Functional characterization of somatic mutations in cancer using network-based inference of protein activity
Open accessAbstract
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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Network connections can nominate a set of genes whose input data should be aggregated to create a composite feature, such as pooling rarely altered genes in cancer into a single pathway that is recurrently altered across a patient population.
“Network connections can also nominate a set of genes whose input data should be aggregated to create a composite feature, such as pooling of rarely altered genes in cancer into a single pathway, which, viewed as a single aggregate, is recurrently altered across a patient population (Alvarez et al., 2016; Chuang et al., 2007).”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Alvarez et al., 2016
Machine-learning systems face input heterogeneity as a challenge that is particularly acute in biomedical applications.
“Machine-learning systems face two recognized challenges that become particularly acute in biomedical applications. The first is input heterogeneity.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Alvarez et al., 2016
Cancer can arise from many different combinations of genetic alterations involving many potential genes, any one of which may be mutated only rarely, so each new patient presents a distinct constellation of molecular changes never before seen in nature.
“For example, cancer can arise as the result of many different combinations of genetic alterations involving many potential genes, any one of which may be mutated only rarely; as a consequence, each new patient presents a distinct constellation of molecular changes never before seen in nature (Alvarez et al., 2016; Kourou et al., 2014).”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Alvarez et al., 2016
Alvarez et al. (2016) used a transcriptional regulatory network to translate patient mRNA expression profiles to activities of regulatory proteins, most of which are difficult to interrogate experimentally.
“For example, Alvarez et al. (2016) used a transcriptional regulatory network to translate patient mRNA expression profiles to activities of regulatory proteins, most of which are difficult to interrogate experimentally.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Alvarez et al., 2016
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- Authors
- Alvarez, M. J., Shen, Y., Giorgi, F. M., Lachmann, A., Ding, B. B., Ye, B. H., & Califano, A.
- Affiliations
- DarwinHealth (United States) (US)
- Columbia University (US)
- Albert Einstein College of Medicine (US)
- Corresponding author
- Alvarez, Mariano J
- Califano, Andrea
- ORCID
- Venue
- Nature Genetics
- Published
- 2016-06-20
- Volume
- 48
- Issue
- 8
- Pages
- 838-847
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC5040167
- OpenAlex
- W2472255748
- Cited by
- 966
- References
- 62
- Citations by year
- 2026: 101 · 2025: 127 · 2024: 131 · 2023: 139 · 2022: 125 · 2021: 90 · 2020: 106 · 2019: 66 · 2018: 38 · 2017: 32 · 2016: 8 · 2013: 1
- FWCI
- 24.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Funders
- Lauri Strauss Leukemia Foundation
- National Institutes of Health (1s10od021764-01, 1s10od012351-01)
- National Cancer Institute (S10OD021764, U54CA121852, S10 OD012351, U01CA164184, R01CA085573, U01CA168426, P30CA013330, R35CA197745)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- vipers, somatic mutations, cancer, gene regulatory networks, oncoprotein, drug response prediction, targeted inhibitors, gene expression dysregulation, precision oncology, TCGA
- MeSH terms
- Algorithms, Antineoplastic Agents, Humans, Mutation, Neoplasm Proteins, Neoplasms, Gene Expression Regulation, Neoplastic, Protein Structure, Tertiary, Computational Biology, Databases, Protein, Gene Regulatory Networks, Protein Interaction Maps, Databases, Pharmaceutical
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