<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-08-26T13:36:10Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/D8C59149-BB78-4279-B795-B2641FC0A06F" ns1:id="D8C59149-BB78-4279-B795-B2641FC0A06F"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/BAC8B54F-7BF2-47BC-93D2-4E74292EE6B6" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/BDFF150E-C682-4C28-8161-F29987F0A868" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/BDFF150E-C682-4C28-8161-F29987F0A868" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/5D22C4D2-843B-4984-9432-D1589854FF37" ns1:rel="FUND" ns1:start="2023-01-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10056682</ns2:identifier></ns2:identifiers><ns2:title>DIOPTRA - EARLY DYNAMIC SCREENING FOR COLORECTAL CANCER VIA NOVEL PROTEIN BIOMARKERS REFLECTING BIOLOGICAL INITIATION MECHANISMS</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>EU-Funded</ns2:grantCategory><ns2:leadFunder>Horizon Europe Guarantee</ns2:leadFunder><ns2:abstractText>DIOPTRA aims to be the driving force for creating change in colorectal cancer screening and everyday medical practice by incorporating new technologies for colorectal cancer (CRC) risk assessment, screening, and progression. It aspires to provide accessible and noninvasive risk estimation while offering robust evidence-based indications of the relationship between undiagnosed cancer patients and CRC, known as Cancer of Unknown Primary (CUP). DIOPTRA's objective is to build a strong evidence base for a unified holistic protocol for primary CRC screening, which can be applied to both undiagnosed cancer patients (through scheduled screening recommendations) and CUP patients with the CRC phenotype. To achieve this goal, we will incorporate lifestyle and environmental factors and evaluate the predictive power of investigated biomarkers and gene-expression profiling, aiming to uncover the molecular mechanisms of CRC development and CUP incidence. In this context, we will also examine the efficacy of network modeling and AI-based Decision Support Systems (DSS) for early primary CRC identification. We will train a set of models using large volumes of primary CRC data, including in situ and metastatic carcinomas, using the latter as a knowledge base for CUP analysis.</ns2:abstractText></ns2:project>