An Australian tech entrepreneur has helped create what appears to be a made-to-measure cancer vaccine for his dog, Rosie, using artificial intelligence tools such as ChatGPT as part of the process. The science behind this sounds intimidating – DNA sequencing, mRNA vaccines, “neoantigens” – but at its core, it is about reading the instructions inside a tumour and then writing a new set of instructions to help the immune system see it. Rosie is an eight-year-old rescue Staffordshire bull terrier cross that developed aggressive mast cell cancer, a common skin cancer in dogs. She had surgery and chemotherapy, but the disease kept coming back and she ended up with large, ugly tumours on her leg. Vets told her owner, Paul Conyngham, that she probably had only months to live. Instead of accepting that, he decided to use the tools he knew from his day job in tech – data analysis, AI and coding – and apply them to his dog’s cancer. Decoding the tumour The first step was to understand what made Rosie’s tumour different from her healthy cells. Every cell in the body carries DNA – a long, chemical molecule that acts like a biological instruction manual. You can think of DNA as a very long string of letters written in a four-letter alphabet. Cancer happens when enough of those letters change, by chance or through damage, so that some cells start to grow and divide out of control. Sequencing a tumour’s or normal cell’s DNA is essentially reading through that long string of letters and comparing it to the “normal” version to see where it has gone wrong. A lot of my own research has focused on this. Conyngham paid a university lab to sequence the DNA from Rosie’s tumour. That produced a huge file listing the mutations – the spelling mistakes in the cancer’s instruction manual – that set her tumour apart from her healthy tissues. On their own, those files are just data. The question is what to do with them. This is where he turned to an AI chatbot. He asked it how scientists design personalised cancer vaccines and how he might go from a list of mutations to specific targets for a vaccine for Rosie. A cancer vaccine in this context is different from the childhood vaccines we are used to. Traditional vaccines prevent infections: you give someone a harmless version or fragment of a virus or bacterium so their immune system can “learn” to recognise it in advance. A cancer vaccine, by contrast, is usually therapeutic rather than preventive. It is given to someone who already has cancer, with the aim of training their immune system to spot markers on the cancer cells that it has previously ignored and then attack them. This is where mRNA comes in. If DNA is the master instruction book, mRNA (messenger RNA) is more like a photocopied page that gets sent to the cell’s protein-making machinery – think of it as a short piece of code that carries a single command: “make this protein”. Some of the COVID vaccines use mRNA: they deliver a strand of mRNA that tells our cells to make the spike protein from the coronavirus, so the immune system can practise on it. The body then breaks down the mRNA; it does not change your DNA. For a personalised cancer vaccine, scientists choose small parts of proteins that are unique to a particular tumour – so-called neoantigens – and encode them in an mRNA sequence. When this mRNA is injected, cells take it up and briefly make those tumour-linked protein fragments. The immune system can then see these fragments and, ideally, begins to treat any cell displaying them as abnormal and dangerous. In effect, it is using mRNA to give the immune system a “most wanted” poster for that individual cancer. With help from AI tools, Conyngham sifted through Rosie’s tumour mutations to pick out candidates that might make good neoantigens. He also used protein structure prediction software to model how some of these mutated proteins would look, trying to guess which ones would be visible to her immune system. Crucially, he did not manufacture a vaccine in his garage. Once he had a shortlist of targets, he approached researchers at the University of New South Wales, including experts in RNA technology, who reviewed the data and designed an mRNA construct based on it. Their team turned this digital design into a physical mRNA vaccine in the lab. It was a one-off product, made just for Rosie, encoding several of the mutations in her tumour. She then received this experimental vaccine at a veterinary research centre, with booster doses over the following months. Reports from her vets and owner suggest that several tumours shrank markedly, her overall tumour burden fell, and her energy and behaviour improved. One resistant tumour has prompted a second round of analysis and a follow-on vaccine targeting a different set of mutations. Promising, but not a cure It should be noted that this is a single dog, not a controlled study, and mast cell tumours can behave unpredictably. We cannot be sure how much of Rosie’s improvement is due to the vaccine, how long it will last, or whether the same approach would help other dogs, let alone humans. The AI did not “cure cancer” by itself. It acted as an always-available guide and assistant, but qualified scientists still had to check its work and do the hard parts in the lab. Even so, this case is a vivid example of several ideas coming together. DNA sequencing allows you to read the specific mutations in an individual cancer. mRNA technology lets you quickly write a custom set of instructions to show those mutations to the immune system. AI systems make the complex biology more navigable for non-experts, suggesting possible targets and explaining concepts – though their outputs still require expert scrutiny. Put those together, and something that would once have required a major pharmaceutical programme – a bespoke cancer vaccine – can now be attempted, at least experimentally, for a single animal. For the informed public, perhaps the most important point is not that AI has magically solved cancer, but that the basic ingredients of high-end personalised medicine are becoming more accessible. A motivated dog owner can now order tumour DNA sequencing, ask an AI to help interpret it, and partner with an academic lab to turn that interpretation into an mRNA vaccine. A significant scientific and ethical challenge ahead is to develop methods for testing such approaches properly, protect patients and animals from false hope and unsafe experiments, and determine who should have access if they prove to be effective.