The identification of a pathogenic variant in the NF1 gene is an important step in the diagnosis of the tumor-predisposing and developmental syndrome neurofibromatosis, and is increasingly important in the characterization of sporadic tumors, in which NF1 loss identifies specific biologic subtypes. However, NF1 variant interpretation is complicated by multiple factors including allelic heterogeneity, sequence homology, and the lack of functional assays to confirm loss of function. Computational tools able to predict variant pathogenicity may represent an ideal complement to the lengthy process of clinical validation, often impossible within an adequate time frame. Here, we present RENOVO-NF1, an evolution of our previously published tool RENOVO algorithm, optimized for the interpretation of NF1 variants. RENOVO-NF1 has an accuracy of 98.6% on (likely) pathogenic/(likely) benign (P/LP/B/LB) variants, and importantly shows high accuracy on P/LP/B/LB variants that were initially classified as of unknown significance (VUS), in particular missense. Based on this, we predict reclassification for 79% of the variants currently classified as missense VUS in ClinVar. Since missense VUS are the most represented and problematic variant class, RENOVO-NF1 may significantly aid in diagnostic challenges in neurofibromatosis and in precision oncology for putatively NF1-mutated patients.

The authors have declared no competing interest.

Research in LM lab is funded by a My First AIRC grant n 25791 and by a European Union. Next Generation EU, PNRR, Project Code: PNRR-MAD-2022-12376934

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