GenomeDx Collaborates with Astellas to Identify Genomic Drivers of Patient Response to XTANDI® Capsu

GenomeDx Biosciences has entered into a research collaboration with Astellas, a leading global pharmaceutical company, to apply genomic tumor profiling using GenomeDx's Decipher® Classifier and Decipher GRID® as a potential aid in the identification of prostate cancer patients undergoing active surveillance who may benefit from treatment with XTANDI® (enzalutamide).  

As part of the agreement, Astellas will provide GenomeDx with tumor samples from its Phase 2 ENACT trial (NCT02799475), which is comparing the time to prostate cancer progression between patients treated with enzalutamide versus patients undergoing active surveillance. GenomeDx will profile all samples to provide Astellas with an analysis of tumor aggressiveness based on its Decipher Classifier score, and a Decipher GRID profile which will assess the biological behavior of a patient's tumor based on a set of signatures that may be associated with enzalutamide response. 

"Profiling our study samples through Decipher Classifier and Decipher GRID will provide us with expansive genomic information, allowing Astellas to potentially deliver innovative, targeted therapies to patients most likely to benefit," said Bruce Brown, M.D., senior medical director, oncology, Astellas. "The genomic data provided by GenomeDx will help us to better understand the molecular drivers of prostate cancer and how those drivers interact with response to enzalutamide."

"This collaboration with Astellas represents the power of the GRID database – the ability to share and analyze what used to be incomprehensible amounts of genomic data to provide a better understanding of how individual patients respond to treatment," said Doug Dolginow, M.D., chief executive officer of GenomeDx. "Together with Astellas, we will be able to compare genomic tumor profiles from patients in their clinical study with thousands of tumor profiles in the GRID database in an effort to identify new and existing genomic signatures that may be predictive of treatment response."