In the biggest study to analyze the genetics of prostate cancer, scientists find no fewer than 80 new potential drug targets. The project opens broad avenues for the design of new treatments. Share on Pinterest Big data provides new ways to approach prostate cancer. Extracting genetic data was, once upon a time, a cumbersome and incredibly time-consuming task. However, as technology continues to improve, the job has become significantly quicker and cheaper. In parallel, the tools available for handling large datasets have vastly improved. Taken together, this means that the oceans of information harvested from genetic code can be analyzed, mapped, and combined with relative ease to provide a new level of clarity. Recently, an international team used this double-pronged approach of DNA analysis and big data to delve into the genetics of prostate cancer. On the hunt for molecular chinks in the disease’s armor, the research was orchestrated by the Institute of Cancer Research in London, United Kingdom.

Prostate cancer challenges Prostate cancer is the second most common cancer among men in the United States. This year, in the U.S., there will be an estimated 164,690 new cases of prostate cancer and almost 30,000 deaths to the disease. Although researchers have made headway in understanding and treating prostate cancer, there are still a number of difficulties. As study leader Prof. Rosalind Eeles explains, “One of the challenges we face in cancer research is the complexity of the disease and the sheer number of ways we could potentially treat it.” Dr. Justine Alford, of Cancer Research U.K., outlines another issue in studying and intervening in prostate cancer. “A major hurdle to making further progress against prostate cancer,” she explains, “is the lack of ways to accurately predict how a person’s disease will progress, making it challenging to know which treatment is best for each patient.”

Harvesting genetic data To approach the problem from a new direction, the researchers took genetic information from 112 men with prostate cancer and combined it with data from a range of other studies. In all, samples from 930 patients were used. Using the latest big data techniques, the team garnered new insights into genetic changes that spark the development and fuel the progress of prostate cancer. Once they understood which genes were involved, they could create a map of the proteins that are coded by these genes. Next, they turned to a database called canSAR, which combines data from studies, applies machine learning, and helps to provide insight into drug discovery. On their website, canSAR explain the questions that their database aims to answer: “What is known about a protein, in which cancers is it expressed or mutated, and what chemical tools and cell line models can be used to experimentally probe its activity? What is known about a drug, its cellular sensitivity profile, and what proteins is it known to bind that may explain unusual bioactivity?” The scientists found that 80 of the proteins that they had uncovered were potential drug targets. And, 11 of these were targeted by existing drugs, and seven others could be targeted by drugs already in clinical trials. Their findings are published this week in the journal Nature Genetics. “Our study applied cutting-edge techniques in big data analysis to unlock a wealth of new information about prostate cancer and possible ways to combat the disease.” Prof. Rosalind Eeles