Comparison of five different gene expression profiles used internationally with breast cancer shows that four yield similar outcome predictions and probably reflect similar molecular events, according to an article in the August 10 issue of the New England Journal of Medicine.

Currently, doctors treating patients with breast cancer make treatment decisions and predictions based largely on the location and size of primary tumor and whether the cancer has metastasized to lymph nodes and distant sites.

Genetic profiling was developed to see if there were molecular biomarkers that could distinguish patients who are similar in terms of the clinical indicators but have very different results to different therapies.

Over the past five years, gene expression profiles have been identified that appear to be predictive for cancer patients, especially for breast cancer patients. However, the tests have very little overlap in their gene lists, and thus it is not known just how distinct these different assays might be.

According to Charles M. Perou, MD, senior author of the paper, some of the predictive assays are available commercially and others are under study in clinical trials in which treatment decisions, including whether or not to use chemotherapy, are being made based on them.

“An important and unanswered question, however, is whether these assays actually disagree or agree concerning outcome predictions for the individual patient,” Perou said. “I think this is a very important point because if they disagree then it becomes difficult to determine which to use and when, and which are more robust and helpful.”

To compare the individual predictions made by different genomic tests, Perou and his American and Dutch collaborators studied the concordance of five different predictors that were all applied to a single data set of 295 tumor samples for which patient survival data was available ? relapse-free survival and overall survival.

Four predictors showed “significant agreement” in their outcome predictions on individual breast cancer patients, despite having little gene overlap. Of the three predictors showing the greatest concordance, two were the main assays that are commercially available and being used to guide clinical trials.

“If one assay said this patient was going to do poorly, then so did the other two,” Perou said, noting that although the two commercial assays overlapped each other only by one gene, they were in 80 percent agreement with each other.

“This is good news for breast cancer patients. It means that different groups have independently arrived at tests which agree with each other and that they all do add information not provided by existing clinical tests,” Perou said.

For example, several of the predictors in this study appear to predict the likelihood of breast cancer recurrence in various populations of women with node-negative disease.

Such information would be useful for identifying women who are unlikely to experience recurrence and, thus, potentially unlikely to benefit from chemotherapy.

“We find our results encouraging and interpret them to mean that although different gene sets are being used, they are each tracking a common set of biological characteristics that are present across different breast cancers and are making similar outcome predictions,” Perou said.