SynthesisPLoS computational biology2015
Prioritizing therapeutics for lung cancer: an integrative meta-analysis of cancer gene signatures and chemogenomic data.
Synthesis in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 49 citations in OpenAlex.
- Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics.Stem cell reports · 2026Article
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Anticancer effect of the antipsychotic agent penfluridol on epithelial ovarian cancer.Journal of gynecologic oncology · 2025Article
- Next generation thiazolyl ketone inhibitors of cytosolic phospholipase ANature communications · 2025Article
- The antipsychotic drug pimozide promotes apoptosis through the RAF/ERK pathway and enhances autophagy in breast cancer cells.Cancer biology & therapy · 2024Article
- Harnessing Drug Repurposing to Combat Breast Cancer by Targeting Altered Metabolism and Epithelial-to-Mesenchymal Transition Pathways.ACS pharmacology & translational science · 2024Review
- CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases.Briefings in bioinformatics · 2024Article
- An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis.Journal of personalized medicine · 2023Review
- Resveratrol inhibits development of colorectal adenoma via suppression of LEF1; comprehensive analysis with connectivity map.Cancer science · 2022Article
- Ligand-based virtual screening, molecular docking, and molecular dynamics of eugenol analogs as potential acetylcholinesterase inhibitors with biological activity against Spodoptera frugiperda.Molecular diversity · 2022Article
- CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual's Disease-Related Signature.Frontiers in pharmacology · 2022Article
- Specific gene module pair-based target identification and drug discovery.Frontiers in pharmacology · 2022Article
- Integrative chemogenomic analysis identifies small molecules that partially rescue ΔF508-CFTR for cystic fibrosis.CPT: pharmacometrics & systems pharmacology · 2021Article
- Repurposing antipsychotics of the diphenylbutylpiperidine class for cancer therapy.Seminars in cancer biology · 2021Review
- Estrogen Receptor 1 Inhibition of Wnt/β-Catenin Signaling Contributes to Sex Differences in Hepatocarcinogenesis.Frontiers in oncology · 2021Article
- Current progress and future perspectives of polypharmacology : From the view of non-small cell lung cancer.Seminars in cancer biology · 2021Review
- Article
- A novel drug repurposing approach for non-small cell lung cancer using deep learning.PloS one · 2020Article
- Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap).International journal of molecular sciences · 2019Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.
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Registered trials
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 OpenQuestion graph.