ArticleGenome biology2017
Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic markers in methotrexate treatments.The pharmacogenomics journal · 2018Pooled it
- Multi-responsive tetrahedral DNA frameworks forChemical science · 2026Article
- Automating candidate gene prioritization with large language models: from naive scoring to literature-grounded validation.Bioinformatics (Oxford, England) · 2025Article
- Metabolism-associated protein network constructing and host-directed anti-influenza drug repurposing.Briefings in bioinformatics · 2025Article
- Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices.Methods in molecular biology (Clifton, N.J.) · 2025Article
- TheMarker: a comprehensive database of therapeutic biomarkers.Nucleic acids research · 2024Article
- The ELK3-DRP1 axis determines the chemosensitivity of triple-negative breast cancer cells to CDDP by regulating mitochondrial dynamics.Cell death discovery · 2023Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?Bioinformatics (Oxford, England) · 2023Article
- RNA-seq of nine canine prostate cancer cell lines reveals diverse therapeutic target signatures.Cancer cell international · 2022Article
- Article
- A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19.PLoS computational biology · 2021Article
- Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study.NPJ systems biology and applications · 2021Article
- ncDRMarker: a computational method for identifying non-coding RNA signatures of drug resistance based on heterogeneous network.Annals of translational medicine · 2020Article
- Identifying Drug Sensitivity Subnetworks with NETPHIX.iScience · 2020Article
- A recursive framework for predicting the time-course of drug sensitivity.Scientific reports · 2020Article
- Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature.Breast cancer research : BCR · 2020Article
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- Tissue-guided LASSO for prediction of clinical drug response using preclinical samples.PLoS computational biology · 2020Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
Corrections and comments
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5 authors.
Funding
Abstract
backgroundIdentification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance.
resultsWe developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group.
conclusionsOur results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.
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