The promoter mutation paucity as part of the dark matter of the cancer genome
Abad, N. A. B.; Glas, I.; Hong, C.; Small, A.; Pageaud, Y.; Maia, A.; Weichenhan, D.; Plass, C.; Hutter, B.; Brors, B.; Körner, C.; Feuerbach, L.
Show abstract
Cancer is a heterogeneous disease caused by genetic alterations. Computational analysis of cancer genomes led to the expansion of the catalog of driver mutations. While individual high-impact mutations have been discovered also in gene promoters, frequency-based approaches have only characterized a few novel candidates. To investigate the promoter mutation paucity in cancer, we developed the REMIND-Cancer workflow to predict activating promoter mutations in silico, irrespective of their recurrence frequency, and applied it to the PCAWG dataset. We positively validated 7 candidates by luciferase assay including mutations within the promoters of ANKRD53 and MYB. Our analysis indicates that particular mutational signatures and necessary co-alterations constrain the creation and positive selection of functional promoter mutations. We conclude that activating promoter mutations are more frequent in the PCAWG dataset than previously observed, which has potential implications for personalized oncology.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers 96%
- HYENA detects oncogenes activated by distal enhancers in cancer 96%
- Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses 95%
Similar papers in this journal
- Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma 96%
- Genomic and transcriptomic landscape of advanced renal cell cancer to individualize treatment strategy 95%
- Mutational Landscape of Cancer-Driver Genes Across Human Cancers 95%
Similar papers in this journal
- Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity 95%
- Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer 95%
- Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.