ArticleNature communications2022
Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The trial behind it
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
12 citing papers in PubMed, 15 citations in OpenAlex.
- FFixR: a machine learning framework for accurate somatic mutation calling from FFPE RNA-seq data in cancer.Bioinformatics (Oxford, England) · 2026Article
- Precision Diagnosis in Cutaneous Head and Neck Squamous Cell Carcinoma.Biomedicines · 2026Review
- Ficoll-based isolation biases the profiling of immune cells and leukemic blasts in AML samples.Frontiers in immunology · 2026Article
- Clinical Implementation of Expanded Solid Tumor Fusion Detection With Whole-Transcriptome Sequencing at an Academic Cancer Center.JCO precision oncology · 2025Article
- Identification of diagnostic and prognostic genetic alterations in uveal melanoma using RNA sequencing.Scientific reports · 2025Article
- Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.BMC research notes · 2025Article
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Overview on biomarkers for immune oncology drugs.Exploration of targeted anti-tumor therapy · 2025Review
- NCI Cancer Research Data Commons: Cloud-Based Analytic Resources.Cancer research · 2024Review
- Current status of molecular diagnostics for lung cancer.Exploration of targeted anti-tumor therapy · 2024Review
- Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies.Cancers · 2023Review
- Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling.Frontiers in immunology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of somatic mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes, detection of mutational signatures, and estimation of tumor mutational burden (TMB). We have previously developed a tool for detection of somatic mutations using tumor RNA and a matched-normal DNA. Here, we further extend it to detect somatic mutations from RNA sequencing data without a matched-normal sample. This is accomplished via a machine-learning approach that classifies mutations as either somatic or germline based on various features. When applied to RNA-sequencing of >450 melanoma samples high precision and recall are achieved, and both mutational signatures and driver genes are correctly identified. Finally, we show that RNA-based TMB is significantly associated with patient survival, showing similar or higher significance level as compared to DNA-based TMB. Our pipeline can be utilized in many future applications, analyzing novel and existing datasets where only RNA is available.
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Registered trials
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.