Colorectal cancer (CRC) is one of the most common cancer and cause of cancer death worldwide. Population-based screening programs for average risk populations have proven effective in reducing both incidence and mortality of CRC through early detection of cancer. The fecal immunochemical testing (FIT), has still a suboptimal diagnostic yield, with both missed adenomas and, mainly, unnecessary colonoscopies.The identification of novel, non-invasive biomarkers is currently one of the research areas driving most expenditure forces in the field of CRC.A large body of evidence shows that alterations of the gut microbiome and the enrichment of specific taxa(e.g. Fusobacterium nucleatum, Parvimonas micra, and others) are involved in the pathogenesis of CRC. Moreover, recent studies, have discovered common microbial signatures able to reproducibly discriminate between patients with CRC and healthy controls.The goal of this observational study to develop a gut microbiome based diagnostic tool for the identification of CRC and advanced colorectal adenomas in patients enrolled in the national colorectal cancer (CRC) screening program (50-74 year-old) and among who refer to all centers involved in this study for screening colonoscopy with positivity of FIT, of both sex. The primary endpoint of the study is to develop a gut microbiome-based diagnostic tool for the identification of CRC and advanced colorectal adenomas in patients involved in the national CRC screening program, using both statistical and machine learning approaches. The secondary endpoints are:
* The association of clinical and colonoscopy outcomes with FIT results; * The characterization of gut microbiome from an ecological, taxonomic, phylogenetic and functional point of view; * The association between microbiome signatures with clinical and colonoscopy outcomes, through statistical and machine-learning algorithms. At baseline, enrolled patients will provide a fecal sample within 2 weeks from enrollment and demographic, clinical characteristics and laboratory data will be recorded. Enrolled patients will be scheduled for colonoscopy, as for clinical practice, within 4 weeks from the positive FIT and histology of resected lesions will be assessed by experienced pathologists according to the WHO classification and the Vienna criteria. Clinical, endoscopic and microbial data will be combined through statistical and machine learning algorithms to identify specific microbial biomarkers associated with CRC and develop a new diagnostic tool, based on a scoring system. This tool will be validated, and its diagnostic performances will be compared with traditional screening methods.
Patients participating in the national CRC screening program (50-74 years old)
Positivity to the FIT;
Ability to provide written informed consent and to be compliant with the study procedures.
Patients unfit for colonoscopy;
Other oncological conditions;
Concomitant severe comorbidities or gastrointestinal (GI) organic diseases (e.g. diverticular disease, inflammatory bowel disease);
Antibiotics, proton pump inhibitors or probiotics within 4 weeks prior to enrollment.
Participants will be selected among those enrolled in the national colorectal cancer (CRC) screening program and among who refer to all centers involved in this study for screening colonoscopy. Patients with all inclusion criteria and none of the exclusion criteria will be considered for this study. A total of 1202 patients will be enrolled, based on sample size calculation and including a validation group. N=911 patients will be needed, and we will add 91 patients to cover a 10% potential drop-out and 200 patients as a validation group (20% of the study cohort). Patients enrolled in this validation cohort will have the same exclusion and inclusion criteria of other patients and will undergo the same study procedures.
Cohort
Cross-sectional
Gut microbiome testing
Diagnostic testGut microbiome testing for the characterization of the patient gut microbiome
Gut microbiome-based diagnostic tool for CRC and advanced colorectal adenomas detection
Discovery of a gut microbial signature able to predict the diagnosis of colorectal cancer and advanced colorectal adenomas
Correlation between clinical and endoscopic outcomes with FIT
The correlation of clinical and colonoscopy outcomes with fecal immunochemical testing results
Microbiome characteristics of fecal samples
The characterization of gut microbiome from an ecological, taxonomic, phylogenetic and functional point of view
Correlation between microbiome signatures with clinical and endoscopic outcomes
The correlation between microbiome signatures with clinical and colonoscopy outcomes, through statistical and machine-learning algorithms
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