A striking advance in understanding and treating prostate cancer: reclassification through genetic analysis

We have begun frequently using genetic analyses in lung, breast and colorectal cancers to guide treatment decisions according to cancer genetic profiles. This makes personalized targeted treatments based on each tumor’s genetic characteristics possible. Until now, however, no genetic tests were used in practice for prostate cancer treatment.

Prostate cancer, the most common cancer among men in our country after lung cancer, has been found to have 5 different types. According to a study whose results were reported last week in EBioMedicine, these findings may substantially change our understanding and treatment approaches.

There are hundreds of cancer types, each with its own subtypes. Scientists aim to identify subgroups without increasing complexity, classifying them by prognosis and treatment response. For example, some prostate cancers grow and spread to other organs (metastasize) rapidly, while others grow and spread slowly.

Until now, early prostate cancer was assessed in 3 groups—low, intermediate and high risk—and treatment selected accordingly, considering age, general health and local disease extent. Risk is calculated from aggressiveness (Gleson score), stage and PSA. Because recurrence risk is high in the high-risk group, aggressive treatments such as surgery, radiotherapy and, recently, chemotherapy shown beneficial early in prostate cancer are preferred. The new genetic analysis identifies prostate cancer as 5 different diseases with distinct genetic and clinical features, more clearly defining subtypes most likely to recur.

The study examined tumor genetic profiles of 259 men with nonmetastatic prostate cancer, identifying 100 key genes. Using this set of 100 genes, prostate cancers were divided into 5 classes. Each class was named, numbered from 1 to 5, iCluster . Groups iCluster 1 and 3, with the most genetic changes, had the highest postoperative recurrence risk. This genetic classification predicted recurrence more effectively than other markers used so far: Gleason score, PSA, cancer extending beyond the prostate capsule and positive surgical margins. It is therefore expected to predict future recurrences more accurately.

In conclusion, One of the greatest problems in prostate cancer treatment is the inability to predict recurrence clearly with current methods. This study was the first to show that genetic analysis may contribute substantially. We believe these findings will make it much easier to determine the most appropriate treatment for each patient in the future.