ArticleScience advances2022
CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- European Association of Neuro-Oncology guideline on molecular testing of meningiomas for targeted therapy selection.Neuro-oncology · 2025Guideline
- Reporting of somatic variants in clinical cancer care: recommendations of the Swiss Society of Molecular Pathology.Virchows Archiv : an international journal of pathology · 2024Guideline
- Genomic profiling of Mexican patients with B-cell precursor acute lymphoblastic leukemia reveals clinically significant somatic and potential germline variants.The journal of pathology. Clinical research · 2026Article
- DeNovoSeer:a deep learning framework for pathogenicity prediction of De novo mutations.Briefings in bioinformatics · 2026Article
- [Artificial intelligence in molecular pathology : Introduction and overview].Pathologie (Heidelberg, Germany) · 2026Review
- Article
- Implementing whole genome and transcriptome sequencing for cancer patients in routine healthcare: a comprehensive guide to costing.British journal of cancer · 2026Article
- OncoRisk: a state-of-the-art web server for bridging the oncogenic databases and pan-cancer cohorts to the translational oncology.Communications biology · 2026Article
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- Molecular Landscape of Advanced Endometrial Cancer: Exploratory Analyses at Modena Cancer Center (MEMO).International journal of molecular sciences · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Integrated Genomic and Transcriptomic Profiling of Isolated Trisomies in AML Reveals Cell Cycle Dysregulation and Therapeutic Vulnerabilities.Journal of cellular and molecular medicine · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- The Mechanisms of Resistance to JAK Inhibitors in Lymphoid Leukemias: A Scoping Review of Evidence from Preclinical Models and Case Reports.International journal of molecular sciences · 2025Article
- Quantitative functional profiling of ERCC2 mutations deciphers cisplatin sensitivity in bladder cancer.The Journal of clinical investigation · 2025Article
- Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and Comparison With AI Model-Based Prediction.JCO precision oncology · 2025Article
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- High Tumor Mutation Burden (TMB) and a Novel Somatic Mutation in the TREX1 Gene in a Patient with Aggressive and Refractory High-Grade B-Cell Lymphoma: A Case Report.International journal of molecular sciences · 2025Article
- Genomic analysis of small renal masses reveals mutations linked with renal cell carcinoma and fast-growing tumors.Journal of cancer research and clinical oncology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.