ReviewFrontiers in cell and developmental biology2026
The glioblastoma ecosystem: clonal evolution, heterogeneity, and therapeutic resistance.
Review in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Small Molecule Modulation of NOS1AP for Molecularly Targeted Glioblastoma Therapy.Life (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Therapeutic resistance and recurrence represent major clinical challenges in glioblastoma (GBM), driven by profound tumor heterogeneity and continuous clonal evolution under therapeutic pressure. Conventional diagnostic and therapeutic strategies, which rely on static sampling, struggle to effectively address this dynamic ecosystem. This review synthesizes recent evidence on how single-cell and spatial multi-omics technologies are uncovering the multidimensional complexity of GBM, spanning its diverse cell states, spatial architecture, and clonal dynamics. We dissect the core mechanistic networks driving this evolution, including genomic instability, microenvironmental selection pressures, cellular plasticity, and the integrative role of core signaling pathways. Furthermore, we critically examine the limitations of static diagnostics and propose the pathways through which heterogeneity mediates therapeutic resistance. Given these challenges, future clinical management should ideally transition from a static classification to a dynamic precision paradigm. To this end, we explore the application prospects of dynamic monitoring technologies based on liquid biopsy and radiomics, as well as novel therapeutic strategies aimed at targeting the evolutionary process itself. Ultimately, reconceptualizing GBM as a dynamically evolving ecosystem provides a foundational framework for understanding therapeutic resistance and is pivotal for developing novel strategies that target the evolutionary process itself. However, the clinical translation of this framework faces significant hurdles, including the restrictive blood-brain barrier, technical constraints in longitudinal monitoring, and the complex signaling redundancies that necessitate more adaptive, evolution-informed clinical trial designs. This review suggests a potential path toward a new paradigm of dynamic precision medicine.
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