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

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.

Diagnóstico molecular de enfermedades genéticas: del diagnóstico genético al diagnóstico genómico con la secuenciación masiva / Molecular diagnosis of genetic diseases: from genetic to genomic diagnosis using next generation sequencing

En la actualidad se conocen 8.000 enfermedades genéticas monogénicas. La mayoría de ellas son heterogéneas, por lo que el diagnóstico molecular por técnicas convencionales de secuenciación suele ser largo y costoso debido al gran número de genes implicados. El tiempo estimado para el diagnóstico molecular se encuentra entre 1 y 10 años, y este retraso impide que los pacientes reciban medidas terapéuticas y de rehabilitación específicas, que sus familiares entren en programas preventivos y que reciban asesoramiento genético. La secuenciación masiva está cambiando el modelo de diagnóstico molecular de los afectos, sin embargo, los médicos y profesionales de la salud se enfrentan al dilema de la selección del método más eficiente, con el menor coste sanitario y con la mayor precisión de sus resultados. El objetivo de este trabajo es revisar la tecnología de secuenciación masiva y definir las ventajas y los problemas en su utilización.

Currently 8000 monogenic genetic diseases are known. Most of them are heterogeneous, so their molecular diagnosis by conventional sequencing techniques is labour intensive and time consuming due to the large number of genes involved. The estimated time is between 1 and 10 years for molecular diagnosis and this delay prevents patients from receiving therapy and rehabilitation measures, and their families from entering prevention programs and being given genetic counselling. Next generation sequencing (NGS) is changing the model of molecular diagnosis of patients; however, doctors and health professionals are faced with the dilemma of choosing the most efficient method, with lower health care costs and the most accurate results. The aim of this paper is to review the NGS technology and define the advantages and problems in the use of this technology.

Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior.

BACKGROUND: Colon cancer patients with the same stage show diverse clinical behavior due to tumor heterogeneity. We aimed to discover distinct classes of tumors based on microarray expression patterns, to analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters and to study differences in the survival. METHODS: Hierarchical clustering was performed for class discovery in 88 colon tumors (stages I to IV). Pathways analysis and correlations between clinical parameters and our classification were analyzed. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the main subtype was generated using the 3-Nearest-Neighbor method. Coincidences with other prognostic predictors were assesed. RESULTS: Hierarchical clustering identified four robust tumor subtypes with biologically and clinically distinct behavior. Stromal components (p < 0.001), nuclear ß-catenin (p = 0.021), mucinous histology (p = 0.001), microsatellite-instability (p = 0.039) and BRAF mutations (p < 0.001) were associated to this classification but it was independent of Dukes stages (p = 0.646). Molecular subtypes were established from stage I. High-stroma-subtype showed increased levels of genes and altered pathways distinctive of tumour-associated-stroma and components of the extracellular matrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected by the increased expression of trefoil factors and mucins as well as by a higher proportion of MSI and BRAF mutations. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the Low-stroma-subtype distinguished low risk patients from high risk patients in the external cohort (Dukes B and C:HR = 8.56(2.53-29.01); Dukes B,C and D:HR = 1.87(1.07-3.25)). Eight different reported survival gene signatures segregated our tumors into two groups the Low-stroma-subtype and the other tumor subtypes. CONCLUSIONS: We have identified novel molecular subtypes in colon cancer with distinct biological and clinical behavior that are established from the initiation of the tumor. Tumor microenvironment is important for the classification and for the malignant power of the tumor. Differential gene sets and biological pathways characterize each tumor subtype reflecting underlying mechanisms of carcinogenesis that may be used for the selection of targeted therapeutic procedures. This classification may contribute to an improvement in the management of the patients with CRC and to a more comprehensive prognosis.