Luco, R. F. | Search bioRxiv

bioRxiv2021

Histone marks are drivers of the splicing changes necessary for an epithelial-to-mesenchymal transition

Cell differentiation and reprogramming depend on coordinated changes in specific alternative splicing events. How these cell type-specific splicing patterns are dynamically modified in response to a stimulus remains elusive. Taking advantage of the epithelial-to-mesenchymal transition (EMT), a reversible cell reprogramming intimately involved in cancer cell invasiveness and metastasis, we found a strong correlation between changes in the alternative splicing of key exons for EMT, such as at the Fgfr2 and Cnntd1 loci, and changes in the enrichment levels of specific histone modifications, namely H3K27ac and H3K27me3. Localised CRISPR epigenome editing of these exon-specific histone marks was sufficient to induce changes in splicing capable of recapitulating important aspects of EMT, such as a motile and invasive cell phenotype. Whereas, impairment of the changes in H3K27 marks observed during EMT, using histone deacetylase inhibitors, repressed inclusion of the mesenchymal isoform despite an EMT induction, supporting a driving effect for H3K27 modifications in establishing the new cell type-specific splicing patterns necessary for EMT cell reprogramming. Finally, H3K27 marks were shown to impact splicing by modulating recruitment of the splicing factor PTB to its RNA binding sites, suggesting a direct link between chromatin modifications and the splicing machinery. Taken together, these results prove the causal role of H3K27 marks in driving the dynamic splicing changes necessary for induction of important aspects of EMT. They also prove that chromatin-mediated splicing changes are sufficient to impact the cells phenotype, which expands the cells toolkit to adapt and respond to diverse stimuli, such as EMT induction.

molecular biology↗

bioRxiv2020

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

BackgroundBreast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers. ResultsTranscriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype. ConclusionsUsing a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

cancer biology↗