bioRxivCancer BiologyPreprint
Landscape of reversion alterations in homologous recombination genes reveals evolutionary constraints
Brennan James DeckeriD, Saumya Dushyant SisoudiyaiD, James Thornton, Chenming CuiiD, Taku HaradaiD, Shahlo O. SolievaiD, Russell William MadisoniD, Arindam DattaiD, Tao Shi, Fabio Guintini, Zoe FleischmanniD, Mark Hartman, Shannon Terrell Bailey, Jeffrey A. LeibowitziD, Ethan S. SokoliD, Alexa Betzig SchrockiD, Garrett Michael FramptoniD, Fergus J. CouchiD, Kim A. ReissiD, Susan M. DomchekiD, Gregory R. BowmaniD, Roger A. GreenbergiD, Smruthy SivakumariD, Katherine L. NathansoniD
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Abstract
Reversion mutations (REVs) restore homologous recombination repair (HRR) and confer resistance to PARP inhibitors (PARPi) in HR-deficient cancers. Yet, their prevalence, mechanisms, and biological constraints remain undefined. We analyzed genomic profiling of 609,464 tissue and liquid biopsy samples across multiple cancer types to delineate the pan-cancer landscape of REVs. REVs were identified in eight HRR genes, most frequently BRCA2 and BRCA1 and notably never in ATM or CHEK2. REVs exclusively impacted truncating pathogenic variants, predominantly through large in-frame and exon-level deletions, associated with repetitive sequences. Conserved functional domains are relatively depleted of REVs. Structural modeling and functional studies support that exon-level deletions preserve critical domain architecture and confer PARPi resistance. The study establishes HRR reversion as a structurally permissive, yet evolutionarily constrained, resistance mechanism with implications on response, monitoring, and therapeutic strategy. One Sentence SummaryHomologous recombination reversion evolves through structurally tolerated, microhomology-driven alterations that restore DNA repair under therapeutic selection across cancers.
- organism: human
- assay: whole-genome sequencing
- research focus: drug response / drug discovery
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: variant effect prediction
- approach: experimental assay
- approach: statistical model
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