ArticleOncotarget2017
Personalized anticancer therapy selection using molecular landscape topology and thermodynamics.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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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
10 citing papers in PubMed.
- Gene Signatures and Oncology Treatment Implications.Hematology/oncology clinics of North America · 2025Review
- Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions.International journal of molecular sciences · 2023Article
- The topology of data: opportunities for cancer research.Bioinformatics (Oxford, England) · 2021Article
- Network potential identifies therapeutic miRNA cocktails in Ewing sarcoma.PLoS computational biology · 2021Article
- Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age.International journal of molecular sciences · 2020Article
- Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks.PloS one · 2020Article
- Gibbs free energy of protein-protein interactions correlates with ATP production in cancer cells.Journal of biological physics · 2019Article
- Comprehensive Molecular Profiling for Relapsed/Refractory Pediatric Burkitt Lymphomas-Retrospective Analysis of Three Real-Life Clinical Cases-Addressing Issues on Randomization and Customization at the Bedside.Frontiers in oncology · 2019Article
- scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2018Article
- Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem.Oncotarget · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalized anticancer therapy requires continuous consolidation of emerging bioinformatics data into meaningful and accurate information streams. The use of novel mathematical and physical approaches, namely topology and thermodynamics can enable merging differing data types for improved accuracy in selecting therapeutic targets. We describe a method that uses chemical thermodynamics and two topology measures to link RNA-seq data from individual patients with academically curated protein-protein interaction networks to select clinically relevant targets for treatment of low-grade glioma (LGG). We show that while these three histologically distinct tumor types (astrocytoma, oligoastrocytoma, and oligodendroglioma) may share potential therapeutic targets, the majority of patients would benefit from more individualized therapies. The method involves computing Gibbs free energy of the protein-protein interaction network and applying a topological filtration on the energy landscape to produce a subnetwork known as persistent homology. We then determine the most likely best target for therapeutic intervention using a topological measure of the network known as Betti number. We describe the algorithm and discuss its application to several patients.
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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.