Cite the benchmark: Ostroverkhova, D., Sheng, Y., & Panchenko, A. (2024). Are next-generation pathogenicity predictors applicable to cancer? Journal of Molecular Biology, 168, 168644. https://doi.org/10.1016/j.jmb.2024.168644
The comprehensive experimental benchmark of cancer driver and neutral mutations. Mutation annotations were obtained from the previous seven experimental studies:
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Martelotto, L.G. et al. Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome Biol 15, 484 (2014).
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Olivier, M. et al. The IARC TP53 database: new online mutation analysis and recommendations to users. Hum Mutat 19, 607-14 (2002)
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Starita, L.M. et al. Massively Parallel Functional Analysis of BRCA1 RING Domain Variants. Genetics 200, 413-22 (2015)
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Mahmood, K. et al. Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics. Hum Genomics 11, 10 (2017)
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Campbell, B.B. et al. Comprehensive Analysis of Hypermutation in Human Cancer. Cell 171, 1042-1056 e10 (2017)
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Ng, P.K. et al. Systematic Functional Annotation of Somatic Mutations in Cancer. Cancer Cell 33(3), 450-462(2018)
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Kim, E. et al. Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles. Cancer Discov. 6, 714–26(2016)
'Driver' and 'passenger' labels were assigned according to Brown AL, Li M, Goncearenco A, Panchenko AR Finding driver mutations in cancer: Elucidating the role of background mutational processes. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006981
Check out our paper at: https://www.sciencedirect.com/science/article/pii/S0022283624002390
This supplementary table (STable_1.xlsx) includes mutations in the benchmark that are observed in the TCGA pan-cancer dataset.