5 questions for
Lucie Loyal recieves funding from the Else Kröner-Fresenius Foundation
In the treatment of tumors such as malignant melanoma (black skin cancer), a therapy can be used in which the patients’ own immune cells are modified in the laboratory and then returned to them to fight the disease. However, selecting the immune cells suitable for this purpose has so far been a lengthy process. A team led by Dr. Lucie Loyal developed the CATCH assay, which makes this quicker, more systematic and possible on a larger scale. The researcher has now received the First and Second Application Funding from the Else Kröner-Fresenius Foundation to conduct a large-scale study to characterize tumor antigens. Based on this she will create a machine learning model that can predict therapy success. We spoke with Dr. Loyal about her project.
Which problem are you trying to solve with your research?
We want to address two central challenges in tumor therapy. Adoptive T-cell transfer therapy, in which tumors are fought by modifying the body’s own T cells in the laboratory, holds great potential. However, treatment is currently only possible to a limited extent, as identifying suitable tumor-specific T-cell receptors (TCR) is technically demanding and only feasible on a limited scale.
At the same time, we want to understand whether melanoma patients who are likely to benefit from so-called immune checkpoint inhibitors (ICI) can be identified before the start of treatment. In this treatment form, ICIs block receptors on the T cell that prevent an effective immune response against the tumor. Through this blockade, the T cell can then react to the tumor again.
What advantages does the CATCH assay have compared to previous methods?
With the CATCH assay that we developed, we can for the first time quickly and precisely determine the avidity of T cell receptors against defined antigens. Avidity is the overall binding strength of the TCR to its respective antigen presented on so-called MHC receptors, which can be, for example, a tumor-specific protein fragment. By integrating this assay, we aim to significantly accelerate the identification of suitable T cell receptors and thus contribute in the long term to building a TCR library. From it, clinicians should then be able to select an optimal therapeutic receptor – based on the individual MHC profile of the respective patients.
Additionally, we want to find out whether the CATCH assay is suitable as a biomarker to identify patients who would benefit from therapy with immune checkpoint inhibitors, either alone or in combination with blood parameters. The goal here is to enable a more precise and individualized therapy decision.
For which diseases can the procedure be used?
T cell receptor avidity for the antigen-MHC complex determines the quality of the T cell response and is thus relevant in all situations where T cells play a role. This includes infections, but also cancers and autoimmune diseases.
We have already demonstrated its performance, for example, in several large COVID-19 studies. Here, the CATCH assay helped us understand that older people increasingly lose certain cross-reactive clones from their repertoire, i.e., T cells that react not only to SARS-CoV-2 but also to endemic coronaviruses. While SARS-CoV-2 vaccinations cannot directly recover this loss, they do cause other clones to proliferate and fill the gaps.
We now want to use the assay in oncology for the first time. Together with the Skin Tumor Center Charité (HTCC) led by Prof. Dr. Thomas Eigentler, we have started a cooperation co-financed by the nTTP-GCT: The goal is to determine T cell receptor avidity before and during therapy in patients with melanoma who receive treatment with immune checkpoint inhibitors.
What are you using the funding from the Else Kröner-Fresenius Foundation for?
The funding now enables us to analyze patients on a larger scale and to characterize T cells with different avidities against tumor antigens in detail. This includes autoantigens, i.e., body's own proteins that are overproduced by the tumor, as well as neoantigens that arise from acquired mutations in somatic cells and are highly individual. Through an optimized pipeline for identifying tumor-specific T cell receptors, we aim to achieve rapid provision of individual TCRs for treatment and – as mentioned – to build an "off-the-shelf" library of tumor antigen-specific T cell receptors in parallel. Such libraries can ideally bridge the individual identification of patient-specific TCRs and enable rapid treatment.
In addition, we want to use the obtained data to construct a machine learning model that will enable the prediction of the success of immune checkpoint inhibitor treatment.
How do patients benefit from this research?
On the one hand, the identification of tumor-specific TCRs is currently too time-consuming and limited to benefit a broad cohort of patients. On the other hand, in the case of ICI treatment, we face a situation where not only about 50% of patients benefit from the therapy, but many also have to accept significant side effects that impair their quality of life. Biomarkers can help make therapy decisions more precise, safer, and more individualized for the benefit of the patient.
