ReviewFrontiers in oncology2015
Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence.
Review in Frontiers in oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
58 citing papers in PubMed, 97 citations in OpenAlex.
- Molecular profiling of glioblastoma-derived extracellular vesicles identifies small nucleolar RNAs as candidate liquid biomarkers for radiation- induced senescence.bioRxiv : the preprint server for biology · 2026Article
- Tumors and their microenvironments: Learning from pediatric brain pathologies.Biochimica et biophysica acta. Reviews on cancer · 2025Review
- The epigenetic mechanisms involved in the treatment resistance of glioblastoma.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells.Science advances · 2024Article
- Article
- RNA cytosine methyltransferase NSUN5 promotes protein synthesis and tumorigenic phenotypes in glioblastoma.Molecular oncology · 2023Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- Validation of a Temperature-Feedback Controlled Automated Magnetic Hyperthermia Therapy Device.Cancers · 2023Article
- G protein inhibitory α subunit 2 is a molecular oncotarget of human glioma.International journal of biological sciences · 2023Article
- Characterization and comparison of human glioblastoma models.BMC cancer · 2022Article
- TIMM44 is a potential therapeutic target of human glioma.Theranostics · 2022Article
- C3G downregulation induces the acquisition of a mesenchymal phenotype that enhances aggressiveness of glioblastoma cells.Cell death & disease · 2021Article
- Putting Proteomics Into Immunotherapy for Glioblastoma.Frontiers in immunology · 2021Review
- The role of extracellular vesicles in acquisition of resistance to therapy in glioblastomas.Cancer drug resistance (Alhambra, Calif.) · 2021Review
- TNFSF13 Is a Novel Onco-Inflammatory Marker and Correlates With Immune Infiltration in Gliomas.Frontiers in immunology · 2021Article
- Neuronal-driven glioma growth requires Gαi1 and Gαi3.Theranostics · 2021Article
- Article
- The impact of initial tumor microenvironment on imaging phenotype.Cancer treatment and research communications · 2021Article
- PDIA3 correlates with clinical malignant features and immune signature in human gliomas.Aging · 2020Article
- The application of histone deacetylases inhibitors in glioblastoma.Journal of experimental & clinical cancer research : CR · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.
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What OpenQuestion holds
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.