The results will also help to determine smaller and more accurate classifiers of different types of tumor. Microarray analyses have previously been used to distinguish between sub-classes of breast tumors based on the differential expression of many hundreds of genes. For example, one sub-class of tumor was identified as basal-like ™ because the tumors expressed a series of genes thought to be expressed in myoepithelial, but not luminal cells. The basal-like tumors appeared to be more aggressive, and a retrospective study showed that those patients with basal-like tumors had a poorer prognosis than those with another sub-class. Applying the base-line dataset from the normal cells to the study that originally proposed the classification, allowed the LICR/Breakthrough team to identify a handful of critical marker ™ genes that may be better able to prospectively diagnose tumor sub-classes, and confer independent prognostic information. These markers might also indicate possible avenues of therapy.

Importantly, according to Dr. Neville, the generation of this more accurate dataset may also have a major impact on patient care. In order to discriminate between different types of breast cancers in the pathology lab, the surgeon often has to take a sample large enough to incorporate the basal myoepithelial cell layer. If, one day, we could use a fine needle biopsy in conjunction with the novel myoepithelial markers we ™ve identified, we could not only potentially improve diagnosis, but also our therapeutic approach.