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Functional characterization of somatic mutations in cancer using network-based inference of protein activity

Abstract

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.

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Figure 1: Overview of the VIPER method.
Figure 2: Effect of network and signature quality on VIPER results.
Figure 3: Reproducibility of VIPER results.
Figure 4: Detecting changes in protein activity induced by nonsilent somatic mutations.
Figure 5: Mutant phenotype score and its association with drug sensitivity.
Figure 6: Effect of specific nonsilent somatic mutation variants on VIPER-inferred protein activity.

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Gene Expression Omnibus

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Gene Expression Omnibus

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Acknowledgements

We thank G. Riekhof for critical insight and help with drafting the manuscript. This work was supported by the National Institutes of Health (NIH) Roadmap National Centers for Biomedical Computing (5U54CA121852), the NIH Library of Integrated Network-based Cellular Signatures program (1U01CA164184), the National Cancer Institute (NCI) Cancer Target Discovery and Development program (1U01CA168426), and the NIH instrumentation grants (S10OD012351 and S10OD021764). Additional support was from NIH (R01CA85573) to B.H.Y. and a fellowship grant from the Lauri Strauss Leukemia Foundation to B.B.D. The results published here are in whole or part based upon data generated by The Cancer Genome Atlas pilot project established by the NCI and NHGRI as of January 2011.

Author information

Authors and Affiliations

Authors

Contributions

M.J.A. conceptualized and developed the algorithms, designed the experiments, analyzed the data and wrote the manuscript. Y.S., F.M.G. and A.L. analyzed the data. B.B.D. generated the BCL6 knockdown experiment and expression profile. B.H.Y. designed the experiments used for benchmarking the algorithms with transcription factor knockdown assays. A.C. conceptualized the algorithm, directed the project, designed the experiments and wrote the manuscript.

Corresponding authors

Correspondence to Mariano J Alvarez or Andrea Califano.

Ethics declarations

Competing interests

M.J.A. is chief scientific officer of DarwinHealth Inc. A.C. is a founder of DarwinHealth Inc.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Tables 1–6 and 9, and Supplementary Figures 1–19. (PDF 8818 kb)

Supplementary Table 7

VIPER-inferred protein activity for 2,956 regulatory proteins (rows) after perturbation of MCF7 cells with 156 small-molecule compounds (columns). Protein activity predictions were based on the Connectivity Map dataset9 and a breast carcinoma regulatory network inferred from 1,037 RNA-seq profiles from TCGA. The table lists the VIPER-inferred relative protein activity as normalized enrichment score values. The first two columns indicate the gene identifier and gene symbol corresponding to the evaluated regulatory protein. The first row lists the small molecule compound, molar concentration and time of exposure. (XLSX 9134 kb)

Supplementary Table 8

Number of samples harboring nonsilent somatic mutations in COSMIC genes. (XLSX 67 kb)

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Alvarez, M., Shen, Y., Giorgi, F. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet 48, 838–847 (2016). https://doi.org/10.1038/ng.3593

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