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MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA

Patients with glioblastoma experience a wide variation in response to standard treatment, with nearly 30% experiencing tumour progression during treatment, and nearly 7% surviving more than 5 years. CEST-MRI is sensitive to treatment-induced changes in tumour metabolism and can be used to evaluate t...

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Detalles Bibliográficos
Autores principales: Nikolopoulos, Marina, Wu, Megan, Bahcheli, Alexander, Kellett, Sorcha, Erickson, Anders, Myrehaug, Sten, Sahgal, Arjun, Spears, Melanie, Bayani, Jane, Das, Sunit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337568/
http://dx.doi.org/10.1093/noajnl/vdad071.040
Descripción
Sumario:Patients with glioblastoma experience a wide variation in response to standard treatment, with nearly 30% experiencing tumour progression during treatment, and nearly 7% surviving more than 5 years. CEST-MRI is sensitive to treatment-induced changes in tumour metabolism and can be used to evaluate treatment response, allowing for the differentiation between early and late progressors before treatment initiation. OBJECTIVE: The purpose of this study is to establish genomic and transcriptomic profiles of early and late progressors in patients with glioblastoma who have undergone CEST-MRI. METHODS: Patients (n=25) were imaged with CEST-MRI at multiple time points throughout standard chemoradiation treatment. DNA and RNA were co-extracted from 25 fresh-frozen matched normal and tumour pairs and processed for whole genome sequencing and gene expression analysis using Nanostring. RESULTS: Early progressors (n = 12) and late progressors (n =13) had significant differences in OS and PFS (log rank <0.0001) as well as in gene expression and pathway deregulation. Genes significantly expressed in early progressors compared to late progressors include CCNA1 (p =0.000557), IGFBP3 (p = 0.000602), ASS1 (p = 0.000698), and ID1 (p= 0.000993), which were also prognostic of PFS and OS in a Cox regression model. CONCLUSION: Upon validation in a larger cohort, this data may serve as a radiogenomic biomarker to assess treatment response within early phases of treatment and adapt the treatment plan to individual biology.