syngeneic models - crownbio

+1.855.827.6968 | [email protected] | www.crownbio.com

Progress immunotherapeutic development with CrownBio’s panel of over 30 syngeneic models

Syngeneic Models

Discover the benefits of using our fully characterized and checkpoint inhibitor benchmarked syngeneic models to accelerate your immuno-oncology drug discovery programs.

The development of novel immunotherapeutics presents many challenges, including the need for immunocompetent preclinical models. Syngeneic mouse models are undergoing a resurgence as an accessible platform to evaluate the efficacy and MOA of novel agents and combination strategies.

CrownBio provides an extensive syngeneic platform of over 30 models covering more than 15 cancer types for immunotherapeutic assessment.

• Select the most appropriate models for checkpoint inhibitor novel agent/combination studies using a vast array of benchmarking data (e.g. anti-PD-1, PD-L1, CTLA-4 antibodies) complemented by immunoprofiling and NGS.

• Choose the right model for bacterial, viral, and vaccine immunotherapy research based on baseline immunophenotyping at the subcutaneous or orthotopic site.

• Evaluate efficacy quickly with standard subcutaneous models, alongside disease relevant tumor microenviroment in orthotopic and metastatic sites with bioluminescent imaging.

• Assess immunomodulatory effects through post-treatment immunoprofiling including T cell infiltration.

• Fast track new immuno-oncology agents using the first large-scale in vivo syngeneic screening platform.

Large scale in vivoscreening

Immune cell pro�ling

Historical origins ofin vitro cell line: murine tumor

Implant into originalinbred mouse strain

Syngeneic Models for I/OResearch

Standard subcutaneous modelsfor I/O agent evaluation

Advanced orthotopic, bioluminescent, and metastic modeling

Checkpoint inhibitor benchmarking

Combination therapystrategies


Syngeneic Models Key Facts

CrownBio provides a well characterized Syngeneic Model Panel:

• Over 30 models covering 15 cancer types, with further models undergoing validation.• Standard subcutaneous models for efficacy evaluation, complemented by orthotopic models to better recapitulate the tumor microenvironment, and metastatic models allowing targeting of clinically relevant metastatic invasion.• Bioluminescent metastatic models to monitor in-life disease progression, and primary to end stage disease.• Full validation data (baseline and post-treatment immunoprofiling, immunotherapy, standard of care, and NGS data) easily searchable through MuBase®, CrownBio’s online collated immuno-oncology model database.• Checkpoint inhibitor benchmarking data including anti-PD-1, PD-L1, and CTLA-4 antibodies to select the appropriate models for single agent and combination studies, including combination immunotherapy and immunotherapy + chemotherapy (including inducer of ICD) strategies.• Validated immunoprofiling including treatment induced T cell infiltration assessment to characterize immunomodulatory effects of novel agents and treatment regimens. • Microbiome analysis to correlate gut microbiomes across our syngeneic models with response to therapy. • CrownBio’s large-scale, in vivo syngeneic screening platform MuScreen™, the first screening platform of its type, to fast track immunotherapy compounds.

Syngeneic Model Use in Preclinical Immuno- Oncology ResearchEvaluating immunotherapeutic agents brings many challenges, including the need for preclinical models within immunocompetent hosts. Syngeneic mouse models have seen a resurgence in use as a straightforward platform enabling efficacy testing and elucidation of the mechanism of action of new immuno-oncology treatments.

Syngeneic mouse tumors are allografts derived from immortalized mouse cancer cell lines which originate from the same inbred strain of mice. The recipient mice have fully competent mouse immunity and are histocompatible to the allografted tumors. Models have now been extensively profiled genomically and immunologically (both pre- and post-treatment), and for agent efficacy to allow simple and rapid model selection for preclinical studies.

Agents commonly tested using syngeneics include checkpoint inhibitors such as anti-PD-1 and PD-L1 antibodies in proof of concept studies. Syngeneics can also be utilized for evaluating a wide range of other immunotherapeutics, including bacterial, viral, and vaccine therapies, all of which have driven syngeneics to become one of the most commonly utilized immuno-oncology models in preclinical investigations.

CrownBio Provides a Large and Well Profiled Panel of Syngeneic ModelsOur large panel of well validated syngeneic models covers over 15 cancer types and more than 30 individual models (summarized in Table 1, availability site by site is covered within our In Vivo Cancer Pharmacology Model catalogs available on request). CrownBio are constantly improving and expanding the syngeneic collection, and our pipeline of models currently undergoing validation includes:

• breast C127I model

• chondrogenic ATDC5 model

• colon CMT-93 model

• liver Hepa1c1c7 model

• lung LA-4 model

• kidney RAG model.


Syngeneic Models Factsheet

Table 1: Summary of Syngeneic Immunotherapy ModelsX = data available; *bioluminescent models established with further validation ongoing.

Standard Subcutaneous Models to Evaluate Novel ImmunotherapiesCrownBio standard syngeneic models shown in Table 1 are fully validated with growth, standard of care (SoC) and/or immunotherapy treatment data available. Complete background information, growth, and treatment data on models are included within MuBase our easy to use, propri-etary online database. Models can be quickly searched and compared to find those appropriate for indivdual studies.

Models with baseline immune cell profiling data are also highlighted in Table 1. CrownBio research has shown that baseline immune cell populations in untreated syngeneic models (T cells and the Teff/Treg ratio) may predict efficacy of anti-CTLA-4 and anti-PD-L1 antibodies, respectively(1). Example FACS analysis baseline data are included within Figure 1 for T cells (CD3+, CD4+, CD8+, CD4+/FOXP3+) and ratio of CD8+ Teffect/Treg cells, with further NK, MDSC, and macrophage data available in MuBase.

Cancer Type Cell Line Model Type Anti-PD-1 Anti-PD-L1 Anti-CTLA-4 RNAseq Immune Cell Profiling

Bladder MBT-2* Subcutaneous X X X X X

Breast 4T1* Subcutaneous, orthotopic, metastatic, bioluminescent X (s.c., ortho) X (s.c., ortho) X (s.c., ortho*) X (s.c., ortho*) X (ortho) Ongoing (s.c.)

EMT6* Subcutaneous, orthotopic, bioluminescent X (s.c.) X (s.c.) X (s.c.) X (s.c.) X (s.c.)

JC Subcutaneous Ongoing Ongoing Ongoing Ongoing X

Colon Colon26 Subcutaneous X X X Ongoing X

CT-26.WT* Subcutaneous X X X X X

Fibrosarcoma WEHI-164 Subcutaneous X Ongoing Ongoing Ongoing Ongoing

Glioma GL261 Subcutaneous, orthotopic Ongoing (s.c., ortho) Ongoing (s.c., ortho) Ongoing (s.c., ortho) X (s.c.) Ongoing (ortho) Ongoing (s.c., ortho)

Kidney Renca* Subcutaneous X X X X X

Leukemia C1498 Subcutaneous Ongoing Ongoing Ongoing Ongoing X

L1210 Subcutaneous X X X X X

Liver H22* Subcutaneous, orthotopic, bioluminescent X X X X X

Hepa 1-6* Subcutaneous, orthotopic, bioluminescent X (s.c.) X (s.c.) X (s.c.) Ongoing (s.c.) X (s.c.)

Lung KLN205 Subcutaneous X X X X X

LL/2 (LLC1)* Subcutaneous, metastatic X X X X X

Lymphoma A20 Subcutaneous X X X X X

E.G7-OVA Subcutaneous X Ongoing Ongoing Ongoing Ongoing

EL4 Subcutaneous X X X X X

L5178-R (LY-R) Subcutaneous X X X Ongoing X

P388D1 Subcutaneous X X X X X

Mastocytoma P815* Subcutaneous Ongoing X Ongoing Ongoing Ongoing

Melanoma B16-BL6 Subcutaneous X X X X X

B16-F0 Subcutaneous Ongoing Ongoing Ongoing Ongoing Ongoing

B16-F1 Subcutaneous Ongoing Ongoing Ongoing Ongoing Ongoing

B16-F10* Subcutaneous, metastatic, bioluminescent X X X X X

Clone M-3 (Cloudman S91)

Subcutaneous Ongoing X Ongoing Ongoing Ongoing

Myeloma J558 Subcutaneous Ongoing Ongoing Ongoing Ongoing X

MPC-11 Subcutaneous X X X X X

P3X63Ag8U.1 Subcutaneous Ongoing Ongoing Ongoing Ongoing Ongoing

Neuroblastoma N1E-115 Subcutaneous Ongoing Ongoing Ongoing Ongoing Ongoing

Neuro-2a Subcutaneous Ongoing Ongoing Ongoing Ongoing Ongoing

Pancreatic Pan02* Subcutaneous, orthotopic, bioluminescent X (s.c.) X (s.c.) X (s.c.) X (s.c.) X (s.c.)

Prostate RM-1* Subcutaneous X X X X X



Figure 1: Basal Level of Immune Cells in Syngeneic TumorsA: T cell mean % of CD3+, CD4+, CD8+, CD4+/FOXP3+ in TIL. B: Ratio of CD8+ Teffect cells to Treg cells in total cell. Data generated at Crown Bioscience Taicang site.

0

10

20

30

40

50

60

Renc

aEL

4

MBT

-2

LL/2

orth

o 4T

1

CT-2

6.W

T

B16-

BL6

RM-1

H22

L121

0

B16-

F10

EMT6

A20

Pan0

2

P388

D1

L517

8-R

Mea

n (%

)

CD3+

CD3+CD4+

CD3+CD8+

Treg

0

5

10

15

20

25

30

35

Renc

aEL

4M

BT-2

LL/2

orth

o 4T

1CT

-26.

WT

B16-

BL6

RM-1

H22

L121

0B1

6-F1

0EM

T6A2

0Pa

n02

P388

D1

Fold

Cha

nge

Table 1 also shows the availability of RNAseq data for our syngeneic models, which has been used to identify biomarkers to predict treatment response. Through generating detailed expression maps and mutational profiles, we have identified alternative gene splicing transcripts and gene fusions within our models. Further mutational analysis has indicated a number of our syngeneic models harbor mutations that may be useful for combination studies of targeted agents and immunotherapy, and we have identified a set of biomarkers that may be useful to predict immunotherapeutic agent response(2).

We also provide syngeneic tumor samples for ex vivo research uses including tumor tissue histology (H&E staining), and frozen and formalin-fixed, paraffin-embedded tumor samples as required.

Advanced Orthotopic and Metastatic Disease Models, and Syngeneic Imaging ModalitiesCrownBio also provides advanced syngeneic modeling options (model availability detailed in Table 1):

• orthotopic models to more closely recapitulate the tumor situation and microenvironment

• clinically relevant metastatic models of disease

• bioluminescent metastatic models to study clinically relevant metastatic invasion, metastatic lesions in secondary organs, and the evaluation of agents to target this metastasis.

For more information on these models and our pipeline of developing bioluminescent syngeneics please request our Optical Imaging FactSheet.

Examine a Vast Array of Checkpoint Inhibitor Benchmarking Data including Anti-PD1, PD-L1, and CTLA-4 AgentsAs checkpoint inhibitors continue to be approved for a variety of cancer types, preclinical evaluation via syngeneic models can be used to identify their potential indications and combination therapy strategies.

CrownBio has extensively profiled our syngeneic panel in vivo response to a variety of checkpoint inhibitors, providing clients with the information necessary to select models and the correct doses for combination therapy (available data shown in Table 1). Waterfall plots for our models tested with anti-PD-1, anti-PD-L1, and anti- CTLA-4 antibodies are shown in Figure 2 through Figure 4.

Figure 2: Anti-PD-1 Antibody Efficacy Benchmarking in Syngeneic ModelsAntibody: RMP1-14. All data mean + SD. Data generated at Crown Bioscience Beijing and Taicang sites.

Hepa 1-610mg/kg

H22 10mg/kg

EMT6

5mg/kg

ortho 4T

1 10mg/kg

L5178-R5mg/kg

CT26.WT10mg/kg

MBT- 2 5mg/kg

L121010mg/k

g

E.G7-OV A

200ug/mouse

A20 10mg/kg

subq4T1 10mg/k

g

LL /210mg/kg

B16-F10 10mg/kg

B16-BL 6 10mg/kg

KLN2055mg/kg

RM-1

10mg/kg

Pan02 5mg/kg

P388D1 10mg/kg

EL 4 10mg/kg

MPC-11 5mg/kg

WEHI-164 5mg/k

g

Renca10mg/kg

Colon265mg/kg

0

50

100

TGI (

%)

-50

Figure 3: Anti-PD-L1 Antibody Efficacy Benchmarking in Syngeneic Models Antibody: 10F.9G2. All data mean + SD. Data generated at Crown Bioscience Beijing and Taicang sites.

Hepa 1-65mg/k

g

CT26.WT10mg/kg

A20 10mg/kg

EMT6

10mg/kg

H22 5mg/kg

L5178-R5mg/kg

CloudmanS91 5mg/kg

RM-1

10mg/kg

MBT-25mg/k

g

P815 5mg/kg

ortho 4T

1 5mg/k

g

Renca5mg/kg

P388D1 5mg/kg

EL 4 5mg/kg

B16-F10 10mg/kg

Pan02 5mg/kg

subq4T-1

5mg/kg

MPC-11 5mg/kg

L121010mg/k

g

KLN205 5mg/kg

LL /210mg/kg

Colon26 5mg/kg

B16-BL 6 5mg/kg

-40

-20

0

20

40

60

80

100

TGI (

%)



Individual control and treated spider plots are available for each model on request, to evaluate model response variability, example data for the liver syngeneic Hepa 1-6 model is included in Figure 5.

Figure 5: Variability of Hepa 1-6 Response: Control and Treatment Spider Plots A: Mean tumor volume ± SEM. B-E: Individual reponse following treatment with IgG2a or checkpoint inhibitor shown. Statistical anal-ysis on Day 25 post inoculation. Data generated at Crown Bioscience Taicang site.

0

100

200

300

400

500

600

700

800

900

3 5 7 9 11 13 15 17 19 21 23 25

Tum

or V

olu

me

(mm

3 )

Days post Tumor Inoculation

IgG2a 3mg/kg b.i.w.Anti-PD-1 (RMP1-14) 10mg/kg b.i.w.Anti-PD-L1 (10F.9G2) 5mg/kg b.i.w.Anti-CTLA-4 (9D9) 5mg/kg b.i.w.

3 5 7 9 11 13 15 17 19 21 23 250

200

400

600

800

1000

1200IgG2a 3mg/kg b.i.w.


Tum

or V

oum

e (m

m3 )

3 5 7 9 11 13 15 17 19 21 23 250

200

400

600

800

1000

1200Anti-PD-1 (RMP1-14) 10mg/kg b.i.w.


Tum

or V

olum

e (m

m3 )

3 5 7 9 11 13 15 17 19 21 23 250

200

400

600

800

1000

1200Anti-PD-L1 (10F.9G2) 5mg/kg b.i.w.

Tum

or V

olum

e (m

m3 )


3 5 7 9 11 13 15 17 19 21 23 250

200

400

600

800

1000

1200


Anti-CTLA-4 (9D9) 5mg/kg b.i.w.

Tum

or V

olum

e (m

m3 )

B.

C.

D.

E.

Treatment T/C (%) TGI (%) p Value

Anti-PD-1 (RMP1-14) 15 85 <0.001

Anti-PD-L1 (10F.9G2) 32 68 0.042

Anti-CTLA-4 (9D9) 13 87 <0.001

Figure 4: Anti-CTLA-4 Antibody Efficacy Benchmarking in Syngeneic Models A: 9D9, data mean + SD; B: 9H10. Data generated at Crown Bioscience Beijing and Taicang sites.

H22 10mg/kg

EMT6

10mg/kg

Hepa 1-65mg/k

g

subq4T-1

10mg/kg

MBT-210mg/kg

CT26.WT10mg/kg

ortho 4T110 mg/kg

RM-1

10mg/kg

Pan02 10mg/kg

A20 10mg/kg

L121010mg/k

g

B16-F10 5mg/kg

B16-BL 6 10mg/kg

LL /210mg/kg

P388D1 10mg/kg

Colon265mg/kg

EL4 10mg/kg

Renca10mg/kg

-40

-20

0

20

40

60

80

100

TGI (

%)

L5178-R 10mg/kg MPC-11 10mg/kg

0

10

20

30

40

50

TGI (

%)

KLN205 10mg/kg

A. B.



Evaluate Combination Checkpoint Inhibitor and Chemotherapy Regimens As researchers discover that chemotherapy, radiotherapy, and targeted therapies may interact or change the tumor immune environment, suitable models are required to evaluate combinations of these agents with immunotherapy.

CrownBio is utilizing our syngeneic panel to investigate combina-tion therapy strategies. Example data treating the H22 liver cancer syngeneic model with a combination of doxorubicin and anti-PD-L1 antibody showed that combined treatment had a greater effect than either treatment alone (Figure 6).

A range of checkpoint inhibitors have been trialed in combination with cyclophosphamide on the A20 B lymphoma model (Figure 7). Response to combination therapy varied, with the greatest tumor growth inhibition observed for cyclophosphamide combined with anti-GITR antibody.

Figure 6: Combination Doxorubicin and Anti-PD-L1 Antibody Induces TGI of H22 Model Greater than Either Agent Alone Data generated at Crown Bioscience Taicang site.

0

400

800

1200

1600

2000

2400

2800

3 5 7 9 11 13 15 17 19 21

Tum

or V

olum

e (m

m3 )


Vehicle q.w.

Doxorubicin 5mg/kg q.4.d.

Anti-PD-L1 (10F.9G2) 5mg/kg b.i.w.

Doxorubicin 5mg/kg q.4.d. + Anti-PD-L1 (10F.9G2) 5mg/kg b.i.w.

Treatment Tumor Volume(mm3)

T/C Value (%) on Day 21 p Value

Vehicle 2291 ± 231 -- --

Anti-PD-L1 (10F.9G2) 519 ± 65 23 <0.001

Anti-CTLA-4 (9D9) 1436 ± 383 63 0.072

Anti-PD-L1 (10F.9G2) 268 ± 28 12 <0.001

Figure 7: Cyclophosphamide and Checkpoint Inhibitor Combined Treatment of A20 Model Elicits a Range of Responses Data generated at Crown Bioscience Taicang site.

0

400

800

1200

1600

2000

2400

2800

3200

10 12 14 16 18 20 22 24

Tum

or V

olum

e (m

m3 )


PBSCTX 20mg/kg CTX 20mg/kg + Anti-CTLA-4 (9D9) 75μg/mouseCTX 20mg/kg + Anti-PD-1 (RPM1-14) 75μg/mouseCTX 20mg/kg + Anti-OX40 (OX-86) 75μg/mouseCTX 20mg/kg + Anti-GITR (DTA-1) 75μg/mouseCTX 20mg/kg + Anti-CD20 (AISB12) 50μg/mouse

Treatment Tumor Volume(mm3)

T/C Value (%) on Day 24 p Value

PBS 2752 ± 240 -- --

Cyclophosphamide (CTX) 776 ± 238 28 <0.001

CTX + Anti-CTLA-4 (9D9) 601 ±154 22 <0.001

CTX + Anti-PD1 (RMP1-14) 1016 ± 363 37 0.004

CTX + Anti-OX40 (OX-86) 538 ± 348 20 0.001

CTX + Anti-GITR (DTA-1) 151 ± 91 5 <0.001

CTX + Anti-CD20 (AISB12) 895 ± 110 33 <0.001

Combining Inducers of Immunogenic Cell Deacth (ICD) with ImmunotherapyA number of anticancer treatment strategies such as chemo- therapeutic agents (e.g. oxaliplatin, doxorubicin, bortezomib, and mitoxantrone), radiotherapy, and oncolytic viruses have been high-lighted as potential inducers of ICD. These treatments are known to increase the presentation of cell-associated antigens to CD4+ and CD8+ T lymphocytes by dendritic cells.

Combination strategies of ICDs with immunotherapies could there-fore provide opportunities to harness the immune system to extend survival, even among metastatic and heavily pretreated cancer patients, and may increase the efficacy of immunotherapy in cancer types with low immunogenic status.



Figure 8: ICD Oxaliplatin and Anti-CTLA-4 Combination Results in Additive TGITGI reduction: Anti-CTLA-4 vs vehicle p<0.05. Oxaliplatin vs vehicle p<0.01. Combination therapy is additive over single agent therapy alone. Data generated at CrownBio UK.

CrownBio has combined the ICD oxaliplatin with anti-CTLA-4 in treating the CT26 colon cancer syngeneic model. Combination of anti-CTLA-4 immunotherapy with oxaliplatin resulted in an additive tumor growth inhibition (Figure 8), and also induced a statistically significant increase in CD8+ TILs compared with oxaliplatin or anti-CTLA-4 antibody alone (p<0.05, Figure 9)(3). These results effec-tively demonstrate the applicability for further exploring combina-tion ICD inducer strategies involving immunotherapy.

Vehicle b.i.w. + vehicle q.w.Anti-CTLA-4 10mg/kg b.i.w. ( ) + vehicle q.w.Oxaliplatin 6mg/kg q.w. ( )+ vehicle q.w.Anti-CTLA-4 10mg/kg b.i.w. ( )+ oxaliplatin 6mg/kg q.w. ( )

Tum

or V

olum

e (m

m3 )

Study Day

1750

1500

1250

1000

750

500

250

0

0 5 10 15 20 25 30

Figure 9: ICD Oxaliplatin and Anti-CTLA-4 Combination Results in CD8+ TIL IncreaseTreatment dosing and regimens as per Figure 8. A: % CD3+/CD8+ T cells, *p<0.05 vs single agent anti-CTLA-4 and oxaliplatin. B: Ratio Teff:Treg. Data generated at CrownBio UK.

CD3+ CD

8+ /Via

ble

Sing

le L

ymph

ocyt

es

Vehicle

40

30

20

10

0

Anti-CTLA-4 Oxaliplatin Anti-CTLA-4 +Oxaliplatin

Treatment

Vehicle Anti-CTLA-4 Oxaliplatin Anti-CTLA-4 +Oxaliplatin

Treatment

Ratio

of C

D8+

: CD

4+ /Fox

P3+

15

10

5

0

*

CD3+ CD

8+ /Via

ble

Sing

le L

ymph

ocyt

es

Vehicle

40

30

20

10

0

Anti-CTLA-4 Oxaliplatin Anti-CTLA-4 +Oxaliplatin

Treatment

Vehicle Anti-CTLA-4 Oxaliplatin Anti-CTLA-4 +Oxaliplatin

Treatment

Ratio

of C

D8+

: CD

4+ /Fox

P3+

15

10

5

0

*

Microbiome Analysis of Responder vs Non-Responder AnimalsMicrobiota play an important role in determining an organism’s response to anticancer treatment, even in tumors far from the gastrointestinal tract, possibly because of their pro-inflammatory properties which activate the immune system.

In order to gain insights into the complex interaction between the microbiome and cancer therapy, CrownBio performs fecal collection and microbiome profiling (16S rRNA sequencing) to compare gut microbiomes across our syngeneic models, which we can correlate with response to therapy.

Example data is shown in Figure 10 for animals implanted with either CT-26 or 4T1 models, and treated with anti-PD-1 antibody or isotype control. Efficacy studies revealed varying response across different tumor models and within tumor models. Gut microbiome sequencing was performed post dosing and showed that:

• the gut microbiome of animals that were responsive to anti-PD-1 treatment differed from animals that were treated with isotype control

• the gut microbiome of animals that were unresponsive to anti-PD-1 treatment clustered closely with animals that were treated with isotype control (Figure 10)(4).

Figure 9: Continuation..

B.

A.



Figure 10: Gut Microbiome Variation between Responder vs Non-Responder AnimalsEfficacy studies: n=8; mean ± SEM. Gut microbiome examined by r16S sequencing of fecal samples collected post last dose of aPD-1 or isotype control. In-between sample difference analyzed using pairwise comparisons of beta-diversity by unweighted uniFrac metric as displayed by Principal Component Analysis. Taxa abundance at the genus level is represented in stacked columns. Data generated at CrownBio San Diego.

0 10 20 300

1000

2000

3000

¤

Treatment Days

Tum

or V

olum

e (m

m3 )

Isotype ControlAnti-PD-1 Antibody 10mg/kg

0

500

1000

1500

2000

Treatment Days

Tum

or V

olum

e (m

m3 )

0 10 20 30


CT26 4T1

Characterizing the Effect of Immune Checkpoint Inhibitors on Syngeneic Tumor Models Through Gut Microbiome Sequencing and Immunophenotyping

Yuki Kato Maves, Hooman Izadi, Elvira Catherina Talaoc, Andrew Calinisan, Deborah Yan, Charlene Echegaray, Melissa Iris Blair, Reina Mizukoshi,Jayant Thatte, Tommy Broudy

Crown Bioscience Inc., San Diego, CA, USA

BackgroundSyngeneic mouse models present an important tool to understand thelocal and systemic effects of immune modulating therapies. Immunecheckpoint inhibitors have become a mainstay of oncology treatment,yet many patients only experience a partial response. Thoroughcharacterization of preclinical models is paramount in order tosuccessfully predict response to treatment in the clinical setting.Material and methodsWe examined the efficacy of anti-PD-1, anti-PD-L1, and anti-CTLA-4therapies on our syngeneic tumor model platform. Immunophenotypingwas conducted at various time points. Gut microbiome was examinedby r16S sequencing to analyze statistically significant shiftslongitudinally and between different arms and animals at cross-sectional time points.ResultsImmune checkpoint inhibitors had variable efficacy across differenttumor models. We also observed shifts in immune cell populationssuch as T regulatory cells and M1/M2 macrophages, as treatmentswere continued. Gut microbial community analysis enableddetermination of enriched microbes between groups and individualanimals, as well as across time points.ConclusionsHere, we report on the establishment of syngeneic mouse modelsincluding efficacy, flow cytometry analysis of tumor-infiltratinglymphocytes, and gut microbial community analysis.

INTRODUCTION DATA

CONCLUSIONS

Table 1. Syngeneic tumor model platform established at Crown BioscienceSan Diego.

• Response to immune checkpoint inhibitor treatment (aPD-1)differs between cell lines.

• Gut microbiome of animals that were responsive to aPD-1treatment differed from animals that were treated with isotypecontrol, whereas gut microbiome of animals that wereunresponsive to aPD-1 treatment clustered closely with animalsthat were treated with isotype control.

• Immunophenotyping allows characterization of various immunecell populations in syngeneic tumors.

T re a tm e n t d a y s

Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

1 0 0 0

2 0 0 0

3 0 0 0


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

1 0 0 0

2 0 0 0

3 0 0 0


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

CT26 4T1

Fig. 1. Immune Checkpoint Inhibitor Efficacy Studies. In vivo growth characteristic of CT26 and 4T1 syngeneic tumor cell lines in subcutaneous models. Eight animals in each group were treated with aPD-1 (n), or respective isotype controls (l) after randomization. Animals were dosed at 10mg/kg on the indicated treatment days (arrows). Data are displayed as mean ± SEM. The inlaid graph displays each curve as measurements from an individual animal.

Fig. 2. Microbiome Sequencing. Gut microbiome was examined by r16S sequencing of fecal samples collected post lastdose of aPD-1 (l) or isotype control (l). In-between sample difference was analyzed using pairwise comparisons of beta-diversity by unweighted uniFrac metric as displayed by Principal Component Analysis. Taxa abundance at the genus level isrepresented in stacked columns.

ACKNOWLEDGMENTS

Many thanks to Diversigen, for performing r16S microbiomesequencing. And gratitude extended to Mitchell Garland and theCrown Bioscience FACS team for immunophenotyping support.

Fig. 3. Immunophenotyping. Immunophenotypic analysis of tumor infiltratinglymphocytes and macrophages in syngeneic tumors. Markers for macrophagepopulations (CD11b), M1 polarization (CD38), and M2 polarization (Egr2) areincluded.

C D 4 5 +

C D 3 +

C D 3 + CD 4 +

C D 3 + CD 8 +

C D 4 + CD 2 5 + F

o x p 3 +0

2 0

4 0

6 0

8 0

1 0 0

M C 3 8 B a s e lin e T IL

Pe

rce

nta

ge

of

mo

the

r p

op

ula

tio

n (

%)

CT26 4T1

Isotype Control aPD-1 Isotype Control aPD-1

Cancer Type Cell Lines In Vivo Growth Data

In Vivo Checkpoint InhibitorData

Breast EMT6 ✔

4T1 ✔ ✔

Colon CT26 ✔ ✔

MC38 ✔ ✔

Leukemia L1210 ✔

Liver H22 ✔

Lung LL/2 ✔ ✔

KLN205 ✔

Lymphoma A20 ✔ ✔

Melanoma B16-BL6 ✔

B16-F10 ✔ ✔

Prostate RM-1 ✔

Characterizing the Effect of Immune Checkpoint Inhibitors on Syngeneic Tumor Models Through Gut Microbiome Sequencing and Immunophenotyping

Yuki Kato Maves, Hooman Izadi, Elvira Catherina Talaoc, Andrew Calinisan, Deborah Yan, Charlene Echegaray, Melissa Iris Blair, Reina Mizukoshi,Jayant Thatte, Tommy Broudy

Crown Bioscience Inc., San Diego, CA, USA

BackgroundSyngeneic mouse models present an important tool to understand thelocal and systemic effects of immune modulating therapies. Immunecheckpoint inhibitors have become a mainstay of oncology treatment,yet many patients only experience a partial response. Thoroughcharacterization of preclinical models is paramount in order tosuccessfully predict response to treatment in the clinical setting.Material and methodsWe examined the efficacy of anti-PD-1, anti-PD-L1, and anti-CTLA-4therapies on our syngeneic tumor model platform. Immunophenotypingwas conducted at various time points. Gut microbiome was examinedby r16S sequencing to analyze statistically significant shiftslongitudinally and between different arms and animals at cross-sectional time points.ResultsImmune checkpoint inhibitors had variable efficacy across differenttumor models. We also observed shifts in immune cell populationssuch as T regulatory cells and M1/M2 macrophages, as treatmentswere continued. Gut microbial community analysis enableddetermination of enriched microbes between groups and individualanimals, as well as across time points.ConclusionsHere, we report on the establishment of syngeneic mouse modelsincluding efficacy, flow cytometry analysis of tumor-infiltratinglymphocytes, and gut microbial community analysis.

INTRODUCTION DATA

CONCLUSIONS

Table 1. Syngeneic tumor model platform established at Crown BioscienceSan Diego.

• Response to immune checkpoint inhibitor treatment (aPD-1)differs between cell lines.

• Gut microbiome of animals that were responsive to aPD-1treatment differed from animals that were treated with isotypecontrol, whereas gut microbiome of animals that wereunresponsive to aPD-1 treatment clustered closely with animalsthat were treated with isotype control.

• Immunophenotyping allows characterization of various immunecell populations in syngeneic tumors.


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

1 0 0 0

2 0 0 0

3 0 0 0


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

1 0 0 0

2 0 0 0

3 0 0 0


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0


Tu

mo

r v

olu

me

(m

m3

)

0 1 0 2 0 3 00

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

CT26 4T1

Fig. 1. Immune Checkpoint Inhibitor Efficacy Studies. In vivo growth characteristic of CT26 and 4T1 syngeneic tumor cell lines in subcutaneous models. Eight animals in each group were treated with aPD-1 (n), or respective isotype controls (l) after randomization. Animals were dosed at 10mg/kg on the indicated treatment days (arrows). Data are displayed as mean ± SEM. The inlaid graph displays each curve as measurements from an individual animal.

Fig. 2. Microbiome Sequencing. Gut microbiome was examined by r16S sequencing of fecal samples collected post lastdose of aPD-1 (l) or isotype control (l). In-between sample difference was analyzed using pairwise comparisons of beta-diversity by unweighted uniFrac metric as displayed by Principal Component Analysis. Taxa abundance at the genus level isrepresented in stacked columns.

ACKNOWLEDGMENTS

Many thanks to Diversigen, for performing r16S microbiomesequencing. And gratitude extended to Mitchell Garland and theCrown Bioscience FACS team for immunophenotyping support.

Fig. 3. Immunophenotyping. Immunophenotypic analysis of tumor infiltratinglymphocytes and macrophages in syngeneic tumors. Markers for macrophagepopulations (CD11b), M1 polarization (CD38), and M2 polarization (Egr2) areincluded.

C D 4 5 +

C D 3 +

C D 3 + CD 4 +

C D 3 + CD 8 +

C D 4 + CD 2 5 + F

o x p 3 +0

2 0

4 0

6 0

8 0

1 0 0

M C 3 8 B a s e lin e T IL

Pe

rce

nta

ge

of

mo

the

r p

op

ula

tio

n (

%)

CT26 4T1

Isotype Control aPD-1 Isotype Control aPD-1

Cancer Type Cell Lines In Vivo Growth Data

In Vivo Checkpoint InhibitorData

Breast EMT6 ✔

4T1 ✔ ✔

Colon CT26 ✔ ✔

MC38 ✔ ✔

Leukemia L1210 ✔

Liver H22 ✔

Lung LL/2 ✔ ✔

KLN205 ✔

Lymphoma A20 ✔ ✔

Melanoma B16-BL6 ✔

B16-F10 ✔ ✔

Prostate RM-1 ✔

0 10 20 300

1000

2000

3000

¤

Treatment Days

Tum

or V

olum

e (m

m3 )


0 10 20 300

1000

2000

3000

¤

Treatment Days

Tum

or V

olum

e (m

m3 )


Treatment Days

Tum

or V

olum

e (m

m3 )

0 10 20 30

0

1000

2000

3000Isotype ControlAnti-PD-1 Antibody 10mg/kg

0 10 20 300

500

1000

1500

2000

2500

Treatment Days

Tum

or V

olum

e (m

m3 ) Isotype Control

Anti-PD-1 Antibody 10mg/kg



Assess Immunotherapy Induced T Cell Infiltration and Immunomodulatory EffectsFollowing checkpoint inhibitor or immunotherapy evaluation, CrownBio can perform immune cell profiling to evalute induced T cell infiltration and immuno-modulatory effects. Our techniques include FACS and IHC immunophenotyping, which have been val-idated with a range of our syngeneic models following checkpoint inhibitor treatment:

• FACS immunophenotyping: MBT-2, 4T1, EMT6, CT-26.WT, L1210, H22 ,B16-F10, and Pan02 models

• IHC immunophenotyping: A20

Example FACS immunophenotyping data for the H22 liver model, and IHC immunophenotyping for the A20 lymphoma model are detailed below.

The H22 model was treated with anti-PD-1 and anti-CTLA-4 antibodies, with response to treatment correlating with an increase in selected tumor infiltrating lymphocytes (TIL) (Figure 11 and Figure 12). T cell infiltration into A20 tumors was analyzed via IHC and immunofluorescence (Figure 13).

Figure 11: H22 Liver Syngeneic Model Responds to Checkpoint Inhibitors: Mean and Individual ResponseT/C values on Day 21: anti-PD-1 (RMP1-14) 16% (p=0.020); anti-CTLA-4 (9D9) 5% (p=0.012). B, C, D: Individual responses to PBS control, anti-PD-1, and anti-CTLA-4, respectively. Data generated at Crown Bioscience Taicang site.

0

500

1000

1500

2000

2500

3000

5 7 9 11 13 15 17 19 21

Tum

or V

olu

me

(mm

3 )


PBS b.i.w.Anti-PD-1 (RMP1-14) 10mg/kg b.i.w.Anti-CTLA-4 (9D9) 10mg/kg b.i.w.

5 7 9 11 13 15 17 19 210

1000

2000

3000PBS b.i.w.


Tum

or V

olum

e (m

m3 )

5 7 9 11 13 15 17 19 210

1000

2000

3000Anti-PD-1 (RMP1-14) 10mg/kg b.i.w.


Tum

or V

olum

e (m

m3 )

5 7 9 11 13 15 17 19 210

1000

2000

3000Anti-CTLA-4 (9D9) 10mg/kg b.i.w.


Tum

or V

olum

e (m

m3 )



Figure 12: H22 Liver Syngeneic Model: Response to Checkpoint Abs Correlates with an Increase in Selected TILsFACS result on Day 21: 2 days post the 5th dose. Data generated at Crown Bioscience Taicang site. *p<0.05, **p<0.01, ***p<0.001.

0

5

10

15

20

25

30

****** *** ***

***

****

*

*

Tum

or In

�ltr

atin

g Im

mun

e Ce

lls

(% in

CD

45+ P

opul

atio

n)

PBS b.i.w., i.p. aPD-1 10mg/kg b.i.w., i.p.

aCTLA-4 10mg/kg b.i.w., i.p.

CD4+ FOXP3++

+

+

+

p Value vs Control CD45+ CD3+ CD3+

CD4+CD3+

CD8+CD4+-

FOXP3+ NK MDSC Macro-phage

Anti-PD-1 10mg/kg 0.082 <0.001 <0.001 <0.001 0.145 0.506 0.056 0.343

Anti-CTLA-4 10mg/kg

0.179 <0.001 <0.001 0.046 <0.001 0.758 0.017 0.028-

Figure 13: A20 Tumor T-Cell InfiltrationIHC (images 20x) of CD4, CD8, CD335, FOXP3, and neutrophils (Ly6G/C) was used to label helper T-cells, cytotoxic T cells, NK, Treg, and neutrophil cells. All IHC assays were run with BondRX Autostainer (Leica) and stained on 4µm FFPE sec-tions of A20 without treatment. IF (image 40x) of CD4 (red) and FOXP3 (green) was stained on frozen sections of the A20 model to label Treg cells (run on Bond RX). DAPI (blue) is used to label the nucleus.

CD4 CD8 CD335

FOXP3 Neutrophil CD4 FOXP3 DAPI

Standard of Care and Experimental Treatment Data also AvailableA range of SoC agents, experimental treatments, and combination chemotherapies have been trialed with our syngeneic models (results shown in Table 2).

Cancer Type Syngeneic Model Treatment T/C (%) p Value

Breast 4T1 Paclitaxel Day 27: 75 0.042

Colon CT-26 VEGF-TRAP Day 15: 50 0.007

Cisplatin Day 20: 52 0.002

Oxaliplatin Day 23: 50 <0.01

Liver H22 Doxorubicin Day 21: 23 <0.001

Sorafenib Day 28: 52 0.047

Lymphoma A20 Cyclophosphamide Day 20: 6.4 <0.001

Melanoma B16-BL6 Cisplatin Day 35: 43 0.016

Melanoma B16-F10 Cisplatin Day 28: 30 0.006

Pancreatic Pan02 Gemcitabine Day 21: 58 <0.001

Gemcitabine + cisplatin Day 21: 40 <0.001

Gemcitabine + paclitaxel Day 45: 33 <0.001

Table 2: Syngeneic Model Standard of Care and Experimental Treatment DataDay: days post-tumor inoculation.

Version 3.1



Fast-Track the In Vivo Screening of Immuno- therapy CompoundsFor immunotherapeutic agents, in vitro screening is not the optimum approach for evaluating PD effect and/or efficacy across multiple cancer types. However, as an alternative, large-scale, parallel, in vivo screening of syngeneic models can provide a cost effective approach.

CrownBio are therefore utilizing our syngeneic platform to offer a unique large scale MuScreen to fast track immunotherapy treatment strategies, the first platform of its type. MuScreen can be used for both single agent and combination studies, reducing variability and improving screening efficiency. We provide a syngeneic efficacy screening panel and tumor microarrays to fit your research needs. For further information please consult the MuScreen FactSheet available from the CrownBio website: www.crownbio.com/ publications/factsheets/.

ConclusionsImmunotherapy research and agents such as anti-PD-1 antibodies are showing considerable success in oncology; providing both patient benefits and commercial success for the pharmaceutical industry. However, progress in the field is hindered through a lack of experimental immunotherapy models featuring a fully competent immune system.

Syngeneic models (allografts derived from immortalized mouse cancer cell lines, which originated from the same inbred strain of mice) are a simple way to evaluate novel immunotherapy treat-ments through eliciting an immune response, in fully immunocompetent mice.

CrownBio has validated a large panel of syngeneic models, covering a variety of cancer types, with a commitment to further extend this model selection. Alongside subcutaneous models, bioluminescent imaging of orthotopic and metastatic tumous allows more clinically relevant stromal interactions to be modeled and investigated.

Full characterization including immunoprofiling, NGS, and check-point inhibitor benchmarking allows rapid selection of appropriate models for client studies. Immunomodulatory effects of novel agents can be evaluated through assessment of immunotherapy induced T cell infiltration, validated for a range of models. Our models are also available for a wide variety of agent assessment from checkpoint inhibitors to other immnunotherapeutics including bacterial, viral, and vaccination research.

As immunotherapies are combined with chemotherapy and target-ed agents, in an effort to extend patient survival, CrownBio is also utilizing its wide ranging Syngeneic Panel to interrogate different combinations including with inducers of ICD and can offer a large scale MuScreen to fast-track strategies.

References1Zhang L, Zhang J, Guo S et al. RNAseq and immune profiling analysis of syngeneic mouse models treated with immune checkpoint inhibitors enable biomarker discovery and model selection for cancer immunotherapy. [abstract]. In: Proceedings of the AACR-NCI-EORTC Inter-national Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Molecular Cancer Therapeutics 2015;14(12 Suppl 2): Abstract nr A6.2 Zhang L, Zhang J, Guo S et al. RNAseq and FACS profiling of syngeneic mouse models treated with immune checkpoint inhibitors enable biomarker discovery and model selection for can-cer immunotherapy [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Research 2016;76(14 Suppl): Abstract nr5177.3Mckenzie A, Kumari R, Shi Q et al. Immune competent syngeneic models demonstrate additive effects of combination strategies using checkpoint immunotherapy and inducers of immunogenic cell death (ICD). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Research 2016;76(14 Suppl): Abstract nr 3994.4Kato Maves Y, Izadi H, Talaoc EC et al. Characterizing the effect of immune checkpoint inhibitors on syngeneic tumor models through gut microbiome sequencing and immuno-phenotyping [abstract].. In: Proceedings of the 28th EORTC-NCI-AACR Symposium: Molecular Targets and Cancer Therapeutics; 2016 Nov 29 – Dec 02; Munich, Germany: European Journal of Cancer 2016;69: S111.

Explore Scientific DataLog into MuBase to review syngeneic model data.mubase.crownbio.com

Contact SalesUS: +1.855.827.6968UK: +44 (0)870 166 [email protected]

Schedule Scientific Consultation Request a consultation to discuss your [email protected]

syngeneic models - crownbio

Documents