Cancer research has always advanced by bringing new tools and disciplines into the lab, the clinic and the wider research ecosystem. AI and data science are part of that, and now is the time to really enable cancer researchers to make the most of them.

They will not replace the biological insight, experimental skill or clinical expertise that great cancer research depends on. But used well, they can enhance it – helping researchers make better use of complex data, ask sharper questions, and, in some cases, ask questions we could not previously frame at all. That matters for cancer. If new technologies are reshaping discovery, cancer research has to be at the centre of that change.

That is why I am so excited that Cancer Research UK is launching its new Data Science in Cancer Research strategy, backed by £60 million of funding. One thing we have been very deliberate about is moving beyond a portfolio of disconnected AI projects. The aim is to build critical mass: connected teams, shared infrastructure, stronger links between disciplines, and partnerships that bring the best of cancer research, data science, clinical insight, patient voice and technology together. It’s about building a stronger, more connected and more responsible data science ecosystem for cancer research in the UK.

Three themes

The strategy is built around three linked themes: data, talent and research.

To some, the data portion of this might sound unglamorous, but it is fundamental. We need cancer research data to be more findable, reusable, linkable and standardised. We need better metadata, better services and clearer routes for researchers to understand what data exists and how it can be used responsibly.

The talent challenge is just as important. Not everyone in cancer research needs to become a machine learning expert. But increasingly, everyone will need enough understanding to ask good questions, work intelligently with these tools and collaborate across disciplines. We need more data scientists in cancer research, more cancer researchers who are confident with data science, and more teams where those skills come together.

And then there is the research itself. The future is not lone genius coders building miracle cancer algorithms in basements. It is clinicians, biologists, mathematicians, software engineers, data scientists and patients working together on ambitious cancer challenges. That is the spirit behind the activities we are planning to fund, including a major AI Research Alliance, an AI and Data Science Skills Catalyst, and a Data Hub to strengthen the data infrastructure underpinning this field.

We have also been clear that this must be done responsibly. The strategy is supported by three cross-cutting enablers: ethics, partnerships and policy. Public and patient involvement, bias mitigation, environmental sustainability – we are very clear that responsible use of data and AI are not add-ons. They are part of what good data science in cancer research has to mean.

That is why the strategy is launching alongside a lay version, so patients and the public can understand what we are doing and why. Public trust is not a communications issue at the end of the process. It is part of the work.

We are also publishing a policy paper setting out what needs to change if the UK is to be an even better place for data science-driven cancer research. CRUK can fund, convene and catalyse but data access, regulation, infrastructure, skills and incentives all shape what is possible. Partnerships are central to this. No single organisation, discipline or sector can unlock the full potential of data science for cancer research on its own. We will need to work across universities, institutes, hospitals, industry, funders, charities, government, regulators and, critically, with patients and the public. The opportunity is too big, and too complex, for single organisations to do alone.

Exciting future

And the £60 million portfolio is not the whole story. Project AURORA, a cross–Cancer Grand Challenges programme funded by CRUK, is a compelling example of the kind of AI ambition we want to see grow. It aims to develop an AI co-scientist for hypothesis-driven cancer research, helping researchers generate and assess competing hypotheses, integrate data with experiments, design assays, and support translation. If successful, it has the potential to accelerate the pace, scale and rigour of discovery across Cancer Grand Challenges, and more widely in cancer research.

I think CRUK has a genuine opportunity here: not just to fund AI, but to help shape what trustworthy, responsible, mission-driven AI looks like in cancer research.

This strategy has been shaped with the help of our Data Advisory Board, and it will only succeed if it belongs to a wider community. That community is already growing through our Data Community and special interest groups, and through our data-driven cancer research conference. The next conference, in February 2028, will be another important moment to build momentum.

I am really excited about the future of data science and AI at CRUK. Quantum computing, robotics and automated experimentation are all moving quickly, and the real power may come when these technologies start to converge. CRUK needs to be close enough to that frontier to understand what is coming, and bold enough to help cancer research benefit from it. Watch this space.