Shawn P. Garbett

The contribution of age structure to cell population responses to targeted therapeutics

Cells grown in culture act as a model system for analyzing the effects of anticancer compounds, which may affect cell behavior in a cell cycle position-dependent manner. Cell synchronization techniques have been generally employed to minimize the variation in cell cycle position. However, synchronization techniques are cumbersome and imprecise and the agents used to synchronize the cells potentially have other unknown effects on the cells. An alternative approach is to determine the age structure in the population and account for the cell cycle positional effects post hoc. Here we provide a formalism to use quantifiable lifespans from live cell microscopy experiments to parameterize an age-structured model of cell population response.

Development of High-Throughput Quantitative Assays for Glucose Uptake in Cancer Cell Lines

PurposeMetabolism, and especially glucose uptake, is a key quantitative cell trait that is closely linked to cancer initiation and progression. Therefore, developing high-throughput assays for measuring glucose uptake in cancer cells would be enviable for simultaneous comparisons of multiple cell lines and microenvironmental conditions. This study was designed with two specific aims in mind: the first was to develop and validate a high-throughput screening method for quantitative assessment of glucose uptake in “normal” and tumor cells using the fluorescent 2-deoxyglucose analog 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxyglucose (2-NBDG), and the second was to develop an image-based, quantitative, single-cell assay for measuring glucose uptake using the same probe to dissect the full spectrum of metabolic variability within populations of tumor cells in vitro in higher resolution.ProcedureThe kinetics of population-based glucose uptake was evaluated for MCF10A mammary epithelial and CA1d breast cancer cell lines, using 2-NBDG and a fluorometric microplate reader. Glucose uptake for the same cell lines was also examined at the single-cell level using high-content automated microscopy coupled with semi-automated cell-cytometric image analysis approaches. Statistical treatments were also implemented to analyze intra-population variability.ResultsOur results demonstrate that the high-throughput fluorometric assay using 2-NBDG is a reliable method to assess population-level kinetics of glucose uptake in cell lines in vitro. Similarly, single-cell image-based assays and analyses of 2-NBDG fluorescence proved an effective and accurate means for assessing glucose uptake, which revealed that breast tumor cell lines display intra-population variability that is modulated by growth conditions.ConclusionsThese studies indicate that 2-NBDG can be used to aid in the high-throughput analysis of the influence of chemotherapeutics on glucose uptake in cancer cells.

An unbiased metric of antiproliferative drug effect in vitro

In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.

Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells

Mapping quantitative cell traits (QCT) to underlying molecular defects is a central challenge in cancer research because heterogeneity at all biological scales, from genes to cells to populations, is recognized as the main driver of cancer progression and treatment resistance. A major roadblock to a multiscale framework linking cell to signaling to genetic cancer heterogeneity is the dearth of large-scale, single-cell data on QCT-such as proliferation, death sensitivity, motility, metabolism, and other hallmarks of cancer. High-volume single-cell data can be used to represent cell-to-cell genetic and nongenetic QCT variability in cancer cell populations as averages, distributions, and statistical subpopulations. By matching the abundance of available data on cancer genetic and molecular variability, QCT data should enable quantitative mapping of phenotype to genotype in cancer. This challenge is being met by high-content automated microscopy (HCAM), based on the convergence of several technologies including computerized microscopy, image processing, computation, and heterogeneity science. In this chapter, we describe an HCAM workflow that can be set up in a medium size interdisciplinary laboratory, and its application to produce high-throughput QCT data for cancer cell motility and proliferation. This type of data is ideally suited to populate cell-scale computational and mathematical models of cancer progression for quantitatively and predictively evaluating cancer drug discovery and treatment.

Not all noise is waste

Stochastic profiling, a method to rank heterogeneity of gene expression in a cell population, shows that quantifying cell-to-cell variability has come of age and leads to biological insight.

Derivation and experimental comparison of cell-division probability densities.

Experiments have shown that, even in a homogeneous population of cells, the distribution of division times is highly variable. In addition, a homogeneous population of cells will exhibit a heterogeneous response to drug therapy. We present a simple stochastic model of the cell cycle as a multistep stochastic process. The model, which is based on our conception of the cell cycle checkpoint, is used to derive an analytical expression for the distribution of cell cycle times. We demonstrate that this distribution provides an accurate representation of cell cycle time variability and show how the model relates drug-induced changes in basic biological parameters to variability in response to drug treatment.

BIASED SAMPLING DESIGNS TO IMPROVE RESEARCH EFFICIENCY: FACTORS INFLUENCING PULMONARY FUNCTION OVER TIME IN CHILDREN WITH ASTHMA.

Substudies of the Childhood Asthma Management Program (CAMP Research Group, 1999, 2000) seek to identify patient characteristics associated with asthma symptoms and lung function. To determine if genetic measures are associated with trajectories of lung function as measured by forced vital capacity (FVC), children in the primary cohort study retrospectively had candidate loci evaluated. Given participant burden and constraints on financial resources, it is often desirable to target a sub-sample for ascertainment of costly measures. Methods that can leverage the longitudinal outcome on the full cohort to selectively measure informative individuals have been promising, but have been restricted in their use to analysis of the targeted sub-sample. In this paper we detail two multiple imputation analysis strategies that exploit outcome and partially observed covariate data on the non-sampled subjects, and we characterize alternative design and analysis combinations that could be used for future studies of pulmonary function and other outcomes. Candidate predictor (e.g. IL10 cytokine polymorphisms) associations obtained from targeted sampling designs can be estimated with very high efficiency compared to standard designs. Further, even though multiple imputation can dramatically improve estimation efficiency for covariates available on all subjects (e.g., gender and baseline age), only modest efficiency gains were observed in parameters associated with predictors that are exclusive to the targeted sample. Our results suggest that future studies of longitudinal trajectories can be efficiently conducted by use of outcome-dependent designs and associated full cohort analysis.

Outcome vector dependent sampling with longitudinal continuous response data: stratified sampling based on summary statistics

The analysis of longitudinal trajectories usually focuses on evaluation of explanatory factors that are either associated with rates of change, or with overall mean levels of a continuous outcome variable. In this article, we introduce valid design and analysis methods that permit outcome dependent sampling of longitudinal data for scenarios where all outcome data currently exist, but a targeted substudy is being planned in order to collect additional key exposure information on a limited number of subjects. We propose a stratified sampling based on specific summaries of individual longitudinal trajectories, and we detail an ascertainment corrected maximum likelihood approach for estimation using the resulting biased sample of subjects. In addition, we demonstrate that the efficiency of an outcome-based sampling design relative to use of a simple random sample depends highly on the choice of outcome summary statistic used to direct sampling, and we show a natural link between the goals of the longitudinal regression model and corresponding desirable designs. Using data from the Childhood Asthma Management Program, where genetic information required retrospective ascertainment, we study a range of designs that examine lung function profiles over 4 years of follow-up for children classified according to their genotype for the IL 13 cytokine.

Abstract 4626: Quantifying erlotinib response variability in EGFR-addicted cells.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Patients with activating epidermal growth factor receptor (EGFR) mutations display drastic initial responses to erlotinib, but tumor reduction and progression-free survival vary widely. An open question is whether response variation derives, in part, from tumor heterogeneity, i.e., differential composition of cells responding to erlotinib with distinct cell fates (death, division, or quiescence). Conventional assays obscure response heterogeneity by taking average measurements of cell populations. To address this question we developed high-throughput live-cell imaging assays that quantify both subpopulation growth dynamics and cell-to-cell fate variability in response to drug. The Dynamic Colony Growth Assay (DCGA) tracks simultaneously the erlotinib response of hundreds of single-cell-derived colonies from within a cell population. In the PC9 lung cancer cell line (widely used to model oncogene addiction, exon19del EGFR) the DCGA reveals that individual colonies span a wide range of erlotinib response, from continued cell division to massive apoptosis. Plotting net growth rates of 207 colonies shows that PC9 parental is comprised of a normally-distributed aggregate of individual single colonies responding to erlotinib with steady-state growth rates from positive to negative. Notably, the positive-growth colonies lie in the tail of this response distribution, suggesting they do not originate from rare genetic variants or cancer stem cells but, rather, are part of a continuous response distribution that is an attribute of parental PC9. To determine whether isolated PC9 colonies maintain unique rate responses, we expanded random-sampled single cells (without erlotinib selection) into 7 discrete sublines (DS), which remain highly sensitive to erlotinib (IC50 ∼50nM). In each DS, erlotinib induced an initial non-linear growth period (72h) followed by a distinct steady-state growth rate that predicts the long-term (10d) response. DS steady-state growth rates were resolved into individual cell fate composition by Fractional Proliferation Assays (FPA). As expected from random selection, the growth rates of treated DS clustered around the median of the DCGA rate distribution, missing the most extreme, less frequent erlotinib responses. Isolating the greatest variance in erlotinib response should facilitate identification of molecular mechanisms underlying drug response variation. Therefore, we isolated 96 new DS and selected the two with highest (DS-B03) and lowest (DS-C03) erlotinib steady state growth rate. We are currently measuring the activity of hundreds of proteins in these two DS by Microwestern Arrays to discover protein signatures and/or mechanistic models that quantitatively link FPA-resolved cell fates to underlying signaling network events. Thus tumor response variability and recurrence may be explained by a continuous distribution of erlotinib response at the level of single cells within a tumor. Citation Format: Peter L. Frick, Darren R. Tyson, Shawn P. Garbett, Carlos F. Lopez, Zach W. Jones, Vito Quaranta. Quantifying erlotinib response variability in EGFR-addicted cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4626. doi:10.1158/1538-7445.AM2013-4626

Outcome vector dependent sampling with longitudinal continuous response data

The analysis of longitudinal trajectories usually focuses on evaluation of explanatory factors that are either associated with rates of change, or with overall mean levels of a continuous outcome variable. In this article, we introduce valid design and analysis methods that permit outcome dependent sampling of longitudinal data for scenarios where all outcome data currently exist, but a targeted substudy is being planned in order to collect additional key exposure information on a limited number of subjects. We propose a stratified sampling based on specific summaries of individual longitudinal trajectories, and we detail an ascertainment corrected maximum likelihood approach for estimation using the resulting biased sample of subjects. In addition, we demonstrate that the efficiency of an outcome-based sampling design relative to use of a simple random sample depends highly on the choice of outcome summary statistic used to direct sampling, and we show a natural link between the goals of the longitudinal regression model and corresponding desirable designs. Using data from the Childhood Asthma Management Program, where genetic information required retrospective ascertainment, we study a range of designs that examine lung function profiles over 4 years of follow-up for children classified according to their genotype for the IL 13 cytokine.