Immuno-oncology has transformed cancer therapy with the utilization of the immune system of the body to combat cancer. Accurate preclinical models that are capable of forecasting human cancer biology are among the most significant driving forces in immunotherapy development and assessment. Cell line-derived xenografts (CDX) have served as a classic preclinical model for cancer drug discovery for many decades. CDX models are subcutaneously implanted human cancer cell lines in immunocompromised mice to explore tumor development, drug sensitivity, and potential therapy.
Although CDX models have proved to be an important tool in the study of oncology, their use in immuno-oncology is complex and potential. The use of CDX models in the study of immunotherapy, the drawbacks, and the possible ways of unlocking their potential in immuno-oncology are detailed below.
Understanding Cell Line-Derived Xenografts (CDX)
CDX models are built upon grafting immunocompromised mice, which are nude, SCID (severe combined immunodeficiency) mice, or NSG (NOD SCID gamma) mice with well-established human cancer cell lines. The immunocompromised animals have a primed immune system where tumors growing from the grafted cancer cell lines will have no immunity rejection. Such tumor-grown-in mice can reproduce all features of human cancers in all those areas where CDX models would be important for exploring chemotherapies, targeted treatments, and radiations.
Nonetheless, immuno-oncology explains the cancer cells’ and immune systems’ interaction more appropriately, and CDX traditional models are unable to do so in the same manner because they lack human functional immune components. Irrespective of these drawbacks’ shortcomings, CDX models also serve well in immuno-oncology research, especially combined with very highly developed types which are appropriate to use in analyzing immune responses.
Advantages of CDX Models in Immuno-Oncology
1. Highest Reproducibility and Accessibility
CDX models are built with characterizable, commercially obtained cancer cell lines that are maximally reproducible for experimentation. CDX tumors, in contrast to patient-derived xenografts (PDX) built with direct patient tumor and increased heterogeneity, form in a normal and reproducible fashion. Predictability is optimally applied to the testing of immunotherapy mechanisms and drug performance in highly controlled environments.
2. Convenient Tumor Development for Drug Screening
CDX tumors are more aggressive than PDX models and are thus an ideal system to test immunotherapy with immune checkpoint inhibitors, monoclonal antibodies, and CAR-T therapy against other therapy. Tumor growth permits high-throughput drug screening in high speed, which speeds up the identification of novel immunotherapies.
3. Genetic Manipulation for Immune Studies
One of the methods researchers seek to improve CDX models in immuno-oncology is that they genetically engineer cancer cell lines to express particular immune-associated proteins or markers. In this way, researchers can model immune evasion mechanisms as well as search for targets against immunotherapy. For example, they employ PD-L1-transduced CDX models that are utilized to screen PD-1/PD-L1 inhibitors such as pembrolizumab and nivolumab.
4. Humanized Mouse Models for Immune Studies
The most promising application of CDX models to immuno-oncology is in humanized mouse models. Mice are genetically modified to acquire a fully functional human immune system by:
- Human hematopoietic stem cells (HSCs) being transplanted
- Human peripheral blood mononuclear cells (PBMCs) being implanted
- Genetically modified mice receiving human immune components transferred
If combined with CDX tumors, these models enable researchers to investigate tumor-immune interaction and evaluate immune-based therapies in a human-relevant setting.
Limitations of CDX Models in Immuno-Oncology
1. Absence of a Complete Immune System
The largest drawback of conventional CDX models in immuno-oncology is that they lack a human immune system. Since immuno-oncology drug development is based on immune activation assumption, the utilization of immunodeficient mice for research can be deceptive in the event of patient response. The drawback has prompted researchers to move towards patient-derived models or humanized mouse models to research immunotherapy.
2. Limited Tumor Heterogeneity
CDX models are lineage-derived from common cancer cell lines, which have been grown in laboratory conditions over the course of decades. Cell lines become more epigenetically and genotypically homogenous to patient tumors and become homogenous. This homogeneity obtained by these characteristics might make CDX models dubious when they predict the responses of various immune cells to more homogenous groups of tumor cells in actual patients.
3. Lack of Tumor Microenvironment (TME) Components
Tumor microenvironment (TME) is also critical to the success of immunotherapy. TME comprises heterogeneous non-tumor cells including:
- T cells, B cells, and macrophages (modulate immune activity)
- Defining fibroblasts and endothelial cells (modulate tumor growth)
- Cytokines and signal molecules (modulate immune cell infiltration)
All these essential elements of TME are absent in retrovintage CDX models, hence not being able to enable an analysis of tumor-immune dynamics and immune checkpoint behaviors.
4. Limited Translation to Clinical Outcomes
Though valuable, CDX models are poor predictors of the clinical response to immunotherapy. Most preclinically active candidate drugs never work in human clinical trials, and the rate of failure is equally high. The reason primarily being that mouse xenograft tumors and actual human cancers in patients are different.
Future Directions and Improvements
1. Better Humanized CDX Models
As a means of overcoming the shortcomings of the immune system, researchers increasingly employ humanized CDX models, in which human immune cells are combined with CDX tumors. These models provide a more physiologically relevant milieu through which to assess immune checkpoint inhibitors, cancer vaccines, and adoptive T-cell therapies.
2. Co-Culture Systems for Immune Interactions
The second mechanism through which the tumors develop is within the co-culture systems where the CDX tumors are cultivated in three-dimensional (3D) cultures with immune cells. From the in vitro system, there is the immune cell invasion, cytokine induction, and evasion of the immune system that can be studied prior to using the in vivo models.
3. CDX and Organoids and PDX collaboration
Others suggest hybrid models where CDX tumors are blended with patient-derived organoids or PDX models to replicate tumor heterogeneity and host-immune interaction. Hybrid systems can enhance predictive validity of preclinical models of immuno-oncology drugs.
4. CRISPR-Based Genetic Manipulation of CDX Models
CRISPR gene editing is utilized in CDX tumor cell editing to design specific immune-associated genes to enable mechanisms of immune evasion and novel drug targets to be explored. CRISPR can be optimized through CDX utilization for immunotherapy construction.
Conclusion
Cell line-derived xenografts (CDX) have been an old standard for preclinical cancer drug discovery over the past several decades with fast-growing and reproducible tumors suitable for screening. Their use in immuno-oncology has been challenged by the absence of intact immune system and tumor heterogeneity.
Despite these limitations, advances in humanized CDX models, co-culture models, and gene editing are enabling the application of these models to immunotherapy research. Through the integration of CDX models with humanized immune systems, PDX models, and more recent in vitro models, scientists can potentially enhance their capacity to develop and validate successful cancer immunotherapies.
With the progress of immuno-oncology, CDX models will continue to be an important asset if supplemented by novel approaches to counter their limitations. The future of preclinical identification of cancer immunotherapies is in the optimization and improvement of existing preclinical models so the next generation of cancer drugs can be effective and translatable to the clinic.