Abstract 5043: reVUE: repository for variants with unexpected effects
Abstract Clinical sequencing of tumor samples is now a component of routine cancer care. By identifying genomic alterations that contribute to tumor initiation or progression, clinical cancer genomic sequencing may be used to identify predictive biomarkers of drug response, refine patient cancer diagnoses, assess heritable cancer risk, or inform patient prognosis. Most genomic alterations are accurately annotated with tools such as the Variant Effect Predictor (VEP) that infer the effects of these alterations on the mRNA and protein by following basic rules of transcription, mRNA post-transcriptional processing, and translation. However, a select subset of variants’ effects cannot be predicted as easily by these rules. While many of these “variants with unexpected effects (VUE)” are functionally characterized and documented in the literature, these VUEs are often mis-annotated during routine clinical cancer genomic sequencing. Importantly, certain VUEs may have therapeutic implications, which, if mis-annotated may lead to suboptimal treatment decisions for individual patients with cancer. To address this unmet clinical need, we created a centralized database resource, the repository for Variants with Unexpected Effects (reVUE - cancerrevue.org), which curates and programmatically stores VUEs to enable the annotation of these variants during routine clinical cancer genomic sequencing. The reVUE resource consists of (1) an intuitive website listing curated VUEs with their observed effects as demonstrated by functional characterization in peer-reviewed literature and (2) an application programming interface (API) for programmatic annotation of variants. We successfully curated 109 VUEs spanning 22 genes from 31 articles, and continue to expand this database. Several curated VUEs were associated with clinical treatment implications, including KIT, MET, ATM, EGFR, and BRCA1/2. The reVUE database has also been integrated into the publicly available bioinformatic ecosystem of cancer variant annotation and interpretation tools that currently includes Genome Nexus, OncoKB, and cBioPortal. By addressing the critical challenge of the accurate annotation of genomic variants with unanticipated protein effects, reVUE enhances our understanding of complex variant interpretation and contributes directly to improved patient care. Notably, the software and all annotated variants are publicly available, allowing for community contributions, and enabling seamless integration into other genomics tools, clinical workflows, and research pipelines. Citation Format: Xiang Li, Ino de Bruijn, Thomas Y. Cong, Walid Chatila, Hongxin Zhang, Moriah Nissan, Amanda Dhaneshwar, Sara E. DiNapoli, Erika Gedvilaite, Bryan Lai, Selcuk Onur Sumer, Aditi Gopalan, Tonatiuh Gonzalez, Madelaine Rangel, Trevor J. Pugh, Rose Brannon, Michael F. Berger, Jianjiong Gao, Nikolaus Schultz, Debyani Chakravarty. reVUE: repository for variants with unexpected effects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5043.
Authors
- Debyani Chakravarty (ORCID: https://orcid.org/0000-0001-8629-5732)
- Aditi Gopalan
- Bryan Lai (ORCID: https://orcid.org/0009-0003-2618-3984)
- Tonatiuh Gonzalez (ORCID: https://orcid.org/0000-0002-2453-9313)
- Jianjiong Gao (ORCID: https://orcid.org/0000-0002-5739-1781)
- Rose Brannon
- S. Onur Sumer (ORCID: https://orcid.org/0000-0001-9726-9441)
- Amanda Dhaneshwar (ORCID: https://orcid.org/0000-0003-1887-1771)
- Walid K. Chatila (ORCID: https://orcid.org/0000-0001-5474-218X)
- Madelaine Rangel
- Sara DiNapoli (ORCID: https://orcid.org/0009-0005-4099-1034)
- Nikolaus Schultz (ORCID: https://orcid.org/0000-0002-0131-4904)
- Michael F. Berger (ORCID: https://orcid.org/0000-0003-3882-5000)
- Erika Gedvilaite (ORCID: https://orcid.org/0000-0003-1749-0045)
- Thomas Y. Cong
- Trevor J. Pugh (ORCID: https://orcid.org/0000-0002-8073-5888)
- Ino de Bruijn (ORCID: https://orcid.org/0000-0001-5427-4750)
- Moriah H. Nissan (ORCID: https://orcid.org/0000-0001-5852-3419)
- Xiang Li (ORCID: https://orcid.org/0000-0002-0016-6233)
- Hongxin Zhang (ORCID: https://orcid.org/0000-0003-2239-5974)
Institutions
- Sage Bionetworks (US)
- Memorial Sloan Kettering Cancer Center (US)
- Cornell University (US)
- Kettering University (US)
- Melanoma Research Alliance (US)
- Massachusetts Institute of Technology (US)
- Ithaca College (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-5043
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00