Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations Journal Article

Authors: Martelotto, L. G.; Ng, C. K.; De Filippo, M. R.; Zhang, Y.; Piscuoglio, S.; Lim, R. S.; Shen, R.; Norton, L.; Reis-Filho, J. S.; Weigelt, B.
Article Title: Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations
Abstract: BACKGROUND: Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations. RESULTS: Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values. CONCLUSIONS: The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.
Keywords: genetics; missense mutation; mutation, missense; comparative study; neoplasm; neoplasms; algorithms; algorithm; dna mutational analysis; procedures; humans; human
Journal Title: Genome Biology
Volume: 15
Issue: 10
ISSN: 1465-6906
Publisher: Biomed Central Ltd  
Date Published: 2014-10-28
Start Page: 484
Language: English
DOI: 10.1186/s13059-014-0484-1
PUBMED: 25348012
PROVIDER: scopus
PMCID: PMC4232638
Notes: Article -- Export Date: 2 November 2016 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Larry Norton
    581 Norton
  2. Ronglai Shen
    129 Shen
  3. Britta Weigelt
    261 Weigelt
  4. Jorge Sergio Reis
    285 Reis
  5. Kiu Yan Charlotte Ng
    152 Ng
  6. Raymond Sear Lim
    54 Lim