Advances and challenges in personalized diagnosis and therapies for the management of recurrent glioblastoma

Abstract

Recurrent glioblastoma (rGBM) remains one of the most formidable challenges in neuro-oncology due to its aggressive evolution, treatment resistance, and profound intratumoral heterogeneity. Despite advances in multimodal first-line therapy, recurrence is nearly universal and represents a genetically divergent, therapy-adapted malignancy. This review dissects the evolutionary dynamics of rGBM, including clonal selection, therapy-induced mutagenesis, and proneural-to-mesenchymal shifts. It explores the translational potential of longitudinal sampling, circulating tumor DNA, and multi-omics profiling to dynamically monitor tumor progression and resistance emergence. Personalized therapeutic strategies are critically evaluated, including targeted inhibition of EGFR, PI3K/AKT/mTOR, and PDGFR pathways, immunotherapeutic approaches such as CAR T-cell therapy and neoantigen vaccines, and functional drug screening using patient-derived organoids. Moreover, the manuscript highlights innovations in AI-assisted therapy mapping, precision-guided re-irradiation, and adaptive trial designs that redefine individualized care in rGBM. Persistent challenges such as blood-brain barrier penetration, immune evasion, and lack of real-world clinical integration are also addressed. The convergence of high-throughput molecular diagnostics, AI analytics, and targeted therapies underscores a shift from static to dynamic, biomarker-guided interventions. Realizing the full promise of personalized medicine in rGBM demands systemic reforms, multi-disciplinary integration, and equitable clinical adoption.

Keywords

Clonal evolution / Multi-omics profiling / Liquid biopsy / Neoantigen vaccine / Radiogenomics / Precision radiotherapy / Adaptive clinical trials

Declarations

Not applicable.

Authors' contributions

Sagar Trivedi: Writing - Review & Editing; Manisha Kawadkar: Literature Review, Data Curation, Formal Analysis; Diksha Pawar and Ujban Hussain: Review & Editing, Critical Revisions; Rishabh Agade: Resources validation.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Sagar Trivedi reports was provided by GH Raisoni University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Not applicable.

Authors' other information

Not applicable.

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