Title: Mathematical Modeling of Normal and Cancerous Hematopoiesis
Abstract: Chronic myeloid leukemia (CML) is a blood cancer in which there is dysregulation of maturing myeloid cells (granulocytes) driven by a chromosomal mutation which creates the fusion gene, BCR-ABL1. Tyrosine kinase inhibitors (TKI) have proved effective in treating CML but there are still a small cohort of patients who, for reasons that are still unknown, do not respond to TKI therapy. Further, a significant proportion of patients who appear to have a complete molecular remission while on TKIs experience a relapse of CML when TKI treatment is discontinued. Mathematical modeling of CML hematopoiesis can provide insight on these processes. Here, we develop physiologically accurate, data-driven mathematical models of CML hematopoiesis that incorporate feedback control and lineage branching to help sort this out. We develop an automated method for model selection that integrates data gleaned from past and new experiments in addition to single-cell RNA sequencing (scRNAseq) to select plausible classes of models. We first apply this approach to normal hematopoiesis and identify models that have desired system properties and make predictions about system behavior upon perturbation. When extended to incorporate CML hematopoiesis, our results show that feedback/branching models are more robust, have a better fit to alternative patterns of patient response than simple linear models, identify mechanisms of primary resistance, and suggest new treatment strategies.