DDLS Fellows identify hidden aggressive features in low-risk prostate cancer

SciLifeLab Group Leaders Arian Lundberg and Golnaz Taheri (KTH), both Fellows within the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), have collaborated on a new approach to identify molecular features associated with aggressive prostate cancer.

The A. Lundberg Lab, together with Taheri’s research team, combined multi-omics data with machine learning and network analysis. The machine-learning model used in the study was developed by Taheri’s research team. The researchers identified molecular characteristics linked to more aggressive disease in a subset of patients who had been classified as low risk using conventional clinical methods.

“The study is a good example of the type of data-driven precision medicine research we are building at KTH, combining large-scale molecular data with machine learning to address clinically relevant questions in cancer,” Lundberg says.

The study highlights how data-driven approaches can provide a more detailed picture of tumor biology and could, in the longer term, contribute to more precise risk assessment and new treatment strategies.

The study has been published in npj Digital Medicine.

Read the full press release: Researchers identify aggressive molecular features in low-risk prostate cancer | KTH