Tomorrow at 14.30 I'll present my poster on the Identification of (ultra-)rare functional promoter mutations in cancer using sequence-based deep learning models at #ashg. Swing by poster Board 9050F if you want to chat or know more! #ashg2025

These genes are enriched for being involved in the cell-cycle, being known cancer driver genes and being more evolutionary constrained. (11/16)

We identify nine high-confident regions (including the well-known TERT, TP53 and PMS2 genes) where in three independent cohorts we find significant enrichment of non-coding mutations and where we can also confirm that these mutations show actual effects on gene expression levels. (9/16)

We subsequently performed a burden analysis to identify a comprehensive set of genes that are enriched for containing non-coding somatic mutations that either preferentially activate or preferentially repress gene expression. (7/16)

Here we used a novel strategy to do this in WGS data of 24,529 cancer patients: we used sequence-based models to predict the transcriptional consequences of these mutations. (6/16)

This is because no algorithms yet existed to distinguish functional from irrelevant non-coding mutations. (5/16)

We are excited to share our manuscript “Identification of (ultra-)rare functional promoter mutations in cancer using sequence-based deep learning models” (www.medrxiv.org/content/10.1...). (1/16) 🧵