ArticleScientific reports2018
Characterizing Cancer Drug Response and Biological Correlates: A Geometric Network Approach.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 23 citations in OpenAlex.
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- ORCO: Ollivier-Ricci Curvature-Omics-an unsupervised method for analyzing robustness in biological systems.Bioinformatics (Oxford, England) · 2025Article
- ORCO: Ollivier-Ricci Curvature-Omics - an unsupervised method for analyzing robustness in biological systems.bioRxiv : the preprint server for biology · 2024Article
- Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.International journal of molecular sciences · 2022Article
- Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion.Frontiers in psychiatry · 2022Article
- Molecular genomic features associated with in vitro response of the NCI-60 cancer cell line panel to natural products.Molecular oncology · 2021Article
- p53: 800 million years of evolution and 40 years of discovery.Nature reviews. Cancer · 2020Review
- The many faces of p53: something for everyone.Journal of molecular cell biology · 2019Review
- aWCluster: A Novel Integrative Network-Based Clustering of Multiomics for Subtype Analysis of Cancer Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
In the present work, we apply a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure, via a link between Ricci curvature and network robustness established by the theory of optimal mass transport, the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For genes identified as important, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in biological processes associated with drug response across cell lines and tissue types which are plausible from the point of view of the biological literature. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.
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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.