Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq
Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequenci...
Ausführliche Beschreibung
Autor*in: |
Andrew S Venteicher [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Science - Washington, DC : AAAS, American Assoc. for the Advancement of Science, 1883, 355(2017), 6332 |
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Übergeordnetes Werk: |
volume:355 ; year:2017 ; number:6332 |
Links: |
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DOI / URN: |
10.1126/science.aai8478 |
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Katalog-ID: |
OLC1992452253 |
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520 | |a Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. | ||
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10.1126/science.aai8478 doi PQ20170501 (DE-627)OLC1992452253 (DE-599)GBVOLC1992452253 (PRQ)p1224-3e670bd30816482708a23773963c47f6cf632774c26685bbf2e5f2d95efac77a0 (KEY)0063888920170000355633200000decouplinggeneticslineagesandmicroenvironmentinidh DE-627 ger DE-627 rakwb eng 500 DNB LING fid Andrew S Venteicher verfasserin aut Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. Ribonucleic acids Itay Tirosh oth Christine Hebert oth Keren Yizhak oth Cyril Neftel oth Mariella G Filbin oth Volker Hovestadt oth Leah E Escalante oth McKenzie L Shaw oth Christopher Rodman oth Shawn M Gillespie oth Danielle Dionne oth Christina C Luo oth Hiranmayi Ravichandran oth Ravindra Mylvaganam oth Christopher Mount oth Maristela L Onozato oth Brian V Nahed oth Hiroaki Wakimoto oth William T Curry oth A John Iafrate oth Miguel N Rivera oth Matthew P Frosch oth Todd R Golub oth Priscilla K Brastianos oth Gad Getz oth Anoop P Patel oth Michelle Monje oth Daniel P Cahill oth Orit Rozenblatt-Rosen oth David N Louis oth Bradley E Bernstein oth Aviv Regev oth Mario L Suvà oth Enthalten in Science Washington, DC : AAAS, American Assoc. for the Advancement of Science, 1883 355(2017), 6332 (DE-627)12931482X (DE-600)128410-1 (DE-576)014533189 0036-8075 nnns volume:355 year:2017 number:6332 http://dx.doi.org/10.1126/science.aai8478 Volltext http://search.proquest.com/docview/1883585485 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-SPO SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-FOR GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_47 GBV_ILN_55 GBV_ILN_59 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_92 GBV_ILN_101 GBV_ILN_110 GBV_ILN_120 GBV_ILN_131 GBV_ILN_170 GBV_ILN_171 GBV_ILN_179 GBV_ILN_181 GBV_ILN_211 GBV_ILN_252 GBV_ILN_259 GBV_ILN_290 GBV_ILN_600 GBV_ILN_601 GBV_ILN_647 GBV_ILN_754 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2116 GBV_ILN_2120 GBV_ILN_2121 GBV_ILN_2173 GBV_ILN_2219 GBV_ILN_2221 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4036 GBV_ILN_4125 GBV_ILN_4219 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4310 GBV_ILN_4314 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4328 GBV_ILN_4333 AR 355 2017 6332 |
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10.1126/science.aai8478 doi PQ20170501 (DE-627)OLC1992452253 (DE-599)GBVOLC1992452253 (PRQ)p1224-3e670bd30816482708a23773963c47f6cf632774c26685bbf2e5f2d95efac77a0 (KEY)0063888920170000355633200000decouplinggeneticslineagesandmicroenvironmentinidh DE-627 ger DE-627 rakwb eng 500 DNB LING fid Andrew S Venteicher verfasserin aut Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. Ribonucleic acids Itay Tirosh oth Christine Hebert oth Keren Yizhak oth Cyril Neftel oth Mariella G Filbin oth Volker Hovestadt oth Leah E Escalante oth McKenzie L Shaw oth Christopher Rodman oth Shawn M Gillespie oth Danielle Dionne oth Christina C Luo oth Hiranmayi Ravichandran oth Ravindra Mylvaganam oth Christopher Mount oth Maristela L Onozato oth Brian V Nahed oth Hiroaki Wakimoto oth William T Curry oth A John Iafrate oth Miguel N Rivera oth Matthew P Frosch oth Todd R Golub oth Priscilla K Brastianos oth Gad Getz oth Anoop P Patel oth Michelle Monje oth Daniel P Cahill oth Orit Rozenblatt-Rosen oth David N Louis oth Bradley E Bernstein oth Aviv Regev oth Mario L Suvà oth Enthalten in Science Washington, DC : AAAS, American Assoc. for the Advancement of Science, 1883 355(2017), 6332 (DE-627)12931482X (DE-600)128410-1 (DE-576)014533189 0036-8075 nnns volume:355 year:2017 number:6332 http://dx.doi.org/10.1126/science.aai8478 Volltext http://search.proquest.com/docview/1883585485 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-SPO SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-FOR GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_47 GBV_ILN_55 GBV_ILN_59 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_92 GBV_ILN_101 GBV_ILN_110 GBV_ILN_120 GBV_ILN_131 GBV_ILN_170 GBV_ILN_171 GBV_ILN_179 GBV_ILN_181 GBV_ILN_211 GBV_ILN_252 GBV_ILN_259 GBV_ILN_290 GBV_ILN_600 GBV_ILN_601 GBV_ILN_647 GBV_ILN_754 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2116 GBV_ILN_2120 GBV_ILN_2121 GBV_ILN_2173 GBV_ILN_2219 GBV_ILN_2221 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4036 GBV_ILN_4125 GBV_ILN_4219 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4310 GBV_ILN_4314 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4328 GBV_ILN_4333 AR 355 2017 6332 |
allfields_unstemmed |
10.1126/science.aai8478 doi PQ20170501 (DE-627)OLC1992452253 (DE-599)GBVOLC1992452253 (PRQ)p1224-3e670bd30816482708a23773963c47f6cf632774c26685bbf2e5f2d95efac77a0 (KEY)0063888920170000355633200000decouplinggeneticslineagesandmicroenvironmentinidh DE-627 ger DE-627 rakwb eng 500 DNB LING fid Andrew S Venteicher verfasserin aut Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. Ribonucleic acids Itay Tirosh oth Christine Hebert oth Keren Yizhak oth Cyril Neftel oth Mariella G Filbin oth Volker Hovestadt oth Leah E Escalante oth McKenzie L Shaw oth Christopher Rodman oth Shawn M Gillespie oth Danielle Dionne oth Christina C Luo oth Hiranmayi Ravichandran oth Ravindra Mylvaganam oth Christopher Mount oth Maristela L Onozato oth Brian V Nahed oth Hiroaki Wakimoto oth William T Curry oth A John Iafrate oth Miguel N Rivera oth Matthew P Frosch oth Todd R Golub oth Priscilla K Brastianos oth Gad Getz oth Anoop P Patel oth Michelle Monje oth Daniel P Cahill oth Orit Rozenblatt-Rosen oth David N Louis oth Bradley E Bernstein oth Aviv Regev oth Mario L Suvà oth Enthalten in Science Washington, DC : AAAS, American Assoc. for the Advancement of Science, 1883 355(2017), 6332 (DE-627)12931482X (DE-600)128410-1 (DE-576)014533189 0036-8075 nnns volume:355 year:2017 number:6332 http://dx.doi.org/10.1126/science.aai8478 Volltext http://search.proquest.com/docview/1883585485 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-SPO SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-FOR GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_47 GBV_ILN_55 GBV_ILN_59 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_92 GBV_ILN_101 GBV_ILN_110 GBV_ILN_120 GBV_ILN_131 GBV_ILN_170 GBV_ILN_171 GBV_ILN_179 GBV_ILN_181 GBV_ILN_211 GBV_ILN_252 GBV_ILN_259 GBV_ILN_290 GBV_ILN_600 GBV_ILN_601 GBV_ILN_647 GBV_ILN_754 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2116 GBV_ILN_2120 GBV_ILN_2121 GBV_ILN_2173 GBV_ILN_2219 GBV_ILN_2221 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4036 GBV_ILN_4125 GBV_ILN_4219 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4310 GBV_ILN_4314 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4328 GBV_ILN_4333 AR 355 2017 6332 |
allfieldsGer |
10.1126/science.aai8478 doi PQ20170501 (DE-627)OLC1992452253 (DE-599)GBVOLC1992452253 (PRQ)p1224-3e670bd30816482708a23773963c47f6cf632774c26685bbf2e5f2d95efac77a0 (KEY)0063888920170000355633200000decouplinggeneticslineagesandmicroenvironmentinidh DE-627 ger DE-627 rakwb eng 500 DNB LING fid Andrew S Venteicher verfasserin aut Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. Ribonucleic acids Itay Tirosh oth Christine Hebert oth Keren Yizhak oth Cyril Neftel oth Mariella G Filbin oth Volker Hovestadt oth Leah E Escalante oth McKenzie L Shaw oth Christopher Rodman oth Shawn M Gillespie oth Danielle Dionne oth Christina C Luo oth Hiranmayi Ravichandran oth Ravindra Mylvaganam oth Christopher Mount oth Maristela L Onozato oth Brian V Nahed oth Hiroaki Wakimoto oth William T Curry oth A John Iafrate oth Miguel N Rivera oth Matthew P Frosch oth Todd R Golub oth Priscilla K Brastianos oth Gad Getz oth Anoop P Patel oth Michelle Monje oth Daniel P Cahill oth Orit Rozenblatt-Rosen oth David N Louis oth Bradley E Bernstein oth Aviv Regev oth Mario L Suvà oth Enthalten in Science Washington, DC : AAAS, American Assoc. for the Advancement of Science, 1883 355(2017), 6332 (DE-627)12931482X (DE-600)128410-1 (DE-576)014533189 0036-8075 nnns volume:355 year:2017 number:6332 http://dx.doi.org/10.1126/science.aai8478 Volltext http://search.proquest.com/docview/1883585485 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-SPO SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-FOR GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_47 GBV_ILN_55 GBV_ILN_59 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_92 GBV_ILN_101 GBV_ILN_110 GBV_ILN_120 GBV_ILN_131 GBV_ILN_170 GBV_ILN_171 GBV_ILN_179 GBV_ILN_181 GBV_ILN_211 GBV_ILN_252 GBV_ILN_259 GBV_ILN_290 GBV_ILN_600 GBV_ILN_601 GBV_ILN_647 GBV_ILN_754 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2116 GBV_ILN_2120 GBV_ILN_2121 GBV_ILN_2173 GBV_ILN_2219 GBV_ILN_2221 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4036 GBV_ILN_4125 GBV_ILN_4219 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4310 GBV_ILN_4314 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4328 GBV_ILN_4333 AR 355 2017 6332 |
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10.1126/science.aai8478 doi PQ20170501 (DE-627)OLC1992452253 (DE-599)GBVOLC1992452253 (PRQ)p1224-3e670bd30816482708a23773963c47f6cf632774c26685bbf2e5f2d95efac77a0 (KEY)0063888920170000355633200000decouplinggeneticslineagesandmicroenvironmentinidh DE-627 ger DE-627 rakwb eng 500 DNB LING fid Andrew S Venteicher verfasserin aut Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. Ribonucleic acids Itay Tirosh oth Christine Hebert oth Keren Yizhak oth Cyril Neftel oth Mariella G Filbin oth Volker Hovestadt oth Leah E Escalante oth McKenzie L Shaw oth Christopher Rodman oth Shawn M Gillespie oth Danielle Dionne oth Christina C Luo oth Hiranmayi Ravichandran oth Ravindra Mylvaganam oth Christopher Mount oth Maristela L Onozato oth Brian V Nahed oth Hiroaki Wakimoto oth William T Curry oth A John Iafrate oth Miguel N Rivera oth Matthew P Frosch oth Todd R Golub oth Priscilla K Brastianos oth Gad Getz oth Anoop P Patel oth Michelle Monje oth Daniel P Cahill oth Orit Rozenblatt-Rosen oth David N Louis oth Bradley E Bernstein oth Aviv Regev oth Mario L Suvà oth Enthalten in Science Washington, DC : AAAS, American Assoc. for the Advancement of Science, 1883 355(2017), 6332 (DE-627)12931482X (DE-600)128410-1 (DE-576)014533189 0036-8075 nnns volume:355 year:2017 number:6332 http://dx.doi.org/10.1126/science.aai8478 Volltext http://search.proquest.com/docview/1883585485 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-SPO SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-FOR GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_47 GBV_ILN_55 GBV_ILN_59 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_92 GBV_ILN_101 GBV_ILN_110 GBV_ILN_120 GBV_ILN_131 GBV_ILN_170 GBV_ILN_171 GBV_ILN_179 GBV_ILN_181 GBV_ILN_211 GBV_ILN_252 GBV_ILN_259 GBV_ILN_290 GBV_ILN_600 GBV_ILN_601 GBV_ILN_647 GBV_ILN_754 GBV_ILN_2001 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2116 GBV_ILN_2120 GBV_ILN_2121 GBV_ILN_2173 GBV_ILN_2219 GBV_ILN_2221 GBV_ILN_2279 GBV_ILN_2286 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4036 GBV_ILN_4125 GBV_ILN_4219 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4302 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4310 GBV_ILN_4314 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4320 GBV_ILN_4328 GBV_ILN_4333 AR 355 2017 6332 |
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Andrew S Venteicher @@aut@@ Itay Tirosh @@oth@@ Christine Hebert @@oth@@ Keren Yizhak @@oth@@ Cyril Neftel @@oth@@ Mariella G Filbin @@oth@@ Volker Hovestadt @@oth@@ Leah E Escalante @@oth@@ McKenzie L Shaw @@oth@@ Christopher Rodman @@oth@@ Shawn M Gillespie @@oth@@ Danielle Dionne @@oth@@ Christina C Luo @@oth@@ Hiranmayi Ravichandran @@oth@@ Ravindra Mylvaganam @@oth@@ Christopher Mount @@oth@@ Maristela L Onozato @@oth@@ Brian V Nahed @@oth@@ Hiroaki Wakimoto @@oth@@ William T Curry @@oth@@ A John Iafrate @@oth@@ Miguel N Rivera @@oth@@ Matthew P Frosch @@oth@@ Todd R Golub @@oth@@ Priscilla K Brastianos @@oth@@ Gad Getz @@oth@@ Anoop P Patel @@oth@@ Michelle Monje @@oth@@ Daniel P Cahill @@oth@@ Orit Rozenblatt-Rosen @@oth@@ David N Louis @@oth@@ Bradley E Bernstein @@oth@@ Aviv Regev @@oth@@ Mario L Suvà @@oth@@ |
publishDateDaySort_date |
2017-01-01T00:00:00Z |
hierarchy_top_id |
12931482X |
dewey-sort |
3500 |
id |
OLC1992452253 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1992452253</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715041841.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170512s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1126/science.aai8478</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20170501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1992452253</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1992452253</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)p1224-3e670bd30816482708a23773963c47f6cf632774c26685bbf2e5f2d95efac77a0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0063888920170000355633200000decouplinggeneticslineagesandmicroenvironmentinidh</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">500</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Andrew S Venteicher</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. 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Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq |
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Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq |
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Andrew S Venteicher |
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Andrew S Venteicher |
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10.1126/science.aai8478 |
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decoupling genetics, lineages, and microenvironment in idh-mutant gliomas by single-cell rna-seq |
title_auth |
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq |
abstract |
Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. |
abstractGer |
Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. |
abstract_unstemmed |
Glioma brain tumors that carry mutant copies of the IDH gene can be subdivided into two major classes. However, the development of and differences between these two classes are not well characterized. Venteicher et al. coupled bulk sequencing and publicly available data with single-cell RNA sequencing data on glioma patient tissue samples. They identified a common lineage program that is shared between glioma subtypes. This suggests that the observed differences between the two glioma classes originate from lineage-specific genetic changes and the tumor microenvironment. Science, this issue p. eaai8478 Tumor fitness, evolution, and resistance to therapy are governed by selection of malignant cells with specific genotypes, by expression programs related to cellular phenotypes, and by influences of the tumor microenvironment (TME). Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. Single-cell analysis of a limited set of representative tumors is then used to distinguish those effects. We applied this approach to understand the differences between two types of isocitrate dehydrogenase (IDH)-mutant gliomas: astrocytoma (IDH-A) and oligodendroglioma (IDH-O). IDH-A and IDH-O are distinguished by co-occurring signature genetic events and by histopathology and are thought to recapitulate distinct glial lineages. By combining 9879 scRNA-seq profiles from 10 IDH-A tumors, 4347 scRNA-seq profiles from six IDH-O tumors, and 165 TCGA bulk RNA profiles, we could decipher differences between these two tumor types at single-cell resolution. We find that differences in bulk expression profiles between IDH-A and IDH-O are primarily explained by the impact of signature genetic events and TME composition, but not by distinct expression programs of glial lineages in the malignant cells. We infer that both IDH-A and IDH-O share the same developmental hierarchy, consisting in each case of three subpopulations of malignant cells: nonproliferating cells differentiated along the astrocytic and oligodendrocytic lineages, and proliferative undifferentiated cells that resemble neural stem/progenitor cells. By analyzing tumors of different clinical grades, we observe that higher-grade tumors present enhanced proliferation, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia programs in the TME. Our approach provides a general framework to decipher differences between classes of human tumors by decoupling cancer cell genotypes, phenotypes, and the composition of the TME. The shared glial lineages and developmental hierarchies observed in IDH-A and IDH-O suggest a common progenitor for all IDH-mutant gliomas, shedding light on a long-standing debate in gliomagenesis. In contrast to the similarity in glial lineages, IDH-A and IDH-O differ significantly in their TME, and in particular in the abundance of microglia/macrophage cells. Microglia and macrophages also differ between IDH-A tumors of different grades. Our study redefines the cellular composition of human IDH-mutant gliomas, with important implications for disease management. (Top ) Human samples were dissociated and analyzed by scRNA-seq. (Bottom ) IDH-O and IDH-A differ in genetics and TME but are both primarily composed of three main types of malignant cells: cycling stem-like cells and noncycling astrocyte-like and oligodendrocyte-like cells. Tumor progression is associated with increased proliferation, decreased differentiation, and increase in macrophages over microglia in the TME. Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses. |
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title_short |
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq |
url |
http://dx.doi.org/10.1126/science.aai8478 http://search.proquest.com/docview/1883585485 |
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Itay Tirosh Christine Hebert Keren Yizhak Cyril Neftel Mariella G Filbin Volker Hovestadt Leah E Escalante McKenzie L Shaw Christopher Rodman Shawn M Gillespie Danielle Dionne Christina C Luo Hiranmayi Ravichandran Ravindra Mylvaganam Christopher Mount Maristela L Onozato Brian V Nahed Hiroaki Wakimoto William T Curry A John Iafrate Miguel N Rivera Matthew P Frosch Todd R Golub Priscilla K Brastianos Gad Getz Anoop P Patel Michelle Monje Daniel P Cahill Orit Rozenblatt-Rosen David N Louis Bradley E Bernstein Aviv Regev Mario L Suvà |
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doi_str |
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up_date |
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Although bulk tumor analysis can interrogate the genetic state of tumor cells with high precision, bulk expression profiles average the diverse cells within each tumor, thereby masking critical differences and providing limited insight into cancer cell programs and TME influences. Single-cell RNA sequencing (scRNA-seq) can help to address those challenges but incurs financial and logistic considerations, including the time required to accrue large cohorts of fresh tumor specimen for single-cell analysis. We reasoned that scRNA-seq of a limited number of representative tumors could be combined with bulk data from large cohorts to decipher differences between tumor subclasses. In this approach, bulk samples collected for large cohorts, such as from The Cancer Genome Atlas (TCGA), are first used to define the combined effects of differences in cancer cell genotypes, phenotypes, and the composition of the TME. 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