ArticleScientific reports2017
Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Systematic Modeling of Risk-Associated Copy Number Alterations in Cancer.International journal of molecular sciences · 2024Article
- The Impact of Mutational Hotspots on Cancer Survival.Cancers · 2024Article
- A New View of Activating Mutations in Cancer.Cancer research · 2022Article
- Biophysical characterisation of human LincRNA-p21 sense and antisense Alu inverted repeats.Nucleic acids research · 2022Article
- Integrative genomic analysis identifies associations of molecular alterations to APOBEC and BRCA1/2 mutational signatures in breast cancer.Molecular genetics & genomic medicine · 2019Article
- Burden of unique and low prevalence somatic mutations correlates with cancer survival.Scientific reports · 2019Article
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
Previous methods proposed for the detection of cancer driver mutations have been based on the estimation of background mutation rate, impact on protein function, or network influence. In this paper, we instead focus on those factors influencing patient survival. To this end, an approximation of the log-rank test has been systematically applied, even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank, independent of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results, achieved through simulations, comparisons with other methods, analyses of TCGA and ICGC cancer datasets, and validations, suggest that VALORATE is accurate, fast, and can identify both known and novel gene mutations. Our proposal and results may have important implications in cancer biology, bioinformatics analyses, and ultimately precision medicine.
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Registered trials
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