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Updated: Dec 25, 2025
Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors
Published on: December 16, 2022
Characterization of human cancer xenografts in humanized mice
Jonathan Rios-Doria1, Christina Stevens2, Christopher Maddage2
1Preclinical Pharmacology, Incyte Research Institute, Wilmington, Delaware, USA jdoria@incyte.com.
Background:
Preclinical evaluation of drugs targeting the human immune system has posed challenges for oncology researchers. Since the commercial introduction of humanized mice, antitumor efficacy and pharmacodynamic studies can now be performed with human cancer cells within mice bearing components of a human immune system. However, development and characterization of these models is necessary to understand which model may be best suited for different agents.
Methods:
We characterized A375, A549, Caki-1, H1299, H1975, HCC827, HCT116, KU-19-19, MDA-MB-231, and RKO human cancer cell xenografts in CD34+ humanized non-obese diabetic-scid gamma mice for tumor growth rate, immune cell profiling, programmed death ligand 1 (PD-L1) expression and response to anti-PD-L1 therapy. Immune cell profiling was performed using flow cytometry and immunohistochemistry. Antitumor response of humanized xenograft models to PD-L1 therapy was performed using atezolizumab.
Results:
We found that CD4+ and CD8+ T-cell composition in both the spleen and tumor varied among models, with A375, Caki-1, MDA-MB-231, and HCC827 containing higher intratumoral frequencies of CD4+ and CD8+ T cells of CD45+ cells compared with other models. We demonstrate that levels of immune cell infiltrate within each model are strongly influenced by the tumor and not the stem cell donor. Many of the tumor models showed an abundance of myeloid cells, B cells and dendritic cells. RKO and MDA-MB-231 tumors contained the highest expression of PD-L1+ tumor cells. The antitumor response of the models to atezolizumab was positively associated with the level of CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs).
Conclusions:
These data demonstrate that there are tumor-intrinsic factors that influence the immune cell repertoire within tumors and spleen, and that TIL frequencies are a key factor in determining response to anti-PD-L1 in tumor xenografts in humanized mice. These data may also aid in the selection of tumor models to test antitumor activity of novel immuno-oncology or tumor-directed agents.
Insights
Humanized mice models enable preclinical cancer drug testing. Tumor type influences immune cell infiltration and response to immunotherapy, guiding the selection of optimal models for novel immuno-oncology agents.
Area of Science:
- Oncology
- Immunology
- Preclinical Research
Background:
- Preclinical evaluation of immunomodulatory drugs in oncology is challenging.
- Humanized mice models allow for studies using human cancer cells and immune systems.
- Characterization of these models is crucial for selecting appropriate agents.
Purpose of the Study:
- To characterize human cancer cell xenografts in humanized mice.
- To assess immune cell infiltration, PD-L1 expression, and response to anti-PD-L1 therapy.
- To identify factors influencing model suitability for immuno-oncology drug development.
Main Methods:
- Xenografts of 10 human cancer cell lines were established in CD34+ humanized NSG mice.
- Immune cell profiling via flow cytometry and immunohistochemistry.
- Tumor growth, PD-L1 expression, and response to atezolizumab (anti-PD-L1) were evaluated.
Main Results:
- Tumor-specific factors, not stem cell donor, dictate immune cell composition (CD4+, CD8+ T cells, myeloid cells, B cells, dendritic cells).
- A375, Caki-1, MDA-MB-231, and HCC827 models showed higher intratumoral T cell frequencies.
- RKO and MDA-MB-231 tumors had highest PD-L1 expression; atezolizumab response correlated with CD4+/CD8+ TILs.
Conclusions:
- Tumor-intrinsic factors shape the immune microenvironment in humanized mouse models.
- Tumor-infiltrating lymphocyte (TIL) frequency is a key predictor of anti-PD-L1 response.
- These findings aid in selecting appropriate models for testing novel immuno-oncology agents.
