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Mathematical modeling of tumor cell proliferation kinetics and label retention in a mouse model of lung cancer.

ABSTRACT: Slowly cycling tumor cells that may be present in human tumors may evade cytotoxic therapies, which tend to be more efficient at destroying cells with faster growth rates. However, the proportion and growth rate of slowly cycling tumor cells is often unknown in preclinical model systems used for drug discovery. Here, we report a quantitative approach to quantitate slowly cycling malignant cells in solid tumors, using a well-established mouse model of Kras-induced lung cancer (Kras(G12D/+)). 5-Bromo-2-deoxyuridine (BrdUrd) was administered to tumor-bearing mice, and samples were collected at defined times during pulse and chase phases. Mathematical and statistical modeling of the label-retention data during the chase phase supported the existence of a slowly cycling label-retaining population in this tumor model and permitted the estimation of its proportion and proliferation rate within a tumor. The doubling time of the slowly cycling population was estimated at approximately 5.7 weeks, and this population represented approximately 31% of the total tumor cells in this model system. The mathematical modeling techniques implemented here may be useful in other tumor models where direct observation of cell-cycle kinetics is difficult and may help evaluate tumor cell subpopulations with distinct cell-cycling rates.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC3703454 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Mathematical modeling of tumor cell proliferation kinetics and label retention in a mouse model of lung cancer.

Zheng Yanyan Y Moore Helen H Piryatinska Alexandra A Solis Trinidad T Sweet-Cordero E Alejandro EA

Cancer research 20130410 12

Slowly cycling tumor cells that may be present in human tumors may evade cytotoxic therapies, which tend to be more efficient at destroying cells with faster growth rates. However, the proportion and growth rate of slowly cycling tumor cells is often unknown in preclinical model systems used for drug discovery. Here, we report a quantitative approach to quantitate slowly cycling malignant cells in solid tumors, using a well-established mouse model of Kras-induced lung cancer (Kras(G12D/+)). 5-Br ...[more]

PMID: 23576555

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Project description:Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. Although past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. In this study, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semiparametric generalized additive model (GAM) on fitting tumor volume data from more than 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models; the latter is often considered the standard growth model. At the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that endpoint gain integrated in time, a GAM-derived efficacy metric, is equivalent to exponential growth rate, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on paclitaxel, anti-PD1 antibody, cetuximab, irinotecan, and sorafenib, we showed that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared exponential growth rate-based association analysis and exponential modeling approach in biomarker discovery and found that they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix.SignificanceWe present a general strategy for mathematically modeling tumor growth in mouse models using data from 30,000 mice and show that modeling and nonmodeling approaches are complementary in biomarker discovery and drug mechanism studies.

Dataset Information

Mathematical modeling of tumor cell proliferation kinetics and label retention in a mouse model of lung cancer.

Publications

Mathematical modeling of tumor cell proliferation kinetics and label retention in a mouse model of lung cancer.

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