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Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival
BACKGROUND: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. G...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462280/ https://www.ncbi.nlm.nih.gov/pubmed/32904984 http://dx.doi.org/10.1093/noajnl/vdaa093 |
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author | Kersch, Cymon N Claunch, Cheryl J Ambady, Prakash Bucher, Elmar Schwartz, Daniel L Barajas, Ramon F Iliff, Jeffrey J Risom, Tyler Heiser, Laura Muldoon, Leslie L Korkola, James E Gray, Joe W Neuwelt, Edward A |
author_facet | Kersch, Cymon N Claunch, Cheryl J Ambady, Prakash Bucher, Elmar Schwartz, Daniel L Barajas, Ramon F Iliff, Jeffrey J Risom, Tyler Heiser, Laura Muldoon, Leslie L Korkola, James E Gray, Joe W Neuwelt, Edward A |
author_sort | Kersch, Cymon N |
collection | PubMed |
description | BACKGROUND: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. METHODS: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. RESULTS: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. CONCLUSIONS: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma. |
format | Online Article Text |
id | pubmed-7462280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-74622802020-09-03 Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival Kersch, Cymon N Claunch, Cheryl J Ambady, Prakash Bucher, Elmar Schwartz, Daniel L Barajas, Ramon F Iliff, Jeffrey J Risom, Tyler Heiser, Laura Muldoon, Leslie L Korkola, James E Gray, Joe W Neuwelt, Edward A Neurooncol Adv Basic and Translational Investigations BACKGROUND: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. METHODS: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. RESULTS: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. CONCLUSIONS: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma. Oxford University Press 2020-08-03 /pmc/articles/PMC7462280/ /pubmed/32904984 http://dx.doi.org/10.1093/noajnl/vdaa093 Text en © The Author(s) 2020. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Basic and Translational Investigations Kersch, Cymon N Claunch, Cheryl J Ambady, Prakash Bucher, Elmar Schwartz, Daniel L Barajas, Ramon F Iliff, Jeffrey J Risom, Tyler Heiser, Laura Muldoon, Leslie L Korkola, James E Gray, Joe W Neuwelt, Edward A Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
title | Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
title_full | Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
title_fullStr | Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
title_full_unstemmed | Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
title_short | Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
title_sort | transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival |
topic | Basic and Translational Investigations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462280/ https://www.ncbi.nlm.nih.gov/pubmed/32904984 http://dx.doi.org/10.1093/noajnl/vdaa093 |
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