Assessing Antitumor Activity
in Preclinical Tumor Xenograft
Model
Department of Biostatistics
St. Jude Children’s Research Hospital
John(Jianrong) Wu
Tumor Xenograft Model
Tumor xenograft models

Subcutaneous tumor model: tumor xenograft is
implanted under the skin and typically located on the
flank of the mouse.
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Orthotopic tumor model: tumor xenograft is either
implanted into the equivalent organ from which the
cancer originated, or where metastatsese are found in
patients.
D456-cisplatin tumor xenograft model
Challenges
A
relative small number of mice (10/per
group) were tested.
 Missing data issue: due to mice die of
toxicity or be sacrificed when tumors grow
to certain size.
 Skewed distribution of tumor volume data
 Various of tumor growth patterns.
Tumor Growth Inhibition
(T/C ratio)
Relative
Tumor
volume
t
time
Skewed tumor volume data
Demidenko, 2010
Anti-tumor activity
Drawback of separate analysis of tumor volume at
each time point


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Multiple tests at different time points inflate type I error.
Excluding animals with missing observation is inefficient
and could result a biased conclusion.
T-test may be not valid due to skewed distribution of
tumor volume data.
Separate analysis at each time point ignores the intrasubject correlation.
Using an arbitrary cut-off point to assess antitumor
activity and without any formal statistical inference
Statistical inference for tumor volume
data

Inference T/C ratio and its 95% confidence interval –
Hothorn (2006), Wu (2009, 2010), Cheng and Wu
(2010)

Multivariate analysis – Tan et al (2002)

MANOVA – Heitjian et al (1995)

Nonparametric multivariate analysis – Koziol et al (1981)
Tumor Growth Delay (T-C)
Relative
Tumor
volume
4
time
Tumor Growth Delay
Tumor doubling and
quadrupling time
4
Tumor quadrupling time
Kaplan-Meier Event-free Survival
Distributions (p<0.0001)
Example: D456-cisplatin tumor
xenograft model

The medians of tumor quadrupling times are 8.7
(days) and 24.9 (days) for control and treatment,
respectively. TGD=16.2 days with standard error
of 1.9 days

The 95% confidence bootstrap percentile
interval of TGD is (10.8, 21.2).
Wu J, Confidence intervals for the difference of median failure times applied
to censored tumor growth delay data, Statistics in Biopharmaceutical
Research, 3:488-496, 2011
Log10 cell kill (LCK)


Log10 cell kill is defined as the negative log10 fraction of tumor cells
surviving (SF).
We illustrate its quantification with assumptions (a) control tumor
growth follows an exponential growth curve (b) treated tumor
regrowth after treament approximates untreated controls, then
LCK = - log10(SF) = (T – C)/(3.32 DT)
where DT is tumor doubling time of control.
or
-log(SF)=Tumor Growth Delay * Rate of Growth
Demidenko, 2010
Log10 cell kill
Anti-tumor activity
SAS macro
 Macro
%long: transform the tumor volume
data to be a longitudinal form
 Macro %day2event: calculate tumor
doubling and quadrupling times.
 Macro %lck: calculate tumor growth delay
(T-C) and log10 cell kill.
References for T/C ratio
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Heitjan DF, Manni A, Santen RJ. Statistical analysis of in vivo tumor growth
experiments. Cancer Research 1993;53:6042–6050
Houghton PJ, Morton CL, et al. (2007). The pediatric preclinical testing program:
Description of models and early testing results. Pediatr. Blood Cancer 49:928–940.
Hothorn L (2006). Statistical analysis of in vivo anticancer experiments: Tumor growth
inhibition. Drug Inform. J. 40:229–238.
Wu J (2010), Statistical Inference for Tumor Growth Inhibition T/C Ratio, JBS,
20:954-964
Wu J and Houghton PJ (2009), Interval approach to assessing antitumor activity for
tumor xenograft studies, Pharmaceutical Statistics, 9:46-54.
Tan, M., Fang, H. B., Tian, G. L., Houghton, P. J. (2002). Small-sample inference for
incomplete longitudinal data with truncation and censoring in tumor xenograft models.
Biometrics 58:612–620.
Koziol et al. (1981). A distribution-free test for tumor-growth curve analyses with
application to an animal tumor immunotherapy experiment. Biometrics, 37:383-390
References for TGD
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Wu J, Confidence intervals for the difference of median failure times
applied to censored tumor growth delay data, Statistics in
Biopharmaceutical Research, 3:488-496, 2011.
Wu J, Assessment of antitumor activity for tumor xenograft studies
using exponential growth models. Journal of Biopharmaceutical
Statistics, 21:472-483, May, 2011.
Demidenko E (2010), Three endpoints of in vivo tumor radiobiology
and their statistical estimation. 86:164-173.
Corbett, T. H., White, K., Polin, L., Kushner, J., Paluch, J., Shih, C.,
Grossman, C. S. (2003).Discovery and preclinical antitumor efficacy
evaluations of LY32262 and LY33169.Investigational New Drugs
21:33–45.
References for LCK
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Demidenko E (2010), Three endpoints of in vivo tumor radiology and
their statistical estimation, Int J Radial Biol. 86:164-173
Lloyd H (1975), Estimation of tumor cell kill from Gompertz growth
curves, Cancer Chemother Rep, 59:267-277.
Corbett TH et al (2003), Discovery and preclinical antitumor efficacy
evaluations of LY32262 and LY33169. Invest New Drugs 21:33-45.
Wu J (2011), Assessment of antitumor activity for tumor xenograft
studies using exponential growth models, JBS, 1:472-483
Wu J and Houghton PJ (2009), Assessing cytotoxic treatment
effects in preclinical tumor xenograft models, JBS,19:755-762
Thank you !