Poster Demonstrates Research Assay and Bayesian Models in Breast Cancer Recurrence

DecisionQ Corporation, Roche Molecular Systems, Inc. and Sharp Memorial Hospital have announced that they have presented a poster demonstrating the use of molecular research assay and Bayesian models in breast cancer recurrence.

Using DecisionQ FasterAnalytics predictive modeling technology, pathology data from Sharp Memorial Hospital, and research genetic testing technology from Roche Molecular Systems, researchers have developed a predictive model that can identify the recurrence of breast cancer in early-stage patients 83.3% of the time.

"We focused on a HER2-negative, small-tumor population because the treatment options for this population are very unclear," said Dr. Howard Robin, Chief of Pathology at Sharp Memorial Hospital.

"With a predictive model we can really use the power of modern laboratory methods to provide the right treatment option at the right time."

FasterAnalytics is able to quickly model complex data sets and uncover hidden correlations that aid in predicting and treating diseases.

This capability was applied to a data set consisting of 64 HER2-negative, T1 breast tumors. The data set included pathology, treatment, and genetic mutation data.

Using this information, FasterAnalytics created a recurrence prediction model that predicted recurrence in this population with an 83.3% predictive value.

"This type of advanced data mining is really the next step toward true personalized medicine," noted John Eberhardt, Executive Vice President of Life Sciences at DecisionQ.

He added, "As the number of treatment and diagnostic options continue to grow, you really need a technology like FasterAnalytics to help match patients with the best therapy."

Additional studies are planned to be performed in the immediate future using Roche's AmpliChip p53 prototype assay to further confirm and validate the findings.