ArticleScientific reports2021
Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Real-World Genomic Landscape of Korean Gastric Cancer: Integrating Biomarker Associations and Clinical Outcomes in Metastatic Gastric Cancer.JCO precision oncology · 2026Article
- Predictive biomarkers in the era of immunotherapy for gastric cancer: current achievements and future perspectives.Frontiers in immunology · 2025Review
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
- Towards a precision approach to anesthetic/analgesic immunomodulation in cancer.Frontiers in anesthesiology · 2024Article
- Tumor Mutational Burden as a Biomarker of Immunotherapy Response: An Immunogram Approach in Onco-immunology.Current molecular medicine · 2024Review
- Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies.Cancers · 2023Review
- Restriction site associated DNA sequencing for tumour mutation burden estimation and mutation signature analysis.Cancer medicine · 2023Article
- Increased diagnostic yield from negative whole genome-slice panels using automated reanalysis.Clinical genetics · 2023Article
- Mutation Burden Independently Predicts Survival in the Pan-Cancer Atlas.JCO precision oncology · 2023Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Bias and inconsistency in the estimation of tumour mutation burden.BMC cancer · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.
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