Abstract 5043: reVUE: repository for variants with unexpected effects
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.044 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".