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Background Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information. Methods A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification. Results Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification. Conclusions These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

Background Colorectal cancer (CRC) remains largely incurable when diagnosed at the metastatic stage. Despite some advances in precision medicine for this disease in recent years, new molecular targets, as well as prognostic/predictive markers, are highly needed. Neuroligin 1 (NLGN1) is a transmembrane protein that interacts at the synapse with the tumor suppressor adenomatous polyposis Coli (APC), which is heavily involved in the pathogenesis of CRC and is a key player in the WNT/β-catenin pathway. Methods After performing expression studies of NLGN1 on human CRC samples, in this paper we used in vitro and in vivo approaches to study CRC cells extravasation and metastasis formation capabilities. At the molecular level, the functional link between APC and NLGN1 in the cancer context was studied. Results Here we show that NLGN1 is expressed in human colorectal tumors, including clusters of aggressive migrating (budding) single tumor cells and vascular emboli. We found that NLGN1 promotes CRC cells crossing of an endothelial monolayer (i.e. Trans-Endothelial Migration or TEM) in vitro, as well as cell extravasation/lung invasion and differential organ metastatization in two mouse models. Mechanistically, NLGN1 promotes APC localization to the cell membrane and co-immunoprecipitates with some isoforms of this protein stimulates β-catenin translocation to the nucleus, upregulates mesenchymal markers and WNT target genes and induces an “EMT phenotype” in CRC cell lines Conclusions In conclusion, we have uncovered a novel modulator of CRC aggressiveness which impacts on a critical pathogenetic pathway of this disease, and may represent a novel therapeutic target, with the added benefit of carrying over substantial knowledge from the neurobiology field.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Background The role of forkhead-box A1 (FOXA1) and Androgen receptor (AR) in breast cancer (BC) has been extensively studied. However, the prognostic role of their co-expression in Estrogen receptor positive (ER+) BC has not been investigated so far. The aim of the present study was thus to assess the co-expression (protein and mRNA) of FOXA1 and AR in BC patients, in order to evaluate their prognostic impact according to ER status. Methods Immunohistochemical expression of AR and FOXA1 was evaluated on 479 consecutive BC, with complete clinical-pathological and follow up data. Fresh-frozen tissues from 65 cases were available. The expression of AR and FOXA1 with ER was validated using mRNA analyses. Survival and Cox proportional hazard analyses were used to evaluate the relationship between FOXA1, AR and prognosis. Results Expression of ER, AR and FOXA1 was observed in 78, 60 and 85% of cases respectively. Most AR+ cases (97%) were also FOXA1+. The level of FOXA1 mRNA positively correlated with level of both AR mRNA (r = 0.8975; P

Colorectal cancer (CRC) transcriptional subtypes have been recently identified by gene expression profiling. Here we describe an analytical pipeline, microRNA master regulator analysis (MMRA), developed to search for microRNAs potentially driving CRC subtypes. Starting from a microRNA–mRNA tumour expression data set, MMRA identifies candidate regulator microRNAs by assessing their subtype-specific expression, target enrichment in subtype mRNA signatures and network analysis-based contribution to subtype gene expression. When applied to a CRC data set of 450 samples, assigned to subtypes by 3 different transcriptional classifiers, MMRA identifies 24 candidate microRNAs, in most cases downregulated in the stem/serrated/mesenchymal (SSM) poor prognosis subtype. Functional validation in CRC cell lines confirms downregulation of the SSM subtype by miR-194, miR-200b, miR-203 and miR-429, which share target genes and pathways mediating this effect. These results show that, by combining statistical tests, target prediction and network analysis, MMRA effectively identifies microRNAs functionally associated to cancer subtypes., Colorectal cancer subtypes can be distinguished by their different biological and molecular properties. Here the authors present microRNA Master Regulator Analysis, a tool to identify microRNAs driving subtype-specific gene expression and cancer variation.

Nat. Genet. 47, 312–319 (2015); published online 23 February 2015; corrected after print 29 August 2016 In the version of this article initially published, an affiliation for author Zsolt Fekete was incorrectly omitted. The missing affiliation was to the Department of Oncology, University of Medicine and Pharmacy Iuliu Hatieganu, Cluj-Napoca, Romania.