A multimodal framework for comprehensive driver variant prediction in cancer.

ABSTRACT:

Background

Cancer genomes contain many mutations, but only a subset drive tumor development. Accurately pinpointing these driver variants remains challenging. We aim to build an accurate and interpretable model by combining DNA sequence, protein 3D structure, and cancer omics data.

Methods

We present ModVAR, a multimodal model that integrates DNA sequences, predicted protein tertiary structures, and cancer omics data to classify driver variants. The approach uses pre-trained models (DNAbert2 and ESMFold) and a self-supervised strategy for cancer omics profiles. We evaluate performance on clinically and experimentally validated driver variants with standard classification metrics, examine therapeutic relevance through molecular docking, assess modeling of variants in intrinsi

SUBMITTER: Yang H 

PROVIDER: S-EPMC12657954 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature