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Evaluating the Evaluation of Cancer Driver Genes

Collin J. Tokheim, Nickolas Papadopoulis, Kenneth W. Kinzler, Bert Vogelstein, Rachel Karchin
doi: https://doi.org/10.1101/060426
Collin J. Tokheim
1Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
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Nickolas Papadopoulis
2Ludwig Center and the Howard Hughes Medical Institute, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
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Kenneth W. Kinzler
2Ludwig Center and the Howard Hughes Medical Institute, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
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Bert Vogelstein
2Ludwig Center and the Howard Hughes Medical Institute, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
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Rachel Karchin
1Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
3Department of Oncology, Cancer Biology Program, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
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  • For correspondence: karchin@jhu.edu
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Abstract

Sequencing has identified millions of somatic mutations in human cancers, but distinguishing cancer driver genes remains a major challenge. Numerous methods have been developed to identify driver genes, but evaluation of the performance of these methods is hindered by the lack of a gold standard, i.e., bona fide driver gene mutations. Here, we establish an evaluation framework that can be applied when a gold standard is not available. We used this framework to compare the performance of eight driver gene prediction methods. One of these methods, newly described here, incorporated a machine learning-based ratiometric approach. We show that the driver genes predicted by each of these eight methods vary widely. Moreover, the p-values reported by several of the methods were inconsistent with the uniform values expected, thus calling into question the assumptions that were used to generate them. Finally, we evaluated the potential effects of unexplained variability in mutation rates on false positive driver gene predictions. Our analysis points to the strengths and weaknesses of each of the currently available methods and offers guidance for improving them in the future.

Significance Modern large-scale sequencing of human cancers seeks to comprehensively discover mutated genes that confer a selective advantage to cancer cells. Key to this effort has been development of computational algorithms to find genes that drive cancer, based on their patterns of mutation in large patient cohorts. However, since there is no generally accepted gold standard of driver genes, it has been difficult to quantitatively compare these methods. We present a new machine learning method for driver gene prediction and a rigorous protocol to evaluate and compare prediction methods. Our results suggest that most current methods do not adequately account for heterogeneity in the number of mutations expected by chance and consequently have many false positive calls. The problem is most acute for cancers with high mutation rates and comprehensive discovery of drivers in these cancers may be more difficult than currently anticipated.

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Posted June 23, 2016.
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Evaluating the Evaluation of Cancer Driver Genes
Collin J. Tokheim, Nickolas Papadopoulis, Kenneth W. Kinzler, Bert Vogelstein, Rachel Karchin
bioRxiv 060426; doi: https://doi.org/10.1101/060426
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Evaluating the Evaluation of Cancer Driver Genes
Collin J. Tokheim, Nickolas Papadopoulis, Kenneth W. Kinzler, Bert Vogelstein, Rachel Karchin
bioRxiv 060426; doi: https://doi.org/10.1101/060426

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