A multidimensional network approach reveals microRNAs as determinants of the mesenchymal colorectal cancer subtype

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Characterizing heterogeneity along EMT and metabolic axes in colorectal cancer reveals underlying consensus molecular subtype-specific trends

Colorectal cancer (CRC) is highly heterogeneous with variable survival outcomes and therapeutic vulnerabilities. A commonly used classification system in CRC is the Consensus Molecular Subtypes (CMS) based on gene expression patterns. However, how these CMS categories connect to axes of phenotypic plasticity and heterogeneity remains unclear. Here, in our analysis of CMS-specific TCGA data and 101 bulk transcriptomic datasets, we found the epithelial phenotype score to be consistently positively correlated with scores of glycolysis, OXPHOS and FAO pathways, while PD-L1 activity scores positively correlated with mesenchymal phenotype scoring, revealing possible interconnections among plasticity axes. Single-cell RNA-sequencing analysis of patient samples revealed that that CMS2 and CMS3 subtype samples were relatively more epithelial as compared to CMS1 and CMS4. CMS1 revealed two subpopulations: one close to CMS4 (more mesenchymal) and the other closer to CMS2 or CMS3 (more epithelial), indicating a partial EMT-like behavior. Consistent observations were made in single-cell analysis of metabolic axes and PD-L1 activity scores. Together, our results quantify the patterns of two functional interconnected axes of phenotypic heterogeneity - EMT and metabolic reprogramming - in a CMS-specific manner in CRC.

LncRNA MIR200CHG inhibits EMT in gastric cancer by stabilizing miR-200c from target-directed miRNA degradation

Gastric cancer (GC) is a heterogeneous disease, threatening millions of lives worldwide, yet the functional roles of long non-coding RNAs (lncRNAs) in different GC subtypes remain poorly characterized. Microsatellite stable (MSS)/epithelial-mesenchymal transition (EMT) GC is the most aggressive subtype associated with a poor prognosis. Here, we apply integrated network analysis to uncover lncRNA heterogeneity between GC subtypes, and identify MIR200CHG as a master regulator mediating EMT specifically in MSS/EMT GC. The expression of MIR200CHG is silenced in MSS/EMT GC by promoter hypermethylation, associated with poor prognosis. MIR200CHG reverses the mesenchymal identity of GC cells in vitro and inhibits metastasis in vivo. Mechanistically, MIR200CHG not only facilitates the biogenesis of its intronic miRNAs miR-200c and miR-141, but also protects miR-200c from target-directed miRNA degradation (TDMD) through direct binding to miR-200c. Our studies reveal a landscape of a subtype-specific lncRNA regulatory network, providing clinically relevant biological insights towards MSS/EMT GC.

Automated exploitation of deep learning for cancer patient stratification across multiple types

MOTIVATION

Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming.

RESULTS

To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types.

AVAILABILITY AND IMPLEMENTATION

The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.

Principles of Molecular Utility for CMS Classification in Colorectal Cancer Management

Colorectal cancer (CRC) is the second cause of cancer-related deaths in both sexes globally and presents different clinical outcomes that are described by a range of genomic and epigenomic alterations. Despite the advancements in CRC screening plans and treatment strategies, the prognosis of CRC is dismal. In the last two decades, molecular biomarkers predictive of prognosis have been identified in CRC, although biomarkers predictive of treatment response are only available for specific biological drugs used in stage IV CRC. Translational clinical trials mainly based on "omic" strategies allowed a better understanding of the biological heterogeneity of CRCs. These studies were able to classify CRCs into subtypes mainly related to prognosis, recurrence risk, and, to some extent, also to treatment response. Accordingly, the comprehensive molecular characterizations of CRCs, including The Cancer Genome Atlas (TCGA) and consensus molecular subtype (CMS) classifications, were presented to improve the comprehension of the genomic and epigenomic landscapes of CRCs for a better patient management. The CMS classification obtained by the CRC subtyping consortium categorizes CRC into four consensus molecular subtypes (CMS1-4) characterized by different prognoses. In this review, we discussed the CMS classification in different settings with a focus on its relationships with precursor lesions, tumor immunophenotype, and gut microbiota, as well as on its role in predicting prognosis and/or response to pharmacological treatments, as a crucial step towards precision medicine.

MicroRNAs as Predictive Biomarkers in Patients with Colorectal Cancer Receiving Chemotherapy or Chemoradiotherapy: A Narrative Literature Review

Colorectal cancer (CRC) is one of the most common malignancies and is associated with high mortality rates worldwide. The underlying mechanism of tumorigenesis in CRC is complex, involving genetic, lifestyle-related, and environmental factors. Although radical resection with adjuvant FOLFOX (5-fluorouracil, leucovorin, and oxaliplatin) chemotherapy and neoadjuvant chemoradiotherapy have remained mainstays of treatment for patients with stage III CRC and locally advanced rectal cancer, respectively, the oncological outcomes of these treatments are often unsatisfactory. To improve patients' chances of survival, researchers are actively searching for new biomarkers to facilitate the development of more effective treatment strategies for CRC and metastatic CRC (mCRC). MicroRNAs (miRs), small, single-stranded, noncoding RNAs, can post-transcriptionally regulate mRNA translation and trigger mRNA degradation. Recent studies have documented aberrant miR levels in patients with CRC or mCRC, and some miRs are reportedly associated with chemoresistance or radioresistance in CRC. Herein, we present a narrative review of the literature on the roles of oncogenic miRs (oncomiRs) and tumor suppressor miRs (anti-oncomiRs), some of which can be used to predict the responses of patients with CRC to chemotherapy or chemoradiotherapy. Moreover, miRs may serve as potential therapeutic targets because their functions can be manipulated using synthetic antagonists and miR mimics.

Molecular Classification of Colorectal Cancer by microRNA Profiling: Correlation with the Consensus Molecular Subtypes (CMS) and Validation of miR-30b Targets

Simple Summary

Colorectal cancer is one of the most significant causes of cancer mortality worldwide. Patients stratification is central to improve clinical practice and the Consensus Molecular Subtypes (CMS) have been validated as a useful tool to predict both prognosis and treatment response. This is the first study describing that microRNA profiling can define colorectal cancer CMS subtypes as well as mRNA profiling. MicroRNAs small size facilitates its analysis in serum facilitating a real-time analysis of the disease course. Three microRNA subtypes are identified: miR-LS is associated with the low-stroma/CMS2-subtype; miR-MI with the mucinous-MSI/CMS1-subtype and miR-HS with the high-stroma/CMS4-subtype. MicroRNA novel subtypes and association to the CMS classification were externally validated using TGCA data. Analyzing both mRNAs and miRs in the same population enabled identification of miR target genes and altered biological pathways. A miR-mRNA interaction screening and regulatory network selected major miR targets and was functionally validated for the miR30b/SCL6A4 pair.

Abstract

Colorectal cancer consensus molecular subtypes (CMSs) are widely accepted and constitutes the basis for patient stratification to improve clinical practice. We aimed to find whether miRNAs could reproduce molecular subtypes, and to identify miRNA targets associated to the High-stroma/CMS4 subtype. The expression of 939 miRNAs was analyzed in tumors classified in CMS. TALASSO was used to find gene-miRNA interactions. A miR-mRNA regulatory network was constructed using Cytoscape. Candidate gene-miR interactions were validated in 293T cells. Hierarchical-Clustering identified three miRNA tumor subtypes (miR-LS; miR-MI; and miR-HS) which were significantly associated (p < 0.001) to the reported mRNA subtypes. miR-LS correlated with the low-stroma/CMS2; miR-MI with the mucinous-MSI/CMS1 and miR-HS with high-stroma/CMS4. MicroRNA tumor subtypes and association to CMSs were validated with TCGA datasets. TALASSO identified 1462 interactions (p < 0.05) out of 21,615 found between 176 miRs and 788 genes. Based on the regulatory network, 88 miR-mRNA interactions were selected as candidates. This network was functionally validated for the pair miR-30b/SLC6A6. We found that miR-30b overexpression silenced 3′-UTR-SLC6A6-driven luciferase expression in 293T-cells; mutation of the target sequence in the 3′-UTR-SLC6A6 prevented the miR-30b inhibitory effect. In conclusion CRC subtype classification using a miR-signature might facilitate a real-time analysis of the disease course and treatment response.

From Dukes-MAC Staging System to Molecular Classification: Evolving Concepts in Colorectal Cancer

This historical review aimed to summarize the main changes that colorectal carcinoma (CRC) staging systems suffered over time, starting from the creation of the classical Duke’s classification, modified Astler–Coller staging, internationally used TNM (T—primary tumor, N—regional lymph nodes’ status, M—distant metastases) staging system, and ending with molecular classifications and epithelial–mesenchymal transition (EMT) concept. Besides currently used staging parameters, this paper briefly presents the author’s contribution in creating an immunohistochemical (IHC)-based molecular classification of CRC. It refers to the identification of three molecular groups of CRCs (epithelial, mesenchymal and hybrid) based on the IHC markers E-cadherin, β-catenin, maspin, and vimentin. Maspin is a novel IHC antibody helpful for tumor budding assessment, which role depends on its subcellular localization (cytoplasm vs. nuclei). The long road of updating the staging criteria for CRC has not come to an end. The newest prognostic biomarkers, aimed to be included in the molecular classifications, exert predictive roles, and become more and more important for targeted therapy decisions.

Molecular characterization of colorectal cancer related peritoneal metastatic disease

A significant proportion of colorectal cancer (CRC) patients develop peritoneal metastases (PM) in the course of their disease. PMs are associated with a poor quality of life, significant morbidity and dismal disease outcome. To improve care for this patient group, a better understanding of the molecular characteristics of CRC-PM is required. Here we present a comprehensive molecular characterization of a cohort of 52 patients. This reveals that CRC-PM represent a distinct CRC molecular subtype, CMS4, but can be further divided in three separate categories, each presenting with unique features. We uncover that the CMS4-associated structural protein Moesin plays a key role in peritoneal dissemination. Finally, we define specific evolutionary features of CRC-PM which indicate that polyclonal metastatic seeding underlies these lesions. Together our results suggest that CRC-PM should be perceived as a distinct disease entity.

Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes

Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329-0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows.

Upregulation of BMI1-suppressor miRNAs (miR-200c, miR-203) during terminal differentiation of colon epithelial cells

BACKGROUND

MicroRNAs (miRNAs) are key regulators of stem cell functions, including self-renewal and differentiation. In this study, we aimed to identify miRNAs that are upregulated during terminal differentiation in the human colon epithelium, and elucidate their role in the mechanistic control of stem cell properties.

METHODS

"Bottom-of-the-crypt" (EPCAM⁺/CD44⁺/CD66a^low) and "top-of-the-crypt" (EPCAM⁺/CD44^neg/CD66a^high) epithelial cells from 8 primary colon specimens (6 human, 2 murine) were purified by flow cytometry and analyzed for differential expression of 335 miRNAs. The miRNAs displaying the highest upregulation in "top-of-the-crypt" (terminally differentiated) epithelial cells were tested for positive correlation and association with survival outcomes in a colon cancer RNA-seq database (n = 439 patients). The two miRNAs with the strongest "top-of-the-crypt" expression profile were evaluated for capacity to downregulate self-renewal effectors and inhibit in vitro proliferation of colon cancer cells, in vitro organoid formation by normal colon epithelial cells and in vivo tumorigenicity by patient-derived xenografts (PDX).

RESULTS

Six miRNAs (miR-200a, miR-200b, miR-200c, miR-203, miR-210, miR-345) were upregulated in "top-of-the-crypt" cells and positively correlated in expression among colon carcinomas. Overexpression of the three miRNAs with the highest inter-correlation coefficients (miR-200a, miR-200b, miR-200c) associated with improved survival. The top two over-expressed miRNAs (miR-200c, miR-203) cooperated synergistically in suppressing expression of BMI1, a key regulator of self-renewal in stem cell populations, and in inhibiting proliferation, organoid-formation and tumorigenicity of colon epithelial cells.

CONCLUSION

In the colon epithelium, terminal differentiation associates with the coordinated upregulation of miR-200c and miR-203, which cooperate to suppress BMI1 and disable the expansion capacity of epithelial cells.

Identification of Serum miRNA Signature and Establishment of a Nomogram for Risk Stratification in Patients With Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The aim of the study was to perform mRNA-miRNA regulatory network analyses to identify a miRNA panel for molecular subtype identification and stratification of high-risk patients with pancreatic ductal adenocarcinoma (PDAC).

BACKGROUND

Recent transcriptional profiling effort in PDAC has led to the identification of molecular subtypes that associate with poor survival; however, their clinical significance for risk stratification in patients with PDAC has been challenging.

METHODS

By performing a systematic analysis in The Cancer Genome Atlas and International Cancer Genome Consortium cohorts, we discovered a panel of miRNAs that associated with squamous and other poor molecular subtypes in PDAC. Subsequently, we used logistic regression analysis to develop models for risk stratification and Cox proportional hazard analysis to determine survival prediction probability of this signature in multiple cohorts of 433 patients with PDAC, including a tissue cohort (n = 199) and a preoperative serum cohort (n = 51).

RESULTS

We identified a panel of 9 miRNAs that were significantly upregulated (miR-205-5p and -934) or downregulated (miR-192-5p, 194-5p, 194-3p, 215-5p, 375-3p, 552-3p, and 1251-5p) in PDAC molecular subtypes with poor survival [squamous, area under the receiver operating characteristic curve (AUC) = 0.90; basal, AUC = 0.89; and quasimesenchymal, AUC = 0.83]. The validation of this miRNA panel in a tissue clinical cohort was a significant predictor of overall survival (hazard ratio = 2.48, P < 0.0001), and this predictive accuracy improved further in a risk nomogram which included key clinicopathological factors. Finally, we were able to successfully translate this miRNA predictive signature into a liquid biopsy-based assay in preoperative serum specimens from PDAC patients (hazard ratio: 2.85, P = 0.02).

CONCLUSION

We report a novel miRNA risk-stratification signature that can be used as a noninvasive assay for the identification of high-risk patients and potential disease monitoring in patients with PDAC.

Adipose stem cell niche reprograms the colorectal cancer stem cell metastatic machinery

Obesity is a strong risk factor for cancer progression, posing obesity-related cancer as one of the leading causes of death. Nevertheless, the molecular mechanisms that endow cancer cells with metastatic properties in patients affected by obesity remain unexplored.

Here, we show that IL-6 and HGF, secreted by tumor neighboring visceral adipose stromal cells (V-ASCs), expand the metastatic colorectal (CR) cancer cell compartment (CD44v6 + ), which in turn secretes neurotrophins such as NGF and NT-3, and recruits adipose stem cells within tumor mass. Visceral adipose-derived factors promote vasculogenesis and the onset of metastatic dissemination by activation of STAT3, which inhibits miR-200a and enhances ZEB2 expression, effectively reprogramming CRC cells into a highly metastatic phenotype. Notably, obesity-associated tumor microenvironment provokes a transition in the transcriptomic expression profile of cells derived from the epithelial consensus molecular subtype (CMS2) CRC patients towards a mesenchymal subtype (CMS4). STAT3 pathway inhibition reduces ZEB2 expression and abrogates the metastatic growth sustained by adipose-released proteins. Together, our data suggest that targeting adipose factors in colorectal cancer patients with obesity may represent a therapeutic strategy for preventing metastatic disease.

Obesity is a major risk factor for cancer related death. Here, the authors show that visceral adipose-derived factors promote vasculogenesis and metastatic dissemination by activation of STAT3, which inhibits miR-200a and enhances ZEB2 expression, effectively reprogramming colorectal cancer cells into a highly metastatic phenotype.

The recurring features of molecular subtypes in distinct gastrointestinal malignancies-A systematic review

In colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC) and gastric cancer (GC) multiple studies of inter-tumor heterogeneity have identified molecular subtypes, which correlate with clinical features. Our aim was to investigate the attributes of molecular subtypes across three different gastrointestinal cancer types. We performed a systematic search for publications on molecular subtypes or classifications in PDAC and GC and compared the described subtypes with the established consensus molecular subtypes of CRC. Examining the characteristics of subtypes across CRC, PDAC and GC resulted in four categories of subtypes. We describe uniting and distinguishing features within a mesenchymal, an epithelial, an immunogenic and a metabolic and digestive subtype category. We conclude that molecular subtypes of CRC, PDAC and GC display relevant overlap in molecular features and clinical outcomes. This finding encourages quantitative studies on subtypes across different cancer types and could lead to a paradigm shift in future treatment strategies.

The Clinical Assessment of MicroRNA Diagnostic, Prognostic, and Theranostic Value in Colorectal Cancer

Simple Summary

MiRNAs are of great interest within colorectal cancers in diagnosis, prognosis, and within the field of personalized treatments; they are present within different biological fluids such as blood and can lead to specific information for daily clinical use. Herein, we review the current literature focusing on miRNAs as potential diagnostic and prognostic biomarkers in patients treated for colorectal cancers. Detection and analysis of miRNA expression are cost-effective and lead to high sensitivity and specificity rates. However, it is now necessary to highlight the most sensitive and specific miRNAs for each goal, either diagnostic, prognostic, or theranostic, thanks to multicentric prospective studies.

Abstract

MiRNAs have recently become a subject of great interest within cancers and especially colorectal cancers in diagnosis, prognosis, and therapy decisions; herein we review the current literature focusing on miRNAs in colorectal cancers, and we discuss future challenges to use this tool on a daily clinical basis. In liquid biopsies, miRNAs seem easily accessible and can give important information toward each step of the management of colorectal cancers. However, it is now necessary to highlight the most sensitive and specific miRNAs for each goal thanks to multicentric prospective studies. Conclusions: by their diversity and the feasibility of their use, miRNAs are getting part of the armamentarium of healthcare management of colorectal cancers.

Clinical Value of Consensus Molecular Subtypes in Colorectal Cancer: A Systematic Review and Meta-Analysis

Background

The consensus molecular subtypes (CMSs) of colorectal cancer (CRC) capture tumor heterogeneity at the gene-expression level. Currently, a restricted number of molecular features are used to guide treatment for CRC. We summarize the evidence on the clinical value of the CMSs.

Methods

We systematically identified studies in Medline and Embase that evaluated the prognostic and predictive value of CMSs in CRC patients. A random-effect meta-analysis was performed on prognostic data. Predictive data were summarized.

Results

In local disease, CMS4 tumors were associated with worse overall survival (OS) compared with CMS1 (hazard ratio [HR] = 3.28, 95% confidence interval = 1.27 to 8.47) and CMS2 cancers (HR = 2.60, 95% confidence interval = 1.93 to 3.50). In metastatic disease, CMS1 consistently had worse survival than CMS2-4 (OS HR range = 0.33-0.55; progression-free survival HR range = 0.53-0.89). Adjuvant chemotherapy in stage II and III CRC was most beneficial for OS in CMS2 and CMS3 (HR range = 0.16-0.45) and not effective in CMS4 tumors. In metastatic CMS4 cancers, an irinotecan-based regimen improved outcome compared with oxaliplatin (HR range = 0.31-0.72). The addition of bevacizumab seemed beneficial in CMS1, and anti-epidermal growth factor receptor therapy improved outcome for KRAS wild-type CMS2 patients.

Conclusions

The CMS classification holds clear potential for clinical use in predicting both prognosis and response to systemic therapy, which seems to be independent of the classifier used. Prospective studies are warranted to support implementation of the CMS taxonomy in clinical practice.

NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data

Targeted therapy has been widely adopted as an effective treatment strategy to battle against cancer. However, cancers are not single disease entities, but comprising multiple molecularly distinct subtypes, and the heterogeneity nature prevents precise selection of patients for optimized therapy. Dissecting cancer subtype-specific signaling pathways is crucial to pinpointing dysregulated genes for the prioritization of novel therapeutic targets. Nested effects models (NEMs) are a group of graphical models that encode subset relations between observed downstream effects under perturbations to upstream signaling genes, providing a prototype for mapping the inner workings of the cell. In this study, we developed NEM-Tar, which extends the original NEMs to predict drug targets by incorporating causal information of (epi)genetic aberrations for signaling pathway inference. An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. Subsequently, we conducted simulation studies to compare three inference methods and found that the greedy hill-climbing algorithm demonstrated the highest accuracy and robustness to noise. Furthermore, two case studies were conducted using multi-omics data for colorectal cancer (CRC) and gastric cancer (GC) in the TCGA database. Using NEM-Tar, we inferred signaling networks driving the poor-prognosis subtypes of CRC and GC, respectively. Our model prioritized not only potential individual drug targets such as HER2, for which FDA-approved inhibitors are available but also the combinations of multiple targets potentially useful for the design of combination therapies.

An integrated workflow for biomarker development using microRNAs in extracellular vesicles for cancer precision medicine

EV-miRNAs are microRNA (miRNA) molecules encapsulated in extracellular vesicles (EVs), which play crucial roles in tumor pathogenesis, progression, and metastasis. Recent studies about EV-miRNAs have gained novel insights into cancer biology and have demonstrated a great potential to develop novel liquid biopsy assays for various applications. Notably, compared to conventional liquid biomarkers, EV-miRNAs are more advantageous in representing host-cell molecular architecture and exhibiting higher stability and specificity. Despite various available techniques for EV-miRNA separation, concentration, profiling, and data analysis, a standardized approach for EV-miRNA biomarker development is yet lacking. In this review, we performed a substantial literature review and distilled an integrated workflow encompassing important steps for EV-miRNA biomarker development, including sample collection and EV isolation, EV-miRNA extraction and quantification, high-throughput data preprocessing, biomarker prioritization and model construction, functional analysis, as well as validation. With the rapid growth of "big data", we highlight the importance of efficient mining of high-throughput data for the discovery of EV-miRNA biomarkers and integrating multiple independent datasets for in silico and experimental validations to increase the robustness and reproducibility. Furthermore, as an efficient strategy in systems biology, network inference provides insights into the regulatory mechanisms and can be used to select functionally important EV-miRNAs to refine the biomarker candidates. Despite the encouraging development in the field, a number of challenges still hinder the clinical translation. We finally summarize several common challenges in various biomarker studies and discuss potential opportunities emerging in the related fields.

Epigenetically regulated gene expression profiles reveal four molecular subtypes with prognostic and therapeutic implications in colorectal cancer

Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of colorectal cancer (CRC). Herein, we first demonstrated that the frequencies of the aberrancies of DNA methylation-correlated (METcor) and microRNA (miRNA)-correlated (MIRcor) genes were significantly co-regulated. Next, through integrative clustering of the expression profiles of METcor and MIRcor genes, four molecular subtypes were identified in CRC patients from The Cancer Genome Atlas and then validated in four independent datasets. More importantly, the four subtypes were well characterized and showed distinct clinical and molecular features: (i) S-I: high metabolic activity, sensitive to 5-fluorouracil-based chemotherapy and good prognosis; (ii) S-II: moderate metabolic activity, marked proliferation, frequent KRAS mutation and intermediate prognosis; (iii) S-III: moderate metabolic activity, marked proliferation, promoter DNA hypermethylation, high mutation burden, frequent BRAF and EGFR mutations, moderate levels of epithelial-mesenchymal transition (EMT) and transforming growth factor β (TGFβ) signals, immune-inflamed phenotype, sensitive to cetuximab and death protein-1 inhibitor treatment and relatively poor prognosis and (iv) S-IV: miRNA overexpression, stem/serrated/mesenchymal-like properties, hypoxia, high levels of EMT and TGFβ signals, immune-excluded phenotype and poor prognosis. Overall, this study established a molecular classification based on epigenetically regulated gene expression profiles, thereby providing a better understanding of the epigenetic mechanisms underlying CRC heterogeneity.

Characterization of Chilean patients with sporadic colorectal cancer according to the three main carcinogenic pathways: Microsatellite instability, CpG island methylator phenotype and Chromosomal instability

Molecular classification of colorectal cancer is difficult to implement in clinical settings where hundreds of genes are involved, and resources are limited. This study aims to characterize the molecular subtypes of patients with sporadic colorectal cancer based on the three main carcinogenic pathways microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and chromosomal instability (CIN) in a Chilean population. Although several reports have characterized colorectal cancer, most do not represent Latin-American populations. Our study includes 103 colorectal cancer patients who underwent surgery, without neoadjuvant treatment, in a private hospital between 2008 and 2017. MSI, CIN, and CIMP status were assessed. Frequent mutations in KRAS, BRAF, and PIK3CA genes were analyzed by Sanger sequencing, and statistical analysis was performed by Fisher's exact and/or chi-square test. Survival curves were estimated with Kaplan-Meier and log-rank test. Based on our observations, we can classify the tumors in four subgroups, Group 1: MSI-high tumors (15%) are located in the right colon, occur at older age, and 60% show a BRAF mutation; Group 2: CIN-high tumors (38%) are in the left colon, and 26% have KRAS mutations. Group 3: [MSI/CIN/CIMP]-low/negative tumors (30%) are left-sided, and 39% have KRAS mutations; Group 4: CIMP-high tumors (15%) were more frequent in men and left side colon, with 27% KRAS and 7% presented BRAF mutations. Three percent of patients could not be classified. We found that CIMP-high was associated with a worse prognosis, both in MSI-high and MSI stable patients (p = 0.0452). Group 3 (Low/negative tumors) tend to have better overall survival compared with MSI-high, CIMP-high, and CIN-high tumors. This study contributes to understanding the heterogeneity of tumors in the Chilean population being one of the few characterizations performed in Latin-America. Given the limited resources of these countries, these results allow to improve molecular characterization in Latin-American colorectal cancer populations and confirm the possibility of using the three main carcinogenic pathways to define therapeutic strategies.

Integrated analysis identifies an immune-based prognostic signature for the mesenchymal identity in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) has been divided into 4 consensus molecular subtypes (CMSs), of which CMS4 has the mesenchymal identity and the highest relapse rate. Our goal is to develop a prognostic signature by integrating the immune system and mesenchymal modalities involved in CMS4.

METHODS

The gene expression profiles collected from 5 public datasets were applied to this study, including 1280 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for CRC (IPSCRC).

RESULTS

We identified 6 immune genes as key factors of CMS4 and established the IPSCRC. The IPSCRC could significantly divide patients into high- and low- risk groups in terms of relapse-free survival (RFS) and overall survival (OS) and in discovery (RFS: P < .0001) and 4 independent validation sets (RFS range: P = .01 to <.0001; OS range: P = .02-.0004). After stage stratification, the IPSCRC could still distinguish poor prognosis patients in discovery (RFS: P = .04) and validation cohorts (RFS range: P = .04-.007) within stage II in terms of RFS. Further, in multivariate analysis, the IPSCRC remained an independent predictor of prognosis. Moreover, Macrophage M2 was significantly enriched in the high-risk group, while plasma cells enriched in the low-risk group.

CONCLUSION

We propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of CRC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.

A Network-Based Approach for Identification of Subtype-Specific Master Regulators in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), the predominant subtype of pancreatic cancer, has been reported with equal mortality and incidence for decades. The lethality of PDAC is largely due to its late presentation, when surgical resection is no longer an option. Similar to other major malignancies, it is now clear that PDAC is not a single disease, posing a great challenge to precise selection of patients for optimized adjuvant therapy. A representative study found that PDAC comprises four distinct molecular subtypes: squamous, pancreatic progenitor, immunogenic, and aberrantly differentiated endocrine exocrine (ADEX). However, little is known about the molecular mechanisms underlying specific PDAC subtypes, hampering the design of novel targeted agents. In this study we performed network inference that integrates miRNA expression and gene expression profiles to dissect the miRNA regulatory mechanism specific to the most aggressive squamous subtype of PDAC. Master regulatory analysis revealed that the particular subtype of PDAC is predominantly influenced by miR-29c and miR-192. Further integrative analysis found miR-29c target genes LOXL2, ADAM12 and SERPINH1, which all showed strong association with prognosis. Furthermore, we have preliminarily revealed that the PDAC cell lines with high expression of these miRNA target genes showed significantly lower sensitivities to multiple anti-tumor drugs. Together, our integrative analysis elucidated the squamous subtype-specific regulatory mechanism, and identified master regulatory miRNAs and their downstream genes, which are potential prognostic and predictive biomarkers.

A pre-existing population of ZEB2+ quiescent cells with stemness and mesenchymal features dictate chemoresistance in colorectal cancer

Background

Quiescent/slow cycling cells have been identified in several tumors and correlated with therapy resistance. However, the features of chemoresistant populations and the molecular factors linking quiescence to chemoresistance are largely unknown.

Methods

A population of chemoresistant quiescent/slow cycling cells was isolated through PKH26 staining (which allows to separate cells on the basis of their proliferation rate) from colorectal cancer (CRC) xenografts and subjected to global gene expression and pathway activation analyses. Factors expressed by the quiescent/slow cycling population were analyzed through lentiviral overexpression approaches for their ability to induce a dormant chemoresistant state both in vitro and in mouse xenografts. The correlation between quiescence-associated factors, CRC consensus molecular subtype and cancer prognosis was analyzed in large patient datasets.

Results

Untreated colorectal tumors contain a population of quiescent/slow cycling cells with stem cell features (quiescent cancer stem cells, QCSCs) characterized by a predetermined mesenchymal-like chemoresistant phenotype. QCSCs expressed increased levels of ZEB2, a transcription factor involved in stem cell plasticity and epithelial-mesenchymal transition (EMT), and of antiapototic factors pCRAF and pASK1. ZEB2 overexpression upregulated pCRAF/pASK1 levels resulting in increased chemoresistance, enrichment of cells with stemness/EMT traits and proliferative slowdown of tumor xenografts. In parallel, chemotherapy treatment of tumor xenografts induced the prevalence of QCSCs with a stemness/EMT phenotype and activation of the ZEB2/pCRAF/pASK1 axis, resulting in a chemotherapy-unresponsive state. In CRC patients, increased ZEB2 levels correlated with worse relapse-free survival and were strongly associated to the consensus molecular subtype 4 (CMS4) characterized by dismal prognosis, decreased proliferative rates and upregulation of EMT genes.

Conclusions

These results show that chemotherapy-naive tumors contain a cell population characterized by a coordinated program of chemoresistance, quiescence, stemness and EMT. Such population becomes prevalent upon drug treatment and is responsible for chemotherapy resistance, thus representing a key target for more effective therapeutic approaches.

DeepCC: a novel deep learning-based framework for cancer molecular subtype classification

Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping.

Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer

Background

Epithelial ovarian cancer (EOC) is a heterogeneous disease, which has been recently classified into four molecular subtypes, of which the mesenchymal subtype exhibited the worst prognosis. We aimed to identify a microRNA- (miRNA-) based signature by incorporating the molecular modalities involved in the mesenchymal subtype for risk stratification, which would allow the identification of patients who might benefit from more rigorous treatments.

Method

We characterized the regulatory mechanisms underlying the mesenchymal subtype using network analyses integrating gene and miRNA expression profiles from The Cancer Genome Atlas (TCGA) cohort to identify a miRNA signature for prognosis prediction.

Results

We identified four miRNAs as the master regulators of the mesenchymal subtype and developed a risk score model. The 4-miRNA signature significantly predicted overall survival (OS) and progression-free survival (PFS) in discovery (p=0.004 and p=0.04) and two independent public datasets (GSE73582: OS, HR: 2.26 (1.26-4.05), p=0.005, PFS, HR: 2.03 (1.34-3.09), p<0.001; GSE25204: OS, HR: 3.07 (1.73-5.46), p<0.001, PFS, HR: 2.59 (1.72-3.88), p<0.001). Moreover, in multivariate analyses, the miRNA signature maintained as an independent prognostic predictor and achieved superior efficiency compared to the currently used clinical factors.

Conclusions

In conclusion, our network analysis identified a 4-miRNA signature which has prognostic value superior to currently reported clinical covariates. This signature warrants further testing and validation for use in clinical practice.

High Expression of LTBP2 Contributes to Poor Prognosis in Colorectal Cancer Patients and Correlates with the Mesenchymal Colorectal Cancer Subtype

Colorectal cancer (CRC) is a complex and heterogeneous disease with four consensus molecular subtypes (CMS1-4). LTBP2 is a member of the fibrillin/LTBP super family and plays a critical role in tumorigenesis by activating TGF-β in the CMS4 CRC subtype. So far, the expression and prognostic significance of LTBP2 in CRC remains obscure. In this study, we aimed to analyze the mRNA and protein expression levels of LTBP2 in CRC tissues and then estimate their values as a potential prognostic biomarker. We detected the mRNA expression of LTBP2 in 28 cases of fresh CRC tissues and 4 CRC cell lines and the protein expression of LTBP2 in 483 samples of CRC tissues, matched tumor-adjacent tissues, and benign colorectal diseases. LTBP2 protein expression was then correlated to patients' clinical features and overall survival. Both LTBP2 mRNA and protein expression levels in CRC tissues were remarkably superior to those in adjacent normal colorectal tissues (P = 0.0071 and P < 0.001, respectively), according to TCGA dataset of CRC. High LTBP2 protein expression was correlated with TNM stage (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), and M stage (P < 0.001). High LTBP2 protein expression was related to poor overall survival in CRC patients and was an independent prognostic factor for CRC. LTBP2 mRNA expression was especially higher in the CMS4 subtype (P < 0.001), which was confirmed in CRC cell lines. Our data suggested that LTBP2 may act as an oncogene in the development of colorectal cancer and have important significance in predicting CRC prognosis. LTBP2 could be a novel biomarker and potential therapeutic target for mesenchymal colorectal cancer and can improve the outcome of high-risk CRC.

Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Similar to many other malignancies, CRC is a heterogeneous disease, making it a clinical challenge for optimization of treatment modalities in reducing the morbidity and mortality associated with this disease. A more precise understanding of the biological properties that distinguish patients with colorectal tumors, especially in terms of their clinical features, is a key requirement towards a more robust, targeted-drug design, and implementation of individualized therapies. In the recent decades, extensive studies have reported distinct CRC subtypes, with a mutation-centered view of tumor heterogeneity. However, more recently, the paradigm has shifted towards transcriptome-based classifications, represented by six independent CRC taxonomies. In 2015, the colorectal cancer subtyping consortium reported the identification of four consensus molecular subtypes (CMSs), providing thus far the most robust classification system for CRC. In this review, we summarize the historical timeline of CRC classification approaches; discuss their salient features and potential limitations that may require further refinement in near future. In other words, in spite of the recent encouraging progress, several major challenges prevent translation of molecular knowledge gleaned from CMSs into the clinic. Herein, we summarize some of these potential challenges and discuss exciting new opportunities currently emerging in related fields. We believe, close collaborations between basic researchers, bioinformaticians and clinicians are imperative for addressing these challenges, and eventually paving the path for CRC subtyping into routine clinical practice as we usher into the era of personalized medicine.

Low miR200c expression in tumor budding of invasive front predicts worse survival in patients with localized colon cancer and is related to PD-L1 overexpression

At the histological level, tumor budding in colon cancer is the result of cells undergoing at least partial epithelial-to-mesenchymal transition. The microRNA 200 family is an important epigenetic driver of this process, mainly by downregulating zinc-finger E-box binding homeobox (ZEB) and transforming growth factor beta (TGF-β) expression. We retrospectively explored the expression of the miR200 family, and ZEB1 and ZEB2, and their relationship with immune resistance mediated through PD-L1 overexpression. For this purpose, we analyzed a series of 125 colon cancer cases and took samples from two different tumor sites: the area of tumor budding at the invasive front and from the tumor center. We found significant ZEB overexpression and a reduction in miR200 in budding areas, a profile compatible with epithelial-to-mesenchymal transition. In multivariate analysis of the cases with localized disease, low miR200c expression in budding areas, but not at the tumor center, was an adverse tumor-specific survival factor (hazard ratio: 0.12; 95% confidence interval: 0.03-0.81; p = 0.02) independent of the clinical stage of the disease. PD-L1 was significantly overexpressed in the budding areas and its levels correlated with a mesenchymal transition profile. These results highlight the importance of including budding areas among the samples used for biomarker evaluation and provides relevant data on the influence of mesenchymal transition in the immune resistance mediated by PD-L1 overexpression.

Circulating inflammation signature predicts overall survival and relapse-free survival in metastatic colorectal cancer

BACKGROUND

Metastatic colorectal cancer (mCRC) is a highly heterogeneous disease from a clinical, molecular, and immunological perspective. Current predictive models rely primarily in tissue based genetic analysis, which not always correlate with inflammatory response. Here we evaluated the role of a circulating inflammatory signature as a prognostic marker in mCRC.

METHODS

Two hundred eleven newly diagnosed patients with mCRC were enrolled in the study. One hundred twenty-one patients had unresectable metastases, whereas ninety patients had potentially resectable liver metastases at presentation. Analysis of miR-21, IL-6, and IL-8 in the plasma of peripheral blood was performed at baseline. Patients with high circulating levels of ≥2 of the three inflammation markers (miR-21, IL-6, and IL-8) were considered to have the "Inflammation phenotype-positive CISIG".

RESULTS

Positive CISIG was found in 39/90 (43%) and 50/121 (45%) patients in the resectable and unresectable cohort, respectively. In the resectable population the median relapse-free survival was 18.4 compared to 31.4 months (p = 0.001 HR 2.09, 95% CI 1.2-3.67) for positive vs. negative CISIG. In contrast, the individual components were not significant. In the same population the median overall survival was 46.2 compared to 66.0 months (p = 0.0003, HR 2.57, 95% CI 1.26-5.27) for positive vs. negative CISIG, but not significant for the individual components. In the unresectable population, the median overall survival was 13.5 compared to 25.0 months (p = 0.0008, HR 2.49, 95% CI 1.46-4.22) for positive vs. negative CISIG. IL-6 was independently prognostic with overall survival of 16.2 compared to 27.0 months (p = 0.004, HR 1.96, 95% CI 1.24-3.11) for high vs. low IL-6, but not the other components. Using a Cox regression model, we demonstrated that CISIG is an independent predictive marker of survival in patients with unresectable disease (HR 1.8, 95% CI 1.2, 2.8, p < 0.01).

CONCLUSION

In two different cohorts, we demonstrated that CISIG is a strong prognostic factor of relapse-free and overall survival of patients with mCRC. Based on these data, analysis of circulating inflammatory signaling can be complimentary to traditional molecular testing.

Back to the Colorectal Cancer Consensus Molecular Subtype Future

PURPOSE OF REVIEW

This review seeks to provide an informed prospective on the advances in molecular profiling and analysis of colorectal cancer (CRC). The goal is to provide a historical context and current summary on how advances in gene and protein sequencing technology along with computer capabilities led to our current bioinformatic advances in the field.

RECENT FINDINGS

An explosion of knowledge has occurred regarding genetic, epigenetic, and biochemical alterations associated with the evolution of colorectal cancer. This has led to the realization that CRC is a heterogeneous disease with molecular alterations often dictating natural history, response to treatment, and outcome. The consensus molecular subtypes (CMS) classification classifies CRC into four molecular subtypes with distinct biological characteristics, which may form the basis for clinical stratification and subtype-based targeted intervention. This review summarizes new developments of a field moving "Back to the Future." CRC molecular subtyping will better identify key subtype specific therapeutic targets and responses to therapy.

Genetic dissection of the miR-200-Zeb1 axis reveals its importance in tumor differentiation and invasion

The epithelial-to-mesenchymal transition (EMT) is an important mechanism for cancer progression and metastasis. Numerous in vitro and tumor-profiling studies point to the miR-200-Zeb1 axis as crucial in regulating this process, yet in vivo studies involving its regulation within a physiological context are lacking. Here, we show that miR-200 ablation in the Rip-Tag2 insulinoma mouse model induces beta-cell dedifferentiation, initiates an EMT expression program, and promotes tumor invasion. Strikingly, disrupting the miR-200 sites of the endogenous Zeb1 locus causes a similar phenotype. Reexpressing members of the miR-200 superfamily in vitro reveals that the miR-200c family and not the co-expressed and closely related miR-141 family is responsible for regulation of Zeb1 and EMT. Our results thus show that disrupting the in vivo regulation of Zeb1 by miR-200c is sufficient to drive EMT, thus highlighting the importance of this axis in tumor progression and invasion and its potential as a therapeutic target.

Genome-wide Discovery and Identification of a Novel miRNA Signature for Recurrence Prediction in Stage II and III Colorectal Cancer

Purpose: The current tumor-node-metastasis (TNM) staging system is inadequate at identifying patients with high-risk colorectal cancer. Using a systematic and comprehensive biomarker discovery and validation approach, we aimed to identify an miRNA recurrence classifier (MRC) that can improve upon the current TNM staging as well as is superior to currently offered molecular assays.Experimental Design: Three independent genome-wide miRNA expression profiling datasets were used for biomarker discovery (N = 158) and in silico validation (N = 109 and N = 40) to identify an miRNA signature for predicting tumor recurrence in patients with colorectal cancer. Subsequently, this signature was analytically trained and validated in retrospectively collected independent patient cohorts of fresh-frozen (N = 127, cohort 1) and formalin-fixed paraffin-embedded (FFPE; N = 165, cohort 2 and N = 139, cohort 3) specimens.Results: We identified an 8-miRNA signature that significantly predicted recurrence-free interval (RFI) in the discovery (P = 0.002) and two independent publicly available datasets (P = 0.00006 and P = 0.002). The RT-PCR-based validation in independent clinical cohorts revealed that MRC-derived high-risk patients succumb to significantly poor RFI in patients with stage II and III colorectal cancer [cohort 1: hazard ratio (HR), 3.44 (1.56-7.45), P = 0.001; cohort 2: HR, 6.15 (3.33-11.35), P = 0.001; and cohort 3: HR, 4.23 (2.26-7.92), P = 0.0003]. In multivariate analyses, MRC emerged as an independent predictor of tumor recurrence and achieved superior predictive accuracy over the currently available molecular assays. The RT-PCR-based MRC risk score = (-0.1218 × miR-744) + (-3.7142 × miR-429) + (-2.2051 × miR-362) + (3.0564 × miR-200b) + (2.4997 × miR-191) + (-0.0065 × miR-30c2) + (2.2224 × miR-30b) + (-1.1162 × miR-33a).Conclusions: This novel MRC is superior to currently used clinicopathologic features, as well as National Comprehensive Cancer Network (NCCN) criteria, and works regardless of adjuvant chemotherapy status in identifying patients with high-risk stage II and III colorectal cancer. This can be readily deployed in clinical practice with FFPE specimens for decision-making pending further model testing and validation. Clin Cancer Res; 24(16); 3867-77. ©2018 AACRSee related commentary by Rodriguez et al., p. 3787.

Consensus molecular subtypes of colorectal cancer are recapitulated in in vitro and in vivo models

Colorectal cancer (CRC) is a highly heterogeneous disease both from a molecular and clinical perspective. Several distinct molecular entities, such as microsatellite instability (MSI), have been defined that make up biologically distinct subgroups with their own clinical course. Recent data indicated that CRC can be best segregated into four groups called consensus molecular subtypes (CMS1-4), each of which has a unique biology and gene expression pattern. In order to develop improved, subtype-specific therapies and to gain insight into the molecular wiring and origin of these subtypes, reliable models are needed. This study was designed to determine the heterogeneity and identify the presence of CMSs in a large panel of CRC cell lines, primary cultures and patient-derived xenografts (PDX). We provide a repository encompassing this heterogeneity and moreover describe that a large part of the models can be robustly assigned to one of the four CMSs, independent of the stromal contribution. We subsequently validate our CMS stratification by functional analysis which for instance shows mesenchymal enrichment in CMS4 and metabolic dysregulation in CMS3. Finally, we observe a clear difference in sensitivity to chemotherapy-induced apoptosis, specifically between CMS2 and CMS4. This relates to the in vivo efficacy of chemotherapy, which delays outgrowth of CMS2, but not CMS4 xenografts. Combined our data indicate that molecular subtypes are faithfully modelled in CRC cell cultures and PDXs, representing tumour cell intrinsic and stable features. This repository provides researchers with a platform to study CRC using the existing heterogeneity.

Colorectal Cancer Consensus Molecular Subtypes Translated to Preclinical Models Uncover Potentially Targetable Cancer Cell Dependencies

Purpose: Response to standard oncologic treatment is limited in colorectal cancer. The gene expression-based consensus molecular subtypes (CMS) provide a new paradigm for stratified treatment and drug repurposing; however, drug discovery is currently limited by the lack of translation of CMS to preclinical models.Experimental Design: We analyzed CMS in primary colorectal cancers, cell lines, and patient-derived xenografts (PDX). For classification of preclinical models, we developed an optimized classifier enriched for cancer cell-intrinsic gene expression signals, and performed high-throughput in vitro drug screening (n = 459 drugs) to analyze subtype-specific drug sensitivities.Results: The distinct molecular and clinicopathologic characteristics of each CMS group were validated in a single-hospital series of 409 primary colorectal cancers. The new, cancer cell-adapted classifier was found to perform well in primary tumors, and applied to a panel of 148 cell lines and 32 PDXs, these colorectal cancer models were shown to recapitulate the biology of the CMS groups. Drug screening of 33 cell lines demonstrated subtype-dependent response profiles, confirming strong response to EGFR and HER2 inhibitors in the CMS2 epithelial/canonical group, and revealing strong sensitivity to HSP90 inhibitors in cells with the CMS1 microsatellite instability/immune and CMS4 mesenchymal phenotypes. This association was validated in vitro in additional CMS-predicted cell lines. Combination treatment with 5-fluorouracil and luminespib showed potential to alleviate chemoresistance in a CMS4 PDX model, an effect not seen in a chemosensitive CMS2 PDX model.Conclusions: We provide translation of CMS classification to preclinical models and uncover a potential for targeted treatment repurposing in the chemoresistant CMS4 group. Clin Cancer Res; 24(4); 794-806. ©2017 AACR.

CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models

Colorectal cancers (CRCs) can be divided into four gene expression-based biologically distinct consensus molecular subtypes (CMS). This classification provides a potential framework for stratified treatment, but to identify novel CMS-drug associations, translation of the subtypes to pre-clinical models is essential. The currently available classifier is dependent on gene expression signals from the immune and stromal compartments of tumors and fails to identify the poor-prognostic CMS4-mesenchymal group in immortalized cell lines, patient-derived organoids and xenografts. To address this, we present a novel CMS classifier based on a filtered set of cancer cell-intrinsic, subtype-enriched gene expression markers. This new classifier, referred to as CMScaller, recapitulated the subtypes in both in vitro and in vivo models (551 in total). Importantly, by analyzing public drug response data from patient-derived xenografts and cell lines, we show that the subtypes are predictive of response to standard CRC drugs. CMScaller is available as an R package.

Distinct gut microbiome patterns associate with consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease and recent advances in subtype classification have successfully stratified the disease using molecular profiling. The contribution of bacterial species to CRC development is increasingly acknowledged, and here, we sought to analyse CRC microbiomes and relate them to tumour consensus molecular subtypes (CMS), in order to better understand the relationship between bacterial species and the molecular mechanisms associated with CRC subtypes. We classified 34 tumours into CRC subtypes using RNA-sequencing derived gene expression and determined relative abundances of bacterial taxonomic groups using 16S rRNA amplicon metabarcoding. 16S rRNA analysis showed enrichment of Fusobacteria and Bacteroidetes, and decreased levels of Firmicutes and Proteobacteria in CMS1. A more detailed analysis of bacterial taxa using non-human RNA-sequencing reads uncovered distinct bacterial communities associated with each molecular subtype. The most highly enriched species associated with CMS1 included Fusobacterium hwasookii and Porphyromonas gingivalis. CMS2 was enriched for Selenomas and Prevotella species, while CMS3 had few significant associations. Targeted quantitative PCR validated these findings and also showed an enrichment of Fusobacterium nucleatum, Parvimonas micra and Peptostreptococcus stomatis in CMS1. In this study, we have successfully associated individual bacterial species to CRC subtypes for the first time.

Multi-omics of 34 colorectal cancer cell lines - a resource for biomedical studies

Background

Colorectal cancer (CRC) cell lines are widely used pre-clinical model systems. Comprehensive insights into their molecular characteristics may improve model selection for biomedical studies.

Methods

We have performed DNA, RNA and protein profiling of 34 cell lines, including (i) targeted deep sequencing (n = 612 genes) to detect single nucleotide variants and insertions/deletions; (ii) high resolution DNA copy number profiling; (iii) gene expression profiling at exon resolution; (iv) small RNA expression profiling by deep sequencing; and (v) protein expression analysis (n = 297 proteins) by reverse phase protein microarrays.

Results

The cell lines were stratified according to the key molecular subtypes of CRC and data were integrated at two or more levels by computational analyses. We confirm that the frequencies and patterns of DNA aberrations are associated with genomic instability phenotypes and that the cell lines recapitulate the genomic profiles of primary carcinomas. Intrinsic expression subgroups are distinct from genomic subtypes, but consistent at the gene-, microRNA- and protein-level and dominated by two distinct clusters; colon-like cell lines characterized by expression of gastro-intestinal differentiation markers and undifferentiated cell lines showing upregulation of epithelial-mesenchymal transition and TGFβ signatures. This sample split was concordant with the gene expression-based consensus molecular subtypes of primary tumors. Approximately ¼ of the genes had consistent regulation at the DNA copy number and gene expression level, while expression of gene-protein pairs in general was strongly correlated. Consistent high-level DNA copy number amplification and outlier gene- and protein- expression was found for several oncogenes in individual cell lines, including MYC and ERBB2.

Conclusions

This study expands the view of CRC cell lines as accurate molecular models of primary carcinomas, and we present integrated multi-level molecular data of 34 widely used cell lines in easily accessible formats, providing a resource for preclinical studies in CRC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0691-y) contains supplementary material, which is available to authorized users.

MiRNA-29b suppresses tumor growth through simultaneously inhibiting angiogenesis and tumorigenesis by targeting Akt3

The traditional anti-angiogenic cancer therapy could trigger hypoxia induced factor (HIF) response, leading to "reactive resistance" to chemotherapy. Simultaneously inhibiting both angiogenesis and tumorigenesis would be ideal to overcome this limitation. MicroRNAs (miRNAs) are increasingly explored as new agents for cancer therapy. In the present study, we identified a microRNA (miR-29b) with the ability of simultaneously inhibiting angiogenesis and tumorigenesis. Ectopic expression of miR-29b inhibits HUVECs formed three-dimensional capillary-like tubular structures, tumor cell proliferation, migration and tumor formation. Systemic administration of miR-29b potently suppressed tumor vascularization and cancer cell activity in vivo, resulting in dramatic suppression of tumor growth without toxicity. Moreover, we demonstrated the role of miR-29b in anti-angiogenesis and anti-tumorigenesis is through targeting Akt3 and inducing VEGF and C-myc arrest in breast cancer cells. These findings indicate that this single miRNA could be used as an efficient anti-cancer therapeutic agent to address a critical challenge in cancer therapy.