It is becoming clear that one solution to the development of new treatments for complex diseases such as HIV-1, cancer, or resistant bacterial strains is to use efficient drug combinations whose properties cannot be achieved by one drug alone. However, studying all possible combinations of drugs is impractical ? if not infeasible ? and thus, there is a critical need for new approaches to tackle this challenge. In an effort to illuminate this currently intractable question, we will develop a computational and experimental framework to predict the effects of multiple stimuli on the induction of immune responses, and evaluate the utility of this principle for the development of new vaccines. In this project, we hypothesize that the effects of higher-order combinations of stimuli (i.e., triplets or quadruplets) can be accurately predicted by using information about the effects of single and pairs of stimuli only. Specifically, we will test this idea by combining novel high-throughput in vitro assays followed by in vivo testing in mouse models of cancer, in an effort to develop innovative anti-tumor vaccines. We will (1) study the effects of millions of immune modulating agents through computations and experiments in vitro by developing high-throughput co-culture assays using live imaging; (2) measure the effects of thousands of immune stimuli combinations on DCs in vitro by developing genomics and proteomics methods; and (3) design and test new vaccine formulations based on immune stimuli combinations in vivo using mouse models of cancer. This innovative and interdisciplinary work marks a departure from current approaches to investigate immunological pathways and their interactions, and is poised to make a significant impact on vaccine design efforts, and more broadly, on how to rationally select combinations of drugs to enable desired effects on biological processes.
One solution to developing new treatments for complex diseases such as HIV-1, cancer, or resistant bacterial strains is to use drug combinations. However, studying all combinations is infeasible, and hence, predictive models of higher-order effects are crucial. In this project, we will address this fundamental challenge in the context of pathogen-sensing pathways, in an effort to build a predictive model for the assembly of potent, anti-tumor vaccines.
