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

• Published in: Physical biology Vol. 18; no. 1; p. 016001
• Main Authors: Johnson, Kaitlyn E, Howard, Grant R, Morgan, Daylin, Brenner, Eric A, Gardner, Andrea L, Durrett, Russell E, Mo, William, Al'Khafaji, Aziz, Sontag, Eduardo D, Jarrett, Angela M, Yankeelov, Thomas E, Brock, Amy
• Format: Journal Article
• Language: English
• Published: England 20-11-2020
• Subjects: Drug Resistance, Neoplasm - genetics; Neoplasms - drug therapy; Neoplasms - genetics; Sequence Analysis, RNA; Single-Cell Analysis; Transcriptome
• ISSN: 1478-3975
• DOI: 10.1088/1478-3975/abb09c

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.