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A Systematic Evaluation of Immune Checkpoint Inhibitors
Sheri Barnes, Paula Miliani de Marval, Jacob Hauser, Tracy Brainard, Daniel Small, Aidan Synnott, Robert Mullin. Charles River Discovery Services, Morrisville, NC, USA
Figure 1. Syngeneic Models Responsive to Anti-PD-1 and Anti-CTLA-4 Therapy
Anti-CTLA-4 Clones + Anti-PD-1
Anti-CTLA-4 9H10 Anti-PD-1
A. Colon26
Anti-CTLA-4 9H10 + Anti-PD-1
B. MC38
Figure 2. Colon26 or MC38 tumor cells were implanted into the flank of mice (Balb/c or C57Bl/6, respectively, Charles River.) Treatment
began once mean tumor volumes reached ~100 mm3 (red arrow). Tumors were measured twice weekly ((w2 x l)/2), and mice were
sacrificed when individual tumors reached a predefined endpoint (1000-2000 mm3).
Table 1. Median Time to Endpoint for Non-Responsive Syngeneic Models
Figure 1. Respective tumor cells from a panel of six syngeneic models (Colon26, MC38, EMT-6, Lewis Lung, 4T1, Madison109, B16F10) were implanted into the flank of mice (C57Bl/6 or Balb/c depending
on the model, Charles River). Mice inoculated with Madison109 or EMT-6 began dosing with anti-PD-1 (BioXCell) on Day 2 after implant (biwk x 2) and with anti-CTLA-4 (BioXCell) on Days 5, 8, and 11
after implant. Treatment of all other models began with anti-PD-1 starting Day 3 after implant (biwk x 2) and with anti-CTLA-4 Days 8, 11, and 14 after implant. Tumors were measured twice weekly ((w2 x l)/
2), and mice were sacrificed when individual tumors reached a predefined endpoint (1000-2000 mm3). Median time to endpoint (TTE) was determined, and data for nonresponsive models are detailed in
Table 1. Data for responsive models are detailed in spider plots in Figures 1A-C.
Figure 3. Flow Cytometry and Gate Strategy
cells
Anti-PD-1
A. Colon26
Anti-CTLA-4 Clones
B. MC38
With the increasing success and subsequent interest in
tumor immunology, we recognized a growing need for
well-characterized preclinical models. While the
literature contains many different experimental models,
the available data includes a variety of reagents and the
side by side comparative evaluation of therapeutics in
multiple models is relatively rare. Lechner et al., (J.
Immunother. 36:477-489, 2013) addressed this issue
and reported on a comparative analysis of a set of six
syngeneic models.
Our effort expands this paradigm towards a
comparative evaluation of the responsiveness of a
collection of syngeneic models to antibody based
checkpoint inhibitor therapeutics.
Specifically, we
examined the response of the Colon26, MC38, B16F10,
Lewis Lung, Madison109, EMT-6, and 4T1 models to
anti-CTLA-4 and anti-PD-1 monotherapies as well as
capturing their response in combination. Our results
clearly show a differential response across this set of
models both in regard to CTLA-4 targeted therapy as
well as PD-1 therapy.
In initiating these studies and evaluating the literature, it
became clear that there were multiple choices in regard
to anti-CTLA-4 preclinical reagents. We have also
performed a series of studies evaluating the efficacy of
multiple anti-CTLA-4 clones as an extension of
previously published work which described the variable
ability of anti-CTLA-4 clones to deplete tumor Treg
populations (Simpson et al. J. Exp. Med. 210(9)
1695-1710, 2013). Our anti-tumor data correlates well
with these cell based observations. This differential
allows one to match efficacy with model and expands
the reagents available for evaluating combination
therapies.
Figure 2. Response in Established Syngeneic Tumors
C. EMT-6
Figure 4. Characterization of Immune Cell Populations Derived From Syngeneic Tumors
A
CD3-
B
C
D
singlets
70
CD11b+
CD3+
Colon26
MC38
LL
4T1
B16F10
60
CD4+ T
Treg
CD8+ T
mMDSC
gMDSC
14
50
Percentage of total TIL
live
Percentage of total TIL
AACR 2015, Philadelphia, PA
Abstract
40
30
20
10
0
Figure 3. Cells isolated from spleen (example above) and tumors were stained with Live/Dead, then with
antibodies against CD3, CD4, CD8, CD11b, CD25, FoxP3, Ly-6G, and Ly-6C. The gating strategy was
determined by first gating for singlets (FSC-H vs. FSCA) and cells (SSC-A vs. FSC-A). The cells gate was
further analyzed for their uptake of the Live/Dead and their expression of the signature markers. Arrows
indicate sequential gating. CD4+ T defined as CD3+CD4+CD8-. CD8+ T defined as CD3+CD4-CD8+. Treg
defined as CD3+CD4+CD25+FoxP3+. Granulocytic MDSC defined as CD3-CD11b+Ly6G+Ly6Clow and
monocytic MDSC defined as CD3-CD11b+Ly6G‑Ly6Chigh .
Control
anti-PD-1
anti-CTLA-4 (9H10)
anti-PD-1+CTLA-4
12
CD3+/4+
MDSC
405
4.50
550
3.49
600
3.37
mm3
CD8:Treg
196
12.32
228
9.05
288
6.37
mm3
CD8:Treg
352
14.97
320
19.29
288
7.05
8
6
4
0
Treg
CD8:Treg
10
p<0.05
2
CD3+/8+
mm3
CD3+/CD8+
CD3+/CD4+
Treg
Figure 4. Total tumor infiltrating leukocytes (TILs) were stained with antibodies against CD3, CD4, CD8, CD11b, CD25,
FoxP3, and Gr1 and analyzed by flow cytometry. (A) Initial gating strategy: Leukocytes were identified in the singlet
population based upon FSC and SSC characteristics and viable cells were sub-divided based upon CD3 expression.
(B) Summary of different TIL populations in syngeneic tumors. (C) TIL populations from Colon26 tumors treated with
immune checkpoint inhibitors. (D) CD8 to Treg ratios and tumor size of individual treated/analyzed tumors. The same
phenotypic markers as in Figure 4B were used and percentages of cell populations were calculated relative to the live
cell gate population, n=3.
Conclusions and Acknowledgements
•  Profiling responses to immune checkpoint inhibitors permits for the rational decision making of
syngeneic models to use in combination with other therapies.
•  Determination of the dosing regimen of individual anti-CTLA-4 mAbs +/- anti-PD1 in specific
syngeneic models allows for the design of combination therapies to evaluate therapeutic
interactions.
•  Generation of base line knowledge of TILs in tumor models assists in the selection of
immunotherapies based upon the presence of T effector and immuno-regulatory cells (Treg and
MDSC).
•  Anti-PD-1 and anti-CTLA-4 treatment have been observed to modulate the T effector: T regulatory
cell ratio in favor a CTL based anti-tumor response.
We would like to thank BioMed Valley Discoveries, Inc. for their contribution. We also thank Alan Meshaw for assistance in
graphical presentation and Dr. Marcio Lasaro for flow cytometry analysis.