Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma
Summary: The aggressive nature of glioblastoma (GBM) – one of the deadliest forms of brain tumors – is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecul...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S258900422400405X |
| _version_ | 1797295722206330880 |
|---|---|
| author | Harshavardhan BV Mohit Kumar Jolly |
| author_facet | Harshavardhan BV Mohit Kumar Jolly |
| author_sort | Harshavardhan BV |
| collection | DOAJ |
| description | Summary: The aggressive nature of glioblastoma (GBM) – one of the deadliest forms of brain tumors – is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts. |
| first_indexed | 2024-03-07T21:53:02Z |
| format | Article |
| id | doaj.art-2510ffd140774162b08ae59d8e6231c6 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-07T21:53:02Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-2510ffd140774162b08ae59d8e6231c62024-02-25T04:36:17ZengElsevieriScience2589-00422024-03-01273109184Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastomaHarshavardhan BV0Mohit Kumar Jolly1IISc Mathematics Initiative, Indian Institute of Science, Bengaluru, Karnataka 560012, IndiaDepartment of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India; Corresponding authorSummary: The aggressive nature of glioblastoma (GBM) – one of the deadliest forms of brain tumors – is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts.http://www.sciencedirect.com/science/article/pii/S258900422400405XMathematical biosciencesExpression studyCancerTranscriptomics |
| spellingShingle | Harshavardhan BV Mohit Kumar Jolly Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma iScience Mathematical biosciences Expression study Cancer Transcriptomics |
| title | Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma |
| title_full | Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma |
| title_fullStr | Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma |
| title_full_unstemmed | Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma |
| title_short | Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma |
| title_sort | proneural mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma |
| topic | Mathematical biosciences Expression study Cancer Transcriptomics |
| url | http://www.sciencedirect.com/science/article/pii/S258900422400405X |
| work_keys_str_mv | AT harshavardhanbv proneuralmesenchymalantagonismdominatesthepatternsofphenotypicheterogeneityinglioblastoma AT mohitkumarjolly proneuralmesenchymalantagonismdominatesthepatternsofphenotypicheterogeneityinglioblastoma |