Integrated Cancer Medicine: Research in Focus

CRUK Cambridge Centre
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The Integrated Cancer Medicine: Research in Focus series highlights the ground-breaking science of the members of the Integrated Cancer Medicine Programme (ICM) at the Cancer Research UK Cambridge Centre and the University of Cambridge, UK. The ICM vision is to revolutionise the patient treatment pathway by utilising information from multiple data streams (imaging, radiogenomics, circulating tumour DNA, and others). Computational advances in artificial intelligence and machine learning are not only employed in the capture of this data but also in its integration. This gives us a powerful tool to assess a patient’s response to treatment, enabling clinicians to tailor therapy according to a patient’s own individual configuration and also to that of their specific disease. In this series, we are proud to showcase the amazing progress being made in ICM towards personalising treatment for cancer patients.

  1. Why patient and public involvement and engagement is important and what it means to those involved

    12/11/2025

    Why patient and public involvement and engagement is important and what it means to those involved

    Have you ever thought about getting involved in research as a patient or supporter? In this episode, Ellie Wolmark talks to the incredible members of the Women⁺s Cancers Programme Patient and Public Involvement and Engagement (PPIE) Group about how they are involved in our research, why they think involvement is so important, and what it means to them to be a part of the group. Not only do they share their stories and thoughts, but they also offer huge insight into their disease and the research surrounding it. They talk about how they can help and make an impact, not just for themselves and each other, but also for patients of the future. Their involvement is about making things easier, supporting quicker diagnoses, and helping to shape better, more personalised treatments. The episode is incredibly uplifting. You will hear how empowering they find being part of the group and engaging with research. They are a shining example of how knowledge is power. Their support for one another, and for the researchers through the ups and downs of this disease, gives real hope for a better future. Further information: -Find out more about the DEMO Project at https://ovarian.org.uk/our-research/improve-uk/demo-uk/ -Join the Wellcome Connecting Science course on 'The Power of Patient Advocacy in Genomics: Influencing Research, Clinical Practice and Decision Making' at https://www.futurelearn.com/courses/the-power-of-patient-advocacy-in-genomics-influencing-research-clinical-practice-and-decision-making

    1hr 17min
  2. The association between radioresistance and tumour evolution

    21/10/2025

    The association between radioresistance and tumour evolution

    Is a tumour's ability to evolve and adapt over time linked to why it becomes resistant to radiotherapy? And if so, what can we do to overcome this radioresistance? In this episode, Dr Christopher Jones and Dr Ashley Nicholls from CRUK RadNet Cambridge at the University of Cambridge explore these questions. They explain why some tumours are resistant to radiation and why some patients are more resistant to treatment than others. They also offer insights into why recurrence happens and how to identify it early in order to treat it more effectively with new technologies. This includes designing new tools to make drug delivery more accurate and new genetic approaches to investigate specific ways to treat cancer in the context of radiation. Chris works predominantly in oesophageal cancer, while Ash studies lung cancer, but both strive to understand why relapse and radioresistance occur to then try to target these cancers with specific drugs or drug combinations to lessen toxicity and help personalise treatment.

    45 min
  3. Using AI to mathematically model the progression of glioblastoma

    07/10/2025

    Using AI to mathematically model the progression of glioblastoma

    Can we use artificial intelligence (AI) to predict where a brain tumour might recur at the point of first diagnosis in order to pre-emptively treat it to stop recurrence? Dr Francesca Cozzi (a PhD student at the University of Cambridge) and Dr Curtis Holliman (Associate Professor in the Mathematics Department at The Catholic University of America, USA) believe this will eventually be possible. In this exciting conversation they talk about their collaboration to find a mechanism to mathematically model the progression of glioblastoma. They highlight why it is important to achieve this in an explainable way using AI, to keep the process of decision making transparent and interpretable to self-validate the models they develop.

    29 min
  4. Studying the tumour microenvironment

    23/09/2025

    Studying the tumour microenvironment

    Could the environment around a cancer tumour hold the key to better treatments? In this episode, PhD students Emily Lythgoe and Ellie Bunce from the University of Cambridge discuss their research into tumour microenvironments. They explain why studying these environments in ovarian cancer (in a clinical setting) and head and neck cancer (in a pre-clinical setting) is so important for predicting how tumours might respond to treatment. They also highlight the value of integrating multiple technologies to create a more complete picture of a patient and their disease. This insightful conversation explores how their work helps to understand the building blocks of these cancers and why such complex research could be crucial for developing new ways to treat patients in the future.

    32 min
  5. Building a tissue time machine

    09/09/2025

    Building a tissue time machine

    What if a time machine could be built, not from metal but from human tissue, to help us understand cancer and how it responds to treatment? Dr Alecia-Jane Twigger, Dr Akanksha Anand, Dr Kui Hua, Dr Pedro Victori and Dr Jo Worley from the University of Cambridge have done just that. In this episode, they talk about their project funded by Wellcome LEAP under the Delta Tissue programme to build a 'tissue time machine'. By studying tumour samples from patients with triple negative breast cancer before and after treatment, they aim to build a model that can predict how patients would respond to therapy. The conversation offers a fascinating look at how a truly multidisciplinary team, bringing together scientists from very different fields, can achieve something remarkable.

    46 min
  6. Using AI in the screening and treatment of kidney cancer

    26/08/2025

    Using AI in the screening and treatment of kidney cancer

    Have you ever wondered how artificial intelligence (AI) is used in research in a healthcare setting? Rebecca Wray and Bill McGough, two PhD students at the University of Cambridge, both use AI in different ways in their research into the screening and treatment of kidney cancer. Rebecca is an MRes PhD student investigating the response and resistance to therapy in patients with kidney cancer. She uses AI to find tumour features at multiple scales. For his PhD, Bill is developing AI models that will sift through the mounds of imaging data produced during the Yorkshire Kidney Screening Trial (funded by Yorkshire Cancer Research), flagging images that show masses in the kidneys. This interesting conversation gives insight into how researchers are using patient data safely in order for AI models to integrate all the information about a patient to help review their treatment and personalise it. The discussion also highlights how using AI in the screening setting is speeding up processes to help save clinicians' time and allowing the potential earlier detection of kidney cancer in a cost-effective manner.

    34 min
  7. Using AI to detect stomach cancer during endoscopy

    12/08/2025

    Using AI to detect stomach cancer during endoscopy

    How can artificial intelligence (AI) support both cancer research and healthcare more generally? Anoushka Harit (University of Cambridge) and Dr Zhongtian Sun (University of Kent) join today's episode to discuss this topic. Anoushka talks about her research in hereditary diffuse gastric cancer (HDGC) and the development of an AI system to enable early detection of signet ring cell carcinoma (SRCC) during endoscopy. This new AI system could help to treat SRCC better or to prevent or delay drastic life-changing treatment, preserving patients' quality of life. Zhongtian shares insights on how AI can support health applications more generally and how he tries to make it as explainable as possible. He even talks about his wish to make the AI mimic as much as possible the way a human brain works!

    48 min
  8. Kevin Brindle – Reflections on a lifetime in imaging research

    01/10/2024

    Kevin Brindle – Reflections on a lifetime in imaging research

    Professor Kevin Brindle joins Ellie Wolmark to discuss his life's work in imaging research as he reaches retirement. Kevin is particularly interested in metabolic imaging. His research group has developed novel magnetic resonance-based molecular imaging techniques to detect the early responses of tumours to therapy, with a view to translating these into clinical application. He talks about how he got into this topic in the first place, his amazing contributions to the field over the past four decades, and about how his techniques can be translated into the clinic to impact patient treatment. Kevin is Emeritus Professor of Biomedical Magnetic Resonance in the Department of Biochemistry and a Senior Group Leader in the CRUK Cambridge Institute at the University of Cambridge, UK. He is also a Fellow of the Academy of Medical Sciences and of the Royal Society.

    35 min
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About

The Integrated Cancer Medicine: Research in Focus series highlights the ground-breaking science of the members of the Integrated Cancer Medicine Programme (ICM) at the Cancer Research UK Cambridge Centre and the University of Cambridge, UK. The ICM vision is to revolutionise the patient treatment pathway by utilising information from multiple data streams (imaging, radiogenomics, circulating tumour DNA, and others). Computational advances in artificial intelligence and machine learning are not only employed in the capture of this data but also in its integration. This gives us a powerful tool to assess a patient’s response to treatment, enabling clinicians to tailor therapy according to a patient’s own individual configuration and also to that of their specific disease. In this series, we are proud to showcase the amazing progress being made in ICM towards personalising treatment for cancer patients.

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