Study protocol: the 'Endoscope CRC' cohort, a prospective biobank study on the development and evaluation of diagnostic and prognostic biomarker profiles for colorectal cancer and premalignant lesions.

Abstract

Introduction: Early detection of colorectal cancer (CRC) and clinically relevant (advanced) adenomas leads to a significant reduction of CRC-related mortality and morbidity. However, the faecal immunochemical test (FIT) suffers from a high number of false-positive results and is insensitive to detecting advanced adenomas, resulting in false-negative results for these premalignant lesions. Therefore, more accurate, non-invasive screening tools are needed for the detection and prognostication of colorectal neoplasia. Previous research on volatile organic compounds (VOCs) analysis in breath and faeces has shown to be promising potential biomarkers for this purpose. Several VOC-sampling methods, including breath sampling, have improved significantly over the recent years resulting in an increased reliability of measurements. Therefore, we aim to identify relevant VOC profiles in exhaled breath and faeces for the diagnosis of colorectal neoplasia while taking into account relevant confounding factors. Follow-up data will be used to identify relevant VOC profiles in exhaled breath and faeces for the prognostication of colorectal neoplasia. Finally, a biobank will be set up for future research questions on this topic.

Methods and analysis: Subjects with positive FIT within the Dutch national CRC cancer screening programme are included. Subjects are asked to fill in questionnaires and exhaled breath, faeces and blood are sampled prior to colonoscopy. All subjects are asked to fill in follow-up questionnaires at years 1 and 5 of the study. In case of surveillance colonoscopies, subjects are asked to provide exhaled breath, faeces and blood prior to the colonoscopy again. Breath sampling is performed using the ReCIVA breath sampler. VOCs in breath and faeces are analysed using gas-chromatography-mass spectrometry (GC-MS). Raw GC-MS data is preprocessed and analysed using machine learning techniques.

Ethics and dissemination: The study is approved by the medical ethics committee at the Maastricht University Medical Center (NL74844.068.20) in November 2021 and started inclusion in January 2022.