Spatial RadiomIcs and TRanscriptomics to the DIscovery of the Cross-link Between Colon Cancer and ChrOnic Kidney Disease

The incidence of colorectal cancer (CRC) is higher in patients with chronic kidney disease (CKD) compared to general population. Causal factors have not been clarified so far, although some hypothesis could be formulated. Tumor microenvironment (TME) impacts cancer progression and is influenced by different chronic diseases associated with systemic inflammation. In our study, we aim to address this relevant issue by an in-deep characterization of TME in CKD patients with CRC undergoing surgery. This will provide genes expression directly on tumor tissues. Our network includes three high national relevance hospitals (INT IRCCS Pascale, AOU Vanvitelli and AOU Brotzu) and a Genetic Research Institute (Biogem Scarl). We will recruit two comparing groups of CRC patients eligible for surgical cancer resection: 1. Patients with I-II stage CRC alone, and 2. Patients with I-II stage CRC and CKD. Anthropological, clinical and laboratory tests data will be collected and organized in a database for epidemiological and descriptive analysis of the enrolled cohorts. Patients will be matched by age, gender and cancer stage and will be stratified by the estimated renal function (application of BSA eGFR formula by Janovitz T et al, J Clin Oncol, 2017). Patients will undergo preoperative high resolution abdominal MRI for high throughput accurate macroscopic assessment of TME by radiomic approach. During surgery, tumor tissue samples will be collected and promptly frozen at -80°C for subsequent analysis. CRC tissue samples will be analyzed combining spatially resolved and single-cell transcriptomic approach, providing a sensitive and unbiased lens for identifying the key mechanisms of the interplay between CRC and immune context, preserving their tissue architecture. Our study involves the usage of Visium Cytassist Spatial Gene Expression, a powerful tool based on in situ capturing methods, providing the whole transcriptome information from tissue sections. Data collected by radiomic