Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Yankeelov, Thomas E.; An, Gary; Saut, Oliver; Luebeck, E. Georg; Popel, Aleksander S.; Ribba, Benjamin; Vicini, Paolo; Zhou, Xiaobo; Weis, Jared A.; Ye, Kaiming; Genin, Guy M.

doi:10.1007/s10439-016-1691-6

Cited by 71 publications

(53 citation statements)

References 99 publications

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“…Mathematical models of gene regulatory networks have been developed to model the dynamics of regulatory networks that lead to transcriptional changes [28,29]. Hybrid, multi-scale approaches that combine these network-based models with the cellular-scale models more accurately model the complexity of system-wide dynamics [16][17][18] and are a promising area for future work to simulate time course omics data. However, the complexity of these gene regulatory models and extensive parameterization will limit the straightforward validation of omics algorithms that is possible from the simplified statistical models employed in CancerInSilico.…”

Section: Availability and Future Directionsmentioning

confidence: 99%

“…Some models simulate cell growth at a cellular level, where the population behavior is driven by the laws governing the individual cells and their interactions [14,15]. To further capture the complexity of biological systems, numerous multiscale and hybrid models linking cellular signaling to the equations of the cellular composition are emerging [16][17][18]. These models often require numerous parameters to simulate high throughput proteomic and transcriptional data and therefore often have similar complexity to real biological systems.…”

Section: Introductionmentioning

confidence: 99%

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CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

Sherman

Kagohara

Cao

et al. 2018

Preprint

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Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated timecourse datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor:

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Section: Availability and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

Sherman

Kagohara

Cao

et al. 2018

Preprint

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“…The ability to describe and predict tumor growth is essential to developing strategies to eradicate cancer cell populations (10,11). Understanding tumor growth kinetics at low cell numbers is of clinical importance as they govern tumor initiation, treatment response, and recurrence.…”

Section: Introductionmentioning

confidence: 99%

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Johnson

Howard

et al. 2019

Preprint

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Models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations from exponential growth only at higher densities due to limited resources such as space and nutrients. However, recent pre-clinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models however, tumor growth data is limited by the lower limit of detection (i.e. a measurable lesion) and confounding variables, such as tumor microenvironment and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to consider the small number of cells in this low-density regime and use the moment approach for stochastic parameter estimation to calibrate the stochastic growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells depends on cell number. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging tumors and relapse. Author SummaryThe growth kinetics of cancer cells at very low cell densities are of utmost clinical importance as the ability of a small number of newly transformed or surviving cells to grow exponentially and thus, to "take off" underlies tumor formation and relapse after treatment. Mathematical models of stochastic tumor cell growth typically assume a stochastic birth-death process of cells impacted by limited nutrients and space when cells reach high density, resulting in the widely accepted logistic growth model. Here we present an in-depth investigation of alternate growth models adopted from ecology to describe potential deviations from a simple cell autonomous birth-death model at low cell densities. We show that our stochastic modeling framework is robust and can be used to identify the underlying structure of stochastic growth trajectories from both simulated and experimental data taken from a controlled in vitro setting in which we can capture data from the relevant low cell density regime. This work suggests that the assumption of cell autonomous proliferation via a constant exponential growth rate at low cell densities may not be appropriate for all cancer cell growth dynamics. Consideration of cooperative behavior amongst tumor cells in this regime is critical for elucidating strategies for controll...

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“…These experimental results focus on specific drug resistance phenotypes that emerge in cell subpopulations following treatment. However, because of the vast complexity of resistance mechanisms, it is difficult to identify a single molecular marker of drug resistance that encompasses all drug resistant cells 13,14 .…”

Section: Introductionmentioning

confidence: 99%

A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer

Howard

Johnson

Ayala

et al. 2017

Preprint

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The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. Although several molecular mechanisms of chemotherapeutic resistance are well studied, a quantitative understanding of the dynamics of resistant subpopulations within a heterogeneous tumor cell population remains elusive. While mathematical models describing the dynamics of heterogeneous cancer cell populations have been proposed, few have been experimentally validated due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently isolate drug resistant subpopulations. In this work, we address this problem with a combined experimental and modeling system that uses drug sensitivity data to reveal the composition of multiple subpopulations differing in their level of drug resistance. We calibrate time-resolved doseresponse data to three mathematical models to interrogate the models' ability to capture the dynamics of drug. All three models demonstrated an increase in population level resistance following drug exposure. The candidate models were compared by Akaike information criterion and the model selection criteria identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity. To validate the ability of this model to identify the composition of subpopulations, we mixed wild-type MCF-7 and MCF-7/ADR resistant cells at various proportions and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell subtypes, with the measured proportions falling within the 95 percent confidence intervals on the parameter estimations and at an R-squared value of 0.986. To the best of our knowledge, this contribution represents the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development.

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Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Cited by 71 publications

References 99 publications

CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer

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