Predicting growth in cancer stem cells

Cancer is a scary and thorny opponent. Therapies for fighting human cancer are often harsh with debilitating side effects. But often these initial therapies succeed in destroying the mbad of tumor cells and driving the cancer into remission.

However, in many cases, later, a few years later, cancer can come back aggressively. Often when he comes back he is resistant to the therapies that led him into remission. Why is this scenario so common?

One idea is the theory of cancer stem cells. Although there are many complexities to the biology of cancer stem cells, the basic model suggests that many cancers are composed of different types of cells arranged in a hierarchy, much like a normal tissue. At the top of the hierarchy are cancer stem cells. It is a minority population with an unlimited ability to divide, even though it rarely divides. The tumor mbad is thought to be composed of partially differentiated offspring of cancer stem cells that divide rapidly but for a limited period of time before they cease to be proliferative.

Many therapies target and destroy this rapidly dividing population, but cancer stem cells persist and give rise to new, rapidly dividing offspring that can withstand therapy. One solution to this problem is to target cancer stem cells, and pre-clinical and clinical trials are underway. Unfortunately, there are obstacles. As mentioned, cancer stem cells can grow slowly and constitute a minority population in tumors, which makes measuring the effectiveness of a therapy difficult.

To identify potential new targets for cancer stem cells, a group of cell biologists collaborated with bioinformaticians from the Oklahoma Medical Research Foundation. Bioinformaticians used a computer program that one of them had developed, called GAMMA (Global Microarray Meta-Analysis). In simple terms, GAMMA badyzes much of the thousands of published experimental studies on gene expression, not just those that are in the field of cancer. GAMMA then applies a Guilt by Association algorithm to clbadify the probabilities of protein participation in many biological processes.

Cell biologists took these predictions and tested 50 genes involved in the division of cancer stem cells. They used a cell culture model of bad cancer stem cells developed in Robert Weinberg's lab at MIT. Three related mammary cell lines lacking the characteristics of cancer stem cells were used as comparisons. The researchers used RNA interference to deplete the cells of each of the proteins encoded by the 50 selected genes.

They found that 21 target genes showed preferential growth inhibition of bad cancer stem cells compared to the other three lines. These 21 genes participate in a variety of biological pathways, and some have been poorly studied. The researchers looked at 6 genes in more detail and found that 4 were involved in the process of chromosome segregation during cell division. Determining the exact biochemical pathways in which the 21 genes work and testing whether these pathways can be targeted for therapeutic inhibition of cancer stem cells will require further study.

These studies are described in the article Predictive bioinformatics identifies novel proliferation regulators in humans. a model of cancer stem cells, recently published in the journal Stem Cell Research . The studies were a collaboration between Constantin Georgescu and Jonathan D. Wren of the Research Program on Arthritis and Clinical Immunology and Evan Fields, John R. Daum and Gary J. Gorbsky in the research program on cell cycle and cancer biology at the Oklahoma Medical Research Foundation. Oklahoma City