Introduction: BAP1 is a tumor suppressor and deubiquitinating protein, which misfunction is associated with the rare hereditary BAP1 tumor predisposition syndrome. Current variants effect predictors (VEP) tools are capable to predict variants effect but lack specificity for the rare cancers associated with BAP1 mutations. Here, we propose a protein centric approach based on highly curated protein information integrated in the model ProFeaTrOL to predict the effect that missense variants might have on the function of BAP1.

Methods: We selected functional relevant BAP1 features from the knowledgebase neXtProt to establish the Protein Feature Training based for Oncology Likelihood (ProFeaTrOL) model. We trained and validated ProFeaTrOL based on literature available datasets of BAP1 mutations and tested its performance on ClinVar reports. we further verified the model on the BAP1 functionally associated protein BRCA1 and the independent gene TP53.

Results: ProFeaTrOL presented an AUC of 0.84 for discriminating functional and non-functional BAP1 variants of the validation set and of 0.89 in the testing set from ClinVar. As a comparison, PolyPhen-2 and SIFT presented AUC of 0.77 and 0.68 in the validation and 0.84 and 0.78 in the testing. Despite being trained on BAP1, if applied to the functionally related protein BRCA1, the model showed a high AUC of 0.81, while for TP53 it was only modest, confirming the specificity of the features selected for ProFeaTrOL.

Conclusion: The protein centric approach of ProFeaTrOL offers a strategy for the accurate SNVs effect prediction specific for the rare cancer conditions associated with germline variants of BAP1.

Co-authors: Joel Spies, Lena Bürgi, Kasun Samarasinghe, Lydie Lane, Ferdinando Cerciello