51. 212. Of States and Fates: Predicting T-Cell Immunity By the Numbers
- Author
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Laurence J.N. Cooper, Luay Nakhleh, Sourindra Maiti, Jianrong Dong, Colleen M. O'Connor, and Sonny Ang
- Subjects
Pharmacology ,medicine.medical_treatment ,Rand index ,Gene regulatory network ,Immunotherapy ,CHOP ,Biology ,medicine.disease ,Chemotherapy regimen ,Pearson product-moment correlation coefficient ,Lymphoma ,symbols.namesake ,Immune system ,Immunology ,Drug Discovery ,symbols ,medicine ,Genetics ,Molecular Medicine ,Molecular Biology - Abstract
The immune system is a complex network of checks and balances in constant flux. Quantitative characterization of the system dynamics, sampled via constituent components such as T cells, combined with mathematical modeling enabled us to obtain “statistical pattern recognition” of immune states and transitions over time. Here we show a statistical framework to characterize immune states for adoptive immunotherapy using serial infusions of activated polyclonal T cells into companion canines diagnosed with B-cell non-Hodgkin Lymphoma (NHL) post CHOP chemotherapy regimen as a model for human disease. We applied multiplexed gene profiling techniques to assess changes in gene expression data from 10 companion canine patient clinical samples and gene regulatory networks (GRN) information to build an “immune landscape” that predicts immune states, state transitions, and plasticity for immunomodulation (“reprogrammability”) over the course of immunotherapy regimens. The potency of immune modulation and immune surveillance, two critical parameters of efficacy through inferred state transitions, were evaluated by measuring distortions and shifts in the immune landscape of canine patients undergoing treatment through system dynamics, Adjusted Rand Index, Pearson Correlation, and ANOVA analyses. Adjust Rand Index validated hierarchical clustering analyses. Pearson correlations isolated genes that were up- or down-regulated with regard to single genes of interest. ANOVA analyses indicated gene expression difference between temporal samples. Applying such analyses to adoptive T-cell immunotherapy, allowed us to examine clinical/tumor remissions through the lens of immune state transitions, affording real-time improvements to therapeutic regimens. In developing a formal framework to capture the effects of molecular signatures and GRN over time to describe distinct immune states, we enhanced the clinical cancer immunologist's analytical arsenal (with a measuring tool/sensor) and helped bridge the gap between bench and bedside.
- Published
- 2015
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