Computationally dissecting autologous and therapy-induced immune responses at tissue and single cell level

The immune system plays a pivotal role in maintaining the organism’s health, acting as a crucial defense mechanism against diseases. Therefore, an imbalance in the immune system can lead to severe health complications. Underactivity of the immune system can result in immunodeficiencies and cancer, while an overactive or dysregulated immune response can lead to autoimmune diseases or excessive cytokine release, resulting in local or systemic damage.
Cancer develops when tumor cells evade immune surveillance and start to proliferate uncontrollably. Traditional treatments, like chemotherapy, while targeting fast-growing cells, often bring significant side effects due to their lack of specificity. In contrast, immunotherapies leverage the host’s immune system to eliminate tumor cells, increasing specificity and reducing toxicity. Despite its promise, immunotherapy’s effectiveness is limited to a small subset of patients, highlighting the need for a deeper understanding of cancer immunopathology and the factors contributing to variability in treatment response.
Bioinformatics is crucial in this context, offering key tools and methods for processing and interpreting complex data from high-throughput and multi-omics technologies. Single-cell technologies further enhance this by providing detailed insights into tumor heterogeneity and the interplay between cancer and the immune system, as well as the impact of immunotherapy on these dynamics.
This thesis, spanning four chapters, explores the complexities of immune profiling and immune monitoring in disease contexts and immunotherapy applications. The first chapter presents a single-cell transcriptomic analysis aimed at understanding the effects of personalized cancer vaccines on the immune systems of patients with clear cell renal cell carcinoma. In our study, we show that a personalized cancer vaccine elicits a durable and effective antitumor immune response, with single-cell analyses offering deeper insights into the immune response triggered by the vaccination.
The second study entails a multi-omics analysis of follicular thyroid cancer, focusing on identifying patient-specific molecular disruptions that contribute to disease development. Here, we show that follicular thyroid cancer is defined by a downregulation of antigen processing and presentation, linked to a disruption of HLA II and proteasome protein modules.
In the third chapter, the potential of CAR-T cell therapies in treating solid tumors is explored, along with the challenges and side effects associated with selecting inappropriate CAR-T targets. Moreover, the chapter highlights the role of bioinformatics as an instrumental resource in target selection.
Finally, the last chapter addresses the detrimental effects of excessive immune responses in severe COVID-19 cases. It entails a single-cell analysis of the immune responses in these patients, investigating the impact of immunomodulatory treatments. Our findings reveal a decrease in proinflammatory cytokines within specific cell types following immunomodulatory treatment.