Immunotherapy has greatly improved the treatment of cancer in the last decade, with novel therapeutic products achieving groundbreaking results in both hematological and solid tumors. However, despite the promising advances, there are still some hurdles to overcome. One of them is patient stratification to tailor therapeutic interventions. Especially regarding personalized therapies, as they tend to work effectively only in specific subsets of patients. Unfortunately, the underlying processes as to why this happens are still mainly unknown.
At the University of Antwerp, we are focusing on "cracking the code" for personalized cancer immunotherapies using immunoinformatic tools. One example of personalized immunotherapy is dendritic cell (DC) vaccination. By administering DCs loaded with tumor antigens, the immune system of the patient is stimulated to initiate a tumor-specific immune response. However, some patients show successful adaptive immune responses leading to an increased overall survival, while others do not. Thus, we aim to identify key differences in response among patients by comparing the T cell repertoire between responding and non-responding patients, as T cells are the main population targeted by DCs.
Another type of cancer immunotherapy centers on administering tumor antigen-specific T cells, instead of activating T cells trough DC vaccination. Tumor-specific T cells can be obtained by the modification of patient-derived T cells with tumor-specific T cell receptors (TCRs). This strategy is called TCR-T cell therapy and relies on the prior identification of T cells with tumor killing capacities. We are currently working on how to prioritize T cells for TCR-T therapy. Here, we start from blood samples of cancer patients who responded positively to a tumor antigen-loaded DC vaccine. Samples both prior to and post vaccination are used to identify those T cells that have actively expanded in response to DC therapy. Both epitope specificity of the T cells and T cell functionality are studied using combined single-cell RNA and TCR sequencing. Thus, we aim to identify TCRs from expanded T cells with an active tumor-killing profile that can be selected as potential leads for TCR-engineering of T cells and be further validated for the development of TCR-T cell therapies. In summary, I will explain the current limitations of cancer immunotherapies and our strategies to overcome these limitations.
