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#gene editing Open access

Supplementary Datasets underlying analysis and results presented in CRISPR-enhanced assessment of variants of unknown significance nominates oncology therapeutic targets and drug repositioning opportunities. Savino et Al. 2026 and Data Package to reproduce results and figures.

Sep 2026 · Figshare

Abstract

Interpreting infrequent somatic variants remains a challenge in cancer genomics. We developed CRISPR-VUS, a framework using public Cancer Dependency Map data to identify Dependency-Associated Mutations (DAMs) - variants linked to increased host-gene dependency - with resolution extending to singleton events. Analysis of 977 cell lines across 36 cancer-types identified 2,376 DAMs in 1,383 genes, including 1,260 not established as cancer drivers. DAM-bearing genes converge on oncogenic networks, while recurrence in matched tumours, functional-impact predictions, tractability and pharmacological associations enable prioritisation. Prime editing showed that the prioritised NSCLC-specific RTN4IP1 p.A80T DAM conferred a significant competitive growth advantage in a lung epithelial model, nominating a candidate driver allele. Exploratory pharmacological testing showed a greater maximal istaroxime response in ATP1B3 p.I189M-bearing RKO cells than in ATP1B3-wild-type HCT15 cells. CRISPR-VUS combines discovery with evidence-guided prioritisation to nominate candidate drivers, therapeutic targets and drug-repositioning hypotheses. Interactive results are available at https://vus-portal.fht.org/.

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