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PreprintCited by 2Cancer Genomics and DiagnosticsGenomics and Rare DiseasesComputational Drug Discovery Methods

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ModVAR: A Multimodal Framework for Comprehensive Driver Variant Prediction in Cancer

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Wang, Zhe, Yang, Hai, Chen, Yijia, Zhou, Tianyi, Wang, Yingzhuo, Zhou, Qin, Xiao, Ting, Zhang, Qian, Zhang, Jing, Li, Dongdong·2024·Communications Medicine
DOI 10.21203/rs.3.rs-4969792/v1

Preprint — published as 10.1038/s43856-025-01209-7 · Paper page

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Abstract

Cancer's genomic complexity poses significant challenges in identifying the crucial driver variants that fuel oncogenesis. To overcome these obstacles, we present ModVAR, an innovative multimodal model that seamlessly integrates DNA sequences, protein tertiary structures, and cancer omics data to predict driver variants with exceptional precision. Leveraging pre-trained models DNAbert2 and ESMFold, combined with a novel self-supervised strategy for omics data, ModVAR consistently outperforms 14 leading prediction methods. It achieves an outstanding AUROC of 0.985 in clinical benchmarks and 0.952 in experimental assay-related benchmarks, underscoring its effectiveness in identifying both clinically and experimentally validated variants. ModVAR also excels in predicting cancer indels, with an AUROC of 0.901, and demonstrates remarkable generalization with an AUROC of 0.981 for non-cancer disease variants. These results underscore ModVAR's robustness and versatility, positioning it as a powerful tool in genomics and precision medicine. Additionally, its application to COSMIC has generated a publicly available resource of 3,971,946 annotated variants, driving an advancement in cancer research and personalized therapy.

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Metadata

Authors
Wang, Z., Yang, H., Chen, Y., Zhou, T., Wang, Y., Zhou, Q., Xiao, T., Zhang, Q., Zhang, J., & Li, D.
Affiliations
  • East China University of Science and Technology (CN)
  • University of Michigan (US)
  • Imperial College London (GB)
Corresponding author
Wang, Zhe
ORCID
Venue
Communications Medicine
Publisher
Springer Science and Business Media LLC
Published
2024-09-17
Volume
5
Issue
1
Pages
499-499
ISSN
2730-664X (ISSN-L)
DOI
10.21203/rs.3.rs-4969792/v1
OpenAlex
W4416720926
Cited by
2
References
1
Citations by year
2026: 2
FWCI
0.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Medicine (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (61902126)
  • National Key Research and Development Program of China (2023YFF1204904)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
multimodal learning, protein structure prediction, cancer omics, ESMFold, self-supervised learning, molecular docking, drug target prioritization, intrinsically disordered regions, variant annotation

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