Abstract
Background/Aim: To evaluate the feasibility of syngeneic mouse models of breast cancer by analyzing the efficacy of immune checkpoint inhibitors (ICIs) and potential predictive biomarkers. Materials and Methods: To establish the murine triple-negative breast cancer (TNBC) models, JC, 4T1, EMT6, and E0771 cells were subcutaneously implanted into female syngeneic mice. When the tumor reached 50-100 mm3, each mouse model was divided into a treatment (using a murine PD-1 antibody) and a no-treatment control group. The treatment group was further divided into the responder and non-responder groups. Potential predictive biomarkers were evaluated by analyzing serum cytokines, peripheral blood T cells and tumor infiltrating immune cells. Results: The EMT6 model showed the highest tumor response rate (54%, 6/11) of the syngeneic models: 4T1 (45%, 5/11), JC (40%, 4/10), or E0771 (23%, 3/13). Early changes in tumor size at 7 days post-PD-1 inhibitor treatment predicted the final efficacy of the PD-1 inhibitor. Peripheral blood CD8+ and CD4+ T cells with or without Ki67 expression at 7 days post-PD-1 inhibitor treatment were higher in the finally designated responder group than in the non-responder group. At the time of sacrifice, analyses of tumor infiltrating lymphocytes consistently supported these results. We also demonstrated that retro-orbital blood sampling procedures (baseline, 7 days post-treatment, time of sacrifice) were safe for serum cytokine analyses, suggesting that our preclinical platform may be used for biomarker research using serum cytokines. Conclusion: Our syngeneic mouse model of TNBC is a feasible preclinical platform to evaluate ICI efficacy combined with other drugs and predictive biomarkers in the screening process of immune-oncology drug development.
- TNBC
- triple-negative breast cancer
- HR-positive
- HER2-positive
- PD-1 inhibitor
- ICI
- immune checkpoint inhibitor
Cancer is one of the leading causes of mortality worldwide, and breast cancer is one of the most common cancers and the second leading cause of cancer-related deaths among women (1). Based on the status of molecular markers, such as estrogen and progesterone receptors, and human epidermal growth factor 2 (HER2), breast cancer is classified into three major subtypes: hormone receptor (HR) positive in ~70% of all breast cancers, HER2 positive in ~15%, and triple negative in ~15% (2). Survival of HR-positive or HER2-positive breast cancer patients has been remarkably improved due to advancements of systemic therapy, including CDK4/6 inhibitors (3) for the HR-positive subtype and various anti-HER2 therapies for the HER2-positive subtype (4). Conversely, triple-negative breast cancer (TNBC) remains the breast cancer subtype with the worst prognosis due to aggressive biology and a limited number of systemic therapies (5).
However, immune checkpoint inhibitors (ICIs) have recently started to shed light on the TNBC subtype. Pembrolizumab or atezolizumab combined with standard chemotherapy as the first-line therapy has improved the progression-free survival in metastatic TNBC in the KEYNOTE 355 (6) or IMpassion 130 trials (7). In addition, pembrolizumab combined with standard chemotherapy also enhanced the pathologic complete response and invasive disease-free survival in early TNBC in the KEYNOTE 522 trial (8). These trials confirmed the PD-L1 positivity as a predictive biomarker for ICIs. However, PDL-1 positivity alone does not sufficiently predict the efficacy of ICIs because only a small proportion of TNBC patients shows good efficacy. Consequently, researchers have attempted to enhance the ICI efficacy in patients with TNBC and develop better predictive biomarkers.
A preclinical model to evaluate ICIs in TNBC is an urgent unmet need. Historically, syngeneic mouse models have been mainly used for immuno-oncology research based on the interaction between the murine tumor cells and a competent murine immune system (9, 10). However, syngeneic mouse models have several disadvantages such as not reflecting the genetic complexity of human tumors due to their lower mutational loads (11, 12). Another disadvantage is the rapid growth of murine tumors, which do not promote the development of the chronic inflammatory environment that is characteristic of human tumors (12, 13). Therefore, humanized mouse models, which are generated by engrafting functional human cells, tissues, or organs, have been developed to overcome the disadvantages of syngeneic mouse models (14). However, the technical complexity, the hard availability of human hematopoietic stem cells, and the high cost to establish humanized mice may limit their usage only in the late stages of drug development. Therefore, the syngeneic mouse model may be a more appropriate preclinical model to potentially screen effective immunotherapies and biomarkers at the earlier stages of drug development due to its easy technique and cost-effectiveness (15). Taken together, various TNBC syngeneic mouse models have been used to analyze the efficacy of PD-1 inhibitors and potential predictive biomarkers, with an expectation that these TNBC syngeneic mouse models could serve as a preclinical platform for screening immunotherapies along with biomarkers.
Materials and methods
Mice and cell lines. Female BALB/c and C57BL/6 mice aged between 5 and 6 weeks were purchased from Orient Bio Inc. (Seongnam, Republic of Korea). Mice were housed in a specific-pathogen-free animal facility at the CHA University (Seongnam, Republic of Korea). All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC, #170062) of CHA University and were performed as per the approved protocols. The mouse TNBC cell lines, such as 4T1, EMT6 and JC were purchased from the American Type Culture Collection (VA, USA). E0771 cells were purchased from CH3 BioSystems (Amherst, NY, USA). The cells were maintained in RPMI 1640 medium (Welgene, Daegu, Republic of Korea), supplemented with 1% penicillin/streptomycin (Welgene) and 10% fetal bovine serum (FBS; Welgene) for 4T1, EMT6 and JC cells and 15% FBS for E0771 cells. All cells were incubated at 37°C with 5% CO2.
Tumor models and treatment regimens. Tumors were xenografted by subcutaneous injection of 1×106 4T1, EMT6, and JC cells and 5×105 E0771 cells into the mammary fat pad of BALB/c and C57BL/6 mice, respectively. When tumors reached 50-100 mm3, mice were treated with either phosphate-buffered saline (PBS) or anti-PD-1 monoclonal antibody (BE0188, clone J116, BioXCell, Lebanon, NH, USA) (200 μg/mice) by an intraperitoneal (IP) injection every 3 days, up to six times. The tumor size was measured every 2 or 3 days using a caliper, and tumor volumes were calculated using the modified ellipsoid formula [1/2 × (length × width2)]. The PD-1 inhibitor treatment group was divided into responder and non-responder groups using the following equations: Responder: tumor volume in the PD-1 inhibitor treatment < mean minus standard deviation in PBS control and non-responder: tumor volume in the PD-1 inhibitor treatment ≥ mean minus standard deviation in PBS control.
Flow cytometric analysis of mouse peripheral blood mononuclear cells (PBMCs). Peripheral blood was collected by retro-orbital bleeding from the mice. Blood sample was lysed using a red blood cell (RBC) lysis buffer (eBioscience, San Diego, CA, USA) and cell pellet was collected and washed with PBS and FACS buffer. After washing cell surface markers were analyzed by staining with mCD45 (30-F11), mCD3 (145-2C11), mCD8 (53-6.7) and mCD4 (RM4-5); similarly, cells were stained for the intracellular marker Ki67 (antibody soLA15), purchased from the e-Bioscience Company. Stained cells were obtained using a CytoFLEX flow cytometer and analyzed using FlowJo v10 (FlowJo LLC, Ashland, OR, USA).
Hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC). Immune cell infiltration in the tumor was determined with IHC using 4% paraformaldehyde fixed, paraffin embedded tissues. All paraffin sections were cut at a 3 μm thickness, deparaffinized through xylene, and dehydrated with graded ethanol. For H&E staining, slides were stained with Harris hematoxylin solution and eosin Y solution. For the IHC analysis, heat-induced antigen retrieval with 0.01 M citrate buffer (pH 6.0) was used for the indicated antibodies. Endogenous peroxidase activity was blocked with 3% H2O2 in methanol, and primary incubations were performed with mouse CD8 (1:500) or CD4 (1:500) antibodies (Abcam, Cambridge, MA, USA) overnight (4°C). Subsequently, sections were incubated with secondary antibody (HRP-conjugated) for 1 h at room temperature, visualized with 3,3-diaminobenzidine tetrahydrochloride (Thermo Fisher Scientific, Waltham, MA, USA) for chromogenic development, washed, and counterstained with hematoxylin. The slides were dehydrated with graded ethanol and mounted with a Canada balsam (Junsei, Tokyo, Japan). To quantify IHC staining, positively stained cells were counted in five random ×400 microscopic fields for each tissue section. Five different sections were counted, and the average percentage with a standard deviation of positive cells per section was reported.
Multiplex cytokine assay. To analyze multiple mouse cytokines, approximately 125 μl of mouse blood was collected using the retro-orbital bleeding method and centrifuged for 10 min at 1,000 × g. Each isolated serum sample was analyzed using the (LEGENDplex Mouse Th1 panel) bead-based immunoassay kit [including interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-2, IL-6, and IL-10] (BioLegend, San Diego, CA, USA), according to the manufacturer’s instructions. Data were obtained with a CytoFLEX flow cytometer and analyzed using LEGENDplex v8.0 software (BioLegend).
Statistical analysis. Statistical analyses were performed using GraphPad Prism 5.0 software (GraphPad Software, La Jolla, CA, USA) and IBM SPSS for Windows Release 19 (SPSS Inc., Chicago, IL, USA). The student’s t-test was used to compare the tumor volume between the two groups. The analysis of variance (ANOVA) test was used to compare the immune cells, cytokines, and tumor volume among the three groups. The least significant difference (LSD) test was used for post hoc analysis. Values are represented as mean±standard deviation unless otherwise indicated. All p-values were two-tailed, and p-values of <0.05 were considered significant.
Results
Efficacy of ICI in various TNBC syngeneic mouse models. Four syngeneic mouse models were produced by xenografting each mouse TNBC cell line, such as 4T1 (n=20), E0771 (n=18), JC (n=20), and EMT6 cells (n=20) (Table I). In mice subcutaneously inoculated with indicated TNBC cells, the PD-1 inhibitor was injected six times, bleeding was performed three times, and tumors were obtained after euthanizing each mouse for profiling cytokines and immune cells as planned (Figure 1a). Differential response to the PD-1 inhibitor was observed in four syngeneic mouse models (Figure 1b). The EMT6 model showed the highest tumor response rate (54%, 6/11) of syngeneic models, followed by the 4T1 (45%, 5/11), JC (40%, 4/10), and E0771 model (23%, 3/13).
Summary of the efficacy of the PD-1 inhibitor in syngeneic mice.
Efficacy of immune checkpoint inhibitors in various triple-negative breast cancer syngeneic mouse models. (a) Scheme of the in vivo ICI efficacy test using syngeneic mice. 4T1, E0771, JC and EMT6 cells were subcutaneously xenografted into 5-6 weeks old female syngeneic mice and treated with the PD-1 inhibitor by intraperitoneal injection every 3 days, for up to six times when tumors reached 50-100 mm3 (black arrow). Peripheral blood samples were collected before injecting the PD-1 inhibitor, 7 days post PD-1 inhibitor treatment, and on the day of sacrifice (blue arrow). Tumor or organ samples were collected at the time of sacrifice (red arrow). (b) The tumor growth inhibition curves of syngeneic mice (4T1, E0771, JC and EMT6 xenograft models) are shown for each group. Tumor growth inhibition curves were drawn as responders versus non-responders. p-Values were calculated using ANOVA followed by the LSD test. Data are presented as mean±standard deviation. The black line indicates the phosphate-buffered saline (PBS) control, the red line indicates responders, and the blue line indicates non-responders. The upper graph for the average value of tumor size is based on responsiveness; the lower graph indicates tumor sizes for individual mice.
Early change of tumor size predicted final efficacy of PD-1 inhibitor treatment. To predict the PD-1 inhibitor efficacy in TNBC syngeneic mouse models, tumor sizes were compared at an early time point (7 days post PD-1 inhibitor treatment) and the final time point (at the time of sacrifice; 15 days for the 4T1, 28 days for the E0771, 29 days for the JC and 24 days for the EMT6 model).
In the control group, relative tumor growth (calculated as ‘tumor size at each time point of day 7 and final day divided by tumor size at first treatment day of each mouse’) increased more rapidly compared to the responder group as shown in Figure 2a. We also analyzed the ‘tumor growth difference between the responder and control group at 7 days post PD-1 inhibitor treatment and the final day (calculated by subtracting the average relative tumor growth in the control from the average relative tumor growth in the responder group at day 7 and the final day). In all syngeneic models, the tumor growth differences at 7 days post PD-1 inhibitor treatment were negative numbers, suggesting that tumor response already began to appear at early time point in responders, and the tumor size differences became larger at the final point (Figure 2b). Finally, to compare tumor growth differences among the 4 syngeneic models at 7 days post PD-1 inhibitor treatment and the final day, we transformed 4 separate bar graphs from each syngeneic model in Figure 2b into one curved-line graph (Figure 2c). The E0771 model showed the greatest tumor growth difference among the 4 syngeneic models at 7 days as well as the final day post PD-1 inhibitor treatment, whereas the 4T1 model demonstrated the smallest tumor growth difference among the 4 syngeneic models at 7 days as well as the final day post PD-1 inhibitor treatment. JC and EMT6 models showed moderate tumor growth differences both at 7 days post PD-1 inhibitor treatment and at the final day. Accordingly, tumor growth difference at the early point (7 days post PD-1 inhibitor injection) predicted the final difference of tumor growth before sacrifice, which suggests that the change of tumor size at 7 days post PD-1 inhibitor treatment can be an early biomarker of final efficacy of PD-1 inhibitor in our preclinical model.
Early changes of tumor size predicted the final efficacy of PD1 inhibitor treatment. (a) ’Relative tumor growth’ (tumor size at each time point of day 7 and final day divided by the tumor size at first treatment day of each mouse) was calculated in the control and responder groups at 7 days post-PD-1 inhibitor treatment and the final day just before sacrifice. Data are presented as mean±standard deviation. (b) ‘Tumor growth difference between responder and control groups’ at 7 days post-PD-1 inhibitor treatment and the final day, was calculated by subtracting the average relative tumor growth in the control from the average relative tumor growth in the responder group at each time point of day 7 and final day. Data are presented as mean±standard deviation. (c) To compare tumor growth differences among the 4 syngeneic models at 7 days post PD-1 inhibitor treatment and the final day, 4 separate bar graphs from each syngeneic model from Figure 2b were transformed into one curved-line graph. The p-Values were calculated using one-way ANOVA and data are presented as mean±standard deviation.
Early biomarkers using peripheral blood T cells for the PD-1 inhibitor efficacy. It may be very advantageous to identify predictive biomarkers for ICI efficacy at an earlier time. Therefore, we further investigated peripheral blood mononuclear cell markers at 7 days post-PD-1 inhibitor treatment to correlate the final efficacies of the PD-1 inhibitor in each syngeneic mouse model. Collected blood samples were stained with T cell markers, such as CD8+ and CD4+ T cells and the proliferation marker Ki67 and analyzed by flow cytometry. Figure 3a shows the gating strategy for flow cytometry analysis.
Early biomarkers using peripheral blood T cells for the PD-1 inhibitor efficacy. (a) The gating strategy for flow cytometry analysis. The collected blood samples were stained with T cell markers, such as CD8+ and CD4+ and proliferation marker Ki67 and analyzed by flow cytometry. The p-Values were calculated using ANOVA followed by the LSD test. Data are presented as mean±standard deviation. (b) Representative flow cytometry plots of peripheral blood CD8+ and CD4+ T cells at 7 days post-PD-1 inhibitor treatment. Responders showed higher CD8+Ki67+ and CD4+Ki67+ cells compared to control or non-responders in the E0771 model. p-Values were calculated using ANOVA followed by the LSD test. Data are presented as mean±standard deviation. (c) Percentage changes of CD8+ and CD4+ T cells and Ki67+ T cells of PD-1 inhibitor-treated mouse blood based on responsiveness from the pre-PD-1 inhibitor treatment to 7 days post-treatment. The average T cell-positive cells were then represented in each group. The p-Values were calculated using the paired samples t-test and ANOVA. Data are presented as mean±standard deviation.
At 7 days post-PD-1 inhibitor treatment, earlier changes in the proportion of CD4+ and CD8+ T cells in the peripheral blood are shown in Figure 3b. In the E0771 model, CD8+ or CD4+ T cells were not different among the groups of control, responders, and non-responders at 7 days post-PD-1 inhibitor treatment. As a proliferation marker, Ki67 was incorporated in the analysis; CD8+Ki67+ T cells significantly increased more in responders than in non-responders (11% vs. 6.2% in responders and non-responders, respectively; ANOVA p=0.002 among the three groups; LSD, p=0.034 between responder and non-responder groups; Figure 3b). Numerically the average number of CD4+Ki67+ T cells was higher in responder compared to the non-responder group but without statistical significance (Figure 3b, lower panel), further validation being necessary.
Furthermore, we analyzed the early dynamic changes of CD8+, CD4+, and Ki67+ T cells from the pre-PD-1 inhibitor treatment to 7 days post-treatment based on responsiveness. In the responder group, CD8+Ki67+T cells increased by 8.9-fold after PD-1 inhibitor treatment and CD8+Ki67+T cells were 1.5-fold higher in the responder than the non-responder group (p=0.034) at 7 days post PD-1 inhibitor treatment. Similarly, CD4+Ki67+ T cells also increased by 5.1-fold after PD-1 inhibitor treatment in the responder group and CD4+Ki67+ T cells were also numerically higher in responders than the non-responder group without statistical significance (Figure 3c).
Serum cytokine analysis as a preclinical biomarker platform. After confirming that peripheral blood CD8+, CD4+, and proliferative T cells started to increase early after a PD-1 inhibitor treatment in the responder group, we subsequently analyzed mouse serum cytokine concentrations to determine early immunological changes that could predict the efficacy of the PD-1 inhibitor treatment.
Figure 4a shows the overview of how we analyzed serum cytokine profiling in our mouse model. In order to use blood for both cytokine profiling and PBMC analysis, we collected approximately 125-150 μl of blood each time, up to 3 times with the retro-orbital bleeding technique. In total, from 78 mice, we drew blood 121 times. During and 48 hours after blood collection, we monitored adverse events, such as weight loss, squinting of eyes, hunched posture etc. We observed death in 1 mouse, weight loss (≥5%) in 1 mouse and squinting of eyes in 2 mice as shown in Table II. However, the mortality was related with the large tumor burden, not the bleeding technique. These results suggest that the bleeding procedure is a safe and reliable technique. Then, serum cytokine was analyzed by using a bead-based assay, LEGENDplex, which relies upon a sandwich immunoassay as shown in Figure 4a. A small amount of sample (75 μl) was sufficient, due to the detection method of cytokines using flow cytometry.
Serum cytokine analysis as a preclinical biomarker platform. Schematic diagram of how to do serum cytokine profiling in our mouse model, including time points of bleeding, retro-orbital blood collection technique application and serum cytokine analysis technique using the bead-based assay, LEGENDplex.
Retro-orbital blood sampling details, clinical signs, and behavioral analysis.
We analyzed the early (at 7 days post-PD-1 inhibitor treatment) and late (at the time of mice sacrifice) serum cytokine changes. Although we could not find any significant changes of serum cytokines (data not shown). In this study, we showed that our preclinical platform was feasible for serum cytokine profiling. Therefore, our preclinical platform may be used for biomarker research using serum cytokines. T cell infiltration in the tumor and proliferation in the spleen based on responsiveness after PD-1 inhibitor treatment. To investigate the T lymphocyte infiltration into the tumor tissue and T cell proliferation in the spleen based on PD-1 inhibitor responsiveness, the tumor tissue of E0771 and 4T1 and the spleen of E0771 syngeneic mice were analyzed by IHC. IHC analysis of infiltrated CD8+ or CD4+ T cells in tumor tissues at the time of sacrifice showed a significantly higher infiltration of CD8+ T cells (ANOVA. p=0.015 among the three groups; LSD, p<0.010 between responder and non-responder groups) and CD4+ T cells (ANOVA, p<0.001 among the three groups; LSD p<0.001 between responder and non-responder groups) in responders compared to the non-responder group (Figure 5a). Likewise, in the 4T1 syngeneic mouse model, we found a consistent pattern of higher tumor infiltration of CD8+ T cells (ANOVA, p=0.001 among the three groups; LSD, p<0.001 between responder and non-responder groups) and CD4+ T cells (ANOVA, p<0.016 among the three groups; LSD p<0.02 between responder and non-responder groups) in responders compared to non-responders (Figure 5b). Similarly, E0771 spleen IHC analysis showed that both CD8+ and CD4+ T cells were significantly higher in responders compared to non-responders (ANOVA, p<0.001 among the three groups; LSD p<0.001 between responder and non-responder groups) for CD8+ T cells and (ANOVA, p=0.009 among the three groups; LSD p=0.007 between responder and non-responder groups) for CD4+ T cells between responders and non-responders (Figure 5c).
T cell infiltration in the tumor and proliferation in the spleen based on responsiveness after a PD-1 inhibitor treatment. (a and b) Tumor infiltrating T lymphocytes in the E0771 and 4T1 tumor tissue were analyzed by immunohistochemistry. (c) T lymphocytes in spleen tissue analyzed by IHC. Representative IHC images in each group are shown, and bar graphs represent the average numbers of CD8+ and CD4+ T cells in each group in five random, non-overlapping fields at ×400 magnification. The p-Values were calculated using ANOVA followed by the LSD test. Data are presented as mean±standard deviation.
Discussion
In this study, we established a protocol to evaluate the treatment effects together with an immune profile of anti-PD-1 inhibitors in syngeneic TNBC mouse models. Overall, our systematic analysis may provide valuable information for the use of syngeneic TNBC mouse models as a preclinical in vivo platform to evaluate the efficacy of various anticancer immunotherapies including anti-PD-1/PDL-1 inhibitors with an invasive approach for biomarker analysis.
The definition of tumor response in this preclinical study, that is, the tumor volume in the PD-1 inhibitor treatment group being smaller than the mean minus standard deviation in the PBS control, is different from the tumor response in human studies based on the Response Evaluation Criteria in Solid Tumors 1.1 (16); the latter is defined as a “minimum 30% decrease in the sum of the longest diameter of target lesions”. In mouse studies, the definition of tumor response is not yet established. In mouse studies, and particularly syngeneic mouse studies, the tumor growth rate is faster than in human studies due to the biological nature of murine tumors (12). Therefore, we adopted a modified definition of the tumor response in this preclinical study using a syngeneic mouse model. Previous preclinical studies for ICIs have demonstrated that ICIs have different tumor response rates according to different syngeneic TNBC models (17, 18). In our preclinical study, we observed various tumor response rates, ranging from the highest (54%) in the EMT6 model to the lowest (23%) in the E0771 model. In patients, ICI efficacies also vary based on the types of cancers or various predictive biomarkers including PD-L1, microsatellite instability (MSI)/mismatch repair deficiency (MMRd), tumor mutation burden (TMB), tumor infiltrating lymphocytes, and cytokines (19-22). In our preclinical study, these different tumor response rates, based on the syngeneic TNBC mice models, may be due to different biological features in several aspects, such as tumor infiltrating lymphocytes, T cell proliferation, and cytokines just like in patients with cancer (23). Other potential predictive biomarkers, such as PD-L1, MSI/MMRd, or TMB may be tested using our preclinical model, if necessary. Researchers may choose an appropriate syngeneic TBNC model with a relatively lower response rate, to better prove the efficacy of combination immunotherapy, based on our study.
It may be very advantageous to be able to identify the benefits of ICIs at an early time. In our study, early tumor response measured at 7 days post-treatment was a good predictor of the final ICI efficacy. To the best of our knowledge, this study is the first to demonstrate this simple concept. This concept of early tumor response by only measuring the physical tumor size could be better enforced with molecular imaging or multimodal imaging techniques, as other researchers have published (24, 25). Furthermore, high CD8+, CD4+, or Ki67+ T cell levels in the peripheral blood or higher serum IFN-γ concentrations are well-known predictive biomarkers for ICI efficacy (26-28). It remains unclear whether early dynamic changes of these markers could serve as predictive biomarkers for ICI efficacy. On-treatment biomarkers during the early treatment period may become better predictive biomarkers if they are validated in clinical trials. Our preclinical model could be used to develop various on-treatment biomarkers to predict efficacy.
Furthermore, the feasibility of our preclinical model to study the immune-oncology drugs should be addressed. In our study, approximately 100-150 μl of blood was collected retro-orbitally on days 0, 7, and 27. A 125 μl volume was sufficient to perform FACS for peripheral blood immune cells or measure serum cytokines. Even better, there were no fatal complications during and after the procedure, and our retro-orbital bleeding technique was considered to be safe. Moreover, at the end of the experiments, fresh tumor tissues were collected, which was sufficient for further experiments.
The main limitation of the syngeneic mouse model is that translation of discoveries from mouse models to clinical trials has been hindered by various genetic and biological differences between humans and mice (29, 30). To overcome this, the necessity of humanized mouse models was suggested (14). However, we know that humanized mouse models are not appropriate in the early screening period of drug development due to their low accessibility. Therefore, syngeneic mouse models, such as our TNBC model in this study is a more suitable bridging preclinical model to determine the efficacy of immunotherapies and biomarker analysis at the early stages of drug development.
Conclusion
In conclusion, we acknowledge that the syngeneic mouse model may have several limitations as mentioned above. The syngeneic mouse model has the advantage of permitting more detailed and invasive biomarker experiments. Our syngeneic mouse protocol can be used to evaluate ICI efficacies in combination with other drugs and biomarkers of immunotherapies in screening for immuno-oncology drug development.
Acknowledgements
This research was supported by two grants; one is the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C1559). The other one is the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2021R1C1C1006882).
Footnotes
Authors’ Contributions
Conceptualization: Nahee Park, Kamal Pandey, Nar Bahadur Katuwal, Seung Ki Kim, Seul-Gi Kim, Yong Wha Moon; Data curation: Nar Bahadur Katuwal, Nahee Park, Kamal Pandey; Formal analysis: Nahee Park, Nar Bahadur Katuwal; Funding acquisition: Yong Wha Moon; Methodology: Nahee Park, Kamal Pandey, Nar Bahadur Katuwal; Project administration: Yong Wha Moon; Supervision: Seung Ki Kim, Yong Wha Moon; Validation: Yong Wha Moon; Writing original draft: Nahee Park, Nar Bahadur Katuwal; Writing review & editing: Nar Bahadur Katuwal, Yong Wha Moon. All Authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
The corresponding Author received research funds from Celltrion, Boryung, Onconic Therapeutics, ImmunoMet Therapeutics, HK inno.N, and Medytox. The other Authors declare no conflicts of interest.
- Received October 25, 2022.
- Revision received November 30, 2022.
- Accepted December 1, 2022.
- Copyright © 2023 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.
