The intricate interplay between epigenetic events, alternative splicing and noncoding RNA deregulation in colorectal cancer

Colorectal cancer (CRC) results from a transformation of colonic epithelial cells into adenocarcinoma cells due to genetic and epigenetic instabilities, alongside remodelling of the surrounding stromal tumour microenvironment. Epithelial-specific epigenetic variations escorting this process include chromatin remodelling, histone modifications and aberrant DNA methylation, which influence gene expression, alternative splicing and function of non-coding RNA. In this review, we first highlight epigenetic modulators, modifiers and mediators in CRC, then we elaborate on causes and consequences of epigenetic alterations in CRC pathogenesis alongside an appraisal of the complex feedback mechanisms realized through alternative splicing and non-coding RNA regulation. An emphasis in our review is put on how this intricate network of epigenetic and post-transcriptional gene regulation evolves during the initiation, progression and metastasis formation in CRC

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data

PURPOSE Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. EXPERIMENTAL DESIGN Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. RESULTS Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. CONCLUSIONS Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples

Epithelial TGFβ engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features

The pro-tumourigenic role of epithelial TGFβ signalling in colorectal cancer (CRC) is controversial. Here, we identify a cohort of born to be bad early-stage (T1) colorectal tumours, with aggressive features and a propensity to disseminate early, that are characterised by high epithelial cell-intrinsic TGFβ signalling. In the presence of concurrent Apc and Kras mutations, activation of epithelial TGFβ signalling rampantly accelerates tumourigenesis and share transcriptional signatures with those of the born to be bad T1 human tumours and predicts recurrence in stage II CRC. Mechanistically, epithelial TGFβ signalling induces a growth-promoting EGFR-signalling module that synergises with mutant APC and KRAS to drive MAPK signalling that re-sensitise tumour cells to MEK and/or EGFR inhibitors. Together, we identify epithelial TGFβ signalling both as a determinant of early dissemination and a potential therapeutic vulnerability of CRC’s with born to be bad traits

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers

RNA systems biology for cancer: From diagnosis to therapy

It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer

MicroRNA-mRNA interactions in colorectal cancer and their role in tumor progression

MicroRNAs (miRNA/miR) play an important role in gene regulatory networks through targeting mRNAs. They are involved in diverse biological processes such as cell proliferation, differentiation, angiogenesis, and apoptosis. Due to their pivotal effects on multiple genes and pathways, dysregulated miRNAs have been reported to be associated with different diseases, including colorectal cancer (CRC). Recent evidence indicates that aberrant miRNA expression is tightly linked with the initiation and progression of CRC. To elucidate the influence of miRNA regulation in CRC, it is critical to identify dysregulated miRNAs, their target mRNA genes and their involvement in gene regulatory and signaling networks. Various experimental and computational studies have been conducted to decipher the function of miRNAs involved in CRC. Experimental studies that are used for this purpose can be classified into two categories: direct/individual and indirect/high-throughput gene expression studies. Here we review miRNA target identification studies related to CRC with an emphasis on experimental data based on Luciferase reporter assays. Recent advances in determining the function of miRNAs and the signaling pathways they are involved in have also been summarized. The review helps bioinformaticians and biologists to find extensive information about downstream targets of dysregulated miRNAs, and their pro-/anti-CRC effects

Naïve Bayes classifier predicts functional microRNA target interactions in colorectal cancer

Alterations in the expression of miRNAs have been extensively characterized in several cancers, including human colorectal cancer (CRC). Recent publications provide evidence for tissue-specific miRNA target recognition. Several computational methods have been developed to predict miRNA targets; however, all of these methods assume a general pattern underlying these interactions and therefore tolerate reduced prediction accuracy and a significant number of false predictions. The motivation underlying the presented work was to unravel the relationship between miRNAs and their target mRNAs in CRC. We developed a novel computational algorithm for miRNA–target prediction in CRC using a Naı¨ve Bayes classifier. The algorithm, which is referred to as CRCmiRTar, was trained with data from validated miRNA target interactions in CRC and other cancer entities. Furthermore, we identified a set of position-based, sequence, structural, and thermodynamic features that identify CRC-specific miRNA target interactions. Evaluation of the algorithm showed a significant improvement of performance with respect to AUC, and sensitivity, compared to other widely used algorithms based on machine learning. Based on miRNA and gene expression profiles in CRC tissues with similar clinical and pathological features, our classifier predicted 204 functional interactions, which involve 11 miRNAs and 41 mRNAs in this cancer entity. While the approach is here validated for CRC, the implementation of disease-specific miRNA target prediction algorithms can be easily adopted for other applications too. The identification of disease-specific miRNA target interactions may also facilitate the identification of potential drug targets