Erin C. Rericha

Clinically relevant modeling of tumor growth and treatment response.

Noninvasive imaging technologies can help create patient-specific mathematical models to predict tumor growth. Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point—for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.

Toward a science of tumor forecasting for clinical oncology.

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.

A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth

While gliomas have been extensively modelled with a reaction–diffusion (RD) type equation it is most likely an oversimplification. In this study, three mathematical models of glioma growth are developed and systematically investigated to establish a framework for accurate prediction of changes in tumour volume as well as intra-tumoural heterogeneity. Tumour cell movement was described by coupling movement to tissue stress, leading to a mechanically coupled (MC) RD model. Intra-tumour heterogeneity was described by including a voxel-specific carrying capacity (CC) to the RD model. The MC and CC models were also combined in a third model. To evaluate these models, rats (n = 14) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging over 10 days to estimate tumour cellularity. Model parameters were estimated from the first three imaging time points and then used to predict tumour growth at the remaining time points which were then directly compared to experimental data. The results in this work demonstrate that mechanical–biological effects are a necessary component of brain tissue tumour modelling efforts. The results are suggestive that a variable tissue carrying capacity is a needed model component to capture tumour heterogeneity. Lastly, the results advocate the need for additional effort towards capturing tumour-to-tissue infiltration.

Modeling and Measuring Signal Relay in Noisy Directed Migration of Cell Groups

We develop a coarse-grained stochastic model for the influence of signal relay on the collective behavior of migrating Dictyostelium discoideum cells. In the experiment, cells display a range of collective migration patterns, including uncorrelated motion, formation of partially localized streams, and clumping, depending on the type of cell and the strength of the external, linear concentration gradient of the signaling molecule cyclic adenosine monophosphate (cAMP). From our model, we find that the pattern of migration can be quantitatively described by the competition of two processes, the secretion rate of cAMP by the cells and the degradation rate of cAMP in the gradient chamber. Model simulations are compared to experiments for a wide range of strengths of an external linear-gradient signal. With degradation, the model secreting cells form streams and efficiently transverse the gradient, but without degradation, we find that model secreting cells form clumps without streaming. This indicates that the observed effective collective migration in streams requires not only signal relay but also degradation of the signal. In addition, our model allows us to detect and quantify precursors of correlated motion, even when cells do not exhibit obvious streaming.

Shear stress induces noncanonical autophagy in intestinal epithelial monolayers

Shear stress applied on the apical side of polarizing intestinal cells induces vacuole formation via the autophagy machinery. This response is relayed through apical microvilli that act as mechanosensors linking the physical environment to the intracellular trafficking pathways.

Abstract 776: Multiscale treatment response model for triple-negative breast cancer linking drug pharmacokinetics to tumor cell population dynamics

Introduction The goal of this study is to establish a predictive model of cytotoxic therapy that incorporates in vitro drug pharmacokinetics and cell-scale therapy response data, on a cell-line specific basis. We report on a series of time-resolved fluorescence microscopy experiments to characterize the uptake of doxorubicin and its effect on the population dynamics of MDA-MB-231 cells, a model of triple negative breast cancer. Experimental Design We leveraged the intrinsic fluorescence of doxorubicin to measure its uptake by MDA-MB-231 cells. Cells, labeled with a fluorescent nuclear marker, were seeded in microtiter plates and incubated with doxorubicin concentrations ranging from 10 nM to 10 μM for 6, 12, or 24 hours. These plates were imaged daily via bright field and fluorescent microscopy after addition of doxorubicin. Nuclei were segmented and automatically counted to quantify cell population size. Counts were normalized to population size at time of treatment and converted to population doublings. On a separate channel, extracellular, cytoplasmic, and nuclear doxorubicin fluorescence were quantified. A compartment model describing the movement of doxorubicin from the extracellular space into cells was fit to these data. We then constructed a cell treatment response model and fit it, coupled with the compartment model, to the population data using MATLAB. Results MDA-MB-231 cellular response to doxorubicin was tightly linked to both drug concentration and exposure time. Higher doses (> 1 μM) invariably induced rapid cell death. Smaller doses ( Conclusion These time-resolved treatment protocols replicate clinically observed pharmacokinetics of cytotoxic therapies more closely than the constant concentrations in previous dose-response assays. By explicitly considering both drug and population dynamics, our mathematical model enables exploration, in silico, of treatment protocols intractable experimentally. Predictions from model simulations can then be tested experimentally, hopefully allowing for computationally-optimized and experimentally validated treatment regimens that maximize cytotoxic effects of doxorubicin. Citation Format: Matthew T. McKenna, Stephanie L. Barnes, Abigail Searfoss, Darren R. Tyson, Erin Rericha, Vito Quaranta, Thomas E. Yankeelov. Multiscale treatment response model for triple-negative breast cancer linking drug pharmacokinetics to tumor cell population dynamics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 776.

Abstract 1845: Sensitivity of PC9 cells to erlotinib is affected by extracellular matrix

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Tyrosine kinase inhibitors of epidermal growth factor receptor, such as erlotinib and gefinitib, have been effective in the initial treatment of non-small cell lung cancer. With time, however, initially responsive tumors almost invariably rebound and proliferate under therapy. Of general interest is the impact of tumor cell heterogeneity on rebound time. Previously, we isolated 89 single-cell derived discrete sublines (DS) from the commonly studied PC9 cell line by monitoring the distribution of cell fates (division, quiescence, and cell death) and the drug-induced proliferation (DIP) rate under erlotinib treatment. In this work we examine the role of the extracellular matrix (ECM) in determining proliferation behavior and the distribution of cell fates. Cells suspended in 3D matrices show increased sensitivity to erlotinb with the rate of cell death depending on the ECM composition. DS sublines that were highly resilient in 2D culture (i.e., positive DIP rate and <10% cell death under treatment) asymptote to about 40% cell death in Matrigel after ten days in 1 µM erlotinb. Both the rate of cell death and the total percentage of the cell population that dies increased in collagen I and in collagen I coated hydrogel. At asymptotic levels, the cell population contained both dividing and quiescent cells. In contrast, DS sublines with negative DIP rates in 2D culture reached 90-99% cell death in each ECM considered. We conclude that the pathways conferring resilience in these cells are sensitive to ECM conditions. Intriguingly, we find that resilient DS sublines tend to have diversity in cluster morphologies in the matrices, suggesting a possible correlation between ECM engagement and/or adhesion and resilience. Molecular studies should clarify mechanistic relationships between ECM and sensitivity to targeted therapy, and whether such relationship may extend to clinical tumors. Citation Format: Halina M. Onishko, Jie Zhao, Katherine L. Jameson, Peter L. Frick, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov, Erin C. Rericha. Sensitivity of PC9 cells to erlotinib is affected by extracellular matrix. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1845. doi:10.1158/1538-7445.AM2014-1845