IMCT-17STATISTICALLY SIGNIFICANT ASSOCIATION OF GLIOBLASTOMA IMMUNOTHERAPY PHASE II CLINICAL STUDY (ICT-107) TREATMENT AND SURVIVAL TO IMMUNE RESPONSE USING A NOVEL COMPREHENSIVE ELISPOT ANALYSIS

Abstract

The main objective of active cancer immunotherapy is the activation of a patient's own T cells to attack tumor cells. The accurate measurement of the expansion and functionality of these T cells upon treatment is a key element in establishing the association between treatment and treatment outcomes or survival. While much research and clinical work has been focused on establishing and optimizing immuno monitoring during clinical trials, direct association of patient immune response to patient clinical endpoints remains difficult. IFN-γ ELISPOT has been used extensively to monitor the immune response in immunotherapy trials. While there have been significant efforts to harmonize ELISPOT procedures and analysis, a standard methodology for classifying a positive immune response in an immunotherapy context has not emerged. Past studies have used techniques ranging from simple quantification of spot counts to published laboratory ELISPOT statistical analysis on single samples, but these studies lack consensus on establishing response to treatment with pre- and post- treatment samples. Here, novel statistical methods are presented for the direct generation of p-values comparing pre- and post- treatment ELISPOT responses evaluated ex-vivo in patients treated with a multi-peptide loaded dendritic cell-based immunotherapy. Then, a comprehensive scoring system is introduced which incorporates these novel methods, along with previously used metrics, into a single numerical scale. Using an analysis of score distribution, this scoring system is shown to distinguish response from non response even in the context of low levels of T cell response detection sometimes associated with ex-vivo evaluation. Consequently, binary classification of patients as responders and non-responders is performed. Finally, this classification system is shown to compare favorably to other methodologies with respect to its ability to find statistically significant associations between the immune response detected ex-vivo and treatment group, IL-12 production of dendritic cells, and both overall and progression-free patient survival.