Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis
Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lang, Alexandra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of neuro-oncology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983, 164(2023), 1 vom: Aug., Seite 211-220 |
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| Übergeordnetes Werk: |
volume:164 ; year:2023 ; number:1 ; month:08 ; pages:211-220 |
| Links: |
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| DOI / URN: |
10.1007/s11060-023-04406-3 |
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| Katalog-ID: |
SPR052897753 |
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| 100 | 1 | |a Lang, Alexandra |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis |
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| 500 | |a © The Author(s) 2023 | ||
| 520 | |a Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. | ||
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| 650 | 4 | |a High-grade glioma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Glioblastoma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Histology |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Immune cells |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CD8 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CD168 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CD63 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a FoxP3 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Automated imaging processing |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Jeron, Raphael L. |4 aut | |
| 700 | 1 | |a Lontzek, Bastian |4 aut | |
| 700 | 1 | |a Kiesel, Barbara |4 aut | |
| 700 | 1 | |a Mischkulnig, Mario |4 aut | |
| 700 | 1 | |a Berghoff, Anna S. |4 aut | |
| 700 | 1 | |a Ricken, Gerda |4 aut | |
| 700 | 1 | |a Wöhrer, Adelheid |4 aut | |
| 700 | 1 | |a Rössler, Karl |4 aut | |
| 700 | 1 | |a Lötsch-Gojo, Daniela |4 aut | |
| 700 | 1 | |a Roetzer-Pejrimovsky, Thomas |4 aut | |
| 700 | 1 | |a Berger, Walter |4 aut | |
| 700 | 1 | |a Hainfellner, Johannes A. |4 aut | |
| 700 | 1 | |a Höftberger, Romana |4 aut | |
| 700 | 1 | |a Widhalm, Georg |4 aut | |
| 700 | 1 | |a Erhart, Friedrich |4 aut | |
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10.1007/s11060-023-04406-3 doi (DE-627)SPR052897753 (SPR)s11060-023-04406-3-e DE-627 ger DE-627 rakwb eng Lang, Alexandra verfasserin aut Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. 5-ALA (dpeaa)DE-He213 High-grade glioma (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Histology (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 CD8 (dpeaa)DE-He213 CD168 (dpeaa)DE-He213 CD63 (dpeaa)DE-He213 FoxP3 (dpeaa)DE-He213 Automated imaging processing (dpeaa)DE-He213 Jeron, Raphael L. aut Lontzek, Bastian aut Kiesel, Barbara aut Mischkulnig, Mario aut Berghoff, Anna S. aut Ricken, Gerda aut Wöhrer, Adelheid aut Rössler, Karl aut Lötsch-Gojo, Daniela aut Roetzer-Pejrimovsky, Thomas aut Berger, Walter aut Hainfellner, Johannes A. aut Höftberger, Romana aut Widhalm, Georg aut Erhart, Friedrich aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 164(2023), 1 vom: Aug., Seite 211-220 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:164 year:2023 number:1 month:08 pages:211-220 https://dx.doi.org/10.1007/s11060-023-04406-3 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_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 164 2023 1 08 211-220 |
| spelling |
10.1007/s11060-023-04406-3 doi (DE-627)SPR052897753 (SPR)s11060-023-04406-3-e DE-627 ger DE-627 rakwb eng Lang, Alexandra verfasserin aut Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. 5-ALA (dpeaa)DE-He213 High-grade glioma (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Histology (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 CD8 (dpeaa)DE-He213 CD168 (dpeaa)DE-He213 CD63 (dpeaa)DE-He213 FoxP3 (dpeaa)DE-He213 Automated imaging processing (dpeaa)DE-He213 Jeron, Raphael L. aut Lontzek, Bastian aut Kiesel, Barbara aut Mischkulnig, Mario aut Berghoff, Anna S. aut Ricken, Gerda aut Wöhrer, Adelheid aut Rössler, Karl aut Lötsch-Gojo, Daniela aut Roetzer-Pejrimovsky, Thomas aut Berger, Walter aut Hainfellner, Johannes A. aut Höftberger, Romana aut Widhalm, Georg aut Erhart, Friedrich aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 164(2023), 1 vom: Aug., Seite 211-220 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:164 year:2023 number:1 month:08 pages:211-220 https://dx.doi.org/10.1007/s11060-023-04406-3 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_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 164 2023 1 08 211-220 |
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10.1007/s11060-023-04406-3 doi (DE-627)SPR052897753 (SPR)s11060-023-04406-3-e DE-627 ger DE-627 rakwb eng Lang, Alexandra verfasserin aut Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. 5-ALA (dpeaa)DE-He213 High-grade glioma (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Histology (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 CD8 (dpeaa)DE-He213 CD168 (dpeaa)DE-He213 CD63 (dpeaa)DE-He213 FoxP3 (dpeaa)DE-He213 Automated imaging processing (dpeaa)DE-He213 Jeron, Raphael L. aut Lontzek, Bastian aut Kiesel, Barbara aut Mischkulnig, Mario aut Berghoff, Anna S. aut Ricken, Gerda aut Wöhrer, Adelheid aut Rössler, Karl aut Lötsch-Gojo, Daniela aut Roetzer-Pejrimovsky, Thomas aut Berger, Walter aut Hainfellner, Johannes A. aut Höftberger, Romana aut Widhalm, Georg aut Erhart, Friedrich aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 164(2023), 1 vom: Aug., Seite 211-220 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:164 year:2023 number:1 month:08 pages:211-220 https://dx.doi.org/10.1007/s11060-023-04406-3 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_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 164 2023 1 08 211-220 |
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10.1007/s11060-023-04406-3 doi (DE-627)SPR052897753 (SPR)s11060-023-04406-3-e DE-627 ger DE-627 rakwb eng Lang, Alexandra verfasserin aut Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. 5-ALA (dpeaa)DE-He213 High-grade glioma (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Histology (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 CD8 (dpeaa)DE-He213 CD168 (dpeaa)DE-He213 CD63 (dpeaa)DE-He213 FoxP3 (dpeaa)DE-He213 Automated imaging processing (dpeaa)DE-He213 Jeron, Raphael L. aut Lontzek, Bastian aut Kiesel, Barbara aut Mischkulnig, Mario aut Berghoff, Anna S. aut Ricken, Gerda aut Wöhrer, Adelheid aut Rössler, Karl aut Lötsch-Gojo, Daniela aut Roetzer-Pejrimovsky, Thomas aut Berger, Walter aut Hainfellner, Johannes A. aut Höftberger, Romana aut Widhalm, Georg aut Erhart, Friedrich aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 164(2023), 1 vom: Aug., Seite 211-220 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:164 year:2023 number:1 month:08 pages:211-220 https://dx.doi.org/10.1007/s11060-023-04406-3 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_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 164 2023 1 08 211-220 |
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10.1007/s11060-023-04406-3 doi (DE-627)SPR052897753 (SPR)s11060-023-04406-3-e DE-627 ger DE-627 rakwb eng Lang, Alexandra verfasserin aut Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. 5-ALA (dpeaa)DE-He213 High-grade glioma (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Histology (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 CD8 (dpeaa)DE-He213 CD168 (dpeaa)DE-He213 CD63 (dpeaa)DE-He213 FoxP3 (dpeaa)DE-He213 Automated imaging processing (dpeaa)DE-He213 Jeron, Raphael L. aut Lontzek, Bastian aut Kiesel, Barbara aut Mischkulnig, Mario aut Berghoff, Anna S. aut Ricken, Gerda aut Wöhrer, Adelheid aut Rössler, Karl aut Lötsch-Gojo, Daniela aut Roetzer-Pejrimovsky, Thomas aut Berger, Walter aut Hainfellner, Johannes A. aut Höftberger, Romana aut Widhalm, Georg aut Erhart, Friedrich aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 164(2023), 1 vom: Aug., Seite 211-220 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:164 year:2023 number:1 month:08 pages:211-220 https://dx.doi.org/10.1007/s11060-023-04406-3 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_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 164 2023 1 08 211-220 |
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Enthalten in Journal of neuro-oncology 164(2023), 1 vom: Aug., Seite 211-220 volume:164 year:2023 number:1 month:08 pages:211-220 |
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Enthalten in Journal of neuro-oncology 164(2023), 1 vom: Aug., Seite 211-220 volume:164 year:2023 number:1 month:08 pages:211-220 |
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However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. 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|
| author |
Lang, Alexandra |
| spellingShingle |
Lang, Alexandra misc 5-ALA misc High-grade glioma misc Glioblastoma misc Histology misc Immune cells misc CD8 misc CD168 misc CD63 misc FoxP3 misc Automated imaging processing Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis |
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Lang, Alexandra |
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1573-7373 |
| topic_title |
Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis 5-ALA (dpeaa)DE-He213 High-grade glioma (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Histology (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 CD8 (dpeaa)DE-He213 CD168 (dpeaa)DE-He213 CD63 (dpeaa)DE-He213 FoxP3 (dpeaa)DE-He213 Automated imaging processing (dpeaa)DE-He213 |
| topic |
misc 5-ALA misc High-grade glioma misc Glioblastoma misc Histology misc Immune cells misc CD8 misc CD168 misc CD63 misc FoxP3 misc Automated imaging processing |
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misc 5-ALA misc High-grade glioma misc Glioblastoma misc Histology misc Immune cells misc CD8 misc CD168 misc CD63 misc FoxP3 misc Automated imaging processing |
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misc 5-ALA misc High-grade glioma misc Glioblastoma misc Histology misc Immune cells misc CD8 misc CD168 misc CD63 misc FoxP3 misc Automated imaging processing |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Journal of neuro-oncology |
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Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis |
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Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis |
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Lang, Alexandra |
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Journal of neuro-oncology |
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Lang, Alexandra Jeron, Raphael L. Lontzek, Bastian Kiesel, Barbara Mischkulnig, Mario Berghoff, Anna S. Ricken, Gerda Wöhrer, Adelheid Rössler, Karl Lötsch-Gojo, Daniela Roetzer-Pejrimovsky, Thomas Berger, Walter Hainfellner, Johannes A. Höftberger, Romana Widhalm, Georg Erhart, Friedrich |
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Elektronische Aufsätze |
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Lang, Alexandra |
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10.1007/s11060-023-04406-3 |
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mapping high-grade glioma immune infiltration to 5-ala fluorescence levels: tcga data computation, classical histology, and digital image analysis |
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Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis |
| abstract |
Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. © The Author(s) 2023 |
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Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. © The Author(s) 2023 |
| abstract_unstemmed |
Purpose Resection of high-grade gliomas has been considerably improved by 5-aminolevulinic acid (5-ALA). However, not all neurobiological properties of 5-ALA are fully understood. Specifically, potential differences in immune infiltration have not been conclusively examined, despite recent reports that immune cells might play a role. Thus, we here provide a systematic mapping of immune infiltration of different 5-ALA fluorescence levels. Methods Tumor-associated macrophages (CD68, CD163), cytotoxic T cells (CD8), and regulatory T cells (FoxP3) were quantified via three methods. First, data from The Cancer Genome Atlas (TCGA) of 172 patients was examined for correlations between 5-ALA fluorescence-related mRNA expression signatures and immune markers. Second, as classical histology, 508 stained slides from 39 high-grade glioma patients were analysed semi-quantitatively by two independent reviewers, generating 1016 data points. Third, digital image analysis was performed with automated scanning and algorithm-based cell quantification. Results TCGA mRNA data from 172 patients showed a direct, significant correlation between 5-ALA signatures and immune markers (p < 0.001). However, we were not able to confirm this finding in the here studied initial set of 39 patient histologies where we found a comparable immune infiltration in different fluorescence levels. Digital image analysis correlated excellently with standard histology. Conclusion With mapping the immune infiltration pattern of different 5-ALA categories, we are adding fundamental basic insights to the field of 5-ALA and glioma biology. The observation that a significant correlation in TCGA data did not fully translate to detectable differences in immune infiltration in first histology data warrants further investigation in larger cohorts. © The Author(s) 2023 |
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Mapping high-grade glioma immune infiltration to 5-ALA fluorescence levels: TCGA data computation, classical histology, and digital image analysis |
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Jeron, Raphael L. Lontzek, Bastian Kiesel, Barbara Mischkulnig, Mario Berghoff, Anna S. Ricken, Gerda Wöhrer, Adelheid Rössler, Karl Lötsch-Gojo, Daniela Roetzer-Pejrimovsky, Thomas Berger, Walter Hainfellner, Johannes A. Höftberger, Romana Widhalm, Georg Erhart, Friedrich |
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| score |
7.401473 |