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Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications

Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous “omics” data to accelerate biomarker discoveries.

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    Title
    • Evolution-Informed Modeling Improves Outcome Prediction for Cancers
    Contributors
    Date Created
    2016-10-21
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1111/eva.12417
    • Identifier Type
      International standard serial number
      Identifier Value
      1752-4563
    • Identifier Type
      International standard serial number
      Identifier Value
      1752-4571
    Note
    • The final version of this article, as published in Evolutionary Applications, can be viewed online at: http://onlinelibrary.wiley.com/doi/10.1111/eva.12417/full

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    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Liu, L., Chang, Y., Yang, T., Noren, D. P., Long, B., Kornblau, S., . . . Ye, J. (2016). Evolution-informed modeling improves outcome prediction for cancers. Evolutionary Applications, 10(1), 68-76. doi:10.1111/eva.12417

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