Researchers have developed a powerful AI tool that reveals previously invisible patterns inside breast cancers. It uncovers how tiny cellular structures called centrosomes change as tumours grow, spread and evolve.
The technology could help clinicians identify high-risk patients, predict how breast cancer might progress, and deliver more targeted treatments.
CenSegNet, an open-source AI platform created by researchers at the University of Southampton, can analyse hundreds of thousands of cells in tumour samples with unprecedented speed and precision.
The technology allows scientists to map centrosome abnormalities across entire tumours and identify how these defects vary between different parts of the same cancer.
The results have been published in * Nature Communications * .
Centrosomes act as the cell's organising hubs, helping cells divide correctly and maintain their structure. When centrosomes become abnormal, cells can accumulate genetic errors - a trait of cancer.
Dr Salah Elias , who led the research at the University of Southampton's School of Biological Sciences and Institute for Life Sciences, said: "For more than a century, centrosome abnormalities have been recognised as a hallmark of cancer, but studying them in patient tissues has been extremely challenging.
“CenSegNet allows us to analyse these defects at single-cell resolution across entire tumours and uncover patterns that were previously impossible to see."
Working with University Hospital Southampton, the team analysed more than 330,000 centrosomes across 911 tumour specimens from 127 breast cancer patients.
The AI uncovered two distinct forms of centrosome abnormality that had previously been considered part of the same process. One form involved cells acquiring too many centrosomes, while the other involved abnormally enlarged centrosomes. Surprisingly, the researchers found that these defects behave independently and can occupy different regions of a tumour.
The study also revealed important links between centrosome abnormalities and clinical features of disease. Tumours with high levels of enlarged centrosomes were associated with more aggressive characteristics, including higher tumour grade, lymph node involvement and certain genetic alterations. Importantly, patients whose tumours contained fewer enlarged centrosomes in the tumour core tended to have better overall survival.
Dr. Elias added: "Rather than viewing centrosome abnormalities as a single phenomenon, our study shows that they have distinct biological states with different spatial distributions and clinical associations.
“Specific combinations of defects may influence how a tumour grows, invades surrounding tissues and responds to treatment. This opens the door to developing new biomarkers and, ultimately, more personalised treatment strategies."
While the technology is not yet ready for routine clinical use, the findings point towards several future applications. First, spatial maps of centrosome abnormalities could help clinicians identify patients with particularly aggressive tumours and refine cancer risk beyond current approaches.
Second, the study highlights new opportunities for precision oncology. Several drugs already in development target proteins that control centrosome function. By identifying which tumours harbour specific centrosome defects, clinicians may one day be able to match patients to therapies that exploit these vulnerabilities.
Third, the work provides a framework for understanding why different parts of the same tumour behave differently, potentially helping researchers predict tumour progression, metastasis and treatment resistance.
Because CenSegNet is freely available as open-source software, the researchers hope it will be adopted by cancer scientists and pathologists around the world. Beyond breast cancer, the team has already demonstrated that the technology can be applied to other tissues, including kidney, colon and appendix samples.
The researchers believe this could help establish a new field where AI is used to track disease by analysing the behaviour of individual cellular structures across entire tissues.
The team now plans to combine CenSegNet with genomic, transcriptomic and proteomic data to determine whether centrosome-based biomarkers can help guide treatment decisions and improve outcomes for patients with cancer.
The University of Southampton has announced plans for a groundbreaking new medical institute which will bring medics, computer scientists and engineers together in one brand new building. The pioneering
[
Institute for Medical Innovation (IMI)
](https://fund.southampton.ac.uk/innovation/)
will be based at Southampton General Hospital and will tackle five devastating disease areas - cancer, dementia, sight loss, infection and respiratory and allergic conditions.