care-crc: microbiome insights and correlations for risk and outcomes in colorectal cancer

Conditions: Colorectal Cancer (CRC); Microbiome; Early Onset Colorectal Cancer

Summary: colorectal cancer (crc) is one of the leading causes of cancer-related deaths globally, with increasing incidence rates. while predominantly affecting older adults, crc cases among individuals under 50 (early-onset crc, or eocrc) are rising. this age group rarely undergoes routine screening, resulting in delayed diagnoses and more advanced disease at presentation. in the usa, eocrc accounts for 10% of crc cases and is the leading cause of cancer-related deaths in men under 50. despite the increase in eocrc incidence, the causes remain unclear. only 25% of cases have a crc family history, suggesting environmental factors. diets low in fibre and rich in fat and red meat, obesity, alcohol consumption, sedentary lifestyle, stress, and chronic inflammation of the gi tract are estimated to account for 70-90% of crc risk. according to the world cancer research fund, 47% of all crc cases could be prevented through lifestyle changes, particularly in diet and physical activity. these lifestyle factors are also strongly linked to changes in the gut microbiome, which differs markedly between crc patients and healthy individuals. the microbiome may influence tumour development by producing metabolites that regulate immune responses or create anti-tumour environments. thus, the gut microbiome is a promising target for early crc detection and prevention. this study aims to develop a non-invasive, microbiome-based diagnostic tool for crc, identifying biomarkers to improve early detection, personalise treatment, and reduce healthcare costs.

this study focuses on identifying faecal microbiome biomarkers for crc across different stages, prognoses, and therapeutic responses, with a particular emphasis on eocrc. recruitment will include individuals recently diagnosed with crc who have not undergone treatment. faecal samples will be collected at three key time points: baseline - at diagnosis and study entry. post-treatment - after completing each therapeutic regimen, such as neoadjuvant chemotherapy, surgery and/or adjuvant chemotherapy follow-up - three years after diagnosis. the study will also monitor survival rates and disease progression over three years. primary objective: to identify and characterise changes in the gut microbiome at different crc stages. secondary objectives: i. identify faecal microbiome biomarkers associated with crc overall and stratify findings by age and sex. ii. compare microbiome profiles and associated clinical and lifestyle data between crc patients and healthy controls, stratified by age and sex. iii. identify lifestyle and dietary factors contributing to crc risk, stratified by age and sex. iv. validate biomarkers specific to eocrc. v. correlate microbiota composition with overall survival. vi. correlate microbiota composition with disease-free survival. this is a longitudinal observational study targeting individuals aged 40-74 who have been newly diagnosed with crc and are treatment-naïve 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. using a significance level of 5% and a power of 80%, the required sample size is 80 individuals per age group (40-49, 50-59, 60-64, 65-69, and 70-74 years). this results in a total of 400 participants. a control group of healthy individuals will be recruited from hospital patients undergoing routine screening colonoscopies. biological and clinical data will be collected at multiple time points throughout the study. faecal samples will be obtained at baseline, post-treatment, and during the three-year follow-up, accompanied by blood sample collection at each of these time points. clinical data will include detailed tumour characteristics, such as location, symptoms, and staging, as well as treatment information, including surgery and chemotherapy. outcomes related to survival and disease progression will also be recorded. lifestyle and dietary data will be gathered through self-reported questionnaires capturing demographic factors such as age, smoking habits, physical activity, stress levels, and body mass index (bmi). dietary habits and adherence to the mediterranean diet will be assessed via telephone interviews. the gut microbiome will be profiled using shotgun metagenomic sequencing, enabling the characterisation of microbial species and functional pathways. this analysis will identify potential correlations between microbiome composition and clinical outcomes, including therapeutic responses and survival rates. for statistical testing, qualitative variables will be analysed using chi-square or fisher's exact tests, while quantitative variables will be assessed using student's t-test or anova. if the normality assumption is not met, mann-whitney or kruskal-wallis tests will be used. all statistical analyses will be two-sided, with a significance level set at 5%. expected outcomes: the study aims to identify biomarkers that enable early, non-invasive crc detection. findings will also provide insights into the interplay between lifestyle, diet, and microbiome changes in crc progression. these insights could lead to personalised preventive strategies and improved therapeutic outcomes.

Eligibility criteria:
inclusion criteria: * be willing and able to provide written informed consent * resident in portugal * age from 40 to 74 years * have a recent diagnosis of crc without initiating any treatment. exclusion criteria: * age \< 40 years or ≥ 75 years * unable to provide informed consent * refusal to provide stool samples * previous or current treatment for crc * first-degree family history of crc * previous diagnosis of inflammatory bowel disease (ulcerative colitis, crohn's disease or indeterminate colitis), inflammatory bowel syndrome, recurrent infection by clostridioides difficile * pregnancy

Keywords: colorectal cancer gut microbiota microbiome early-onset colorectal cancer biomarkers diet risk factors metagenomics