Tuesday, December 11, 2018
GeneCentric Therapeutics, Inc. today announced that it has entered into a research collaboration with the University of North Carolina at Chapel Hill (UNC) to assess patient response to immunotherapeutic drugs such as PD-1 and PDL-1 inhibitors based on bladder cancer subtypes. The collaboration will augment the GeneCentric’s Bladder Cancer Subtype Profiler (BSP™) to predict disease progression and drug response and expand the company’s Cancer Subtype Platform (CSP®).
“There is a significant need for selecting the right patients for clinical development of new therapies and ultimately for medical practice,” said Dr. Myla Lai-Goldman, CEO/Founder of GeneCentric. “While the five-year survival rate for all bladder cancer patients is 77 percent, when the cancer has metastasized, survival is less than 12 months. Checkpoint inhibitor immunotherapy has been effective in treating some bladder cancer patients but has been associated with significant adverse events in others. Our UNC collaboration will build on our knowledge of bladder cancer subtypes and other biomarkers to determine their potential to predict disease progression and drug response.”
The collaboration, with the laboratory of Dr. William Kim, Distinguished Professor of Medicine and Genetics in the Department of Medicine and Division of Hematology and Oncology at UNC-Chapel Hill, will involve a retrospective study of ‘real world’ data augmented by extensive molecular characterization of patients with metastatic bladder cancer. This includes investigation of the links between certain bladder cancer alterations, disease progression, and clinical response to anti-PD1/anti-PD1-L1 checkpoint inhibitor therapy as well as other treatments. Dr. Kim is also a consultant to GeneCentric.
“While our understanding of the tumor and immune biology related to bladder cancer is evolving rapidly, meaningful progress for invasive disease has been difficult,” said Dr. Kim. “This collaboration will generate data to support important research intended to generate new insights regarding disease progression and response signatures to current therapies as well as accelerate the advancement of novel treatments.”
