Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy
Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC)...
Ausführliche Beschreibung
Autor*in: |
Patil, Shivaprasad [verfasserIn] Linge, Annett [verfasserIn] Grosser, Marianne [verfasserIn] Lohaus, Fabian [verfasserIn] Gudziol, Volker [verfasserIn] Kemper, Max [verfasserIn] Nowak, Alexander [verfasserIn] Haim, Dominik [verfasserIn] Tinhofer, Inge [verfasserIn] Budach, Volker [verfasserIn] Guberina, Maja [verfasserIn] Stuschke, Martin [verfasserIn] Balermpas, Panagiotis [verfasserIn] Rödel, Claus [verfasserIn] Schäfer, Henning [verfasserIn] Grosu, Anca-Ligia [verfasserIn] Abdollahi, Amir [verfasserIn] Debus, Jürgen [verfasserIn] Ganswindt, Ute [verfasserIn] Belka, Claus [verfasserIn] Pigorsch, Steffi [verfasserIn] Combs, Stephanie E. [verfasserIn] Boeke, Simon [verfasserIn] Zips, Daniel [verfasserIn] Baretton, Gustavo B. [verfasserIn] Baumann, Michael [verfasserIn] Krause, Mechthild [verfasserIn] Löck, Steffen [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Übergeordnetes Werk: |
Enthalten in: Radiotherapy and oncology - Amsterdam [u.a.] : Elsevier Science, 1983, 171, Seite 91-100 |
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Übergeordnetes Werk: |
volume:171 ; pages:91-100 |
DOI / URN: |
10.1016/j.radonc.2022.04.006 |
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Katalog-ID: |
ELV007989369 |
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100 | 1 | |a Patil, Shivaprasad |e verfasserin |4 aut | |
245 | 1 | 0 | |a Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy |
264 | 1 | |c 2022 | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. | ||
650 | 4 | |a Head and neck squamous cell carcinoma | |
650 | 4 | |a Gene signature | |
650 | 4 | |a Postoperative radiotherapy | |
650 | 4 | |a Hypoxia | |
650 | 4 | |a Cancer stem cells | |
650 | 4 | |a Machine learning | |
700 | 1 | |a Linge, Annett |e verfasserin |4 aut | |
700 | 1 | |a Grosser, Marianne |e verfasserin |4 aut | |
700 | 1 | |a Lohaus, Fabian |e verfasserin |4 aut | |
700 | 1 | |a Gudziol, Volker |e verfasserin |4 aut | |
700 | 1 | |a Kemper, Max |e verfasserin |4 aut | |
700 | 1 | |a Nowak, Alexander |e verfasserin |4 aut | |
700 | 1 | |a Haim, Dominik |e verfasserin |4 aut | |
700 | 1 | |a Tinhofer, Inge |e verfasserin |4 aut | |
700 | 1 | |a Budach, Volker |e verfasserin |4 aut | |
700 | 1 | |a Guberina, Maja |e verfasserin |4 aut | |
700 | 1 | |a Stuschke, Martin |e verfasserin |4 aut | |
700 | 1 | |a Balermpas, Panagiotis |e verfasserin |4 aut | |
700 | 1 | |a Rödel, Claus |e verfasserin |4 aut | |
700 | 1 | |a Schäfer, Henning |e verfasserin |4 aut | |
700 | 1 | |a Grosu, Anca-Ligia |e verfasserin |4 aut | |
700 | 1 | |a Abdollahi, Amir |e verfasserin |4 aut | |
700 | 1 | |a Debus, Jürgen |e verfasserin |4 aut | |
700 | 1 | |a Ganswindt, Ute |e verfasserin |4 aut | |
700 | 1 | |a Belka, Claus |e verfasserin |4 aut | |
700 | 1 | |a Pigorsch, Steffi |e verfasserin |4 aut | |
700 | 1 | |a Combs, Stephanie E. |e verfasserin |4 aut | |
700 | 1 | |a Boeke, Simon |e verfasserin |4 aut | |
700 | 1 | |a Zips, Daniel |e verfasserin |4 aut | |
700 | 1 | |a Baretton, Gustavo B. |e verfasserin |4 aut | |
700 | 1 | |a Baumann, Michael |e verfasserin |4 aut | |
700 | 1 | |a Krause, Mechthild |e verfasserin |4 aut | |
700 | 1 | |a Löck, Steffen |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Radiotherapy and oncology |d Amsterdam [u.a.] : Elsevier Science, 1983 |g 171, Seite 91-100 |h Online-Ressource |w (DE-627)306710110 |w (DE-600)1500707-8 |w (DE-576)082435731 |x 1879-0887 |7 nnns |
773 | 1 | 8 | |g volume:171 |g pages:91-100 |
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allfields |
10.1016/j.radonc.2022.04.006 doi (DE-627)ELV007989369 (ELSEVIER)S0167-8140(22)00187-6 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.64 bkl Patil, Shivaprasad verfasserin aut Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning Linge, Annett verfasserin aut Grosser, Marianne verfasserin aut Lohaus, Fabian verfasserin aut Gudziol, Volker verfasserin aut Kemper, Max verfasserin aut Nowak, Alexander verfasserin aut Haim, Dominik verfasserin aut Tinhofer, Inge verfasserin aut Budach, Volker verfasserin aut Guberina, Maja verfasserin aut Stuschke, Martin verfasserin aut Balermpas, Panagiotis verfasserin aut Rödel, Claus verfasserin aut Schäfer, Henning verfasserin aut Grosu, Anca-Ligia verfasserin aut Abdollahi, Amir verfasserin aut Debus, Jürgen verfasserin aut Ganswindt, Ute verfasserin aut Belka, Claus verfasserin aut Pigorsch, Steffi verfasserin aut Combs, Stephanie E. verfasserin aut Boeke, Simon verfasserin aut Zips, Daniel verfasserin aut Baretton, Gustavo B. verfasserin aut Baumann, Michael verfasserin aut Krause, Mechthild verfasserin aut Löck, Steffen verfasserin aut Enthalten in Radiotherapy and oncology Amsterdam [u.a.] : Elsevier Science, 1983 171, Seite 91-100 Online-Ressource (DE-627)306710110 (DE-600)1500707-8 (DE-576)082435731 1879-0887 nnns volume:171 pages:91-100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.64 Radiologie AR 171 91-100 |
spelling |
10.1016/j.radonc.2022.04.006 doi (DE-627)ELV007989369 (ELSEVIER)S0167-8140(22)00187-6 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.64 bkl Patil, Shivaprasad verfasserin aut Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning Linge, Annett verfasserin aut Grosser, Marianne verfasserin aut Lohaus, Fabian verfasserin aut Gudziol, Volker verfasserin aut Kemper, Max verfasserin aut Nowak, Alexander verfasserin aut Haim, Dominik verfasserin aut Tinhofer, Inge verfasserin aut Budach, Volker verfasserin aut Guberina, Maja verfasserin aut Stuschke, Martin verfasserin aut Balermpas, Panagiotis verfasserin aut Rödel, Claus verfasserin aut Schäfer, Henning verfasserin aut Grosu, Anca-Ligia verfasserin aut Abdollahi, Amir verfasserin aut Debus, Jürgen verfasserin aut Ganswindt, Ute verfasserin aut Belka, Claus verfasserin aut Pigorsch, Steffi verfasserin aut Combs, Stephanie E. verfasserin aut Boeke, Simon verfasserin aut Zips, Daniel verfasserin aut Baretton, Gustavo B. verfasserin aut Baumann, Michael verfasserin aut Krause, Mechthild verfasserin aut Löck, Steffen verfasserin aut Enthalten in Radiotherapy and oncology Amsterdam [u.a.] : Elsevier Science, 1983 171, Seite 91-100 Online-Ressource (DE-627)306710110 (DE-600)1500707-8 (DE-576)082435731 1879-0887 nnns volume:171 pages:91-100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.64 Radiologie AR 171 91-100 |
allfields_unstemmed |
10.1016/j.radonc.2022.04.006 doi (DE-627)ELV007989369 (ELSEVIER)S0167-8140(22)00187-6 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.64 bkl Patil, Shivaprasad verfasserin aut Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning Linge, Annett verfasserin aut Grosser, Marianne verfasserin aut Lohaus, Fabian verfasserin aut Gudziol, Volker verfasserin aut Kemper, Max verfasserin aut Nowak, Alexander verfasserin aut Haim, Dominik verfasserin aut Tinhofer, Inge verfasserin aut Budach, Volker verfasserin aut Guberina, Maja verfasserin aut Stuschke, Martin verfasserin aut Balermpas, Panagiotis verfasserin aut Rödel, Claus verfasserin aut Schäfer, Henning verfasserin aut Grosu, Anca-Ligia verfasserin aut Abdollahi, Amir verfasserin aut Debus, Jürgen verfasserin aut Ganswindt, Ute verfasserin aut Belka, Claus verfasserin aut Pigorsch, Steffi verfasserin aut Combs, Stephanie E. verfasserin aut Boeke, Simon verfasserin aut Zips, Daniel verfasserin aut Baretton, Gustavo B. verfasserin aut Baumann, Michael verfasserin aut Krause, Mechthild verfasserin aut Löck, Steffen verfasserin aut Enthalten in Radiotherapy and oncology Amsterdam [u.a.] : Elsevier Science, 1983 171, Seite 91-100 Online-Ressource (DE-627)306710110 (DE-600)1500707-8 (DE-576)082435731 1879-0887 nnns volume:171 pages:91-100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.64 Radiologie AR 171 91-100 |
allfieldsGer |
10.1016/j.radonc.2022.04.006 doi (DE-627)ELV007989369 (ELSEVIER)S0167-8140(22)00187-6 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.64 bkl Patil, Shivaprasad verfasserin aut Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning Linge, Annett verfasserin aut Grosser, Marianne verfasserin aut Lohaus, Fabian verfasserin aut Gudziol, Volker verfasserin aut Kemper, Max verfasserin aut Nowak, Alexander verfasserin aut Haim, Dominik verfasserin aut Tinhofer, Inge verfasserin aut Budach, Volker verfasserin aut Guberina, Maja verfasserin aut Stuschke, Martin verfasserin aut Balermpas, Panagiotis verfasserin aut Rödel, Claus verfasserin aut Schäfer, Henning verfasserin aut Grosu, Anca-Ligia verfasserin aut Abdollahi, Amir verfasserin aut Debus, Jürgen verfasserin aut Ganswindt, Ute verfasserin aut Belka, Claus verfasserin aut Pigorsch, Steffi verfasserin aut Combs, Stephanie E. verfasserin aut Boeke, Simon verfasserin aut Zips, Daniel verfasserin aut Baretton, Gustavo B. verfasserin aut Baumann, Michael verfasserin aut Krause, Mechthild verfasserin aut Löck, Steffen verfasserin aut Enthalten in Radiotherapy and oncology Amsterdam [u.a.] : Elsevier Science, 1983 171, Seite 91-100 Online-Ressource (DE-627)306710110 (DE-600)1500707-8 (DE-576)082435731 1879-0887 nnns volume:171 pages:91-100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.64 Radiologie AR 171 91-100 |
allfieldsSound |
10.1016/j.radonc.2022.04.006 doi (DE-627)ELV007989369 (ELSEVIER)S0167-8140(22)00187-6 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.64 bkl Patil, Shivaprasad verfasserin aut Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning Linge, Annett verfasserin aut Grosser, Marianne verfasserin aut Lohaus, Fabian verfasserin aut Gudziol, Volker verfasserin aut Kemper, Max verfasserin aut Nowak, Alexander verfasserin aut Haim, Dominik verfasserin aut Tinhofer, Inge verfasserin aut Budach, Volker verfasserin aut Guberina, Maja verfasserin aut Stuschke, Martin verfasserin aut Balermpas, Panagiotis verfasserin aut Rödel, Claus verfasserin aut Schäfer, Henning verfasserin aut Grosu, Anca-Ligia verfasserin aut Abdollahi, Amir verfasserin aut Debus, Jürgen verfasserin aut Ganswindt, Ute verfasserin aut Belka, Claus verfasserin aut Pigorsch, Steffi verfasserin aut Combs, Stephanie E. verfasserin aut Boeke, Simon verfasserin aut Zips, Daniel verfasserin aut Baretton, Gustavo B. verfasserin aut Baumann, Michael verfasserin aut Krause, Mechthild verfasserin aut Löck, Steffen verfasserin aut Enthalten in Radiotherapy and oncology Amsterdam [u.a.] : Elsevier Science, 1983 171, Seite 91-100 Online-Ressource (DE-627)306710110 (DE-600)1500707-8 (DE-576)082435731 1879-0887 nnns volume:171 pages:91-100 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.64 Radiologie AR 171 91-100 |
language |
English |
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Enthalten in Radiotherapy and oncology 171, Seite 91-100 volume:171 pages:91-100 |
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Enthalten in Radiotherapy and oncology 171, Seite 91-100 volume:171 pages:91-100 |
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Onkologie Radiologie |
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Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning |
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Radiotherapy and oncology |
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Patil, Shivaprasad @@aut@@ Linge, Annett @@aut@@ Grosser, Marianne @@aut@@ Lohaus, Fabian @@aut@@ Gudziol, Volker @@aut@@ Kemper, Max @@aut@@ Nowak, Alexander @@aut@@ Haim, Dominik @@aut@@ Tinhofer, Inge @@aut@@ Budach, Volker @@aut@@ Guberina, Maja @@aut@@ Stuschke, Martin @@aut@@ Balermpas, Panagiotis @@aut@@ Rödel, Claus @@aut@@ Schäfer, Henning @@aut@@ Grosu, Anca-Ligia @@aut@@ Abdollahi, Amir @@aut@@ Debus, Jürgen @@aut@@ Ganswindt, Ute @@aut@@ Belka, Claus @@aut@@ Pigorsch, Steffi @@aut@@ Combs, Stephanie E. @@aut@@ Boeke, Simon @@aut@@ Zips, Daniel @@aut@@ Baretton, Gustavo B. @@aut@@ Baumann, Michael @@aut@@ Krause, Mechthild @@aut@@ Löck, Steffen @@aut@@ |
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2022-01-01T00:00:00Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV007989369</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524150545.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230507s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.radonc.2022.04.006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV007989369</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-8140(22)00187-6</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.81</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.64</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Patil, Shivaprasad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. 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Patil, Shivaprasad |
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Patil, Shivaprasad ddc 610 bkl 44.81 bkl 44.64 misc Head and neck squamous cell carcinoma misc Gene signature misc Postoperative radiotherapy misc Hypoxia misc Cancer stem cells misc Machine learning Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy |
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610 DE-600 44.81 bkl 44.64 bkl Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy Head and neck squamous cell carcinoma Gene signature Postoperative radiotherapy Hypoxia Cancer stem cells Machine learning |
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ddc 610 bkl 44.81 bkl 44.64 misc Head and neck squamous cell carcinoma misc Gene signature misc Postoperative radiotherapy misc Hypoxia misc Cancer stem cells misc Machine learning |
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Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy |
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Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy |
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Patil, Shivaprasad Linge, Annett Grosser, Marianne Lohaus, Fabian Gudziol, Volker Kemper, Max Nowak, Alexander Haim, Dominik Tinhofer, Inge Budach, Volker Guberina, Maja Stuschke, Martin Balermpas, Panagiotis Rödel, Claus Schäfer, Henning Grosu, Anca-Ligia Abdollahi, Amir Debus, Jürgen Ganswindt, Ute Belka, Claus Pigorsch, Steffi Combs, Stephanie E. Boeke, Simon Zips, Daniel Baretton, Gustavo B. Baumann, Michael Krause, Mechthild Löck, Steffen |
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Patil, Shivaprasad |
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10.1016/j.radonc.2022.04.006 |
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verfasserin |
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development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with hpv-negative locally advanced hnscc treated by postoperative radio(chemo)therapy |
title_auth |
Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy |
abstract |
Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. |
abstractGer |
Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. |
abstract_unstemmed |
Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy. |
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title_short |
Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy |
remote_bool |
true |
author2 |
Linge, Annett Grosser, Marianne Lohaus, Fabian Gudziol, Volker Kemper, Max Nowak, Alexander Haim, Dominik Tinhofer, Inge Budach, Volker Guberina, Maja Stuschke, Martin Balermpas, Panagiotis Rödel, Claus Schäfer, Henning Grosu, Anca-Ligia Abdollahi, Amir Debus, Jürgen Ganswindt, Ute Belka, Claus Pigorsch, Steffi Combs, Stephanie E. Boeke, Simon Zips, Daniel Baretton, Gustavo B. Baumann, Michael Krause, Mechthild Löck, Steffen |
author2Str |
Linge, Annett Grosser, Marianne Lohaus, Fabian Gudziol, Volker Kemper, Max Nowak, Alexander Haim, Dominik Tinhofer, Inge Budach, Volker Guberina, Maja Stuschke, Martin Balermpas, Panagiotis Rödel, Claus Schäfer, Henning Grosu, Anca-Ligia Abdollahi, Amir Debus, Jürgen Ganswindt, Ute Belka, Claus Pigorsch, Steffi Combs, Stephanie E. Boeke, Simon Zips, Daniel Baretton, Gustavo B. Baumann, Michael Krause, Mechthild Löck, Steffen |
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306710110 |
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c |
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hochschulschrift_bool |
false |
doi_str |
10.1016/j.radonc.2022.04.006 |
up_date |
2024-07-06T18:09:52.560Z |
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1803854170094043136 |
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