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Novel Insights into Glioblastoma and Brain Metastases (2nd Edition)
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Novel Insights into Glioblastoma and Brain Metastases (2nd Edition)

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Metastasis".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3517

Special Issue Editor

Prof. Dr. Sanjay K. Srivastava

E-Mail Website
Guest Editor
Department of Immunotherapeutics and Biotechnology, Texas Tech University Health Sciences Center, 1718 Pine Street, Abilene, TX 79601, USA
Interests: development of phytochemicals for cancer prevention and therapeutics; targeting STAT-3, NF-kB, HER2, MCL-1, AKT/FOXO, GLI1/2, and related signaling pathways with agents such as capsaicin, piperlongumine, penfluridol, isothiocyanates, diindolylmethane, panabinostat, cucurbitacin B, and deguelin in pancreatic, ovarian, breast, melanoma, and brain cancer; drug repurposing
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Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of the Special Issue “Novel Insights into Glioblastoma and Brain Metastases”, available at https://www.mdpi.com/journal/cancers/special_issues/UOW92IR7VI.

Glioblastoma is an aggressive grade IV brain tumor leading to severe fatalities globally. Moreover, several cancers lead to brain metastasis which is a major cause of mortality. Tackling these conditions has several challenges, with the most important being the blood–brain barrier (BBB) permeability. Significant research is required to overcome these challenges. With surgery and radiation therapy being the major treatment strategy for brain tumors, intensive research has also been conducted on chemotherapies. Targeted therapies are currently being developed with beneficial effects in cancers such as colorectal, breast, lung, and melanoma. Radiation therapy has also proven to be quite successful against brain metastasis. Thus, in this Special Issue, we invite you to contribute your work concerning glioblastoma, novel insights into its treatment and management, and different cancers leading to brain metastasis. This Special Issue aims to highlight all types of work, such as clinical, pre-clinical, translational, and basic research. We invite both original research articles and review articles in this Special Issue. This critical topic would appeal to a number of scientists and clinicians in the field. The articles may include (but are not limited to) treatment, management, diagnosis, clinical trials, and molecular insights for glioblastoma and brain metastasis caused by different cancers such as breast, melanoma, renal, lung, and colorectal, etc.

Prof. Dr. Sanjay K. Srivastava
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • glioblastoma
  • brain metastasis
  • surgery
  • radiation
  • chemotherapy
  • blood–brain barrier
  • immunotherapy
  • novel targets
  • oncogenes
  • tumor heterogeneity
  • drug discovery

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Published Papers (3 papers)

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49 pages, 2911 KB  
Article
From LQ to AI-BED-Fx: A Unified Multi-Fraction Radiobiological and Machine-Learning Framework for Gamma Knife Radiosurgery Across Intracranial Pathologies
by Răzvan Buga, Călin Gheorghe Buzea, Valentin Nedeff, Florin Nedeff, Diana Mirilă, Maricel Agop, Letiția Doina Duceac and Lucian Eva
Cancers 2026, 18(6), 985; https://doi.org/10.3390/cancers18060985 - 18 Mar 2026
Viewed by 544
Abstract
Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell [...] Read more.
Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy2.47; MEN 41–85 Gy3.5; VS 46–68 Gy3; BM 37–75 Gy10) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R2 = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned. Full article
(This article belongs to the Special Issue Novel Insights into Glioblastoma and Brain Metastases (2nd Edition))
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28 pages, 10458 KB  
Article
Whole-Genome Sequencing Reveals a Novel GATA2 Mutation in Lower-Grade Glioma: Bioinformatics Analysis of Functional and Therapeutic Implications
by Handoko, Vincent Lau, Eka Susanto, Renindra Ananda Aman, Didik Setyo Heriyanto and Soehartati A. Gondhowiardjo
Cancers 2025, 17(20), 3338; https://doi.org/10.3390/cancers17203338 - 16 Oct 2025
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Abstract
Background/Objectives: Lower-grade gliomas, particularly IDH-mutant astrocytomas, represent a distinct molecular subtype with unique therapeutic challenges. Whole-genome sequencing (WGS) plays a crucial role in uncovering genetic alterations that drive glioma pathogenesis and therapeutic resistance. This study identifies and evaluates a novel GATA2 p.Arg396Trp [...] Read more.
Background/Objectives: Lower-grade gliomas, particularly IDH-mutant astrocytomas, represent a distinct molecular subtype with unique therapeutic challenges. Whole-genome sequencing (WGS) plays a crucial role in uncovering genetic alterations that drive glioma pathogenesis and therapeutic resistance. This study identifies and evaluates a novel GATA2 p.Arg396Trp mutation in a clinical sample of lower-grade glioma, assessing its structural impact and implications for drug binding. Methods: A WHO Grade II astrocytoma specimen from a 33-year-old female patient was analyzed using WGS with Oxford Nanopore sequencing, followed by comprehensive bioinformatics processing to identify genomic variants. The GATA2 p.Arg396Trp mutation was evaluated using protein modeling, structural analysis, and pathogenicity prediction tools. Drug affinity analysis was conducted using molecular docking simulations to assess the computational impact of the mutation on drug binding. Results: The GATA2 p.Arg396Trp mutation was identified as a computationally predicted pathogenic variant, potentially disrupting protein interactions within critical functional domains. Structural analysis revealed altered binding dynamics with key anti-neoplastic agents, suggesting potential implications for therapeutic response. These findings represent computational predictions requiring experimental validation. Conclusions: Our preliminary findings suggest a potential role of the GATA2 p.Arg396Trp mutation in lower-grade glioma pathogenesis. The mutation predicted impact on transcriptional regulation and drug affinity suggests GATA2 as a possible biomarker candidate. Extensive experimental validation in larger patient cohorts is needed to establish clinical relevance and explore targeted therapeutic strategies. Full article
(This article belongs to the Special Issue Novel Insights into Glioblastoma and Brain Metastases (2nd Edition))
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19 pages, 808 KB  
Systematic Review
Ex Vivo Organotypic Brain Slice Models for Glioblastoma: A Systematic Review
by Cateno C. T. Petralia, Agata G. D’amico, Velia D’Agata, Giuseppe Broggi and Giuseppe M. V. Barbagallo
Cancers 2026, 18(3), 372; https://doi.org/10.3390/cancers18030372 - 25 Jan 2026
Viewed by 1036
Abstract
Background/Objective: This systematic review aims to evaluate ex vivo brain slice models in glioblastoma (GBM) research, with a specific focus on tumour invasion, tumour–microenvironment interactions, and therapeutic response. Methods: A systematic search looking for studies employing ex vivo organotypic brain slice models in [...] Read more.
Background/Objective: This systematic review aims to evaluate ex vivo brain slice models in glioblastoma (GBM) research, with a specific focus on tumour invasion, tumour–microenvironment interactions, and therapeutic response. Methods: A systematic search looking for studies employing ex vivo organotypic brain slice models in GBM research was conducted across multiple databases (January 2010–July 2025) in accordance with PRISMA guidelines. The study was registered in PROSPERO database (CRD420251138341). Inclusion criteria encompassed patient-derived brain slices, hybrid rodent–human slice co-cultures, and microfluidic-integrated ex vivo platforms designed to assess tumour invasion, microenvironmental interactions and therapeutic responses. Exclusion criteria included reviews, abstracts, conference proceedings, in vivo-only studies, purely in vitro models without organotypic integration, and studies not focused on GBM. Results: Twenty-six studies met the inclusion criteria. Among these, 18/26 (69%) investigated GBM invasion, 18/26 (69%) evaluated therapeutic responses, and 5/26 (19%) examined tumour–microenvironment interactions, with several studies spanning multiple domains. Across platforms, organotypic slices consistently recapitulated key features of GBM biology—including perivascular and white-matter-aligned invasion, stromal–immune interactions, and patient-specific drug sensitivity—while engineered systems enhanced perfusion and exposure control. Methodological variability, particularly regarding slice preparation, oxygenation and viability assessment, limits direct comparability between studies. Conclusions: Organotypic brain slice models represent an extremely relevant tool for translational investigations of GBM biology and treatment response. However, substantial methodological heterogeneity together with limited standardisation hamper reproducibility and cross-study validation. Future work should focus on enhancing reproducibility and harmonising protocols to support the development of clinically meaningful precision oncology strategies. Full article
(This article belongs to the Special Issue Novel Insights into Glioblastoma and Brain Metastases (2nd Edition))
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