Complex Immunotherapeutic Response and Computational Models in Single-Subject Studies

Over the last decade, a large number of new immunotherapies have been developed, leading to a renewed interest in targets for immune-mediated diseases (i.e., Lupus, Alzheimer’s, Multiple Sclerosis, Rheumatoid Arthritis, atopic dermatitis, cancers). High-throughput experimentation (e.g., ImmPort) and pharmaceutical clinical trials are generating so much novel data that these observations are exceeding our understanding of immunobiology and the design of immunotherapy protocols. In addition, single-gene product biomarkers have been insufficient in predicting therapeutic responses. Furthermore, alternating combinations of activation and suppression immunotherapies are probably required over long periods of time to obtain disease control or remission, increasing the complexity of modeling responses. Taken together, these issues create unique opportunities for improving the multiscale modeling of immunological response at the molecular, cellular, cellular communication, tissular, and individual levels, as well as the development of more complex multianalyte or multiscale biomarkers.  We will present the framework for new assays and novel single-subject analytics that have the potential to substantially increase the efficiency of clinical trials by reducing the sample size and the time of observations via surrogate complex biomarkers. We will demonstrate a clinical trial evaluating a genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus that identifies children prone to asthma exacerbations.