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Predicting protein networks in cancer

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Characterization of the mutational landscape of tumors is important to understanding disease etiology but does not provide mechanistic insight into the functional role of specific mutations. A new study introduces a statistical mechanical framework that draws on biophysical data from SH2 domain–phosphoprotein interactions to predict the functional effects of mutations in cancer.

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Figure 1: The multiscale statistical mechanical (MSM) framework.

Debbie Maizels/Nature Publishing Group

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Authors and Affiliations

  1. Andrea Califano is in the Department of Systems Biology, the Department of Biochemistry and Molecular Biophysics, and the Department of Biomedical Informatics at Columbia University, New York, New York, USA.,

    Andrea Califano

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  1. Andrea Califano
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Correspondence to Andrea Califano.

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The author declares no competing financial interests.

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Califano, A. Predicting protein networks in cancer. Nat Genet 46, 1252–1253 (2014). https://doi.org/10.1038/ng.3156

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