An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers

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• 34 methods
• 49 configurations
• 3 protocols
• 1 dataset
• 147 evaluations
• 294 results
• 10 evidence claims
• 1 use case

Results

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Claims

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• CADD: description: Description Combined Annotation-Dependent Depletion: a genome-wide variant deleteriousness score that integrates more than 60 genomic features in a machine learning model trained to separate simulated de novo variants from variants fixed in human populations since the human-chimpanzee split. It scores single nucleotide variants and short insertions and deletions anywhere in the reference assembly, not only missense variants. (Record description; cited Rentzsch et al. 2019, Nucleic Acids Research 47:D886)
• Evidence summary: somatic small-variant oncogenicity: Summary Two predictor comparisons join the existing OncoVI evidence; their values cannot be pooled. In Chen et al. 2020 (peer reviewed), 33 predictors scored somatic missense mutations labelled by OncoKB curation or by the authors' own cell viability assays, with likely neutral or assay-neutral somatic negatives. The authors co-developed one scored tool, CanDrA, and developed none of the others. With each tool's own default calls, the highest accuracy was 0.70 on OncoKB labels (DEOGEN2, sensitivity 0.87, specificity 0.51), and several tools called most neutral mutations damaging, for example M-CAP with sensitivity 0.97 and specificity 0.11, and CanDrA with specificity 0.17 and the lowest accuracy, 0.49. On the assay labels the highest default accuracy was 0.65 (PrimateAI). When each tool's median score on the test draw was used as the threshold, which ranks tools but is not an operating point a reviewer could apply, the highest accuracy was 0.69 on OncoKB labels (PROVEAN and VEST4) and 0.70 on assay labels (CTAT-cancer and PrimateAI), and FATHMM-disease scored 0.50 on OncoKB labels. Chen et al. print each value as a mean with a range of two standard deviations over 100 draws, not a confidence interval, and do not say which OncoKB positive set fed these tables. In a single-author bioRxiv preprint that is not peer reviewed (Lee 2026), 49 scores were compared on Cancer Gene Census missense variants whose negatives are germline-benign labels (ClinVar benign or likely benign, and gnomAD common variants), which this use case does not accept as somatic controls. In that table 42 of the 49 scores had a lower AUROC on oncogene variants than on tumour-suppressor variants, for example AlphaMissense 0.885 against 0.900 and CADD 0.821 against 0.894, while popEVE went the other way (0.900 against 0.842). The highest all-gene AUROC was 0.876 (MutScore); these AUROC values are rounded here to three decimals from the table's full precision. None of these comparisons covers small indels, calibrated probabilities, criterion-level evidence strength, drug response or reviewer agreement. (somatic-oncogenicity-20261009-source-chen2020-additional-file-9 and -10: Benchmark 2; somatic-oncogenicity-20261009-source-chen2020-additional-file-21 and -22: Benchmark 5; somatic-oncogenicity-20261009-source-chen2020: Methods and reference 7; somatic-oncogenicity-20261009-source-lee2026-oncocal-tool-performance: tables/tool_performance.tsv; somatic-oncogenicity-20261009-source-lee2026: Methods and Results)
• PROVEAN rank score, dbNSFP 5.3.1a (Lee 2026): facets.method_types: Facets: method types ["conventional_pipeline"] (Chen et al. 2020 Table 1: PROVEAN uses a delta alignment score)
• PROVEAN: facets.method_types: Facets: method types ["conventional_pipeline"] (Chen et al. 2020 Table 1: PROVEAN uses a delta alignment score)
• Relevance of CGC missense oncogenicity, all genes (Lee 2026) to "Assess somatic small-variant oncogenicity": Assessed by CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) (somatic-oncogenicity-20261009-source-lee2026-oncocal-tool-performance: group 'all'; somatic-oncogenicity-20261009-source-lee2026: Results first section; Methods 'Labels' and 'Features')
• Relevance of CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026) to "Assess somatic small-variant oncogenicity": Assessed by CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv) (somatic-oncogenicity-20261009-source-lee2026-oncocal-tool-performance: group 'oncogene'; somatic-oncogenicity-20261009-source-lee2026: Results first section; Methods 'Labels' and 'Features')
• Relevance of CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026) to "Assess somatic small-variant oncogenicity": Assessed by CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv) (somatic-oncogenicity-20261009-source-lee2026-oncocal-tool-performance: group 'TSG'; somatic-oncogenicity-20261009-source-lee2026: Results first section; Methods 'Labels' and 'Features')
• SIFT rank score, dbNSFP 5.3.1a (Lee 2026): facets.method_types: Facets: method types ["conventional_pipeline"] (Chen et al. 2020 Table 1: SIFT uses a position-specific scoring matrix)
• SIFT: facets.method_types: Facets: method types ["conventional_pipeline"] (Chen et al. 2020 Table 1: SIFT uses a position-specific scoring matrix)
• SIFT4G rank score, dbNSFP 5.3.1a (Lee 2026): facets.method_types: Facets: method types ["conventional_pipeline"] (Chen et al. 2020 Table 1: SIFT uses a position-specific scoring matrix)

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Evidence

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