Mathematics Faculty Proceedings, Presentations, Speeches, Lectures

Using Fusion of Response Metrics and Monte Carlo Simulation to Determine Immune Response in Cancer Immunotherapy Patients

Event Name/Location

The 2016 Duke-Industry Statistical Symposium; 2016 September 14-16; Durham, NC

Presentation Date

9-2016

Document Type

Conference Presentation

Description

Determining immune responders in the post-treatment clinical context of cancer immunotherapy, in which patients are treated with one or more antigens for the purpose of eliciting an immune response against the cancer can be challenging. In general, the effectiveness of threshold-based criteria, such as spot count difference from control in ELISpot, can vary widely, depending on patient population latent response and also on experimental choices that increase background variation such as IVS testing. On the other hand, inferential statistical tests such as mDFR or the binomial test can be impacted by varying numbers of samples per patient and also by general differences in patient population distribution of response. Furthermore, measuring differences between pre- and post-treatment response using either a direct statistical test, or a difference of some kind between independently determined pre- and post-treatment response are options when determining immune responders. The end result is that no single approach is applicable in all cases; this, in turn, can lead to the data itself dictating the definition of immune responder, a non-objective process that is difficult to apply broadly. In this presentation, a novel heuristic for determining immune responders using multiple metrics combined in a fusion scoring approach will be shown. Monte Carlo simulation is then used to put these fusion scores into a clear context for the purpose of assigning responder status to individual patient samples, and hence to each patient . A specific implementation of this approach will be shown using data from a recent phase 2 glioblastoma immunotherapy trial (ICT-107) in which HLA-A2 patients were treated with six synthetic peptides. Patient samples were tested for immune response using both ELISpot and Multimer, and it will be shown how the method was used for both types of data. Promising associations between responders designated in this manner and survival endpoints suggest that this method of designating patients as immune responders captures some of the underlying mechanism of action of this treatment.

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