As unique as one’s ‘John Hancock,’ a genetic “signature” is helping scientists develop a new technique to identify aggressive forms of prostate cancer. Currently, a grading system known as Gleason score determines the severity of prostate cancer, with a high score typically being assigned to those determined to have a poor prognosis. But there are times when men with a low Gleason score still go on to develop aggressive tumors.
Investigators from The Cancer Institute of New Jersey, along with colleagues from the Institute for Advanced Study in Princeton and Chugai Pharmaceutical in Japan, wanted to further understand the molecular characteristics associated with poor prognosis – all through the help of Systems Biology methodologies.
In their study which appeared in the December 27, 2011, edition of Proceedings of the National Academy of Sciences (doi/10.1073/pnas.1117029108), investigators started by examining gene expression data from 281 prostate cancer tissue samples from a 2010 Swedish study. In this study, samples from each patient’s tumor were collected by biopsy, stored, and analyzed using a technology known as microarray, by which a sample can be scanned for the expression of thousands of genes using probes – or ‘baits’ - affixed on glass slides. The final data published from this study contained the levels of each gene measured in each sample, resembling a vast ‘light field’: green lights for inactive genes, red lights for active genes. Investigators examined these data looking for patterns that might identify specific tumor characteristics; these patterns can be thought of as traces – or ‘signatures’ –of genetic aberrations causing various malfunctions in the tumor cells. The team then looked at the combination of these signatures that were present in each sample.
Researchers identified five prostate cancer subtypes. Three of those groups were determined to have a non-malignant outcome, but the molecular profiles of the other two groups exhibited a distinct high-risk prognosis, as confirmed by clinical outcome data. The data were re-confirmed using a different study set. The authors note, the two study sets differed in patient characteristics including age, ethnicity and treatment regimens, but had comparable molecular features in the two high-risk subtypes.
“Gleason score remains the best indicator of overall prostate cancer survival to date, but if we are able to identify patients with a low Gleason score who, nevertheless, exhibit characteristics for developing an aggressive prostate cancer, clinicians may be able to better manage the disease,” said CINJ resident member Arnold J. Levine, PhD, senior author of the research and professor of pediatrics and biochemistry at UMDNJ-Robert Wood Johnson Medical School. He is also a professor emeritus at the Simons Center for Systems Biology at the Institute for Advanced Study.
In order to do that, investigators need to determine the relevance of this subtype classification for the development of diagnostic methods to influence treatment decisions, including treatment versus follow up and the identification of new targeted therapies. Using Systems Biology, a pilot study is now being conducted at CINJ to determine the suitability of Next Generation Sequencing technologies in reading out even more detailed genetic information from prostate tumor samples stored in paraffin wax blocks.
By utilizing shared resources at CINJ (Biospecimen Repository Service, Functional Genomics and Bioinformatics) and the Center for Systems Biology, researchers will be able to reconfirm their findings and further investigate more efficient strategies to achieve this classification. An example of the latter is to identify a set of surface markers on prostate cancer cells that would allow investigators to classify prostate cancer patients into the subtypes uncovered by the gene expression analysis. Using a specialized cyber infrastructure to analyze tissue slides, the presence of surface markers could then be identified.
With translational science traditionally being an effort “from laboratory bench to patient bedside,” the Center for Systems Biology at CINJ further expands that to "from computer to bench to bedside” with advancing technologies in between.