Microbiome Testing for the Screening of Colorectal Cancer (NI-GUILTI)

A Non-invasive Gut Microbiome-based Diagnostic Tool for the Screening of Colorectal Cancer

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 years 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 at 24 months, using both statistical and machine learning approaches

The secondary endpoints are:

• The association of clinical and colonoscopy outcomes with FIT results at 24 months
• The characterization of gut microbiome from an ecological, taxonomic, phylogenetic and functional point of view at 24 months
• The association between microbiome signatures with clinical and colonoscopy outcomes at 24 months, 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.

Study Overview

• Status: Recruiting
• Conditions: Colorectal Cancer
• Intervention / Treatment: Diagnostic test: Gut microbiome testing

Detailed Description

Colorectal cancer (CRC) is the 3rd most common cancer and the 2nd most common cause of cancer death worldwide, with nearly 2 million new cases and one million deaths in 2020. In the last decades, a population-based screening programs for average-risk populations have established as an effective strategy to reduce both incidence and mortality of CRC through early detection of cancer. The fecal immunochemical testing (FIT), the reference diagnostic tool in most countries, has still a suboptimal diagnostic yield, with both missed adenomas and, mainly, unnecessary colonoscopies. For these reasons, in the last years the identification of novel, non-invasive biomarkers is absolutely advocatein 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, including metagenomic meta-analyses from our group, have discovered common microbial signatures able to reproducibly discriminate between patients with CRC and healthy controls. Based on this evidence, international guidelines have recently advocated the exploitation of microbiome-based biomarkers for the screening of CRC in clinical practice, but such studies are not yet available to date.

The primary objective is to develop a gut microbiome-based diagnostic tool for the identification of CRC and advanced colorectal adenomas.

The secondary objectives are:

• To associate FIT with clinical and colonoscopy outcomes
• To characterize gut microbiome of enrolled patients
• To associate microbiome signatures with clinical and colonoscopy outcomes

This is an observational prospective multicenter study, in which patients 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 (detailed in the specific section of this website) will be considered for this study. A total of n=1006 subjects will be enrolled. More specifically, 838 patients will be enrolled based on sample size calculation and to cover a 10% dropout risk. Moreover, an additional cohort of 168 patients (20% of the total) will be enrolled to be used as an internal validation set for rigorous validation of the identified biomarkers (validation 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.

At baseline all enrolled patients will provide a fecal sample (collected using a buffer for genome preservation) within 2 weeks from enrollment and will be stored at -80°C at each clinical center and assigned de-identified IDs.

Moreover demographic, clinical characteristics and laboratory data will be recorded.

For all enrolled patients clinicians will record the following data:

-Familiar history of CRC; -Comorbidities; -Drug intake; -Gastrointestinal (GI) and alarm symptoms (iron-deficiency anemia, hematochezia, unexplained weight loss, abdominal pain, sudden change in bowel habits); -Dietary habits; -Smoking and alcohol consumption; -BMI; -Information about previous FIT and/or colonoscopies and/or virtual colonoscopies

All enrolled patients will undergo a colonoscopy within 4 weeks from the positive FIT, after colonoscopy endoscopic characteristics (size, site, shape based on Paris class.) and histopathological characteristics of detected lesions will be collected.

Advanced colorectal adenomas will be defined as adenomas larger than or equal to 10 mm, and/or with villous components higher than or equal to 25%, and/or high-grade dysplasia.

Moreover, patients diagnosed with CRC, will also undergo a total-body CT scan to stage the disease and will be referred to an oncological pathway in clinical practice, and the following data will be collected: presence of lymph node metastasis and of organ metastasis, TNM class.

The enrollment phase will last 20 months.

At the end of the enrollment phase, the investigators will perform the microbiome analysis, and the investigators will combined clinical, endoscopic and microbial data through statistical and machine learning algorithms to identify specific microbial biomarkers associated with CRC and advanced colorectal adenomas, to develop a new diagnostic tool, based on a scoring system. The analytic phase will last 4 months.

Study Outcomes are detailed in the specific section of this website.

Microbiome analysis will be performed with shotgun sequencing techniques. Moreover to obtain a panel of microbial species mostly associated with CRC and advanced colorectal adenomas, the investigators will exploit machine learning applications, for instance, using the widely adopted Random Forest algorithms, as well as meta-analytical approaches integrating our large cohort with previously published metagenomic cohorts. A defined panel of microbial species mostly associated with CRC and advanced colorectal adenomas will provide a comprehensive risk score based on microbiome features, with two specific goals, to improve the accuracy testing of the FIT diagnostic tool and to be easily interpretable by clinicians.

All the collected clinical data will be statistically combined to rank microbial features from the most to least associated. These rankings will be exploited to evaluate a microbiome profile from a new individual, and to report how many of the defined species in the previous panel are found, and hence linked with CRC and advanced colorectal adenomas. Associations between identified species and makers of interest will be estimated through robust statistical analysis methods, such as partial correlation or linear modeling. Then the associations will be ranked and compared across markers for the same species, giving a priority score for each species. Particular care will be dedicated to identifying potential confounding markers known to be associated with differences in microbiome composition, for instance, age, that instead should be used as covariates in the statistical modeling. In addition to statistical modeling, associations between species and markers will also be evaluated via ML approaches, particularly those providing feature importance scores for the input features (species, in our case), that can be used to rank the species across the different markers (e.g. Random Forest, LASSO, etc.).

Continuous variables will be reported as mean ± standard deviation or as median and interquartile range (IQR), and categorical variables were summarized as frequency and percentage. Comparisons of variables will be made by t-test (or Mann-Whitney/Kruskal-Wallis), Chi-square test, or Fisher's exact test, as appropriate. A p-value <0.05 will be considered to indicate statistical significance. Logistic regression models will be performed to identify the presence of variables independently associated with outcomes. Variables considered in the models were selected through stepwise model selection and guided by clinical relevance. All statistical analyses were performed using SPSS v. 28.0 for Macintosh (SPSS Inc., Chicago, USA). The investigators will employ a Machine Learning (ML) framework based on the scikit-learn Python package using a cross-validation approach with 100 bootstrap iterations and an 80/20 random splitting into training and testing folds. Classification and regression ML algorithms will be trained on both microbiome species-level taxonomic relative abundances and functional potential profiles. Species-level taxonomic abundances will be estimated by MetaPhlAn 4 (using the latest databases available, currently named Oct22) and normalized using the arcsin-sqrt transformation for compositional data. Functional potential profiles will be estimated using the latest HUMAnN 4 version and the investigators will consider both abundance estimations of single microbial gene families and of metabolic pathways. Partial correlations between species and markers will be computed using the Spearman index, to avoid biases due to outlier values and corrected for covariates, such as sex, age, and body-mass index, known to have an impact on microbiome composition. Partial correlation values will be corrected using the Benjamini-Hochberg procedure for the false-discovery rate.

• Study Type: Observational
• Enrollment (Estimated): 1006

Contacts and Locations

• Study Contact: Name: Gianluca Ianiro, MD, PhD; Phone Number: 0630159539; Email: gianluca.ianiro@unicatt.it
• Study Contact Backup: Name: Serena Porcari, MD, PhD; Phone Number: 0630159539; Email: serena.porcari@guest.policlinicogemelli.it

Study Locations

• Italy
  • Roma, Italy, 00168
    • Recruiting
    • Fondazione Policlinico Agostino Gemelli, IRCCS
    • Contact: Fondazione Policlinico Agostino Gemelli, IRCCS; Phone Number: 00390630151; Email: gianluca.ianiro@unicatt.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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 n=1006 subjects will be enrolled. More specifically, 838 patients will be enrolled based on sample size calculation and to cover a 10% dropout risk. Moreover, an additional cohort of 168 patients (20% of the total) will be enrolled to be used as an internal validation set for rigorous validation of the identified biomarkers. Patients enrolled in this validation cohort will have the same exclusion and inclusion criteria of other patients, and will undergo the same study procedures.

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Study cohort consists of patients enrolled in the national colorectal cancer (CRC) screening program; 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. In this cohort 1006 patients will be enrolled (838 patients by sample size + 168 patients as validation cohort)

Diagnostic test: Gut microbiome testing; Gut microbiome testing for the characterization of the patient gut microbiome

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gut microbiome-based diagnostic tool for CRC and advanced colorectal adenomas detection	The development of 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	24 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Association between clinical and endoscopic outcomes with FIT	The association of clinical and colonoscopy outcomes with fecal immunochemical testing results.	24 months
Microbiome characteristics of fecal samples	The characterization of gut microbiome from an ecological, taxonomic, phylogenetic and functional point of view	24 months
Association between microbiome signatures with clinical and endoscopic outcomes	The association between microbiome signatures with clinical and colonoscopy outcomes, through statistical and machine-learning algorithms	24 months

Collaborators and Investigators

• Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
• Investigators: Principal Investigator: Gianluca Ianiro, MD, PhD, Fondazione Policlinico Agostino Gemelli IRCCS

Publications and helpful links

General Publications

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• Gupta S, Halm EA, Rockey DC, Hammons M, Koch M, Carter E, Valdez L, Tong L, Ahn C, Kashner M, Argenbright K, Tiro J, Geng Z, Pruitt S, Skinner CS. Comparative effectiveness of fecal immunochemical test outreach, colonoscopy outreach, and usual care for boosting colorectal cancer screening among the underserved: a randomized clinical trial. JAMA Intern Med. 2013 Oct 14;173(18):1725-32. doi: 10.1001/jamainternmed.2013.9294.
• Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
• Ladabaum U, Dominitz JA, Kahi C, Schoen RE. Strategies for Colorectal Cancer Screening. Gastroenterology. 2020 Jan;158(2):418-432. doi: 10.1053/j.gastro.2019.06.043. Epub 2019 Aug 5.
• Thomas AM, Manghi P, Asnicar F, Pasolli E, Armanini F, Zolfo M, Beghini F, Manara S, Karcher N, Pozzi C, Gandini S, Serrano D, Tarallo S, Francavilla A, Gallo G, Trompetto M, Ferrero G, Mizutani S, Shiroma H, Shiba S, Shibata T, Yachida S, Yamada T, Wirbel J, Schrotz-King P, Ulrich CM, Brenner H, Arumugam M, Bork P, Zeller G, Cordero F, Dias-Neto E, Setubal JC, Tett A, Pardini B, Rescigno M, Waldron L, Naccarati A, Segata N. Author Correction: Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat Med. 2019 Dec;25(12):1948. doi: 10.1038/s41591-019-0663-4.
• Schlemper RJ, Riddell RH, Kato Y, Borchard F, Cooper HS, Dawsey SM, Dixon MF, Fenoglio-Preiser CM, Flejou JF, Geboes K, Hattori T, Hirota T, Itabashi M, Iwafuchi M, Iwashita A, Kim YI, Kirchner T, Klimpfinger M, Koike M, Lauwers GY, Lewin KJ, Oberhuber G, Offner F, Price AB, Rubio CA, Shimizu M, Shimoda T, Sipponen P, Solcia E, Stolte M, Watanabe H, Yamabe H. The Vienna classification of gastrointestinal epithelial neoplasia. Gut. 2000 Aug;47(2):251-5. doi: 10.1136/gut.47.2.251.
• Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N, Armanini F, Beghini F, Manghi P, Tett A, Ghensi P, Collado MC, Rice BL, DuLong C, Morgan XC, Golden CD, Quince C, Huttenhower C, Segata N. Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. Cell. 2019 Jan 24;176(3):649-662.e20. doi: 10.1016/j.cell.2019.01.001. Epub 2019 Jan 17.
• Blanco-Miguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, Manghi P, Dubois L, Huang KD, Thomas AM, Nickols WA, Piccinno G, Piperni E, Puncochar M, Valles-Colomer M, Tett A, Giordano F, Davies R, Wolf J, Berry SE, Spector TD, Franzosa EA, Pasolli E, Asnicar F, Huttenhower C, Segata N. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol. 2023 Nov;41(11):1633-1644. doi: 10.1038/s41587-023-01688-w. Epub 2023 Feb 23.
• Beghini F, McIver LJ, Blanco-Miguez A, Dubois L, Asnicar F, Maharjan S, Mailyan A, Manghi P, Scholz M, Thomas AM, Valles-Colomer M, Weingart G, Zhang Y, Zolfo M, Huttenhower C, Franzosa EA, Segata N. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021 May 4;10:e65088. doi: 10.7554/eLife.65088.
• Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Bohm J, Brunetti F, Habermann N, Hercog R, Koch M, Luciani A, Mende DR, Schneider MA, Schrotz-King P, Tournigand C, Tran Van Nhieu J, Yamada T, Zimmermann J, Benes V, Kloor M, Ulrich CM, von Knebel Doeberitz M, Sobhani I, Bork P. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol. 2014 Nov 28;10(11):766. doi: 10.15252/msb.20145645.
• Wong CC, Yu J. Gut microbiota in colorectal cancer development and therapy. Nat Rev Clin Oncol. 2023 Jul;20(7):429-452. doi: 10.1038/s41571-023-00766-x. Epub 2023 May 11.
• Chan FKL, Wong MCS, Chan AT, East JE, Chiu HM, Makharia GK, Weller D, Ooi CJ, Limsrivilai J, Saito Y, Hang DV, Emery JD, Makmun D, Wu K, Ali RAR, Ng SC. Joint Asian Pacific Association of Gastroenterology (APAGE)-Asian Pacific Society of Digestive Endoscopy (APSDE) clinical practice guidelines on the use of non-invasive biomarkers for diagnosis of colorectal neoplasia. Gut. 2023 Jul;72(7):1240-1254. doi: 10.1136/gutjnl-2023-329429. Epub 2023 Apr 5.
• Morikawa T, Kato J, Yamaji Y, Wada R, Mitsushima T, Shiratori Y. A comparison of the immunochemical fecal occult blood test and total colonoscopy in the asymptomatic population. Gastroenterology. 2005 Aug;129(2):422-8. doi: 10.1016/j.gastro.2005.05.056.
• Thomas AM, Manghi P, Asnicar F, Pasolli E, Armanini F, Zolfo M, Beghini F, Manara S, Karcher N, Pozzi C, Gandini S, Serrano D, Tarallo S, Francavilla A, Gallo G, Trompetto M, Ferrero G, Mizutani S, Shiroma H, Shiba S, Shibata T, Yachida S, Yamada T, Wirbel J, Schrotz-King P, Ulrich CM, Brenner H, Arumugam M, Bork P, Zeller G, Cordero F, Dias-Neto E, Setubal JC, Tett A, Pardini B, Rescigno M, Waldron L, Naccarati A, Segata N. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat Med. 2019 Apr;25(4):667-678. doi: 10.1038/s41591-019-0405-7. Epub 2019 Apr 1.
• Quintero E, Castells A, Bujanda L, Cubiella J, Salas D, Lanas A, Andreu M, Carballo F, Morillas JD, Hernandez C, Jover R, Montalvo I, Arenas J, Laredo E, Hernandez V, Iglesias F, Cid E, Zubizarreta R, Sala T, Ponce M, Andres M, Teruel G, Peris A, Roncales MP, Polo-Tomas M, Bessa X, Ferrer-Armengou O, Grau J, Serradesanferm A, Ono A, Cruzado J, Perez-Riquelme F, Alonso-Abreu I, de la Vega-Prieto M, Reyes-Melian JM, Cacho G, Diaz-Tasende J, Herreros-de-Tejada A, Poves C, Santander C, Gonzalez-Navarro A; COLONPREV Study Investigators. Colonoscopy versus fecal immunochemical testing in colorectal-cancer screening. N Engl J Med. 2012 Feb 23;366(8):697-706. doi: 10.1056/NEJMoa1108895.

Study record dates

Study Major Dates

• Study Start (Actual): November 29, 2024
• Primary Completion (Estimated): August 30, 2026
• Study Completion (Estimated): February 28, 2027

Study Registration Dates

• First Submitted: September 5, 2024
• First Submitted That Met QC Criteria: September 5, 2024
• First Posted (Actual): September 19, 2024

Study Record Updates

• Last Update Posted (Actual): March 19, 2026
• Last Update Submitted That Met QC Criteria: March 18, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: colorectal cancer; microbiome; colorectal cancer screening; microbiome testing
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Intestinal Diseases; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Colonic Diseases; Colorectal Neoplasms
• Other Study ID Numbers: 6902; PNRR-POC-2023-12377319 (Other Grant/Funding Number: Italian Health Minister)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The individual data of patients will be shared
• IPD Sharing Time Frame: Data will be available after the completion of the study, for 5 years
• IPD Sharing Access Criteria: Data will be given upon reasonable request to the PI
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No