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SVelfie: A method for discovering cancer drivers based on enrichment of likely functional structural variants

Loinaz, X.; Dagan, J.; Alberge, J.-B.; Kowalewski, A.; Miller, M.; Hess, J. M.; Rheinbay, E.; Stewart, C.; Getz, G.

2025-03-10 bioinformatics
10.1101/2025.03.09.642284 bioRxiv
Show abstract

Structural variants (SVs) can drive tumorigenesis, yet discovering SV cancer drivers remains challenging1. Here, we present SVelfie (Structural Variants enriched with likely functional/impactful events), a statistical method to infer driver genes from SVs detected across a cohort of cancer genomes, based on enrichment of likely functional events. When testing SVelfie on lymphoma samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG)2, it corroborates known tumor suppressor genes and also yields novel driver candidates.

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"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.