Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Evolutionary action score of TP53 coding variants is predictive of platinum response in head and neck cancer patients

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma (HNSCC), with mutations occurring in over two thirds of cases; however, the predictive response of these mutations to cisplatin-based therapy remains elusive. In the current study, we evaluate the ability of the Evolutionary Action score of TP53-coding variants (EAp53) to predict the impact of TP53 mutations on response to chemotherapy. The EAp53 approach clearly identifies a subset of high-risk TP53 mutations associated with decreased sensitivity to cisplatin both in vitro and in vivo in preclinical models of HNSCC. Furthermore, EAp53 can predict response to treatment and, more importantly, a survival benefit for a subset of head and neck cancer patients treated with platinum-based therapy. Prospective evaluation of this novel scoring system should enable more precise treatment selection for patients with HNSCC

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

10.1038/s41467-021-23143-7Nature Communications121329