Abstract 5180: Bayesian multi-endpoint analysis of syngeneic mouse immunotherapeutic trials

Recent and notable immunotherapeutic successes for the treatments of melanoma and lung cancer have catalyzed wide-spread development of novel immunotherapies and combinations for a variety of cancers. While promising, responses to immunotherapies are a focal point of present investigations, being as of yet challenging to explain. Amongst the many translational challenges, pre-clinical in bred syngeneic mouse experiments often display heterogeneous tumor responses, whereby a fraction of tumors show resistance, partial, or complete response to the same immuno-intervention(s) implanted in identical in bred mouse strains. We mathematically modeled heterogeneous tumor responses in syngeneic mouse trials with Bayesian statistical methodology. Our decision criteria for drugs and drug combinations can simultaneously evaluate two or more endpoints. At the heart of our methodology is Bayesian learning with prior information that did not compromise statistical performance; actually, we achieved statistical gains to identify superior immunotherapies, rendering quantifiable efficiencies by design. We illustrate an augmented approach to discovery by Bayesian learning in real syngeneic mouse studies. We propose our powerful multi-endpoint Bayesian statistical criteria to ascertain superior pre-clinical immunotherapeutic activity. Citation Format: David Gold, Ningning Chen. Bayesian multi-endpoint analysis of syngeneic mouse immunotherapeutic trials. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5180.