed that lithium chloride enhanced detection sensitivity to elucidate the structure of low abundant lipids in plasma EVs. pSi SALDI can be used as an effective system for label-free and high throughput analysis of low-volume patient samples, allowing rapid and sensitive analysis for CRC classification.

Abstract 5759: Molecular classification and risk stratification of colorectal cancer

Abstract Introduction: Currently, no widely accepted molecular classification of colorectal cancer (CRC) has been developed except for the distinction between hypermutated and nonhypermutated CRC. No reliable prognostication model has been available to optimize the effect of adjuvant chemotherapy either. To develop novel molecular classification and prognostication systems, we conducted large-scale studies of gene mutations and transcriptomes involving 3056 well-annotated CRC patients. Methods: All patients were analyzed for mutations in 169 known/putative driver genes in CRC using targeted-capture sequencing, which also include1,630 SNPs sequenced to assess genome-wide copy numbers. RNA sequencing was also performed for gene expression profiling and comprehensive detection of gene fusions. Results: Based on the strong mutually exclusive and co-occuring mutation patterns of mutations in Wnt pathway, non-hypermutated CRC patients (90%) were further classified into APC-mutated (81%) and unmutated (9%) case. The former was further divided into TP53-mutated (70%) and unmutated (11%), while the latter was into CTNNB1-mutated (2.7%), RNF43-mutated (1.7%), RSPO-fusion-positive (2%), and other cases (2.7%). Among these, TP53-unmutated CRC was characterized by significantly higher and lower numbers of mutations and copy number alterations, respectively, while accounting for 70% of all CRCs, the APC/TP53-double mutated ‘canonical CRC’ cases were classified into 13 unique subtypes having unique mutational profiles using non-negative matrix factorization consensus clustering. Next we modeled overall (OS) and relapse free (RFS) survival using Cox proportional hazard modeling with 1000 times cross validations using lasso. The final prediction model included gene mutations and copy number abnormalities, together with age, sex, stage, and tumor location, based on which we classified patients into groups having distinct OS and RFS in each stage. Importantly, adjuvant chemotherapy (ACT) was shown to have significantly different impacts on RFS between different risk groups within Stage2 and 3 CRC. Conclusions: On the basis of comprehensive mutation analysis in a large cohort, we developed novel molecular classification of CRC. We further identified distinct risk groups showing different RFS and OS, which showed relationship to the efficacy of ACT, which could be used for risk stratification in Stage2 and Stage3 CRC for optimized use of ACT. Citation Format: Yoshikage Inoue, Nobuyuki Kakiuchi, Kenichi Yoshida, Yasuhito Nanya, Yasuhide Takeuchi, Yoichi Fujii, Yuichi Shiraishi, Kenichi Chiba, Satoshi Nagayama, Kazutaka Obama, Seishi Ogawa. Molecular classification and risk stratification of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5759.

A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification

Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to reach high classification accuracy. Gray wolf optimization (GWO) and random forest (RF) algorithm were utilized to find the most suitable features in the histological images of the human colorectal cancer dataset. Then, based on the best-selected features, the artificial neural networks (ANNs) classifier was applied to classify multiclass texture analysis in colorectal cancer. A comparison between the GWO and another optimizer technique particle swarm optimization (PSO) was also conducted to determine which technique is the most successful in the enhancement of the RF algorithm. Furthermore, it is crucial to select an optimizer technique having the capability of removing redundant features and attaining the optimal feature subset and therefore achieving high CRC classification performance in terms of accuracy, precision, and sensitivity rates. The Heidelberg University Medical Center Pathology archive was used for performance check of the proposed method which was found to outperform benchmark approaches. The results revealed that the proposed feature selection method (GWO-RF) has outperformed the other state of art methods where it achieved overall accuracy, precision, and sensitivity rates of 98.74%, 98.88%, and 98.63%, respectively.

Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological systems. Due to the advancements of medical imaging in healthcare decision making, significant attention has been paid by the computer vision and deep learning (DL) models. At the same time, the detection and classification of colorectal cancer (CC) become essential to reduce the severity of the disease at an earlier stage. The existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning (ML) to recognize the existence of diseases. In this aspect, this study focuses on the design of intelligent DL based CC detection and classification (IDL-CCDC) model for bioinformatics applications. The proposed IDL-CCDC technique aims to detect and classify different classes of CC. In addition, the IDL-CCDC technique involves fuzzy filtering technique for noise removal process. Moreover, water wave optimization (WWO) based EfficientNet model is employed for feature extraction process. Furthermore, chaotic glowworm swarm optimization (CGSO) based variational auto encoder (VAE) is applied for the classification of CC into benign or malignant. The design of WWO and CGSO algorithms helps to increase the overall classification accuracy. The performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

Progress in the Application of Immune Checkpoint Inhibitor-Based Immunotherapy for Targeting Different Types of Colorectal Cancer.

Colorectal cancer (CRC), a common malignant disease, has the second highest mortality rate among all cancer types. Due to the diversity and heterogeneity of CRC, few effective treatment strategies have been developed in recent years, except for surgical resection. As immunotherapy has become a revolutionary treatment after surgery, along with chemoradiotherapy and targeted therapy, numerous basic research studies and clinical trials have been conducted on CRC. Therefore, immune checkpoint inhibitor (ICI) therapy has become the main anti-CRC immunotherapy method used at present. With the rapid development of biotechnology and cell research, an increasing number of monotherapy or combination therapy strategies using ICIs for CRC have been designed in recent years. Methods to classify and review ICI strategies for different types of CRC to better guide treatment are continuously investigated. However, the identification of why the ICIs would be more effective in targeting particular subtypes of CRC such as high microsatellite instability (MSI-H) is more important because of the different immune backgrounds in patients. This review intends to classify different subtypes of CRC and summarizes the basic and clinical studies on ICIs for each subtype of CRC currently available. In addition, we also attempt to briefly discuss the progress in immunotherapy methods other than ICI therapy, such as chemoimmunotherapy strategy, chimeric antigen receptor-modified T (CAR-T) cells, or immunotherapy based on oncolytic viruses. Finally, we provide a perspective on the development of immunotherapy in the treatment of CRC and attempt to propose a new systematic classification of CRC based on immunological strategies, which may improve guidance for the selection of immunotherapy strategies for different subtypes of CRC in the future.

General insight into cancer: An overview of colorectal cancer (Review).

Cancer is currently among the leading causes of mortality globally. Colorectal cancer (CRC) ranks second among the most common types of cancer in terms of mortality worldwide. This type of cancer arises from mutations in the colonic and rectal epithelial tissues that target oncogenes, tumor suppressor genes and genes related to DNA repair mechanisms. The aim of the present review was to provide an explanation of CRC classification, which is carried out according to the histological subtype, location and molecular pathways implicated in its development. The pathogenic mechanisms implicated in CRC may involve one of three different molecular pathways: Chromosomal instability, microsatellite instability and cytosine preceding guanine island methylator phenotype. In addition, a variety of mutated genes associated with CRC, which affect certain signaling pathways, including DNA mismatch repair, cell cycle checkpoints and apoptotic pathways, were discussed. Moreover, a brief description of the risk factors and the symptoms associated with CRC was also provided. Finally, the treatment approaches to CRC were outlined.

Clinicopathological, Immunohistochemical, and PMS2 Gene Expression Profiling of Patients with Sporadic Colorectal Cancer.

The DNA mismatch repair (MMR) system is one of the molecular pathways involved in colorectal cancer (CRC) carcinogenesis that consists of several genes, including MLH1 (MutL homolog 1), MSH6 (MutS homolog 6), MSH2 (MutS homolog 2), and MSH3 (MutS homolog 3). The protein encoded by PMS2 (post-meiotic segregation 2) is also essential for MMR. Here, we address the correlation between immunohistochemical and transcriptional expression of PMS2 with the tumor grade and clinical stage of non-hereditary/sporadic CRC disease. This study retrospectively analyzed 67 colorectal resections performed for 38 male and 29 female patients. Random biopsies were taken by a gastroenterologist from patients referring to three hospitals in the cities of Zanjan, Urmia and Qazvin (Iran) during 2017-2019. All specimens were examined and classified for localization of tumor, pathological stage and grade. The PMS2 protein expression was studied immunohistochemically and analysis of mRNA expression was performed in the same tissue sections. Immunohistochemistry and quantitative real-time polymerase chain reaction (PCR) analysis showed a decrease in PMS2 expression compared with paracancerous tissue (P<0.001), which correlated with tumor stage. In addition, reduced PMS2 expression was correlated with the tumor differentiation grade, underlining a connection between downregulation of PMS2 and progression of CRC. Comparing the PMS2 mRNA levels in different groups showed the following results: 0.92 ± 0.18 in patients with Stage I CRC tumor, 0.86 ± 0.38 in Stage Ⅱ, 0.50 ± 0.29 in Stage Ⅲ, and 0.47 ± 0.23 in Stage Ⅳ. These findings suggest that PMS2 may provide a potential reliable biomarker for CRC classification by combined immunohistochemical and mRNA analysis.

A Combined Epithelial Mesenchymal Transformation and DNA Repair Gene Panel in Colorectal Cancer With Prognostic and Therapeutic Implication.

BackgroundEpithelial mesenchymal transformation (EMT) and DNA repair status represent intrinsic features of colorectal cancer (CRC) and are associated with patient prognosis and treatment responsiveness. We sought to develop a combined EMT and DNA repair gene panel with potential application in patient classification and precise treatment.MethodsWe comprehensively evaluated the EMT and DNA repair patterns of 1,652 CRC patients from four datasets. Unsupervised clustering was used for classification. The clinical features, genetic mutation, tumor mutation load, and chemotherapy as well as immunotherapy sensitivity among different clusters were systematically compared. The least absolute shrinkage and selection operator regression method was used to develop the risk model.ResultsThree distinct CRC clusters were determined. Clustet1 was characterized by down-regulated DNA repair pathways but active epithelial markers and metabolism pathway and had intermediate prognosis. Clustet2 was characterized by down-regulated both epithelial markers and DNA repair pathways and had poor outcome. Clustet3 presented with activation of DNA repair pathway and epithelial markers had favorable prognosis. Clustet1 might benefit form chemotherapy and Clustet3 had a higher response rate to immunotherapy. An EMT and DNA repair risk model related to prognosis and treatment response was developed.ConclusionsThis work developed and validated a combined EMT and DNA repair gene panel for CRC classification, which may be an effective tool for survival prediction and treatment guidance in CRC patients.

Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in next generation sequencing technology have led to increased availability of sequence data and enabled an environment for the growth of gut microbiome research. The use of conventional machine learning algorithms for automatic detection of CRC based on the microbiome is limited by factors such as low accuracy and the need for manual selection of features. Despite their success in other fields, Deep Neural Network (DNN) algorithms have limitations in microbiome-based CRC classification. These limitations include high dimensionality of microbiome data and other characteristics associated with sequence data such as feature dominance. In this paper, we propose a feature augmentation approach that aggregates data normalization methods to extend existing features of a dataset. The proposed method combines feature extension with data augmentation to improve CRC classification performance of a DNN model. The proposed model obtained area under the curve (AUC) scores of 0.96 and 0.89 on two publicly available microbiome datasets.

A Novel TNM Classification for Colorectal Cancers based on the Metro-ticket Paradigm.

Background: Several revisions of the TNM classifications for colorectal cancer (CRC) have acknowledged that the oncological outcomes of stage IIB/IIC CRC are worse than those of stage IIIA. We aimed to develop a novel TNM (nTNM) classification based on the metro-ticket paradigm.Methods: We identified eligible CRC patients from the Surveillance, Epidemiology, and End Results database. The nTNM was developed using distance from the origin on a Cartesian plane incorporating the pN (x-axis) and pT (y-axis) stages, and was compared with the AJCC TNM classification. The areas under the curves (AUCs), calibration curves, and Akaike's information criterion (AIC) were used to evaluate the predictive performances of the two classifications. Clinical benefits were further estimated by decision curve analyses. The validation cohort was applied to validate these findings.Results: A total of 58,192 CRC patients (40,736 training cohort, 17,456 validation cohort) were finally included. In the training cohort, 18,476 patients (45.4%) experienced upstaging and 15,907 patients (39.0%) experienced downstaging in the nTNM classification compared with the TNM classification. Taking the prognosis of stage I as the reference, survival decreased with increasing nTNM stage. The nTNM classification showed better discrimination (AUC, 0.678 vs. 0.667, P<0.001), model-fitting (AIC, 236,525 vs. 237,741), and clinical benefits than the TNM classification. Similar results were found in the validation cohort.Conclusions: The nTNM classification for CRC has better predictive performances and superior accuracy for predicting prognosis compared with the TNM classification. The nTNM classification should therefore be considered in future revisions of the TNM classification.

Association of Tumor Budding With Immune Evasion Pathways in Primary Colorectal Cancer and Patient-Derived Xenografts

Tumor budding has been found to be of prognostic significance for several cancers, including colorectal cancer (CRC). Additionally, the molecular classification of CRC has led to the identification of different immune microenvironments linked to distinct prognosis and therapeutic response. However, the association between tumor budding and the different molecular subtypes of CRC and distinct immune profiles have not been fully elucidated. This study focused, firstly, on the validation of derived xenograft models (PDXs) for the evaluation of tumor budding and their human counterparts and, secondly, on the association between tumor budding and the immune tumor microenvironment by the analysis of gene expression signatures of immune checkpoints, Toll-like receptors (TLRs), and chemokine families. Clinical CRC samples with different grades of tumor budding and their corresponding PDXs were included in this study. Tumor budding grade was reliably reproduced in early passages of PDXs, and high-grade tumor budding was intimately related with a poor-prognosis CMS4 mesenchymal subtype. In addition, an upregulation of negative regulatory immune checkpoints (PDL1, TIM-3, NOX2, and IDO1), TLRs (TLR1, TLR3, TLR4, and TLR6), and chemokine receptors and ligands (CXCR2, CXCR4, CXCL1, CXCL2, CXCL6, and CXCL9) was detected in high-grade tumor budding in both human samples and their corresponding xenografts. Our data support a close link between high-grade tumor budding in CRC and a distinctive immune-suppressive microenvironment promoting tumor invasion, which may have a determinant role in the poor prognosis of the CMS4 mesenchymal subtype. In addition, our study demonstrates that PDX models may constitute a robust preclinical platform for the development of novel therapies directed against tumor budding in CRC.

Clasificación molecular del cáncer colorrectal, su impacto pronóstico y terapéutico: un paso crucial hacia la medicina personalizada

Colorectal cancer (CRC) has a high incidence and mortality and is the second leading cause of cancer death worldwide. A total of 25% of patients debut in metastatic stages, and 50% of patients with resectable disease subsequently have disseminated disease, thus their systemic therapy constitutes an important challenge. Although there are advances in their treatment, patients have a variable and unpredictable response due to their molecular heterogeneity, making it necessary to identify the specific mutations that leads oncogenesis in each patient. The CRC classification into 4 molecular subtypes or consensus molecular subtypes (CMS) has had wide clinical acceptance. CMS1 (immune), affects young patients, is rapidly progressive and refractory to conventional therapies, and could benefit from aggressive therapy and immunotherapy. CMS2 (canonical) is characterized by mutation in specific pathways linked to cellular metabolism. The CMS3 (metabolic), where its specific mutation is KRAS. Finally, CMS4 (mesenchymal) is associated with metastasis, worse prognosis and mutation in pathways of fibrogenesis and epithelial-mesenchymal transition. Specific treatments have been proposed for each of these types with the purpose of reaching a personalized medicine. The identification and better understanding of molecular subtypes will lead us to improve the prognostic and therapeutic prediction in this disease.

The role of transcription factor caudal-related homeobox transcription factor 2 in colorectal cancer.

Colorectal cancer (CRC) is one of the most malignant tumors in humans and causes mass mortality. In the age of precise medicine, more and more subtypes of CRC were classified. The caudal-related homeobox transcription factor 2 (CDX2) is an intestine-specific transcription factor which is implicated in differentiation, proliferation, cell-adhesion, and migration. The loss of CDX2 in immunohistochemical stain was reported to be a prognostic factor of colon cancer, but the clinical application remained controversial. Most of the CRCs expressed or over-expressed CDX2. Homeobox genes can display either an oncogenic or a tumor-suppressing activity. CDX2 regulates the developing intestinal epithelium and CRC by different pathways. The complex regulation of CDX2 and its complex targets cause the difficulties of application for CDX2 in the prediction of prognosis. However, CDX2 is a potential biomarker applied in the precise classification of CRC for personalized medicine. This review partially clarifies the role of CDX2 in CRC.

Low expression of adenomatous polyposis coli 2 correlates with aggressive features and poor prognosis in colorectal cancer

ABSTRACT Currently, there are no relevant findings on the diagnostic and prognostic roles of adenomatous polyposis coli 2 (APC2) in colorectal cancer (CRC). This study investigated the clinical value of APC2 dysregulation in the prognosis of CRC. Immunohistochemical scores obtained from tissue microarrays were used to quantify the expression of APC2 protein in 201 CRC tissues and 139 adjacent normal tissues. A chi-squared test was performed to analyze the association between APC2 expression and various clinical characteristics. Differences in 5-year survival between groups were analyzed. A receiver operating characteristic (ROC) curve was generated to investigate the potential association between APC2 and CRC diagnosis. Compared with adjacent normal tissues, APC2 was downregulated in CRC tissues (P = 0.0004). Survival analyses revealed that CRC patients with high APC2 expression (96.74%) obtained better 5-year survival rates than those with low APC2 expression (88.07%). CRC patients with low APC2 expression exhibited obvious lymphovascular invasion (P = 0.010), lymph node metastasis (P = 0.007), and high tumor node metastasis (TNM) stage (P = 0.007). Furthermore, ROC curves confirmed that APC2 was associated with lymphovascular invasion (P = 0.004), lymph node metastasis (P = 0.002), and TNM staging (P = 0.002). In summary, low APC2 expression in CRC tissues was associated with poor prognosis and may be a useful biomarker for the prognosis and clinical classification of CRC.