Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences

Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clini...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Wesley [verfasserIn] Tugaoen, Jonah Domingo Fadda, Paolo Toland, Amanda Ewart Ma, Qin Elder, J. Brad Giglio, Pierre Otero, José Javier

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Glioblastoma Pseudo-progression Novel enhancement Pathology informatics Clinical decision-making

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Acta Neuropathologica Communications - London : Biomed Central, 2013, 11(2023), 1 vom: 04. Dez.
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:1 ; day:04 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s40478-023-01587-w

Katalog-ID:	SPR053980271

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spelling	10.1186/s40478-023-01587-w doi (DE-627)SPR053980271 (SPR)s40478-023-01587-w-e DE-627 ger DE-627 rakwb eng Wang, Wesley verfasserin aut Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. Glioblastoma (dpeaa)DE-He213 Pseudo-progression (dpeaa)DE-He213 Novel enhancement (dpeaa)DE-He213 Pathology informatics (dpeaa)DE-He213 Clinical decision-making (dpeaa)DE-He213 Tugaoen, Jonah Domingo aut Fadda, Paolo aut Toland, Amanda Ewart aut Ma, Qin aut Elder, J. Brad aut Giglio, Pierre aut Otero, José Javier (orcid)0000-0003-0680-4520 aut Enthalten in Acta Neuropathologica Communications London : Biomed Central, 2013 11(2023), 1 vom: 04. Dez. (DE-627)746066465 (DE-600)2715589-4 2051-5960 nnns volume:11 year:2023 number:1 day:04 month:12 https://dx.doi.org/10.1186/s40478-023-01587-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 04 12
allfields_unstemmed	10.1186/s40478-023-01587-w doi (DE-627)SPR053980271 (SPR)s40478-023-01587-w-e DE-627 ger DE-627 rakwb eng Wang, Wesley verfasserin aut Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. Glioblastoma (dpeaa)DE-He213 Pseudo-progression (dpeaa)DE-He213 Novel enhancement (dpeaa)DE-He213 Pathology informatics (dpeaa)DE-He213 Clinical decision-making (dpeaa)DE-He213 Tugaoen, Jonah Domingo aut Fadda, Paolo aut Toland, Amanda Ewart aut Ma, Qin aut Elder, J. Brad aut Giglio, Pierre aut Otero, José Javier (orcid)0000-0003-0680-4520 aut Enthalten in Acta Neuropathologica Communications London : Biomed Central, 2013 11(2023), 1 vom: 04. Dez. (DE-627)746066465 (DE-600)2715589-4 2051-5960 nnns volume:11 year:2023 number:1 day:04 month:12 https://dx.doi.org/10.1186/s40478-023-01587-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 04 12
allfieldsGer	10.1186/s40478-023-01587-w doi (DE-627)SPR053980271 (SPR)s40478-023-01587-w-e DE-627 ger DE-627 rakwb eng Wang, Wesley verfasserin aut Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. Glioblastoma (dpeaa)DE-He213 Pseudo-progression (dpeaa)DE-He213 Novel enhancement (dpeaa)DE-He213 Pathology informatics (dpeaa)DE-He213 Clinical decision-making (dpeaa)DE-He213 Tugaoen, Jonah Domingo aut Fadda, Paolo aut Toland, Amanda Ewart aut Ma, Qin aut Elder, J. Brad aut Giglio, Pierre aut Otero, José Javier (orcid)0000-0003-0680-4520 aut Enthalten in Acta Neuropathologica Communications London : Biomed Central, 2013 11(2023), 1 vom: 04. Dez. (DE-627)746066465 (DE-600)2715589-4 2051-5960 nnns volume:11 year:2023 number:1 day:04 month:12 https://dx.doi.org/10.1186/s40478-023-01587-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 04 12
allfieldsSound	10.1186/s40478-023-01587-w doi (DE-627)SPR053980271 (SPR)s40478-023-01587-w-e DE-627 ger DE-627 rakwb eng Wang, Wesley verfasserin aut Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. Glioblastoma (dpeaa)DE-He213 Pseudo-progression (dpeaa)DE-He213 Novel enhancement (dpeaa)DE-He213 Pathology informatics (dpeaa)DE-He213 Clinical decision-making (dpeaa)DE-He213 Tugaoen, Jonah Domingo aut Fadda, Paolo aut Toland, Amanda Ewart aut Ma, Qin aut Elder, J. Brad aut Giglio, Pierre aut Otero, José Javier (orcid)0000-0003-0680-4520 aut Enthalten in Acta Neuropathologica Communications London : Biomed Central, 2013 11(2023), 1 vom: 04. Dez. (DE-627)746066465 (DE-600)2715589-4 2051-5960 nnns volume:11 year:2023 number:1 day:04 month:12 https://dx.doi.org/10.1186/s40478-023-01587-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 04 12
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author	Wang, Wesley
spellingShingle	Wang, Wesley misc Glioblastoma misc Pseudo-progression misc Novel enhancement misc Pathology informatics misc Clinical decision-making Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences
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topic_title	Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences Glioblastoma (dpeaa)DE-He213 Pseudo-progression (dpeaa)DE-He213 Novel enhancement (dpeaa)DE-He213 Pathology informatics (dpeaa)DE-He213 Clinical decision-making (dpeaa)DE-He213
topic	misc Glioblastoma misc Pseudo-progression misc Novel enhancement misc Pathology informatics misc Clinical decision-making
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title	Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences
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title_full	Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences
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title_sort	glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences
title_auth	Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences
abstract	Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. © The Author(s) 2023
abstractGer	Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. © The Author(s) 2023
abstract_unstemmed	Abstract Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence. © The Author(s) 2023
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title_short	Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences
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Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences

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