Consensus Molecular Subtypes Research Articles

Tumor-immune hybrid cells evade the immune response and potentiate colorectal cancer metastasis through CTLA4.

Understanding the metastatic cascade is critical for the treatment and prevention of cancer-related death. Within a tumor, immune cells have the capacity to fuse with tumor cells to generate tumor-immune hybrid cells (THCs). THCs are hypothesized to be a subset of cancer cells with the capacity to enter circulation as circulating hybrid cells (CHC) and seed metastases. To understand the mechanism of THC metastasis, we investigated CHCs in peripheral blood from patients with stage IV colorectal cancer (CRC), as well as THCs in tissues of primary colorectal cancers and their liver metastasis sites using immunofluorescence, spatial proteomic, spatial transcriptomic, molecular classification, and molecular pathway analyses. Our findings indicated a high prevalence of CHCs and THCs in patients with stage IV CRC. THCs expressed CTLA4 in primary CRC lesions and correlated with upregulation of CD68, CD4, and HLA-DR in metastatic liver lesions, which is found in the consensus molecular subtype (CMS) 1 of primary CRC tissue. Pathway analysis of these genes suggested that THCs are associated with neutrophils due to upregulation of neutrophil extracellular trap signaling (NET) and neutrophil degranulation pathways. These data provide molecular pathways for the formation of THCs suggesting fusion with neutrophils, which may facilitate extravasation and metastatic seeding.

TP53 mutation status and consensus molecular subtypes of colorectal cancer in patients from Rwanda

BackgroundMutations 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.MethodsFormalin-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.ResultsSequencing 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 5ʹ 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%).ConclusionOur 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.

Role of CD47 gene expression in colorectal cancer: a comprehensive molecular profiling study

BackgroundIn patients with colorectal cancer (CRC), the therapeutic effects of conventional immune checkpoint inhibitors targeting the adaptive immune system are largely limited to those with microsatellite instability-high tumors. Meanwhile, new...

Molecular profiling of risk factors for relapse in Japanese patients with stage II colorectal cancer: a retrospective cohort study.

The association between the molecular profiles and prognosis of Stage II colorectal cancer remains unclear. This study aimed to examine the risk factors for relapse of Stage II colorectal cancer using molecular profiling. We retrospectively enrolled patients with pStage II colorectal cancer who did not receive perioperative adjuvant therapy and whose surgically resected specimens were evaluated using gene expression and whole-exome analyses between January 2014 and December 2018. We evaluated the long-term outcomes and examined the risk factors for relapse-free survival. We evaluated 322 patients with pStage II colorectal cancer, including 126 (39.1%) with right colon cancer. Eighty-seven patients (27.0%) had pT4 tumor, 175 (54.3%) had positive venous invasion, 120 (37.3%) had positive lymphatic invasion, and 68 (21.1%) had perineural invasion. The presence of mutations in key genes for colorectal cancer development based on whole-exome analyses was as follows: APC, 245 (76.1%); TP53, 208 (64.6%); and KRAS, 134 (41.6%). According to the consensus molecular subtype classification based on gene expression, 76 patients (23.6%) had consensus molecular subtype 4 and a significantly lower relapse-free survival than the other patients (5-year relapse-free survival: 83.8% vs. 92.9%, p = 0.017). Perineural invasion (hazard ratio: 5.316, p < 0.001) and consensus molecular subtype 4 (hazard ratio: 2.399, p = 0.020) were identified as independent risk factors for relapse-free survival. Molecular profiling of Stage II colorectal cancer to assess the risk factors for relapse may contribute to the indication and drug selection for adjuvant chemotherapy.

Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes

BackgroundColorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition.ResultsIn this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results.ConclusionsThese results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups.

Immune microenvironmental heterogeneity according to tumor DNA methylation phenotypes in microsatellite instability-high colorectal cancers

The detailed association between tumor DNA methylation, including CpG island methylation, and tumor immunity is poorly understood. CpG island methylator phenotype (CIMP) is observed typically in sporadic colorectal cancers (CRCs) with microsatellite instability-high (MSI-H). Here, we investigated the differential features of the tumor immune microenvironment according to CIMP status in MSI-H CRCs. CIMP-high (CIMP-H) or CIMP-low/negative (CIMP-L/0) status was determined using MethyLight assay in 133 MSI-H CRCs. All MSI-H CRCs were subjected to digital pathology-based quantification of CD3 + /CD8 + /CD4 + /FoxP3 + /CD68 + /CD204 + /CD177 + tumor-infiltrating immune cells using whole-slide immunohistochemistry. Programmed death-ligand 1 (PD-L1) immunohistochemistry was evaluated using the tumor proportion score (TPS) and combined positive score (CPS). Representative cases were analyzed using whole-exome and RNA-sequencing. In 133 MSI-H CRCs, significantly higher densities of CD8 + tumor-infiltrating lymphocytes (TILs) were observed in CIMP-H tumors compared with CIMP-L/0 tumors. PD-L1 TPS and CPS in CIMP-H tumors were higher than in CIMP-L/0 tumors. Next-generation sequencing revealed that, compared with CIMP-L/0 tumors, CIMP-H tumors had higher fractions of CD8 + T cells/cytotoxic lymphocytes, higher cytolytic activity scores, and activated immune-mediated cell killing pathways. In contrast to CIMP-L/0 tumors, most CIMP-H tumors were identified as consensus molecular subtype 1, an immunogenic transcriptomic subtype of CRC. However, there were no differences in tumor mutational burden (TMB) between CIMP-H and CIMP-L/0 tumors in MSI-H CRCs. In conclusion, CIMP-H is associated with abundant cytotoxic CD8 + TILs and PD-L1 overexpression independent of TMB in MSI-H CRCs, suggesting that CIMP-H tumors represent a typical immune-hot subtype and are optimal candidates for immunotherapy in MSI-H tumors.

Consensus molecular subtyping of metastatic colorectal cancer expands biomarker-directed therapeutic benefit for patients with CMS1 and CMS2 tumors.

We developed a whole transcriptome sequencing (WTS)-based Consensus Molecular Subtypes (CMS) classifier using FFPE tissue and investigated its prognostic and predictive utility in a large clinico-genomic database of CRC patients (n = 24,939). The classifier was trained against the original CMS datasets using an SVM model and validated in an independent blinded TCGA dataset (88.0% accuracy). Kaplan-Meier estimates of overall survival (OS) and time-on-treatment (TOT) were calculated for each CMS (p < 0.05 considered significant). CMS2 tumors were enriched on left-side of colon and conferred the longest median OS. In RAS-wildtype mCRC, left-sided tumors and CMS2 classification were associated with longer TOT with anti-EGFR antibodies (cetuximab and panitumumab). When restricting to only CMS2, there was no significant difference in TOT between right- versus left-sided tumors. CMS1 tumors were associated with a longer median TOT with pembrolizumab relative to other CMS groups, even when analyzing only microsatellite stable (MSS) tumors. A WTS-based CMS classifier allowed investigation of a large multi-institutional clinico-genomic mCRC cohort, suggesting anti-EGFR therapy benefit for right-sided RAS-WT CMS2 tumors and immune checkpoint inhibitor benefit for MSS CMS1. Routine CMS classification of CRC provides important treatment associations that should be further investigated.

Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma

BackgroundThe radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment.MethodsWe apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA).ResultsThe generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes.ConclusionsGlobal DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies.

Colorectal cancer (CRC) represents a significant global health burden, with high incidence and mortality rates worldwide. Recent progress in research highlights the distinct clinical and molecular characteristics of colon versus rectal cancers, underscoring tumor location's importance in treatment approaches. This article provides a comprehensive review of our current understanding of CRC epidemiology, risk factors, molecular pathogenesis, and management strategies. We also present the intricate cellular architecture of colonic crypts and their roles in intestinal homeostasis. Colorectal carcinogenesis multistep processes are also described, covering the conventional adenoma-carcinoma sequence, alternative serrated pathways, and the influential Vogelstein model, which proposes sequential APC, KRAS, and TP53 alterations as drivers. The consensus molecular CRC subtypes (CMS1-CMS4) are examined, shedding light on disease heterogeneity and personalized therapy implications.

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1,987 ovarian cancer patients spanning 26 independent GEO cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust 10-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

Association of genetic ancestry with molecular tumor profiles in colorectal cancer

BackgroundThere are known disparities in incidence and outcomes of colorectal cancer (CRC) by race and ethnicity. Some of these disparities may be mediated by molecular changes in tumors that occur at different rates across populations. Genetic ancestry is a measure complementary to race and ethnicity that can overcome missing data issues and better capture genetic similarity in admixed populations. We aimed to identify somatic mutations and tumor gene expression differences associated with both genetic ancestry and imputed race and ethnicity.MethodsSequencing was performed with the Tempus xT NGS 648-gene panel and whole exome capture RNA-Seq for 8454 primarily late-stage CRC patients. Genetic ancestry proportions for five continental groups—Africa (AFR), American indigenous (AMR), East Asia (EAS), Europe (EUR), and South Asia (SAS)—were estimated using ancestry informative markers. To address data gaps, race and ethnicity categories were imputed, resulting in assignments for 952 Hispanic/Latino, 420 non-Hispanic (NH) Asian, 1061 NH Black, and 5763 NH White individuals. We assessed association of genetic ancestry proportions and imputed race and ethnicity categories with somatic mutations in relevant CRC genes and in 2608 expression profiles, as well as 1957 consensus molecular subtypes (CMS).ResultsIncreased AFR ancestry was associated with higher odds of somatic mutations in APC, KRAS, and PIK3CA and lower odds of BRAF mutations. Additionally, increased EAS ancestry was associated with lower odds of mutations in KRAS, EUR with higher odds in BRAF, and the Hispanic/Latino category with lower odds in BRAF. Greater AFR ancestry and the NH Black category were associated with higher rates of CMS3, while a higher proportion of Hispanic/Latino patients exhibited indeterminate CMS classifications.ConclusionsMolecular differences in CRC tumor mutation frequencies and gene expression that may underlie observed differences by race and ethnicity were identified. The association of AFR ancestry with increased KRAS mutations aligns with higher CMS3 subtype rates in NH Black patients. The increase of indeterminate CMS in Hispanic/Latino patients suggests that subtype classification methods could benefit from enhanced patient diversity.

Benefit of adjuvant chemotherapy on recurrence free survival per consensus molecular subtype in stage III colon cancer.

The consensus molecular subtype (CMS) classification divides colon tumors into four subtypes holding promise as a predictive biomarker. However, the effect of adjuvant chemotherapy on recurrence free survival (RFS) per CMS in stage III patients remains inadequately explored. With this intention, we selected stage III colon cancer (CC) patients from the MATCH cohort (n = 575) and RadboudUMC (n = 276) diagnosed between 2005 and 2018. Patients treated with and without adjuvant chemotherapy were matched based on tumor location, T- and N-stage (n = 522). Tumor material was available for 464 patients, with successful RNA extraction and CMS subtyping achieved in 390 patients (surgery alone group: 192, adjuvant chemotherapy group: 198). In the overall cohort, CMS4 was associated with poorest prognosis (HR 1.55; p = .03). Multivariate analysis revealed favorable RFS for the adjuvant chemotherapy group in CMS1, CMS2, and CMS4 tumors (HR 0.19; p = .01, HR 0.27; p < .01, HR 0.19; p < .01, respectively), while no significant difference between treatment groups was observed within CMS3 (HR 0.68; p = .51). CMS subtyping in this non-randomized cohort identified patients with poor prognosis and patients who may not benefit significantly from adjuvant chemotherapy.

Molecular biomarkers of progression in non-muscle-invasive bladder cancer - beyond conventional risk stratification.

The global incidence of bladder cancer is more than half a million diagnoses each year. Bladder cancer can be categorized into non-muscle-invasive bladder cancer (NMIBC), which accounts for ~75% of diagnoses, and muscle-invasive bladder cancer(MIBC). Up to 45% of patients with NMIBC develop disease progression to MIBC, which is associated with a poor outcome, highlighting a clinical need to identify these patients. Current risk stratification has a prognostic value, but relies solely on clinicopathological parameters that might not fully capture the complexity of disease progression. Molecular research has led to identification of multiple crucial players involved in NMIBC progression. Identified biomarkers of progression are related to cell cycle, MAPK pathways, apoptosis, tumour microenvironment, chromatin stability and DNA-damage response. However, none of these biomarkers has been prospectively validated. Reported gene signatures of progression do not improve NMIBC risk stratification. Molecular subtypes of NMIBC have improved our understanding of NMIBC progression, but these subtypes are currently unsuitable for clinical implementation owing to a lack of prospective validation, limited predictive value as a result of intratumour subtype heterogeneity, technical challenges, costs and turnaround time. Future stepsinclude thedevelopment of consensus molecular NMIBC subtypes that mightimprove conventional clinicopathological risk stratification. Prospective implementation studies of biomarkers and the design ofbiomarker-guided clinical trials are required for the integration of molecular biomarkersinto clinical practice.

Exon-Skipping–Based Subtyping of Colorectal Cancers

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. Unsupervised clustering was used to measure the strength of association between different categories of alternative splicing and CMS. 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). 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. 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.

Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors

It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications. We identified two cohorts of patients (total N=249) with RC treated with neoadjuvant radiotherapy (45Gy/25) plus fluoropyrimidine. This discovery set included 57 cases with pathological complete response (pCR) to chemoradiotherapy (23%). Pre-treatment cancer biopsies were assessed using transcriptome-wide mRNA expression and targeted DNA sequencing for copy number and driver mutations. Biological candidate and machine learning (ML) approaches were used to identify predictors of pCR to radiotherapy independent of tumour stage. Findings were assessed in 107 cases from an independent validation set (GSE87211). Three gene expression sets showed significant independent associations with pCR: Fibroblast-TGFβ Response Signature (F-TBRS) with radioresistance; and cytotoxic lymphocyte (CL) expression signature and consensus molecular subtype CMS1 with radiosensitivity. These associations were replicated in the validation cohort. In parallel, a gradient boosting machine model comprising the expression of 33 genes generated in the discovery cohort showed high performance in GSE87211 with 90% sensitivity, 86% specificity. Biological and ML signatures indicated similar mechanisms underlying radiation response, and showed better AUC and p-values than published transcriptomic signatures of radiation response in RC. RCs responding completely to chemoradiotherapy (CRT) have biological characteristics of immune response and absence of immune inhibitory TGFβ signalling. These tumours may be identified with a potential biomarker based on a 33 gene expression signature. This could help select patients likely to respond to treatment with a primary radiotherapy approach as for anal cancer. Conversely, those with predicted radioresistance may be candidates for clinical trials evaluating addition of immune-oncology agents and stromal TGFβ signalling inhibition. The Stratification in Colorectal Cancer Consortium (S:CORT) was funded by the Medical Research Council and Cancer Research UK (MR/M016587/1).

Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges.

Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker-drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter- or intra-tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. In recent years, exploration of subtypes through cell lines, animal models, patient-derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype-specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.

Correction: A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues

Correction: A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues

Predictors of response to immunotherapy in colorectal cancer.

Colorectal cancer (CRC) is a major cause of cancer-related deaths globally. While treatment advancements have improved survival rates, primarily through targeted therapies based on KRAS, NRAS, and BRAF mutations, personalized treatment strategies for CRC remain limited. Immunotherapy, mainly immune checkpoint blockade, has shown efficacy in various cancers but is effective in only a small subset of patients with CRC with deficient mismatch repair (dMMR) proteins or high microsatellite instability (MSI). Recent research has challenged the notion that CRC is immunologically inert, revealing subsets with high immunogenicity and diverse lymphocytic infiltration. Identifying precise biomarkers beyond dMMR and MSI is crucial to expanding immunotherapy benefits. Hence, exploration has extended to various biomarker sources, such as the tumor microenvironment, genomic markers, and gut microbiota. Recent studies have introduced a novel classification system, consensus molecular subtypes, that aids in identifying patients with CRC with an immunogenic profile. These findings underscore the necessity of moving beyond single biomarkers and toward a comprehensive understanding of the immunological landscape in CRC, facilitating the development of more effective, personalized therapies.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis.

Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis. CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay. Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls. This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Charting heterogeneity and spatial landscape of consensus molecular subtypes in colorectal cancer.

e15647 Background: High inter-patient heterogeneity and spatial diversity within tumors represent significant challenges to improving outcomes in colorectal cancer (CRC). Despite elucidation of consensus molecular subtypes (CMS), little progress has been made in translating this knowledge into clinical gain. Intratumoral CMS heterogeneity remains a relatively unexplored area and understanding of the interplay between subtypes holds promise for improving prognostication. We leverage spatial transcriptomics (ST) to enhance understanding of CMS heterogeneity and spatial distribution, with the goal of advancing personalized therapy through high resolution molecular sub-typing. Methods: ST using the 10x Genomics Visium platform was performed on paraffin-embedded CRC specimens for which comprehensive clinicopathological and long-term clinical outcome data were available. ST data were deconvoluted with respect to CMS single-cell RNA sequencing (scRNA-seq) data, yielding maps of CMS landscapes at a resolution of 55 microns. A pre-trained deep learning model was used to identify distinct CRC tissue classes (tumor epithelium, stroma, etc.) within H&amp;E images, enabling spatial correlation of histomorphology with CMS. Cellular composition within these CMS maps was quantified through deconvolution of six major cell types using reference scRNA-seq data. Pseudo-bulk samples were generated from the ST data to evaluate CMS behaviors and compute pathway activities. Results: 40 CRC patients were assayed using ST and H&amp;E profiling (13, 8, 14, and 5 from stages 1-4, respectively). Median follow-up was 112 months (Range 1–192). Intratumoral CMS heterogeneity was evident in all patients, with diverse CMS compositions. Using Simpson diversity index to quantify this heterogeneity, we observed significant correlation of heterogeneity with disease specific survival (DSS) (p = .03). CMS landscapes displayed spatial patterns that were distinct among tissue categories (average F1-score: testing = .92, validation = .89. Tissue categories based on deep learning from H&amp;E images). Tumors with higher levels of CMS1 or 4 were associated with worse outcome. Stratification by the composite CMS biomarker (CMS1+CMS4)/(CMS2+CMS3) yielded significant separation of the Kaplan-Meier curve for DSS (p = .018). Conclusions: This study demonstrates that CRC tumors contain significant intratumoral CMS heterogeneity that is not detectable using bulk classification methods. Spatial patterns in CMS transcriptional topographies correlate with H&amp;E-derived morphology, suggesting the potential for estimating CMS composition from histopathological features. Importantly, we show that spatially composite CMS-derived biomarkers possess strong prognostic potential. The capacity to decode CMS topographic patterns and associate these with clinical outcome offers great potential to advance personalized therapy for CRC.