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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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. |
format | Online Article Text |
id | pubmed-10337568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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