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The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis

BACKGROUND: The optimal second-line systemic treatment model for recurrent and/or metastatic head and neck squamous cell carcinoma (R/M HNSCC) remains controversial. A Bayesian network meta-analysis (NMA) was performed to address this issue with regard to efficacy and toxicity. METHODS: By searching...

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Autores principales: Zhan, Ze-Jiang, Yao, Wen-Yu, Zhang, Fang, Qiu, Wen-Ze, Liao, Kai-, Feng, Jian-Hui, Tan, Jin-Yun, Liu, Hui, Yuan, Tai-Ze, Zheng, Rong-Hui, Yuan, Ya-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367750/
https://www.ncbi.nlm.nih.gov/pubmed/34413862
http://dx.doi.org/10.3389/fimmu.2021.719650
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author Zhan, Ze-Jiang
Yao, Wen-Yu
Zhang, Fang
Qiu, Wen-Ze
Liao, Kai-
Feng, Jian-Hui
Tan, Jin-Yun
Liu, Hui
Yuan, Tai-Ze
Zheng, Rong-Hui
Yuan, Ya-Wei
author_facet Zhan, Ze-Jiang
Yao, Wen-Yu
Zhang, Fang
Qiu, Wen-Ze
Liao, Kai-
Feng, Jian-Hui
Tan, Jin-Yun
Liu, Hui
Yuan, Tai-Ze
Zheng, Rong-Hui
Yuan, Ya-Wei
author_sort Zhan, Ze-Jiang
collection PubMed
description BACKGROUND: The optimal second-line systemic treatment model for recurrent and/or metastatic head and neck squamous cell carcinoma (R/M HNSCC) remains controversial. A Bayesian network meta-analysis (NMA) was performed to address this issue with regard to efficacy and toxicity. METHODS: By searching MEDLINE (via PubMed), Embase, the Cochrane Central Register of Controlled Trials and Web of Science, we extracted eligible studies. Efficacy, represented as overall survival (OS) and progression-free survival (PFS), and overall toxicity, represented as ≥ grade 3 severe acute events (sAE), were assessed to compare the following 7 treatment models through an NMA: standard-of-care therapy (SoC), single targeted therapy different from SoC (ST), double targeted therapy (DT), targeted therapy combined with chemotherapy (T+C), single immune checkpoint inhibitor therapy (SI), double immune checkpoint inhibitor therapy (DI) and single chemotherapy different from SoC (SC). Rank probabilities according to the values of the surface under the cumulative ranking curve (SUCRA) were separately determined for efficacy and toxicity. RESULTS: In total, 5285 patients from 24 eligible studies were ultimately screened, with 5184, 4532 and 4026 involved in the NMA of OS, PFS and sAE, respectively. All qualifying studies were absent from first-line immune checkpoint inhibitor therapy. In terms of OS, SI was superior to the other treatments, followed by DI, ST, T+C, SoC, DT and SC. Other than SI and SC, all treatments tended to be consistent, with hazard ratios (HRs) close to 1 between groups. For PFS, ST ranked first, while DT ranked last. For the toxicity profiles, compared with the other models, SI resulted in the lowest incidences of sAE, with statistical significance over SoC (odds ratio [OR] 0.31, 95% credible interval [CrI] 0.11 to 0.90), ST (OR 0.23, 95% CrI 0.06 to 0.86) and DT (OR 0.11, 95% CrI 0.02 to 0.53), while DT was the worst. When the SUCRA values of OS and sAE were combined, a cluster plot illustrated the superiority of SI, which demonstrated the best OS and tolerability toward sAE. CONCLUSION: For R/M HNSCC patients without immune checkpoint inhibitors in the first-line setting, SI may serve as the optimal second-line systemic treatment model, demonstrating the best OS and least sAE.
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spelling pubmed-83677502021-08-18 The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis Zhan, Ze-Jiang Yao, Wen-Yu Zhang, Fang Qiu, Wen-Ze Liao, Kai- Feng, Jian-Hui Tan, Jin-Yun Liu, Hui Yuan, Tai-Ze Zheng, Rong-Hui Yuan, Ya-Wei Front Immunol Immunology BACKGROUND: The optimal second-line systemic treatment model for recurrent and/or metastatic head and neck squamous cell carcinoma (R/M HNSCC) remains controversial. A Bayesian network meta-analysis (NMA) was performed to address this issue with regard to efficacy and toxicity. METHODS: By searching MEDLINE (via PubMed), Embase, the Cochrane Central Register of Controlled Trials and Web of Science, we extracted eligible studies. Efficacy, represented as overall survival (OS) and progression-free survival (PFS), and overall toxicity, represented as ≥ grade 3 severe acute events (sAE), were assessed to compare the following 7 treatment models through an NMA: standard-of-care therapy (SoC), single targeted therapy different from SoC (ST), double targeted therapy (DT), targeted therapy combined with chemotherapy (T+C), single immune checkpoint inhibitor therapy (SI), double immune checkpoint inhibitor therapy (DI) and single chemotherapy different from SoC (SC). Rank probabilities according to the values of the surface under the cumulative ranking curve (SUCRA) were separately determined for efficacy and toxicity. RESULTS: In total, 5285 patients from 24 eligible studies were ultimately screened, with 5184, 4532 and 4026 involved in the NMA of OS, PFS and sAE, respectively. All qualifying studies were absent from first-line immune checkpoint inhibitor therapy. In terms of OS, SI was superior to the other treatments, followed by DI, ST, T+C, SoC, DT and SC. Other than SI and SC, all treatments tended to be consistent, with hazard ratios (HRs) close to 1 between groups. For PFS, ST ranked first, while DT ranked last. For the toxicity profiles, compared with the other models, SI resulted in the lowest incidences of sAE, with statistical significance over SoC (odds ratio [OR] 0.31, 95% credible interval [CrI] 0.11 to 0.90), ST (OR 0.23, 95% CrI 0.06 to 0.86) and DT (OR 0.11, 95% CrI 0.02 to 0.53), while DT was the worst. When the SUCRA values of OS and sAE were combined, a cluster plot illustrated the superiority of SI, which demonstrated the best OS and tolerability toward sAE. CONCLUSION: For R/M HNSCC patients without immune checkpoint inhibitors in the first-line setting, SI may serve as the optimal second-line systemic treatment model, demonstrating the best OS and least sAE. Frontiers Media S.A. 2021-08-02 /pmc/articles/PMC8367750/ /pubmed/34413862 http://dx.doi.org/10.3389/fimmu.2021.719650 Text en Copyright © 2021 Zhan, Yao, Zhang, Qiu, Liao, Feng, Tan, Liu, Yuan, Zheng and Yuan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Zhan, Ze-Jiang
Yao, Wen-Yu
Zhang, Fang
Qiu, Wen-Ze
Liao, Kai-
Feng, Jian-Hui
Tan, Jin-Yun
Liu, Hui
Yuan, Tai-Ze
Zheng, Rong-Hui
Yuan, Ya-Wei
The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis
title The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis
title_full The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis
title_fullStr The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis
title_full_unstemmed The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis
title_short The Optimal Second-Line Systemic Treatment Model for Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: A Bayesian Network Meta-Analysis
title_sort optimal second-line systemic treatment model for recurrent and/or metastatic head and neck squamous cell carcinoma: a bayesian network meta-analysis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367750/
https://www.ncbi.nlm.nih.gov/pubmed/34413862
http://dx.doi.org/10.3389/fimmu.2021.719650
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