Immunotherapy: The Need for Immunocompetent Models

An improved understanding of cancer pathogenesis has given rise to new treatment options, including cancer immunotherapy. Traditional cancer treatments, such as chemotherapy and radiotherapy, are effective, but are associated with severe side effects and treatment resistance.

Immunotherapy focuses on the ability of an individual patient’s immune system to recognize and eliminate cancer cells. Latest studies have shown that combination therapies, involving immunotherapy, appear to have enhanced effects when compared to the use of one therapy alone. This also has the potential to reduce treatment resistance. Combining traditional treatments with immunotherapeutic agents can increase a patient’s comfort while also increasing the treatment efficacy of administered active compounds.

There is a great unmet need for improved preclinical models, with functional immunity, to drive forward promising immunotherapy research in oncology and to enable the successful transition of immunotherapeutics from the laboratory to the clinic. The benefits these models can provide could drive the development of combination therapy, improving patient comfort and saving lives.

Syngeneic tumor models

Immunotherapy has shown impressive results in clinical trials producing unprecedented long lasting anticancer effects. Combination therapies consisting of more than one antibody in the same course of treatment or of antibodies together with standard of care agents are currently being evaluated. However, research efforts to answer important biological questions before moving new compounds or therapeutic strategies to the clinic are being limited by a distinct lack of experimental immunotherapy models which feature a functioning immune system. These provide an invaluable tool to assess the efficacy of any anticancer agent and design the correct treatment strategy.

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Syngeneic mouse models are tumor bearing mice created by grafting tumor cells into recipient mice with the same genetic backgroud. These models offer a series of undeniable advantages. They provide an effective approach for studying how cancer therapies perform in the presence of a functional immune system, are relatively inexpensive, and – importantly – reproducible. Moreover as syngeneic tumor models have long been used in cancer research, there is a strong baseline of drug response data and they come in a wide variety of tumor types. The models are also readily available so studies are easily conducted with statistically meaningful numbers of mice per group.

Antibodies and anti-cancer agents

A recent study explored the anti-tumor effect of immunotherapy agents currently being tested in clinical trials, including anti-PD1, anti-PD-L1 and anti-CTLA-4 antibodies, on a panel of syngeneic models to provide valuable information in selecting the appropriate models and doses for combination therapy. Programmed Cell Death 1 (PD-1), Programmed Death-Ligand 1 (PD-L1) and Cytotoxic T-Lymphocyte-Associated Protein 4 (CTLA-4) are all proteins shown to down regulate the response by the immune system. Research has suggested that up-regulation of PD-L1 by the tumor may allow them to evade the host immune system. Antibodies have been used as inhibitors to prevent this natural suppression, showing great potential for cancer treatment. With our growing understanding of the immune system and of drugs’ mechanism of action, it has become clear that many of the anticancer agents, including the most advanced targeted agents, can synergize with immunotherapy by damaging the integrity of tumor cell and rendering them more sensitive to the immune system attack. Researchers are now exploring the feasibility of combining traditional treatment with immunotherapy. To perform these studies, suitable models in which to evaluate the efficacy of combination therapies and design new therapeutic strategies are in great demand. Importantly, in the clinic it is still unknown why some patients respond to immunotherapies while others do not. Syngeneic models can be used to answer these questions.

Methods

The study extensively profiled syngeneic models in vivo using antibodies and inhibitors against immune suppressing proteins such as PD1, PD-L1, and CTLA-4. This has provided necessary information for selecting the appropriate models to conduct preclinical studies and importantly to establish the appropriate dosing regimen while developing new combination therapies. Moreover syngeneic models were genomically profiled in details to identify genetic events such as mutations, alternative gene splicing, and gene fusion in search for new targets for combination studies and immunotherapy.

Results

In some cancer models, such as colon, liver and pancreatic, the immunotherapy showed high levels of tumor growth inhibition (TGI) and increased numbers of cancer fighting immune cells. However, other types of tumor didn’t respond as well. The results suggested that the effectiveness of immunotherapy agents is highly dependent on the tumor cell type. This discrepancy of responses demonstrates the need for relevant immunocompetent pre-clinical models, to test new agents and dosing regimens before moving to more sophisticated and clinically relevant models. The mutational analysis performed on syngeneic models indicated that a number of them harbor mutations that may be used as targets for new therapeutic approaches.

Conclusion

Immunotherapy has shown promising anticancer results in the clinic. However, not all tumor types or all patients respond. In order to successfully identify the responder population and design the best clinical strategy for immunotherapy agents, appropriate preclinical models are needed with a functional immune system. The main advantage of syngeneic models is indeed represented by their fully efficient immune system, one of the tumor microenvironment components, which, similarly to what happens in patients, influences tumor responses to therapy.

However it should be kept in mind that in syngenic models the tumor cells are rodent, and therefore express the mouse/rat homologues of the desired human targets; moreover tumors tend to grow faster than in other systems. However, there is little doubt that the scientific information obtained by using these models is essential to evaluate which compond or combination strategy are worth moving forward to more complex studies and are the most likely to produce the best response.

In addition, predictive biomarkers obtained from the mutational analysis may be useful tools in understanding patient response in the clinic.