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# AI reveals a hidden split inside a century-old sign of breast cancer
- URL: https://www.metatalks.ai/ai-tool-splits-breast-cancer-centrosome-marker-in-two/
- Published: 2026-09-06T14:26:00.000Z
- Updated: 2026-09-06T14:25:59.000Z
- Author: Al
- Tags: News, Health AI, Science AI, #newswire

**What biologists have read as one sign of cancer for a century turns out to be two — and only one of them tracks with how the disease behaves.**

Centrosomes are the cell’s organising hubs, steering division and holding the cell’s shape, and when they go wrong cells accumulate the genetic errors that typify cancer. An AI analysis of breast tumour tissue found that what has been counted as one defect is two: cells that acquire too many centrosomes, and centrosomes swollen to an abnormal size. The two vary independently and can occupy different regions of the same tumour, a result the researchers called surprising.

The split came out of CenSegNet, an open-source AI platform built at the University of Southampton; the study is published in Nature Communications. Working with University Hospital Southampton, the team examined more than 330,000 centrosomes across 911 tumour samples from 127 breast cancer patients. Studying them in patient tissue, said Dr Salah Elias, who led the work, has been extremely hard.

Tumours thick with enlarged centrosomes tended to show more aggressive traits: a higher grade, spread to the lymph nodes and particular genetic changes. Overall survival was generally better among patients whose tumour cores held fewer of them. The study reports these as associations within the same samples, not demonstrated causes.

Several drugs in development target proteins that control centrosome function, so knowing which defect a tumour carries could one day point to a therapy that exploits it. In the University of Southampton’s [announcement of the work](https://www.southampton.ac.uk/news/2026/08/ai-reveals-hidden-patterns-inside-breast-cancer.page?ref=metatalks.ai), Elias said specific combinations of defects “may influence how a tumour grows, invades surrounding tissues and responds to treatment”, and that this “opens the door to developing new biomarkers and, ultimately, more personalised treatment strategies”.

Routine clinical use is some way off. CenSegNet is free and open-source, and the team has already run it on kidney, colon and appendix tissue.