Overview
- The University of Southampton released CenSegNet, an open-source AI that maps centrosomes at single-cell, spatial resolution and was used to analyse more than 330,000 centrosomes across 911 tumour specimens from 127 breast cancer patients.
- CenSegNet separates centrosome abnormalities into two independent defect types: cells with excess centrosomes and cells with enlarged centrosomes, which tend to occupy different regions within tumours.
- Tumours with high levels of enlarged centrosomes were associated with more aggressive features, including higher grade, lymph node involvement, and specific genetic changes.
- Researchers have applied the tool to other tissues such as kidney and colon, but CenSegNet remains a research tool and the team plans to combine its spatial outputs with genomic, transcriptomic, and proteomic data to develop and validate centrosome‑based biomarkers.
- Centrosome abnormalities have long been seen as a cancer hallmark, yet direct study in patient tissue was hard; this AI approach makes large-scale, single‑cell mapping possible but requires independent validation before it can change patient care.