<?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/477B127C-BDFD-42BD-91DD-636D2BC5FA7E" ns1:id="477B127C-BDFD-42BD-91DD-636D2BC5FA7E"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/90310C55-0C34-4B1C-A118-95860F1E7E99" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A46A8268-6BE3-4418-9C20-D3B9E57AB4D1" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/BFB1B239-E29B-4473-82BE-32D359289B83" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A46A8268-6BE3-4418-9C20-D3B9E57AB4D1" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2024-10-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/3BEEAD3D-A764-4FBF-8622-075525ACCE9F" ns1:rel="FUND" ns1:start="2023-11-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10067478</ns2:identifier></ns2:identifiers><ns2:title>Guiding treatment decisions in breast cancer with artificial intelligence</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Triple negative breast cancer (TNBC) is the most aggressive form of breast cancer, affecting around 20% of all people with breast cancer. It disproportionately affects younger women and ethnic minorities, and has profound effects on quality of life, fertility, and ability to work.

There is overwhelming scientific evidence that TNBC is complex with diverse clinical outcomes. Unlike many other cancers, there is currently no test that enables us to identify those at risk of the worst outcomes and those likely to have excellent outcomes. This means that we must take a 'one size fits all' approach and recommend systematic chemotherapy for most patients. Unfortunately, chemotherapy produces significant side-effects, including damage to nerves in the hands and feet and life-threatening infections. Some side effects can be life-long, debilitating, cause anxiety and impact close relationships.

This imminent need to improve quality of life and outcomes led us to develop a novel clinical biomarker test known as **SIPA: Spatial Immuno-Ecology Prognostic Assessment**. SIPA is a rapid, artificial intelligence-powered test that only requires routinely generated samples. It accurately measures each individual patient's unique immune response to their tumor, and it is envisaged to be routinely used across several tissue cancers to accelerate precision medicine pathways.

Currently, SIPA operates primarily as a prognostication test in TNBC, it can be used to predict which patients are likely to have the best outcomes and be spared chemotherapy, whilst identifying others that may benefit from new treatments such as immunotherapy. Ultimately, we intend for SIPA to be used to help inform personalized therapies for cancer patients.

Through this project, we aim to develop high-standard quality management system to prospectively test SIPA in clinical settings under careful expert supervision, whilst working towards an (In-Vitro Diagnostic) IVD CE-mark regulatory plan in preparation to launch in the UK and Europe. This project brings us a step closer to maximize the reach of cutting-edge, AI-based technologies to benefit cancer patients in the UK and beyond. Precise patient stratification enabled by SIPA addresses key NHS priorities and could revolutionize treatment for patients with TNBC.

**Our added values are for patients --- prevent unnecessary toxicity and lost opportunities to pursue more effective treatments in the most crucial time of their lives; for healthcare providers --- save tremendous costs on potentially ineffective treatments; and for clinicians ---efficient precision oncology by enabling them to offer personalized treatments.**</ns2:abstractText></ns2:project>