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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

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          Abstract

          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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          Most cited references42

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

              Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2022
                8 June 2022
                : 12
                : 6
                : e061674
                Affiliations
                [1 ]departmentDepartment of Thoracic Oncology , National Kyushu Cancer Center , Fukuoka, Japan
                [2 ]departmentMedical Information Center , Kyushu University , Fukuoka, Japan
                [3 ]departmentDepartment of Thoracic Surgery , National Hospital Organisation Kyushu Medical Center , Fukuoka, Japan
                Author notes
                [Correspondence to ] Dr Fumihiro Shoji; fumshojifumshoji@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-0636-8696
                Article
                bmjopen-2022-061674
                10.1136/bmjopen-2022-061674
                9185567
                35676015
                980cbfdc-3c0e-4ef2-9326-7ba19c62c1a9
                © 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.

                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/.

                History
                : 02 February 2022
                : 30 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008732, Uehara Memorial Foundation;
                Award ID: N/A
                Funded by: Grant-in-Aid for Scientific Research;
                Award ID: 20K09188
                Funded by: FundRef http://dx.doi.org/10.13039/100007434, Suzuken Memorial Foundation;
                Award ID: N/A
                Funded by: FundRef http://dx.doi.org/10.13039/100017684, Japan Dairy Association;
                Award ID: N/A
                Categories
                Oncology
                1506
                1717
                Protocol
                Custom metadata
                unlocked

                Medicine
                respiratory tract tumours,respiratory medicine (see thoracic medicine)
                Medicine
                respiratory tract tumours, respiratory medicine (see thoracic medicine)

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