bioRxivGenomicsPreprint
Global signatures of Ribonucleotide Excision Repair deficiency and its therapeutic vulnerabilities
Ping Jing ToongiD, Soraya Boushaki, Salome Jingchen ZhaoiD, Rachel BroughiD, Fei SongiD, Dragomir B. KrasteviD, Daniella Black, Giuseppe RinaldiiD, Cherif BadjaiD, Andrea DegasperiiD, Jan CzarneckiiD, Yasin MemariiD, Helen Davies, Daniel Hüebschmann, Hanno GlimmiD, Stefan Froehling, Andrew N.J. TuttiD, Stephen J. PettittiD, Christopher J. LordiD, Ching Chiek KohiD, Serena Nik‐ZainaliD
Abstract
Cancer therapies increasingly exploit DNA repair deficiencies, yet many clinically actionable repair-deficient states remain undetectable because they lack robust biomarkers and are not identifiable through gene-based diagnostics. Here, we show that deficiency of ribonucleotide excision repair (RER), a pathway that removes misincorporated ribonucleotides from genomic DNA, generates a distinct genome-wide mutational phenotype comprising characteristic insertion-deletion, substitution, and structural variation signatures. Using these features, we developed RERDetect, a whole-genome classifier that identifies RER-deficient tumors across diverse cancer types. Application of RERDetect to more than 11,000 primary and metastatic cancers revealed a recurrent subset of tumors with evidence of RER deficiency, the majority lacking detectable genetic alterations in RNASEH2 genes. We identified promoter hypermethylation of RNASEH2C as a recurrent mechanism of pathway inactivation. RER-deficient cells exhibited distinct vulnerabilities, including sensitivity to EXO1 and FANCM inhibition. These findings establish RER deficiency as a previously unrecognized and therapeutically actionable DNA repair-deficient state in cancer, demonstrating the power of mutational scars to identify clinically relevant tumor phenotypes that evade conventional genomic diagnostics.
- organism: human
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 0
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