The initial engraftment of tumor cells is critical for the future growth pattern: a mathematical study based on simulations and animal experiments.
Animal welfare
Exponential model
Gompertz model
Logistic model
Nonlinear least squares
Nonlinear systems
Parameter estimation
Power model
Replacement, reduction and refinement (3Rs)
Tumor growth
Journal
BMC cancer
ISSN: 1471-2407
Titre abrégé: BMC Cancer
Pays: England
ID NLM: 100967800
Informations de publication
Date de publication:
05 Jun 2020
05 Jun 2020
Historique:
received:
05
03
2020
accepted:
28
05
2020
entrez:
7
6
2020
pubmed:
7
6
2020
medline:
20
1
2021
Statut:
epublish
Résumé
Xenograft mouse tumor models are used to study mechanisms of tumor growth and metastasis formation and to investigate the efficacy of different therapeutic interventions. After injection the engrafted cells form a local tumor nodule. Following an initial lag period of several days, the size of the tumor is measured periodically throughout the experiment using calipers. This method of determining tumor size is error prone because the measurement is two-dimensional (calipers do not measure tumor depth). Primary tumor growth can be described mathematically by suitable growth functions, the choice of which is not always obvious. Growth parameters provide information on tumor growth and are determined by applying nonlinear curve fitting. We used self-generated synthetic data including random measurement errors to research the accuracy of parameter estimation based on caliper measured tumor data. Fit metrics were investigated to identify the most appropriate growth function for a given synthetic dataset. We studied the effects of measuring tumor size at different frequencies on the accuracy and precision of the estimated parameters. For curve fitting with fixed initial tumor volume, we varied this fixed initial volume during the fitting process to investigate the effect on the resulting estimated parameters. We determined the number of surviving engrafted tumor cells after injection using ex vivo bioluminescence imaging, to demonstrate the effect on experiments of incorrect assumptions about the initial tumor volume. To select a suitable growth function, measurement data from at least 15 animals should be considered. Tumor volume should be measured at least every three days to estimate accurate growth parameters. Daily measurement of the tumor volume is the most accurate way to improve long-term predictability of tumor growth. The initial tumor volume needs to have a fixed value in order to achieve meaningful results. An incorrect value for the initial tumor volume leads to large deviations in the resulting growth parameters. The actual number of cancer cells engrafting directly after subcutaneous injection is critical for future tumor growth and distinctly influences the parameters for tumor growth determined by curve fitting.
Sections du résumé
BACKGROUND
BACKGROUND
Xenograft mouse tumor models are used to study mechanisms of tumor growth and metastasis formation and to investigate the efficacy of different therapeutic interventions. After injection the engrafted cells form a local tumor nodule. Following an initial lag period of several days, the size of the tumor is measured periodically throughout the experiment using calipers. This method of determining tumor size is error prone because the measurement is two-dimensional (calipers do not measure tumor depth). Primary tumor growth can be described mathematically by suitable growth functions, the choice of which is not always obvious. Growth parameters provide information on tumor growth and are determined by applying nonlinear curve fitting.
METHODS
METHODS
We used self-generated synthetic data including random measurement errors to research the accuracy of parameter estimation based on caliper measured tumor data. Fit metrics were investigated to identify the most appropriate growth function for a given synthetic dataset. We studied the effects of measuring tumor size at different frequencies on the accuracy and precision of the estimated parameters. For curve fitting with fixed initial tumor volume, we varied this fixed initial volume during the fitting process to investigate the effect on the resulting estimated parameters. We determined the number of surviving engrafted tumor cells after injection using ex vivo bioluminescence imaging, to demonstrate the effect on experiments of incorrect assumptions about the initial tumor volume.
RESULTS
RESULTS
To select a suitable growth function, measurement data from at least 15 animals should be considered. Tumor volume should be measured at least every three days to estimate accurate growth parameters. Daily measurement of the tumor volume is the most accurate way to improve long-term predictability of tumor growth. The initial tumor volume needs to have a fixed value in order to achieve meaningful results. An incorrect value for the initial tumor volume leads to large deviations in the resulting growth parameters.
CONCLUSIONS
CONCLUSIONS
The actual number of cancer cells engrafting directly after subcutaneous injection is critical for future tumor growth and distinctly influences the parameters for tumor growth determined by curve fitting.
Identifiants
pubmed: 32503458
doi: 10.1186/s12885-020-07015-9
pii: 10.1186/s12885-020-07015-9
pmc: PMC7275472
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
524Références
Br J Cancer. 1964 Sep;13:490-502
pubmed: 14219541
IEEE Trans Biomed Eng. 2005 May;52(5):808-15
pubmed: 15887530
Br J Cancer. 1998 Aug;78(3):382-7
pubmed: 9703287
Q Rev Biol. 1957 Sep;32(3):217-31
pubmed: 13485376
PLoS Comput Biol. 2014 Aug 28;10(8):e1003800
pubmed: 25167199
Br J Cancer. 2019 Jul;121(2):101-108
pubmed: 31231121
J Theor Biol. 2000 Mar 21;203(2):177-86
pubmed: 10704301
Proc Natl Acad Sci U S A. 2019 Feb 5;116(6):1918-1923
pubmed: 30674661
Bull Math Biol. 2014 Aug;76(8):2010-24
pubmed: 25081547
Mol Cancer. 2014 Nov 05;13:244
pubmed: 25373310
PLoS One. 2017 Nov 6;12(11):e0187144
pubmed: 29107953
Methods Mol Biol. 2019;1878:263-277
pubmed: 30378082
BMC Cancer. 2017 Mar 7;17(1):174
pubmed: 28270135
Clin Cancer Res. 2008 Dec 1;14(23):7733-40
pubmed: 19047100
Clin Cancer Res. 2004 Apr 1;10(7):2512-24
pubmed: 15073131
Cancer Res. 2016 Feb 1;76(3):535-47
pubmed: 26511632
Comput Methods Programs Biomed. 2020 Mar;185:105165
pubmed: 31710982
Front Psychol. 2019 May 21;10:1067
pubmed: 31164847
Am J Roentgenol Radium Ther Nucl Med. 1956 Nov;76(5):988-1000
pubmed: 13362715
Immunol Lett. 2007 Nov 30;114(1):16-22
pubmed: 17920694
Eur J Cancer. 2011 Feb;47(3):479-90
pubmed: 21074409
Methods Mol Biol. 2014;1070:107-16
pubmed: 24092435
Bull Math Biol. 2015 Oct;77(10):1934-54
pubmed: 26481497
Br J Cancer. 2010 May 25;102(11):1555-77
pubmed: 20502460
Cell Tissue Kinet. 1987 May;20(3):343-55
pubmed: 3690626
Cancer. 1993 Mar 15;71(6):2013-9
pubmed: 8443753
Cell Tissue Kinet. 1982 Sep;15(5):545-54
pubmed: 7127402
Cancer Lett. 2004 Jul 8;210(1):7-15
pubmed: 15172115
Cancer Res. 2017 Sep 15;77(18):5183-5193
pubmed: 28729417
Cancer Res. 2004 Jan 15;64(2):547-53
pubmed: 14744768
Nat Med. 2011 Apr;17(4):504-9
pubmed: 21441917
Eur J Cancer. 1978 Jun;14(6):681-8
pubmed: 658092
BMC Cancer. 2016 Feb 26;16:163
pubmed: 26921070
Bull Math Biol. 1994 Jul;56(4):617-31
pubmed: 8054889
ILAR J. 2006;47(1):5-14
pubmed: 16391426
ILAR J. 2002;43(4):244-58
pubmed: 12391400
Int J Biomed Comput. 1982 Jan;13(1):19-36
pubmed: 7061168
Cancer Lett. 2011 Sep 1;308(1):54-61
pubmed: 21570176