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Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study

INTRODUCTION: Immunotherapy is the fourth leading therapy for lung cancer following surgery, chemotherapy and radiotherapy. Recently, several studies have reported about the potential association between the gut microbiome and therapeutic response to immunotherapy. Nevertheless, the specific composi...

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Autores principales: Shoji, Fumihiro, Yamashita, Takanori, Kinoshita, Fumihiko, Takamori, Shinkichi, Fujishita, Takatoshi, Toyozawa, Ryo, Ito, Kensaku, Yamazaki, Koji, Nakashima, Naoki, Okamoto, Tatsuro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185567/
https://www.ncbi.nlm.nih.gov/pubmed/35676015
http://dx.doi.org/10.1136/bmjopen-2022-061674
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author Shoji, Fumihiro
Yamashita, Takanori
Kinoshita, Fumihiko
Takamori, Shinkichi
Fujishita, Takatoshi
Toyozawa, Ryo
Ito, Kensaku
Yamazaki, Koji
Nakashima, Naoki
Okamoto, Tatsuro
author_facet Shoji, Fumihiro
Yamashita, Takanori
Kinoshita, Fumihiko
Takamori, Shinkichi
Fujishita, Takatoshi
Toyozawa, Ryo
Ito, Kensaku
Yamazaki, Koji
Nakashima, Naoki
Okamoto, Tatsuro
author_sort Shoji, Fumihiro
collection PubMed
description INTRODUCTION: Immunotherapy is the fourth leading therapy for lung cancer following surgery, chemotherapy and radiotherapy. Recently, several studies have reported about the potential association between the gut microbiome and therapeutic response to immunotherapy. Nevertheless, the specific composition of the gut microbiome or combination of gut microbes that truly predict the efficacy of immunotherapy is not definitive. METHODS AND ANALYSIS: The present multicentre, prospective, observational study aims to discover the specific composition of the gut microbiome or combination of gut microbes predicting the therapeutic response to immunotherapy in lung cancer using artificial intelligence. The main inclusion criteria are as follows: (1) pathologically or cytologically confirmed metastatic or postoperative recurrent lung cancer including non-small cell lung cancer and small cell lung cancer; (2) age≥20 years at the time of informed consent; (3) planned treatment with immunotherapy including combination therapy and monotherapy, as the first-line immunotherapy; and (4) ability to provide faecal samples. In total, 400 patients will be enrolled prospectively. Enrolment will begin in 2021, and the final analyses will be completed by 2024. ETHICS AND DISSEMINATION: The study protocol was approved by the institutional review board of each participating centre in 2021 (Kyushu Cancer Center, IRB approved No. 2021-13, 8 June 2021 and Kyushu Medical Center, IRB approved No. 21-076, 31 August 2021). Study results will be disseminated through peer-reviewed journals and national and international conferences. TRIAL REGISTRATION NUMBER: UMIN000046428.
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spelling pubmed-91855672022-06-16 Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study Shoji, Fumihiro Yamashita, Takanori Kinoshita, Fumihiko Takamori, Shinkichi Fujishita, Takatoshi Toyozawa, Ryo Ito, Kensaku Yamazaki, Koji Nakashima, Naoki Okamoto, Tatsuro BMJ Open Oncology INTRODUCTION: Immunotherapy is the fourth leading therapy for lung cancer following surgery, chemotherapy and radiotherapy. Recently, several studies have reported about the potential association between the gut microbiome and therapeutic response to immunotherapy. Nevertheless, the specific composition of the gut microbiome or combination of gut microbes that truly predict the efficacy of immunotherapy is not definitive. METHODS AND ANALYSIS: The present multicentre, prospective, observational study aims to discover the specific composition of the gut microbiome or combination of gut microbes predicting the therapeutic response to immunotherapy in lung cancer using artificial intelligence. The main inclusion criteria are as follows: (1) pathologically or cytologically confirmed metastatic or postoperative recurrent lung cancer including non-small cell lung cancer and small cell lung cancer; (2) age≥20 years at the time of informed consent; (3) planned treatment with immunotherapy including combination therapy and monotherapy, as the first-line immunotherapy; and (4) ability to provide faecal samples. In total, 400 patients will be enrolled prospectively. Enrolment will begin in 2021, and the final analyses will be completed by 2024. ETHICS AND DISSEMINATION: The study protocol was approved by the institutional review board of each participating centre in 2021 (Kyushu Cancer Center, IRB approved No. 2021-13, 8 June 2021 and Kyushu Medical Center, IRB approved No. 21-076, 31 August 2021). Study results will be disseminated through peer-reviewed journals and national and international conferences. TRIAL REGISTRATION NUMBER: UMIN000046428. BMJ Publishing Group 2022-06-08 /pmc/articles/PMC9185567/ /pubmed/35676015 http://dx.doi.org/10.1136/bmjopen-2022-061674 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Oncology
Shoji, Fumihiro
Yamashita, Takanori
Kinoshita, Fumihiko
Takamori, Shinkichi
Fujishita, Takatoshi
Toyozawa, Ryo
Ito, Kensaku
Yamazaki, Koji
Nakashima, Naoki
Okamoto, Tatsuro
Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
title Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
title_full Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
title_fullStr Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
title_full_unstemmed Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
title_short Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
title_sort artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185567/
https://www.ncbi.nlm.nih.gov/pubmed/35676015
http://dx.doi.org/10.1136/bmjopen-2022-061674
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