This aim focuses on determinants of dose-response at a single-cell level. We will test the hypothesis that non-genetic cell-to-cell variability (arising from variation in the relative levels or activities of network components) is critical in determining the shape of dose-response curves and the maximum therapeutic effect that can be achieved at high drug concentrations. The impact of stochastic variation will be contrasted with that of cell cycle state and of special lineages (e.g. tumor stem cells). Experiments in this Aim also examine the importance of timing and order-of-exposure in combination cancer therapy. All but aim 1.4 will be performed using panels of ~10-40 cancer cell lines grown in 2D culture supplemented by a smaller number of patient- derived cultures obtained through the Translational Pharmacology Core (Aim 5). The influence of the tumor microenvironment on dose-response will be examined based on progress with Aim 3.4. Studies in Aim 1 are distinguished from those in Aim 2 by their focus on phenotypes as opposed to modeling intracellular signaling.
Aim 1. 1 will focus on genetically diverse panels of cancer cell lines and their responses to anti-cancer drugs, primarily investigational and approved kinase inhibitors.
Aim 1. 1.1 will use fixed cell microscopy to discriminate among drug response phenotypes at a single-cell level using molecular markers of cell division, induction of senescence and apoptosis (and other forms of cell death such as autophagy). Variation in response with time after drug addition, physiological state and genotype will be studied across cell types and within single cells in a genetically homogenous population.
Aim 1. 1.2. will wills use mutational information (MI) and other methods to associate dose response parameters from Aims 1.1.1-1.1.2 with features of the drug, target of cell type.
Aim 1. 1.3 will supplement fixed-cell analysis with live-cell imaging of selected drug-cell line combinations to determine how response evolves over time and distinguish among phenotypes that appear similar by endpoint assays.
Aim 1. 1.4 will extend these studies to patient-derived lines and cultures with the goal of increasing the relevance of our findings to human disease.
Aim 1. 2 will determine the role of cell-to-cell heterogeneity on fractional response and dose-response curves that are unusually shallow. Mutual information analysis of panels of related kinase inhibitors will reveal whether submaximal and shallow dose-response associates with drug, target or phenotype.
Aim 1. 3 examines the role of time in pharmacology.
Aim 1. 3.1 investigates the phenomenon of sequential drug synergy involving EGFR inhibitors and DNA damaging agents.
Aim 1. 3.2 investigates transient drug resistance induced by paradoxical responses to compounds that are thought to be pro-apoptotic.
Aim 1. 4 extends the analysis to a different therapeutic area, the response of Mycobacterium tuberculosis (Mtb) to antibiotics;these studies follow up recent data showing that asymmetric division by Mtb results in a cell-to-cell heterogeneity that impacts drug response.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Specialized Center (P50)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard Medical School
United States
Zip Code
Wang, Rui-Sheng; Loscalzo, Joseph (2018) Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications. J Mol Biol 430:2939-2950
Weinstein, Zohar B; Kuru, Nurdan; Kiriakov, Szilvia et al. (2018) Modeling the impact of drug interactions on therapeutic selectivity. Nat Commun 9:3452
Leopold, Jane A; Loscalzo, Joseph (2018) Emerging Role of Precision Medicine in Cardiovascular Disease. Circ Res 122:1302-1315
Spady, Emma S; Wyche, Thomas P; Rollins, Nathanael J et al. (2018) Mammalian Cells Engineered To Produce New Steroids. Chembiochem 19:1827-1833
Cheng, Feixiong; Desai, Rishi J; Handy, Diane E et al. (2018) Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun 9:2691
Vinegoni, Claudio; Feruglio, Paolo Fumene; Gryczynski, Ignacy et al. (2018) Fluorescence anisotropy imaging in drug discovery. Adv Drug Deliv Rev :
Oldham, William M; Oliveira, Rudolf K F; Wang, Rui-Sheng et al. (2018) Network Analysis to Risk Stratify Patients With Exercise Intolerance. Circ Res 122:864-876
Sampattavanich, Somponnat; Steiert, Bernhard; Kramer, Bernhard A et al. (2018) Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases. Cell Syst 6:664-678.e9
Monteiro, Maria B; Ramm, Susanne; Chandrasekaran, Vidya et al. (2018) A High-Throughput Screen Identifies DYRK1A Inhibitor ID-8 that Stimulates Human Kidney Tubular Epithelial Cell Proliferation. J Am Soc Nephrol 29:2820-2833
Cokol-Cakmak, Melike; Bakan, Feray; Cetiner, Selim et al. (2018) Diagonal Method to Measure Synergy Among Any Number of Drugs. J Vis Exp :

Showing the most recent 10 out of 77 publications