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Gene Expression Analysis
Further pathway analysis of the 2,596 gene list was performed by using a fold change
greater than ±1.2 (in log2 space) comparing all samples to each other and observing pathway
relations using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City,
CA). To understand the observed tumor dynamics, IPA software found the key molecular
differences occurring between each age group. Upstream regulator analysis from IPA identifies
any molecule that affected the expression or function of downstream target molecules. This
includes transcription regulators, growth factors, cytokines, enzymes, transmembrane receptors,
and kinases. This analysis uses expected causal effects from the gene expression list compiled
in this study and uses a Fisher’s test with a comprehensive database of known upstream
regulators from the literature to determine significance. The activation state of each upstream
regulator from the experimental data set is determined by calculating the z-score (≥ 2, activated
or ≤ -2, inhibited).
Gene Set Enrichment Analysis (GSEA) (1) was also performed using the entire list of genes
and with leading edge analysis. Significant gene sets between age groups were considered with
FDR < 0.05.
Immunofluorescence Antibodies
For immunofluorescence, anti-TGF beta 1 antibody [2Ar2] mouse monoclonal (Abcam,
Cambridge, MA) at 1:250, Vimentin antibody (H-84), rabbit polyclonal (Santa Cruz
Biotechnology, Santa Cruz, CA) at 1:100, anti-Ki67 antibody, rabbit polyclonal (Abcam) at
1:500, CD31 antibody, rat polyclonal (BD Pharmingen, San Jose, CA) at 1:100, and anti-goat
Alexa 488 or 555 secondary antibodies (Molecular Probes, Carlsbad, CA) at 1:300 were used.
Immunofluorescence staining on tissue
Frozen 15µm tissue sections mounted on slides were placed at room temperature for 30min
prior to staining. For antibodies that require permeabilization, the tissue sections were rinsed
once in PBS and were treated in 100% methanol at -20ºC for 10min. All tissue sections were
fixed in 4% paraformaldehyde for 5min at room temperature. To block nonspecific sites tissue
sections were incubated with 1% BSA at room temperature for 1hr. An additional blocking was
done using Mouse Detective (Biocare Medical, Concord, CA) for 4hr at room temperature.
Tissue sections were incubated with primary antibodies in 1% BSA at 4ºC overnight, following
washing with 10% goat serum. Tissue sections were incubated with secondary antibodies for
1hr in dark. For nuclear staining To-Pro-3 (Molecular Probes) was used. Tissue was mounted
with anti-fade mounting solution (ProLong® Gold, Invitrogen).
Confocal Microscopy
Tissue sections were viewed using a Zeiss LSM 510 Meta Confocal Scanning System (Carl
Zeiss, Jena, Germany) equipped with an Argon laser, HeNe laser 543nm, HeNe laser 633nm,
two 1-channel detectors, and one 8-channel Meta detector. Images were captured using three
photomultiplier tubes for each detector at 1024×1024 pixels. Z-stacks were obtained when
necessary. Reconstruction of z-stacks was done with Zeiss LSM software.
Model for Tumor Dynamics
A two-dimensional ordinary differential equation model for tumor growth (2), having as variables
the tumor volume, V, and its carrying capacity, K, characterizing the tumor microenvironment
was used. Equations are:

K 
dV
 aV ln  ,
V 
dt
V (0)  V0
2
dK
 bV  dV 3 K,
dt
K(0)  K 0
����
The model has three parameters: a, cancer cells kinetics, and b and d interactions of the tumor
mass with host carrying capacity. These two last parameters stand respectively for the
stimulatory and inhibitory impacts on tumor on the host support (2).
Parameters and initial carrying capacity were fitted to individual data of tumor growth, using
the Levenberg-Marquardt algorithm (nonlinear regression method) implemented in Matlab
(Mathworks Inc, R2012, Natick, MA) for minimization of the sum of squared residuals between
logarithm of model simulation and logarithm of data. This logarithm transformation was used to
acknowledge that variation (and thus unreliability) increases in proportion to size. Constrained
optimization was used with bounds defining a compact set in the parameter space where the
model had numerically been observed to be identifiable (uniqueness of the best fit set of
parameters whatever the initial guess). Initial values used were a0 = 0.86, b0 = 0.54, d0 =
0.00188, K0 = 100 based on preliminary analysis. Bounds were set to a range [0.1p0, 10p0] for
parameter p except for K0 for which [0.1K0, 50K0] had to be used due to different range of initial
values depending on the batch. No bound constraint was active at convergence of the
minimization algorithm ensuring that a local minimum had been reached and no excessive
restriction of the bounds. One outlier (of 60) showing incoherent data and escaping the fitting
ability of the model was removed. Apart from this, all the fits were consistent as quantified by an
R2 value above 0.9, where R 2  1   y i  f (t i )
2
 y
 y  , where the yi are the data
2
i
points, y is the mean value of the data and the f(ti) are the values of the model at times ti.

The second purpose of the model is to correct for any batch differences in tumor sizes at
 first detection, due to subtle differences in experimental conditions (3). This was done by taking
into account differences in the initial carrying capacity, K0, (which is statistically different
between the two experimental groupings, with exception of the adolescent-young adult and oldyoung adult comparison), along with differences in V0 (Fig 1D). Among the possible
explanations include subtle variations in positioning of the small tumor inoculants under the skin,
which would be expected to diminish as the tumors grow. In any event, the cell inoculants
themselves, as injected, should be indistinguishable in character. By simulating tumor growth for
each individual (i.e. using its set of parameters (a, b, d)) with the same initial conditions (V0 and
K0) and averaging the resulting curves, these simulations (Fig 1C) show a clear trend of slower
growth with age due to factors related to the host.
Statistical Analysis
To quantify tumor development we used nonlinear regression to fit the tumor growth phase
data with curves of the form V = bect, where V is tumor volume and t is time. Here c is
interpreted as the tumor growth rate once the tumor starts to grow more or less exponentially; b
is interpreted as the extrapolated “effective” volume at the implant time, t=0. “Effective” means
the volume is inferred by backward extrapolation from the fit to later time points. It is thus not a
measure of the number of injected cells (which is the same for all mice). Instead, it indirectly
takes into account intermediate events; for example, the inclusion of stroma into its measured
mass or the fact that different individual host environments may be more or less supportive of
having the injected cells assemble into an organized, growing tumor.
The regression procedure is now described in detail. For the kth (k = 1,...,10) of the 10 mice
in Group g (g = 1 and 2, respectively, are “young adult” and “middle-aged”), and for the kth (k =
1,..., 20) of the 20 mice in Group g (g = 3 and 4, respectively, are “adolescent” and “old”) the pgk
time points tgki (i = 1,...,pgk) and corresponding tumor size measurements Vgki represent data
acquired after the last recorded zero tumor size measurement for that mouse. We fit these
measurements (tgki, Vgki) to the curve V=b’gkexp(c’gkt) using standard non-linear regression sum
of squares. After thus calculating the (b’gk, c’gk) corresponding to each of the 10 or 20 mice in a
given group g, we computed the average of the c’gk and recorded it as the group growth rate cg
(Table 1). Similarly, the geometric mean of the b’gk was recorded as the “effective initial volume”
bg of the group, interpreted as described above.
To check the robustness of this procedure, especially as regards the omission of some early
time points, we used a secondary method. Starting now with the p’g time points t’gi (i = 1,…,p’g)
on and after which a non-zero tumor volume was consistently recorded for at least one of the 10
mice for each of the g groups, we fit the averages
of the volumes V ’gki for the 10 or 20
mice measured at time point t’gi, with an inverse-variance-weighted non-linear regression of the
form V=Bgexp(Cgt) (plots not shown). The striking similarity of these curves to those calculated
as per above based on the individual mouse parameters b’gk and c’gk, respectively, lends
support to the robustness of our method of deducing fits to the composite tumor data for each
group based on fits to the individual mouse tumors in that group.
1. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set
enrichment analysis: a knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci U S A. 2005;102:15545-50.
2. Hahnfeldt P, Panigrahy D, Folkman J, Hlatky L. Tumor development under angiogenic
signaling: a dynamical theory of tumor growth, treatment response, and postvascular
dormancy. Cancer Res. 1999;59:4770-5.
3. Ayers GD, McKinley ET, Zhao P, Fritz JM, Metry RE, Deal BC, et al. Volume of preclinical
xenograft tumors is more accurately assessed by ultrasound imaging than manual caliper
measurements. J Ultrasound Med. 2010;29:891-901.