Multi-scale Spatio-Temporal Analysis of Research Data

Recruiting

• Conditions: Colorectal Cancer

• Registration Number: NCT07189312
• Lead Sponsor: Universidad de Granada

Brief Summary

MuSTARD is a singular project in the sense that investigators will develop a computational framework that is conceived as a common solution for disparate applications. This framework responds to a common need: the derivation of multi-scale spatio-temporal (ST) models equipped with statistical inference capabilities. Since the sequencing of the human genome, a significant portion of clinical research has shifted to the study of precision medicine (PM), resulting in numerous breakthroughs in both non-communicable and infectious diseases. PM refers to disease treatment and prevention that considers variability in genes, environment, and lifestyle for each person. Main drivers of PM are the omics technologies . Omics data is obtained from new high-throughput instruments that generate massive volumes of data. It is also notoriously complex data to analyze. In the last years, new separation technology has allowed us to measure omics data at single-cell resolution, creating a new (and even more complex) type of data generally referred to as single-cell omics. This allow us to study the spatial distribution of omics in a sample. Spatial and single cell omics represents one of the most powerful approaches today to understand cancer propagation in both time and space. Spatial omics. Unfortunately, there are no computational tools that can readily combine the nature of this new type of data with powerful statistical inference. Furthermore, spatially resolved omics experiments with repeated measures is another form of complex ST data. MuSTARD will provide a new computational framework that can handle ST omics to understand colorectal cancer propagation in both time and space, and leverage it in a new clinical study of reduced sample size (15 patients). The MuSTARD computational framework applied to ST omics will be made available for the general community in the form of open software.

Detailed Description

Not available

Study Timeline

• Study Start: 2025-02-02
• Status Verified: 2025-09
• Study First Submitted: 2025-09-09
• Study First Submitted QC: 2025-09-17
• Study First Posted: 2025-09-23
• Last Update Submitted: 2025-09-24
• Last Update Posted: 2025-09-30
• Primary Completion: 2026-02-02
• Study Completion: 2026-12-02

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 15

Inclusion Criteria

• Diagnosis for colorectal cancer
• Patients with cancer planned surgery
• Absence of instability microsatellites
• Absence of any other associated disease that can affect the cancer pathology

Exclusion Criteria

• Patients who need an urgent surgery

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
To develop a framework of computational tools and services capable of handling multi-scale spatio-temporal structures in massive scientific data and providing interpretable models and visualizations with sound statistical support.	three years	Sample Collection A total of 15 patients with a diagnosis of colorectal cancer, that undergoes surgery, will be recruited. Blood and fecal samples will be collected at two time points (baseline and endpoint). In addition, three spatially distinct tissue samples will be obtained at a single time point (during cancer surgery).; Measurements; * Variation of the intestinal microbiota composition before and after surgery. Intestinal microbiota composition willbe determined by shot-gun sequencing techniques.; * Variation of the plasma metabolite before and after surgery, by untargeted metabolimics.; * Variation in specific metabolites and gene expression in the colorectal cancer's tissue samples. Next-generation sequencing and untargeted metabolomic techniques will be used.; The multi-omics data will be processed and integrated using Artificial Intelligence (AI) techniques, allowing the development

Trial Locations

Locations (1)

Virgen de las Nieves Hospital; 🇪🇸 Granada, Granada, Spain