In 2019, the Digital Cancer Research team within the CRUK Manchester Institute Cancer Biomarker Centre, along with our EU colleagues, including those from the Fondazione IRCCS Istituto Nazional Tumori Milan and Vall d’Hebron Barcelona, were awarded a CRUK Accelerator Award to enable SMART Experimental Cancer Medicine Trials. The ambition of this programme, entitled UpSMART, is to digitalise up experimental cancer medicine centres across the UK, Italy, and Spain, providing clinical teams with digital tools for real-time access to a wealth of patient data allowing faster decision making. The UpSMART consortium consists of 23 participating centres across Experimental Cancer Medicine Centre (ECMCs) in the UK, and Early Drug Development Units (EDDUs) in Spain and Italy. Furthermore, 2 collaborating centres in France have been onboarded to UpSMART and are actively involved in advancing various aspects of the programme.
UpSMART has developed and released its open-source, new digital healthcare technology approaches to improve early phase cancer clinical trials and better enable patients access to tomorrow’s medicine today. Our goal is for these and other digital healthcare products to be shared and implemented more widely, together with training in digital healthcare product approaches.
Since 2019 UpSMART has developed and open sourced 9 digital tools to meet the needs of early phase cancer clinical trials. We also held the UpSMART digital clinical trials conference in 2024, to bring together experts in the field. You can read more at the UpSMART website.
Key collaborators:
CCE-DART aims to incorporate newer and more effective methods to the design, conduct and analysis of academic clinical trials. The project has been built around Cancer Core Europe (CCE), a network of comprehensive cancer research centres, and the Basket of Baskets (BoB) Clinical trial, a modular multi-country investigator-led precision medicine trial. The objectives of CCE-DART are:
The DCR team is leading the following work packages of CCE-DART:
Key Collaborators
The National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) is the largest BRC outside the South East of England and the beating heart of translational research across Greater Manchester, Lancashire, and South Cumbria, transforming scientific breakthroughs into diagnostic tests and life-saving treatments for patients.
Awarded £64.1million (2022-28) – the largest single award given by the NIHR to the city region – Manchester BRC brings together world-leading researchers based at The University of Manchester and six of the country’s foremost NHS Trusts, with a vision to drive health improvements and lasting change for all through creative, inclusive and proactive research that identifies and bridges gaps between new discoveries and individualised care.
The DCR team contributes to the Precision Cancer Medicine Theme of the Manchester BRC.
The BRC has supported both our AI multiomic biomarker work and our technology clinical trials. these trials use novel technology solutions to allow in-home monitoring of cancer treatment toxicities (damaging side effects), with patients as co-investigators in their research.
Our trials include:
The Manchester Experimental Cancer Medicine Team (ECMT) has a large portfolio of early phase clinical trials including ‘first-in-human’ and ‘first-in-combination’ studies across all major solid tumour types. Our ambition is to offer cancer patients from Greater Manchester and beyond more clinical trial options, with the aspiration to provide patients with the opportunity to receive tomorrow’s treatments today. The DCR team lead the Digital Innovation theme within the ECMT.
We developed an explainable AI model to interpret OCT scans to detect and classify types of retinal damage. This model could be used for cancer patients who are having treatment that is known to cause eye toxicities.
To collect images needed to train this algorithm, we ran the A-EYE study in which we asked patients to share their OCT images with us, and asked patients and doctors about their views on how AI decision support could serve cancer patients.
Key Collaborators: Manchester Eye Hospital
During the COVID-19 pandemic we developed Coronet, an online decision support tool to help hospital teams decide whether to admit or discharge cancer patients presenting with COVID-19 symptoms.
Key Collaborators: We worked with researchers from over 30 different cancer centres to develop this tool.
BioLunar is an advanced AI tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large Language Models (LLMs) to facilitate complex scientific reasoning across distributed evidence spaces, enhancing the capability for harmonising and reasoning over heterogenous data sauces.
Key collaborators: IDIAP Switzerland, Department of Computer Sciences University of Manchester
Currently, around 40% of patients who enrol into an early phase clinical trial are not well enough to proceed through the eligibility checks and the 28 day does limiting toxicity window, which is an essential part of a clinical trial. We aim to develop an AI tool to predict which patients are unlikely to make it through these points, then we could feed that prediction into discussions on enrolling into trials. This could mean that we avoid wasting patients’ time, and unnecessary tests and procedures. In this project we are aiming to use a LLM system to see if we can make this prediction accurately.
Key collaborators: Centre Leon Berard France, Christie Hospital NHS Foundation Trust
Under UpSMART we have developed and open-sourced 9 digital tools to improve early phase cancer clinical trials
Enable study teams to visualise trial data such as Adverse Events, laboratory results, response and genomic profile data.
Used at University Hospital Southampton (UHS) and in commercial trials/industry by Athenix, Cellcentric and Carrick.
Data visualisation tool for use in Molecular Tumour Board meetings, designed to meet the requirements of the TARGET study. Integrates patient clinical and genomic NGS data to support decision making.
Our eTARGET virtual molecular tumour board solution and dECMT’s flagship TARGET study (Tumour chARacterisation to Guide Experimental Targeted therapy), an academic precision medicine study developed in Manchester ECMT which has now recruited over 3000 patients across 20 ECMCs
We developed eTARGET for the TARGET study and have further developed and open-sourced this tool under our UpSMART programme.
eTARGET and our dECMT clinical trial matching tool have both been used in the TARGET, TARGET National and CUP-COMP studies.
The cancer trial finder template can be used to create an online tool that displays clinical trial options. The aim of the tool is to raise awareness of recruiting trials by making the trial information accessible to clinicians and the wider multidisciplinary team from any location.
Phase I prognostic online (PIPO) is a user-friendly interactive tool to calculate specific survival probabilities for a patient before enrolment in a phase I trial.
Being used in a validation study that is being run by collaborators at VIHO and recruiting at 4 other sites in Spain, with a target to recruit 586 patients.
Allows clinicians to find suitable clinical trials for their patients, based on the tumour’s genetic profile. Ingests trials data from clinicaltrials.gov and applies natural language processing techniques to support precise matching based on genetic inclusion criteria
Around 60-100 visits per month. Vall d’Hebron Institute of Oncology (VHIO) developed and used a Spanish instance in 2022. A new instance of this DHP has been launched in the Centre Leon Berard (2024)
PROACT 2.0 is an open source patient application for secure, direct communication between clinical trial participants and their medical team.
In use in CCE-DART study, Building Data Rich Clinical Trials. To be used in VHIO brain metastasis study.
Developed during the COVID-19 pandemic, Coronet is an online tool to support decisions regarding hospital admissions or discharge in cancer patients presenting with symptoms of COVID-19.
Automatic AI-enabled classification and visualisation of free text protocol deviations. Can be used for all trials across a site, or all sites across a trial. Implemented in the Christie ECMT in 2025.
A survey tool to allow clinical sites to review how sustainable their waste management processes are with regards to clinical trials kit supplies and pharmacy processes.
