ArticleOncotarget2017
Age-associated and therapy-induced alterations in the cellular microenvironment of experimental gliomas.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Integrative analysis of single-cell and bulk transcriptome data reveals age-related immune cell alterations in primary glioblastoma associated with prognosis.Cancer immunology, immunotherapy : CII · 2025Article
- Immunological and tumor-intrinsic mechanisms mediate the synergistic growth suppression of experimental glioblastoma by radiotherapy and MET inhibition.Acta neuropathologica communications · 2023Article
- Article
- Immunocompetent Mouse Models in the Search for Effective Immunotherapy in Glioblastoma.Cancers · 2020Review
- CHD7 promotes glioblastoma cell motility and invasiveness through transcriptional modulation of an invasion signature.Scientific reports · 2019Article
- Interferon-β exposure induces a fragile glioblastoma stem cell phenotype with a transcriptional profile of reduced migratory and MAPK pathway activity.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The poor prognosis associated with advanced age in patients with glioblastoma remains poorly understood. Glioblastoma in the elderly has been particularly associated with vascular endothelial growth factor (VEGF)-dependent angiogenesis, and early uncontrolled studies suggested that the anti-angiogenic agent bevacizumab (BEV), an antibody to VEGF, might be preferentially active in this patient population. Accordingly, we explored host age-dependent differences in survival and benefit from radiotherapy (RT) or BEV in syngeneic mouse glioma models. Survival was inferior in older mice in the SMA-540 and and less so in SMA-560, but not in the SMA-497 or GL-261 models. Detailed flow cytometric studies revealed increased myeloid and decreased effector T cell population frequencies in SMA-540 tumors of old compared to young mice, but no such difference in the SMA-497 model. Bone marrow transplantation (BMT) from young to old mice had no effect, whereas survival was reduced with BMT from old to young mice. BEV significantly decreased vessel densities in gliomas of old, but not young mice. Accordingly, old, but not young SMA-540 tumor-bearing mice benefited from BEV alone or in combination with RT. End-stage tumors of old BEV- and BEV/RT-treated mice exhibited increased infiltration of T helper and cytotoxic T cells compared to tumors of young mice. The SMA-540 model may provide a valuable tool to evaluate the influence of host age on glioblastoma progression and treatment response. The biological host factors that modulate glioma growth in old as opposed to young mice remain to be identified.
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Identifiers
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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.