Data from Clinical Studies of bioTheranostics' CancerTYPE IDÃ‚® and Breast Cancer Index(SM) Molecular Diagnostic Tests Presented at ASCO 2011 Annual Meeting
SAN DIEGO, June 7, 2011 /PRNewswire/ -- bioTheranostics, Inc., a bioMerieux company that develops innovative oncology diagnostic tests to drive personalized treatment, reported today findings from clinical studies utilizing the company's CancerTYPE ID®, and Breast Cancer Index(SM). Data from the studies were presented this week at the American Society of Clinical Oncology (ASCO) 2011 Annual Meeting in Chicago.
On June 6, scientists from bioTheranostics and the Institut Gustave-Roussy (France) presented results from a study evaluating the utility of the Breast Cancer Index(SM) (BCI) for predicting pathologic complete response (pCR) in breast cancer patients treated with neoadjuvant chemotherapy. In the study, patients with a high BCI score had a more favorable response to standard chemotherapies such as anthracycline and taxane (P=0.02).
"Patients determined as high-risk by BCI had a 10-fold greater probability of pCR with the chemotherapy regimen when compared to the low risk patients," said Mark Erlander, Ph.D., chief scientific officer, bioTheranostics. "These results suggest that BCI is predictive of chemotherapy response."
bioTheranostics also presented diagnostic utility data for CancerTYPE ID® from a study of 754 metastatic cancer cases. The results showed that pre-CancerTYPE ID® pathological workup involved a mean of seven immunohistochemistry (IHC) stains to identify the cancer site of origin. Increasing the number of IHC stains beyond seven lengthened the diagnostic process without improving the accuracy of the final result.
"Quickly and accurately diagnosing the site of origin is critical in the care of metastatic cancer patients," said Richard Ding, chief executive officer, bioTheranostics. "This study illustrated that the current diagnostic process is not standardized and often time- consuming." In the study, CancerTYPE ID required only five days of lab processing to predict a