Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.
Meningiomas, the most common intracranial tumor, though mostly benign can be recurrent and fatal. WHO grading does not always identify high risk meningioma and better characterizations of their aggressive biology is needed. To approach this problem, we combined 13 bulk RNA-Seq datasets to create a dimension-reduced reference landscape of 1298 meningiomas. Clinical and genomic metadata effectively correlated with landscape regions which led to the identification of meningioma subtypes with specific biological signatures. Time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape where nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcome. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 28. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Thirimanne, H. Nayanga [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| doi: |
10.1101/2024.02.23.581766 |
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| PPN (Katalog-ID): |
XBI042661749 |
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| 100 | 1 | |a Thirimanne, H. Nayanga |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome. |
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| 520 | |a Meningiomas, the most common intracranial tumor, though mostly benign can be recurrent and fatal. WHO grading does not always identify high risk meningioma and better characterizations of their aggressive biology is needed. To approach this problem, we combined 13 bulk RNA-Seq datasets to create a dimension-reduced reference landscape of 1298 meningiomas. Clinical and genomic metadata effectively correlated with landscape regions which led to the identification of meningioma subtypes with specific biological signatures. Time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape where nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcome. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
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| 700 | 1 | |a Bonnin, Damian Almiron |4 aut | |
| 700 | 1 | |a Nuechterlein, Nicholas |4 aut | |
| 700 | 1 | |a Arora, Sonali |4 aut | |
| 700 | 1 | |a Jensen, Matt |4 aut | |
| 700 | 1 | |a Parada, Carolina A |4 aut | |
| 700 | 1 | |a Qiu, Chengxiang |4 aut | |
| 700 | 1 | |a Szulzewsky, Frank |4 aut | |
| 700 | 1 | |a English, Collin W |4 aut | |
| 700 | 1 | |a Chen, William C |4 aut | |
| 700 | 1 | |a Sievers, Philipp |4 aut | |
| 700 | 1 | |a Nassiri, Farshad |4 aut | |
| 700 | 1 | |a Wang, Justin Z |4 aut | |
| 700 | 1 | |a Klisch, Tiemo J |4 aut | |
| 700 | 1 | |a Aldape, Kenneth D |4 aut | |
| 700 | 1 | |a Patel, Akash J |4 aut | |
| 700 | 1 | |a Cimino, Patrick J |0 (orcid)0000-0003-0441-4502 |4 aut | |
| 700 | 1 | |a Zadeh, Gelareh |4 aut | |
| 700 | 1 | |a Sahm, Felix |0 (orcid)0000-0001-5441-1962 |4 aut | |
| 700 | 1 | |a Raleigh, David R |4 aut | |
| 700 | 1 | |a Shendure, Jay |4 aut | |
| 700 | 1 | |a Ferreira, Manuel |4 aut | |
| 700 | 1 | |a Holland, Eric C |4 aut | |
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