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Publications of Eduardo D. Sontag jointly with K. Johnson
Articles in journal or book chapters
and A. Brock.
Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.
The development of resistance to chemotherapy is a major cause of treatment failure in cancer. Intratumoral heterogeneity and phenotypic plasticity play a significant role in therapeutic resistance. Individual cell measurements such as flow and mass cytometry and single cell RNA sequencing (scRNA-seq) have been used to capture and analyze this cell variability. In parallel, longitudinal treatment-response data is routinely employed in order to calibrate mechanistic mathematical models of heterogeneous subpopulations of cancer cells viewed as compartments with differential growth rates and drug sensitivities. This work combines both approaches: single cell clonally-resolved transcriptome datasets (scRNA-seq, tagging individual cells with unique barcodes that are integrated into the genome and expressed as sgRNA's) and longitudinal treatment response data, to fit a mechanistic mathematical model of drug resistance dynamics for a MDA-MB-231 breast cancer cell line. The explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens.
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Last modified: Wed Aug 17 10:22:06 2022
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