Two Genes Together Drive Aggressive Prostate Cancer

An approach has been investigated for accurate cross-species analysis of conserved cancer pathways based on reverse engineering of genome-wide regulatory networks, known as interactomes, a whole set of molecular interactions in a particular cell, representing both human and murine prostate cancer.

A team of scientists led by those at Columbia University Medical Center (CUMC; New York, NY, USA) assembled genome-wide regulatory networks (interactomes) for human and mouse prostate cancer from expression profiles of human tumors and of genetically engineered mouse models, respectively. Gene silencing of two genes as well as forced expression of one of the genes were done using lentiviral small hairpin RNAs (shRNAs, Open Biosystems; Pittsburgh, PA, USA) or the expression vectors from the CCSB Human ORFeome Library from the same company. Analysis of protein expression of two genes was performed using a high-density tissue microarray analyses (TMA) and a metastasis TMA, and slides were scanned using an iScan microarray scanner (Illumina; San Diego, CA, USA)

The team identified the gene for the Forkhead box M1 protein (FOXM) and the gene encoding for the centromere protein F (CENPF) as a synergistic driver pair in aggressive prostate cancer in both mice and humans, as these regulators jointly control genetic programs associated with the most prominent tumor hallmarks in both species. They analyzed prostate cancers from a group of more than 900 patients who had undergone prostate removal surgery. This analysis showed a striking correlation between the co-expression of FOXM1 and CENPF and the poorest disease outcome. In sharp contrast, expression of either gene alone did not correlate with aggressive disease. In addition, tumors in which neither gene was aberrantly expressed had the best prognosis.

Michael M. Shen, PhD, professor of medical sciences and coauthor of the study, said, “This is just a first step toward a deeper understanding of the genetics of cancer. The tools and approaches developed in this study may have broad utility in studying prostate cancer; cross-species computational analyses also could be used to identify the causes of other cancers, as well as that of other complex diseases.” The study was published on May 12, 2014, in the journal Cancer Cell.

Related Links:
Columbia University Medical Center
Open Biosystems