Researchers identify aggressive molecular features in low-risk prostate cancer
Researchers at KTH has developed a new approach that reveals molecular features of aggressive prostate cancer that are not captured by conventional clinical risk classification. The results could lead to new therapeutic strategies.
The A. Lundberg Lab, together with Golnaz Taheri’s research team, has combined multi-omics analysis of different molecular layers with machine learning modelling and network analysis. The approach revealed molecular features linked to more aggressive prostate cancer in a subset of patients who had been classified as low risk, highlighting biological differences that may not be apparent from clinical measures alone. The results were published in npj Digital Medicine.
“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,” assistant professor Arian Lundberg says.
The multi-omics approach means that the researchers look at different molecular measurements to tell the complete story of a tumor.
“For example, genomics tells us about DNA alterations, transcriptomics tells us which genes are active, and epigenomics tells us about regulatory changes that can influence gene activity,” Lundberg says.
“Instead of looking at these layers separately, we integrate them to build a more complete, patient-specific picture of tumor biology.”
The machine-learning model used in the study was developed by KTH researcher Golnaz Taheri’s research team, whose machine-learning and network-analysis contributions were central to the study and its findings.
At the moment, the new method is at the research stage and not yet ready for clinical use. The next step is to test some of the potential therapeutic vulnerabilities suggested by the analysis, through laboratory experiments. This will help Lundberg’s team understand whether the molecular changes they identified are useful for risk prediction only or could also point towards new therapeutic strategies.
“In the longer term, the goal is to translate these findings into more precise risk stratification and potentially identify patients who could benefit from earlier or more targeted treatment,” Lundberg says.
The work was supported by SciLifeLab-Knut and Alice Wallenberg Data-Driven Life Science (DDLS) program, Vetenskapsrådet (Swedish Research Council), Prostatacancerförbundet and Digital Futures.
Jon Lindhe