I'm only partially kidding cuz he definitely is forcing me to modify some of my slides 😂. But I did want to express some thoughts on the lightweight specialized single task (ST) models vs kitchensink multi task (MT) model paradigms a bit. 2/

This paper is also supported by resources from intOGen (@nlbigas.bsky.social ) and Cosmic Cancer Gene Census. (16/16)

Also big thanks to the authors of PARM (@luciabarmar.bsky.social et al) and Borzoi (Johannes Linder and @drkbio.bsky.social et al) for sharing their models. (15/16)

Thanks to all the other co-authors, Lucía Barbadilla-Martínez, Minh Chau Luong Boi, Harm-Jan Westra, Noud H.M. Klaassen, Vinícius H. Franceschini-Santos, Miguel Parra-Martínez, Jeroen de Ridder, Bas van Steensel, Emile Voest and Lude H. Franke for the collaboration! (14/16)

Many thanks to all the co-authors (in particular the close collaboration with Carlos Urzúa-Traslaviña) for their input, discussions, collaboration and reviews! (13/16)

Our results suggests that the promoter regions of many genes contain non-coding somatic mutations that contribute to cancer development. This is substantially more than what has been reported previously. (12/16)

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

A meta-analysis across all 24,529 samples reveals 492 regions to be significantly enriched for non-coding somatic mutations with a predicted functional impact. (10/16)

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 identified 492 genes where this is the case, indicating that somatic non-coding mutations play a substantial role in causing cancer. This is more than earlier studies which had to rely on smaller sample sizes and mutational background models that can be quite challenging to apply. (8/16)

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)

This was surprising, considering that 97% of all mutations occur in non-coding regions. However, these statistical approaches had to lump together all mutations, irrespective of whether these mutations may alter gene regulation or not. (4/16)

However, previous statistical approaches to find non-coding “driver” mutations in large cancer genome datasets have so far revealed a few genes that showed mutation frequencies in their regulatory elements that were higher than expected by chance. (3/16)