Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

iTIME.219: An Immortalized KSHV Infected Endothelial Cell Line Inducible by a KSHV-Specific Stimulus to Transition From Latency to Lytic Replication and Infectious Virus Release.

Kaposi's sarcoma-associated herpesvirus (KSHV/HHV-8) is the causative agent of Kaposi's sarcoma and two B cell lymphoproliferative disorders: primary effusion lymphoma and KSHV-associated multicentric Castleman's disease. These distinct pathologies involve different infected cell types. In Kaposi's sarcoma, the virus is harbored in spindle-like tumor cells of endothelial origin, in contrast with the two pathologies of B cells. These distinctions highlight the importance of elucidating potential differences in the mechanisms of infection for these alternate target cell types and in the properties of virus generated from each. To date there is no available chronically KSHV-infected cell line of endothelial phenotype that can be activated by the viral lytic switch protein to transition from latency to lytic replication and production of infectious virus. To advance these efforts, we engineered a novel KSHV chronically infected derivative of TIME (telomerase immortalized endothelial) cells harboring a previously reported recombinant virus (rKSHV.219) and the viral replication and transcription activator (RTA) gene under the control of a doxycycline-inducible system. The resulting cells (designated iTIME.219) maintained latent virus as indicated by expression of constitutively expressed (eGFP) but not a lytic phase (RFP) reporter gene and can be sustained under long term selection. When exposed to either sodium butyrate or doxycycline, the cells were activated to lytic replication as evidenced by the expression of RFP and KSHV lytic genes and release of large quantities of infectious virus. The identity of the iTIME.219 cells was confirmed both phenotypically (specific antigen expression) and genetically (short tandem repeat analysis), and cell stability was maintained following repeated serial passage. These results suggest the potential utility of the iTime.219 cells in future studies of the KSHV replication in endothelial cells, properties of virus generated from this biologically relevant cell type and mechanisms underlying KSHV tropism and pathogenesis.