A study analyzing nearly 1,000 prostate tumor samples has revealed eight distinct mutational patterns that account for most genetic variations in prostate cancer, some of which correlate with how aggressive the disease progresses. Published in Nature, the findings propose a method to re-examine existing genomic data instead of creating new tests, which could refine how clinicians evaluate risk and select treatments. “We have effectively created a map of the biological processes that drive prostate cancer,” said Joachim Weischenfeldt, PhD, professor at the Biotech Research & Innovation Centre at the University of Copenhagen and Rigshospitalet, and co-lead author of the study. “It is not going to change how any man is treated tomorrow. But it runs on the kind of DNA sequencing that several health systems already carry out for cancer patients. What we are proposing is to read existing data differently, not to build a new test from scratch.”
Researchers from the University of Copenhagen and Rigshospitalet investigated why prostate cancer outcomes vary so widely, ranging from slow-growing tumors to fast-spreading ones. Previous studies often examined individual mutation types separately, but this team analyzed whole-genome sequencing data from 959 primary prostate cancers at different stages and with varying clinical outcomes. Their broader approach combined single-base substitutions, insertions-deletions, copy number changes, and structural variants. The study’s integrative method allowed them to dissect how different mutational processes—such as those driven by DNA replication errors or oxidative stress—interact within the same tumor, rather than treating each class of mutation in isolation.
Their work identified eight integrated mutational footprints (IMFs), covering 85% of mutational processes in prostate cancer genomes. Most of these patterns are linked to three core biological mechanisms: hormone signaling, DNA repair failures, including deficiencies in homologous recombination, and cellular aging. The authors emphasized that these IMFs represent the key mutational processes operating in prostate cancer, providing a unified framework to explain its genomic diversity.
Four mutational patterns linked to faster metastasis
When the researchers examined whether these IMFs could forecast disease behavior, they found four, present in 37% of primary tumors—strongly tied to faster metastasis. These included patterns driven by reactive oxygen species and homologous recombination deficiency, both of which accelerate tumor progression. Two additional IMFs differed between early-onset and late-onset prostate cancer, with late-onset tumors showing a pattern that suggests responsiveness to androgen receptor-blocking drugs. The distinction between early- and late-onset tumors highlights how timing of disease onset may influence underlying mutational processes.
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The researchers conclude that their study “delineates the aetiologies and mutational processes that drive the genomic and clinical heterogeneity of prostate cancer, introduces IMFs as a unifying framework, and highlights their potential to improve both risk stratification and biomarker-guided treatment selection.” However, they caution: “While these findings are encouraging towards addressing an urgent clinical unmet need, more extensive and well-powered prospective biomarker-driven studies are warranted.” Weischenfeldt noted that while the work provides a stronger foundation, further validation is essential. “Our goal is to tailor treatment to each individual patient’s disease, and this brings us one step closer to making that a reality.”
How existing data could redefine prostate cancer treatment
Unlike earlier research that treated mutation types in isolation, this study integrated multiple genomic signals to explain why some prostate cancers advance rapidly while others remain inactive. The method leverages data already collected in many cancer clinics, eliminating the need for additional testing. By analyzing existing sequencing data differently, the researchers demonstrated how a full mutational signature approach could uncover clinically relevant insights without requiring new infrastructure.
The authors acknowledged the findings are still preliminary. For now, the study offers a clearer understanding of the genetic factors driving prostate cancer and a potential guide for more accurate, personalized treatment strategies. The research also shows the importance of biomarker-driven studies to validate these patterns in diverse patient populations before they can inform clinical decisions.
This research reflects a growing trend in oncology: maximizing the utility of existing genomic data. Similar techniques have been applied to other cancers, where mutational signatures have helped uncover the roots of tumor development. In prostate cancer, such insights could eventually improve risk stratification and enable more effective targeted therapies, though widespread clinical adoption remains years away. The study’s emphasis on repurposing existing data aligns with broader efforts to optimize genomic resources in healthcare systems.
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