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  Toxic effects of breast cancer treatment on the heart: using artificial intelligence to predict the potential for heart failure

   Department of Biomedical Sciences

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  Prof A Clerk, Prof W Holderbaum, Prof S Nasuto  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Project Overview:


Breast cancer is the most common cancer worldwide with ~15% of cases caused by a single gene, HER2. These women are treated with an antibody therapy (e.g. Herceptin) which blocks HER2 function. Anti-HER2 therapy causes cardiac dysfunction or heart failure (cardiotoxicity) in 4-10% of patients, so all patients are screened using ultrasound (echocardiography) to monitor heart function before and during treatment. This increases the investigative burden for patients, most of whom have no cardiac problems. Meanwhile, patients who develop cardiotoxicity would benefit from more precise monitoring for earlier detection and treatment for the effects on the heart. 

Our aim is to develop systems to predict if someone about to start anti-cancer treatment is at risk of cardiotoxicity and, for patients who experience cardiotoxicity, to detect this before there is serious damage to the heart.   

We seek individuals interested in developing AI and machine learning approaches to assess electrocardiograms (ECGs) and echocardiograms from cancer patients before and after treatment.  The aim is to use automated analysis for accurate prediction of abnormalities or changes in cardiac function. Individuals will increase their understanding of heart function, and develop their skills in AI and machine learning. Opportunities are available for participation in wet-laboratory research. 

The project will benefit particularly individuals excited by the prospect of working with an interdisciplinary team with a focus on improving patient welfare. 


Cardio-oncology at Reading (CORe)


Our team brings together the Departments of Cardiology and Oncology at the Royal Berkshire NHS Foundation Trust (Reading) and investigators at the University of Reading (Biomedical Sciences, Biomedical Engineering and Psychology), with links to the BRIC (Building Resilience in Breast Cancer) Centre (


School of Biological Sciences, University of Reading:

University of Reading, located west of London, England, provides world-class research education programs. The University’s main Whiteknights Campus is in 130 hectares of beautiful parkland, 30-minutes from central London and 40 minutes from London Heathrow airport. 

The School of Biological Sciences conducts high-impact research, tackling global challenges to society and the planet. Our research ranges from understanding and improving human health and combating disease, through to understanding evolutionary processes and uncovering new ways to protect the natural world. In 2020, we occupied a stunning new ~£60 million Health & Life Sciences building. This state-of-the-art facility is purpose-built for science research and teaching, housing the Cole Museum of Zoology, a café and social spaces.

In the School of Biological Sciences, you will join a vibrant community of ~180 PhD students representing ~40 nationalities. Our students publish in high-impact journals, present at international conferences, and organise exciting outreach and public engagement activities.

During your PhD at the University of Reading, you will expand your research knowledge and skills, receiving supervision in one-to-one and small group sessions. You will have access to cutting-edge technology and learn the latest research techniques. We also provide dedicated training in transferable skills to support your career aspirations. If English is not your first language, the University's excellent International Study and Language Institute will help you develop your academic English skills.

The University of Reading is a welcoming community for people of all faiths and cultures. We are committed to a healthy work-life balance and will work to ensure that you are supported personally and academically.


Applicants should have a good degree (minimum of a UK Upper Second (2:1) undergraduate degree or equivalent) in computer science, engineering or related analytic field. Good understanding of modern AI, and statistical analysis is expected, with experience in programming languages. Experience with processing of medical imaging data would be useful. Individuals working in multi-disciplinary projects such as this usually display excellent problem-solving and communication skills, with an ability to work independently as well as collaboratively. 

We also welcome interest from individuals with Biological Sciences or other relevant training. 

Applicants will need to meet the University’s English Language requirements. We offer pre-sessional courses that can help with this. With a commitment to improving diversity in science and engineering, we encourage applications from underrepresented groups.

How to apply:

Submit an application for a PhD in Biomedical Sciences or Biomedical Engineering at


Further information:



Prof. Angela Clerk (), Biomedical Sciences.

Prof. William Holderbaum (), Biomedical Engineering.

Prof. Slawomir Nasuto (), Biomedical Engineering.

Biological Sciences (4) Computer Science (8) Engineering (12) Mathematics (25) Medicine (26) Physics (29)

Funding Notes

We welcome applications from self-funded students worldwide for this project.
If you are applying to an international funding scheme, we encourage you to get in contact as we may be able to support you in your application.


Recent publications
A Clerk
• Alharbi HO, Hardyman MA, Cull JJ, Markou T, Cooper STE, Glennon PE, Fuller SJ, Sugden PH, Clerk A. Cardiomyocyte BRAF is a key signalling intermediate in cardiac hypertrophy in mice. Clin Sci (Lond). 2022; 136:1661-1681.
• Meijles DN, Cull JJ, Cooper STE, Markou T, Hardyman MA, Fuller SJ, Alharbi HO, Haines ZHR, Alcantara-Alonso V, Glennon PE, Sheppard MN, Sugden PH, CLERK A. The anti-cancer drug dabrafenib is not cardiotoxic and inhibits cardiac remodelling and fibrosis in a murine model of hypertension. Clin Sci (Lond). 2021; 135:1631-1647.
• Meijles DN, Cull JJ, Markou T, Cooper STE, Haines ZHR, Fuller SJ, O’Gara P, Sheppard MN, Harding S, Sugden PH, CLERK A. Redox regulation of cardiac ASK1 controls p38-MAPK and orchestrates cardiac remodelling to hypertension. Hypertension. 2020; 76:1208-1218.
SJ Nasuto
• Abdalbari H, Durrani M, Pancholi S, Patel N, Nasuto SJ, Nicolaou N. Brain and brain-heart Granger causality during wakefulness and sleep. Frontiers in Neuroscience. 2022; 16: 927111.
• Williams NJ, Daly I, Nasuto SJ. Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Frontiers in Computational Neuroscience. 2018. 12: 76.
• Kadirvelu B, Hayashi Y, Nasuto SJ. Inferring structural connectivity using Ising couplings in models of neuronal networks. Sci Rep. 2017; 7: 8156.
W Holderbaum
• Haben, S., Holderbaum, W. and Voss, M. (2023) Load forecasting: core concepts and methods with applications in distribution networks. Springer Nature, pp384
• Armengol, M. , Zoulias, I. D., Gibbons, R. S., McCarthy, I., Andrews, B. J., Harwin, W. S. and Holderbaum, W. (2022) The effect of functional electrical stimulation-assisted posture-shifting in bone mineral density: case series-pilot study. Spinal cord series and cases, 8 (1). 60. ISSN 2058-6124
• King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W. and Harwin, W. S. (2017) Application of data fusion techniques and technologies for wearable health monitoring. Medical Engineering & Physics, 42. pp. 1-12. ISSN 1350-4533

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