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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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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
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author Nikolopoulos, Marina
Wu, Megan
Bahcheli, Alexander
Kellett, Sorcha
Erickson, Anders
Myrehaug, Sten
Sahgal, Arjun
Spears, Melanie
Bayani, Jane
Das, Sunit
author_facet Nikolopoulos, Marina
Wu, Megan
Bahcheli, Alexander
Kellett, Sorcha
Erickson, Anders
Myrehaug, Sten
Sahgal, Arjun
Spears, Melanie
Bayani, Jane
Das, Sunit
author_sort Nikolopoulos, Marina
collection PubMed
description 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.
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spelling pubmed-103375682023-07-13 MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA Nikolopoulos, Marina Wu, Megan Bahcheli, Alexander Kellett, Sorcha Erickson, Anders Myrehaug, Sten Sahgal, Arjun Spears, Melanie Bayani, Jane Das, Sunit Neurooncol Adv Posters 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. Oxford University Press 2023-07-12 /pmc/articles/PMC10337568/ http://dx.doi.org/10.1093/noajnl/vdad071.040 Text en © The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Posters
Nikolopoulos, Marina
Wu, Megan
Bahcheli, Alexander
Kellett, Sorcha
Erickson, Anders
Myrehaug, Sten
Sahgal, Arjun
Spears, Melanie
Bayani, Jane
Das, Sunit
MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA
title MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA
title_full MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA
title_fullStr MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA
title_full_unstemmed MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA
title_short MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE IN GLIOBLASTOMA
title_sort molecular profiling to understand treatment resistance and response in glioblastoma
topic Posters
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337568/
http://dx.doi.org/10.1093/noajnl/vdad071.040
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