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An argument for mechanism-based statistical inference in cancer

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Abstract

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.

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Acknowledgments

The work of D. Geman and L. Younes was partially supported by the National Science Foundation under NSF DMS1228248. N. Price’s work was supported by a Camille Dreyfus Teacher-Scholar Award and NIH 2P50GM076547.

Author contributions

D.G. supervised the project. All authors wrote the manuscript.

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Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21210, USA

    Donald Geman & Laurent Younes

  2. Mathematics and Statistics, The College of New Jersey, Ewing Township, USA

    Michael Ochs

  3. Institute for Systems Biology, Seattle, USA

    Nathan D. Price

  4. Division of Biostatistics and Bioinformatics, and Department of Biostatistics, Johns Hopkins University, Baltimore, USA

    Cristian Tomasetti

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  1. Donald Geman
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  2. Michael Ochs
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  3. Nathan D. Price
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  4. Cristian Tomasetti
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  5. Laurent Younes
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Correspondence to Donald Geman.

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Geman, D., Ochs, M., Price, N.D. et al. An argument for mechanism-based statistical inference in cancer. Hum Genet 134, 479–495 (2015). https://doi.org/10.1007/s00439-014-1501-x

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