From the examination of tumor genomes of nearly 300 prostate cancer patients, it was found that changes in the prostate cells genetic information lead to the development of cancer. A computer model has been developed by the scientists that predicts the course of disease in each patient, which allows doctors to tailor treatments accordingly.
In Germany, prostate cancer is the most common malignancy in men, with close to 60,000 new cases diagnosed every year. These Tumors are usually slow-growing, meaning that not all patients require immediate treatment. Until recently, physicians had been unable to distinguish between benign and aggressive forms of the disease, particularly when dealing with tumors diagnosed at an early stage in the disease process.
Working alongside a number of other research groups from within Germany and abroad, Charité-based researchers helped to develop criteria that would make this type of classification possible. To do so, they studied the molecular profiles of close to 300 prostate tumors. They sequenced the information encoded within the cells' genetic material, recorded chemical changes to the genetic code, and measured the activity of specific genes within cancerous tissues. An analysis of their data has shed light upon the temporal order of mutational events involved in the development of prostate cancer. "We were able to identify tumor subtypes that progress at different rates and therefore require different types of treatment," says one of the study's lead authors, Prof. Dr. Thorsten Schlomm, Director of Charité's Department of Urology.
In an effort to improve the reliability of prognoses, the research consortium is planning to spend the next few years collating additional data on thousands of patients, which they will then use to further develop and enhance their computer model. They will achieve this by working with Berlin's newly established urology network (Hauptstadt-Urologie-Netzwerk), which brings together urology specialists from Charité and private practice. Their ultimate aim is to make it easier for physicians to decide on the most suitable treatments for individual patients.