Innovate UK has awarded a £1m ($1.26m) grant for a collaborative initiative called AI-VISION to aid treatment decisions for breast cancer patients.
The project involves the Institute of Cancer Research (ICR), Durham University, the Royal Marsden Hospital and techbio company Concr.
It will support clinical decision-making, including directing breast cancer patients on the use of immunotherapy.
AI-VISION is a 24-month observational clinical study to assess tissue samples from early triple-negative breast cancer (TNBC) patients.
These subjects will be analysed to define and establish chemotherapy response biomarkers, irrespective of immunotherapy status.
The study aims to validate the safety and performance of new computational methods to offer precision therapies for patients.
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Concr will apply Bayesian computational frameworks, inspired by astrophysics, to interconnect diverse oncology data, enabling researchers to detect and develop biomarkers indicative of drug response.
The ICR will contribute genomic data from its genomics facility, which will be integrated with clinical findings from TNBC samples through Concr’s FarrSight platform.
The current TNBC treatment protocol typically involves physicians making treatment choices based on clinical and pathology findings.
However, despite significant strides in molecular genomic profiling, no licensed platform presently exists that links tumour molecular data with drug response predictions for TNBC.
The AI-VISION study seeks to fill this gap and enhance precision therapy.
Institute of Cancer Research London clinician-scientist and Royal Marsden clinical oncologist and project lead Dr Navita Somaiah stated: “It’s exciting to announce our new partnership, which is bringing together experts in cancer, genomics, astrophysics and artificial intelligence for an innovative project that is seeking to improve treatment for triple-negative breast cancer.
“The aim of our project is to uncover new potential ways to treat triple-negative breast cancer more effectively, by combining data from a variety of different sources and analysing it using AI. For example, we hope to uncover genomic characteristics that indicate sensitivity to immunotherapy, and could guide us to selecting the patients most likely to benefit from these newer therapies.”