Overlap of biological markers of prostate cancer reveals information about how progression occurs.

What is it about?

How prostate cancer develops is highly variable and this has led to the development of multiple tests using biological markers detectable in cells to try and predict how long it will take for the disease to progress. In this study we review all the known tests and look for overlap in their constituents. It is known that the biological markers selected for tests are very sensitive to the data and tools used to develop the test. We propose that if overlaps are found then they are more likely to be important and suggest to us how and why the disease develops. We found two groups of biomarkers that were highly overlapped. The first group that we designate SIG-HES6 defines a core set linking genes linking hormone signalling, cell cycle progression and a molecular subgroup of patients. The second set (designated SIG-DESNT) consisted of the a signature of aggressive disease that we had previously proposed and a second molecular subtype. Frequently these two sets of biomarkers predicted that the same individual was high-risk, suggesting that both the underlying mechanisms act together to promote aggressive cancer development. We also show that if a patient is determined to be a high-risk cancer by both of these groups then they are more likely to have a disease that progresses quickly and causes a poorer outcome. Finally, we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways.

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Photo by Vincent van Zalinge on Unsplash

Photo by Vincent van Zalinge on Unsplash

Why is it important?

To our knowledge this is the first publication to systematically analyze the relationships between multiple distinct tests for prostate cancer. First, we conclude that our results support a model whereby SIG-HES6 and SIG-DESNT genes co-operated to cause cancer progression. Secondly, consistent with this model, the use of a SIG-HES6 signature in combination with DESNT provides a much better predictor of poor outcome than the use of either alone. Thirdly, this observation may have relevance to approaches for therapeutic treatment where we make the prediction that drugs that target the two highlighted underlying mechanisms together may produced improved results.

Perspectives