ArticleScientific reports2017
Revealing protein networks and gene-drug connectivity in cancer from direct information.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
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- ELIHKSIR Web Server: Evolutionary Links Inferred for Histidine Kinase Sensors Interacting with Response Regulators.Entropy (Basel, Switzerland) · 2021Article
- The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations.Journal of clinical medicine · 2020Review
- Epistatic contributions promote the unification of incompatible models of neutral molecular evolution.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Evidence-Based Network Approach to Recommending Targeted Cancer Therapies.JCO clinical cancer informatics · 2020Article
- Coevolutionary Couplings Unravel PAM-Proximal Constraints of CRISPR-SpCas9.Biophysical journal · 2019Article
- Phylogenetic correlations can suffice to infer protein partners from sequences.PLoS computational biology · 2019Article
- Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset.Nucleic acids research · 2019Article
- Statistical investigations of protein residue direct couplings.PLoS computational biology · 2018Article
- Global pairwise RNA interaction landscapes reveal core features of protein recognition.Nature communications · 2018Article
- Genotypic and Phenotypic Factors Influencing Drug Response in Mexican Patients With Type 2 Diabetes Mellitus.Frontiers in pharmacology · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.