Network-based inference of protein activity helps functionalize the genetic landscape of cancer

By Mariano J. Alvarez, Yao Shen, Federico Manuel Giorgi, Alexander Lachmann, B. B. Ding, B. Ye, Andrea Califano. Nature Genetics, 2016

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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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0cb82aeef6706924b9acd30be77549ff280458b0

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Citations in the corpus

Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. (2019, methodology): “To confirm these pathways are active in our two CAF subpopulations, and to identify novel regulatory proteins that are differentially activated between human iCAFs and myCAFs, we applied the virtual inference of protein activity by enriched regulon analysis (VIPER) algorithm (50).”
Gene regulatory network inference in the era of single-cell multi-omics (2023, methodology): “In bulk studies, inference of TF activities through enrichment methods has enabled, for example, the identification of druggable oncoproteins 94 , stratification of cell lines in response to drug treatment 97 and identification of a master regulator as a metastasis promoter in breast carcinoma 98 .”
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data (2021, positive): “Transcription factor activity was inferred using the master regulator inference algorithm (MARINa) 59 in the viper R package (version 1.20.0).”
Benchmark and integration of resources for the estimation of human transcription factor activities (2018, methodology): “and The Cancer Genome Atlas (TCGA) cancer types inferred from expressiondata (Alvarez et al. 2016)usingARACNE (Fig.”
Precision Oncology: The Road Ahead (2017, background): “Third, beyond providing information about the expression of tumor driver genomic variations, the inclusion of transcriptomics allows the mapping of non-oncogene vulnerabilities, and provides information about oncogenic pathway activities, even in the absence of mutated driver genes [63, 70].”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “html) (Alvarez et al., 2016); and hMARINa was performed by extending the functionality of the package.”