Understanding how a healthy cell turns into a malignant one has been a challenge for scientists. Researchers at Charité - Universitätsmedizin in Berlin may have come close to solving this enigma.
In a study published in Cancer Cell, the researchers analyzed the tumor genomes of 300 prostate cancer patients by examining each molecular profile, sequencing encoded material within the cells' genetic substance, documenting chemical variations to the genetic code, and assessing the activity of specific genes within cancerous tissues. By looking at all of these data, the investigators were able to develop precise criteria that can be used to classify tumor types ranging from benign to aggressive.
"We were able to identify tumor subtypes that progress at different rates and therefore require different types of treatment. We now know which of these mutations occur first, initiating the process of change from prostate cells to tumor cells, and which of them are more likely to follow later," explained Thorsten Schlomm, MD, Director of Charité - Universitätsmedizin's Department of Urology and one of the study's senior authors.
"When an individual patient's tumor shows a specific mutation, we are now able to predict which mutation is likely to follow and how good the patient's prognosis is," Dr. Schlomm added. "Our team is currently busy incorporating our computer model into the treatment process at Charité. This will enable clinicians to model a particular treatment's likelihood of success. As for the timescale involved, we expect it will take two to three years for this algorithm-based method to become clinical routine."
Further studies will be conducted on a larger population to improve the reliability of this computer model. The researchers are hopeful that in the near future, clinicians will be able to use this system in order to customize prostate cancer treatments depending on the future course of the disease.
For More Information
Gerhauser C, Favero F, Risch T, et al (2018). Molecular evolution of early-onset prostate cancer identifies molecular risk markers and clinical trajectories. Cancer Cell, 6(34):996-1011. DOI:10.1016/j.ccell.2018.10.016
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