A new study suggests that a genetic fingerprint associated with normal embryonic stem cells may be important for the development and function of cancer stem cells. The research, published by Cell Press in the April 10th issue of Cell Stem Cell, demonstrates that embryonic stem cells and multiple types of human cancer cells share a genetic expression pattern that is repressed in normal differentiated cells, a finding that may have significant clinical implications for cancer therapeutics.
Self-renewal is a hallmark of stem cells and cancer, but existence of a shared stemness program remains controversial, explains study co-author, Dr. Howard Y. Chang from Stanford University. Dr. Chang, Dr. Eran Segal from the Weizmann Institute in Israel and their colleagues constructed a gene module map to systematically relate transcriptional programs in embryonic stem cells (ESCs), adult tissue stem cells and human cancers.
The researchers identified two predominant gene modules that distinguish ESCs and adult tissue stem cells. Importantly, the ESC-like transcriptional program was activated in diverse human epithelial cancers and strongly predicted metastasis and death, says Dr. Segal. Conversely, the adult tissue stem gene module had an opposite pattern, activated in normal tissues relative to cancer and repressed in various human cancers when compared to normal tissues.
The researchers went on to demonstrate that c-Myc, but not other oncogenes, was sufficient to reactivate the ESC-like program in normal and cancer cells. In primary cells transformed by tumor-inducing genes Ras and I"B", c-Myc increased the number of tumor-initiating cells that exhibited key properties associated with cancer stem cells and dramatically increased the frequency of tumor formation in mice."
These findings suggest that activation of an ESC-like transcriptional program in differentiated adult cells may induce pathologic self-renewal characteristics of cancer stem cells. Further, the map of gene modules may prove to be a valuable tool for establishing improved standards for classifying and defining stem cells by using the expression signature modules as fingerprints rather than reliance on just a few molecular markers.
|Contact: Cathleen Genova|