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The Role of Oxygen in Avascular Tumor Growth.

Grimes DR, Kannan P, McIntyre A, Kavanagh A, Siddiky A, Wigfield S, Harris A, Partridge M - PLoS ONE (2016)

Bottom Line: These describe the basic rate of growth well, but do not offer an explicitly mechanistic explanation.The model is fitted to growth curves for a range of cell lines and derived values of OCR are validated using clinical measurement.Finally, we illustrate how changes in OCR due to gemcitabine treatment can be directly inferred using this model.

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK/MRC Oxford Institute for Radiation Oncology, Gray Laboratories, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom.

ABSTRACT
The oxygen status of a tumor has significant clinical implications for treatment prognosis, with well-oxygenated subvolumes responding markedly better to radiotherapy than poorly supplied regions. Oxygen is essential for tumor growth, yet estimation of local oxygen distribution can be difficult to ascertain in situ, due to chaotic patterns of vasculature. It is possible to avoid this confounding influence by using avascular tumor models, such as tumor spheroids, a much better approximation of realistic tumor dynamics than monolayers, where oxygen supply can be described by diffusion alone. Similar to in situ tumours, spheroids exhibit an approximately sigmoidal growth curve, often approximated and fitted by logistic and Gompertzian sigmoid functions. These describe the basic rate of growth well, but do not offer an explicitly mechanistic explanation. This work examines the oxygen dynamics of spheroids and demonstrates that this growth can be derived mechanistically with cellular doubling time and oxygen consumption rate (OCR) being key parameters. The model is fitted to growth curves for a range of cell lines and derived values of OCR are validated using clinical measurement. Finally, we illustrate how changes in OCR due to gemcitabine treatment can be directly inferred using this model.

No MeSH data available.


Related in: MedlinePlus

Plots of experimental data and model growth curves for (a) HCT 116 (b) LS 174T (c) MDA-MB-468 and (d) SCC-25 spheroids.In all plots the growth curve due to mean experimentally estimated OCR a is denoted by a solid blue line, with one standard deviation above average OCR marked by a dashed red line and one standard deviation below average consumption marked with a dotted green line. Best fit doubling times td and co-efficient of determination are shown for each value with high goodness of fit obtained for each estimated consumption rate within the confidence intervals of experimental data. The shaded area corresponds to range of ± 2 standard deviations for OCR.
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pone.0153692.g004: Plots of experimental data and model growth curves for (a) HCT 116 (b) LS 174T (c) MDA-MB-468 and (d) SCC-25 spheroids.In all plots the growth curve due to mean experimentally estimated OCR a is denoted by a solid blue line, with one standard deviation above average OCR marked by a dashed red line and one standard deviation below average consumption marked with a dotted green line. Best fit doubling times td and co-efficient of determination are shown for each value with high goodness of fit obtained for each estimated consumption rate within the confidence intervals of experimental data. The shaded area corresponds to range of ± 2 standard deviations for OCR.

Mentions: As curve-fitting suggests the model fits the data well, it is possible for some cell lines to avoid potential degeneracy and directly contrast theoretical curves with experimental data, provided OCR can be determined. In this case curve-fitting is not required and model and data can be directly compared. Cell volume and hence mass estimates were obtained for a number of cells in four distinct cell lines; HCT 116 (n = 36), LS 147T (n = 36), MDA-MB-468 (n = 27) and SCC-25 (n = 22). For these cell lines, multiple individual cells could be isolated and cell mass was estimated by the procedure outlined in the methods section. This was combined with the extracellular flux measurements SH to yield an estimate for the consumption rate a and the resultant OCR a. Best estimates for oxygen consumption are shown in Table 2. The diffusion constant was assumed to be close to that of water so D = 2 × 10−9 m2 s−1. The oxygen partial pressure in the medium at the spheroid boundary was po = 100 mmHg. Results for these cell lines are shown in Fig 4, where model results are contrasted to experimental data. Results are shown with their respective best fit doubling times, td. Error bars on the time axis are a day to capture uncertainty on exact time which growth curves were measured on a daily basis. The model data illustrated in Fig 4 are independent of fitting, directly contrasting the model with OCR taken from the experimental data in Table 1.


The Role of Oxygen in Avascular Tumor Growth.

Grimes DR, Kannan P, McIntyre A, Kavanagh A, Siddiky A, Wigfield S, Harris A, Partridge M - PLoS ONE (2016)

Plots of experimental data and model growth curves for (a) HCT 116 (b) LS 174T (c) MDA-MB-468 and (d) SCC-25 spheroids.In all plots the growth curve due to mean experimentally estimated OCR a is denoted by a solid blue line, with one standard deviation above average OCR marked by a dashed red line and one standard deviation below average consumption marked with a dotted green line. Best fit doubling times td and co-efficient of determination are shown for each value with high goodness of fit obtained for each estimated consumption rate within the confidence intervals of experimental data. The shaded area corresponds to range of ± 2 standard deviations for OCR.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835055&req=5

pone.0153692.g004: Plots of experimental data and model growth curves for (a) HCT 116 (b) LS 174T (c) MDA-MB-468 and (d) SCC-25 spheroids.In all plots the growth curve due to mean experimentally estimated OCR a is denoted by a solid blue line, with one standard deviation above average OCR marked by a dashed red line and one standard deviation below average consumption marked with a dotted green line. Best fit doubling times td and co-efficient of determination are shown for each value with high goodness of fit obtained for each estimated consumption rate within the confidence intervals of experimental data. The shaded area corresponds to range of ± 2 standard deviations for OCR.
Mentions: As curve-fitting suggests the model fits the data well, it is possible for some cell lines to avoid potential degeneracy and directly contrast theoretical curves with experimental data, provided OCR can be determined. In this case curve-fitting is not required and model and data can be directly compared. Cell volume and hence mass estimates were obtained for a number of cells in four distinct cell lines; HCT 116 (n = 36), LS 147T (n = 36), MDA-MB-468 (n = 27) and SCC-25 (n = 22). For these cell lines, multiple individual cells could be isolated and cell mass was estimated by the procedure outlined in the methods section. This was combined with the extracellular flux measurements SH to yield an estimate for the consumption rate a and the resultant OCR a. Best estimates for oxygen consumption are shown in Table 2. The diffusion constant was assumed to be close to that of water so D = 2 × 10−9 m2 s−1. The oxygen partial pressure in the medium at the spheroid boundary was po = 100 mmHg. Results for these cell lines are shown in Fig 4, where model results are contrasted to experimental data. Results are shown with their respective best fit doubling times, td. Error bars on the time axis are a day to capture uncertainty on exact time which growth curves were measured on a daily basis. The model data illustrated in Fig 4 are independent of fitting, directly contrasting the model with OCR taken from the experimental data in Table 1.

Bottom Line: These describe the basic rate of growth well, but do not offer an explicitly mechanistic explanation.The model is fitted to growth curves for a range of cell lines and derived values of OCR are validated using clinical measurement.Finally, we illustrate how changes in OCR due to gemcitabine treatment can be directly inferred using this model.

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK/MRC Oxford Institute for Radiation Oncology, Gray Laboratories, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom.

ABSTRACT
The oxygen status of a tumor has significant clinical implications for treatment prognosis, with well-oxygenated subvolumes responding markedly better to radiotherapy than poorly supplied regions. Oxygen is essential for tumor growth, yet estimation of local oxygen distribution can be difficult to ascertain in situ, due to chaotic patterns of vasculature. It is possible to avoid this confounding influence by using avascular tumor models, such as tumor spheroids, a much better approximation of realistic tumor dynamics than monolayers, where oxygen supply can be described by diffusion alone. Similar to in situ tumours, spheroids exhibit an approximately sigmoidal growth curve, often approximated and fitted by logistic and Gompertzian sigmoid functions. These describe the basic rate of growth well, but do not offer an explicitly mechanistic explanation. This work examines the oxygen dynamics of spheroids and demonstrates that this growth can be derived mechanistically with cellular doubling time and oxygen consumption rate (OCR) being key parameters. The model is fitted to growth curves for a range of cell lines and derived values of OCR are validated using clinical measurement. Finally, we illustrate how changes in OCR due to gemcitabine treatment can be directly inferred using this model.

No MeSH data available.


Related in: MedlinePlus