Advancing precision medicine in cancer and COVID-19 with bioinformatics: a multifaceted affair

Abstract

[eng] Advancing precision medicine requires integrating bioinformatics to unravel complex biological data and translate these insights into clinical applications. This thesis explores the role of bioinformatics in enhancing our understanding of cancer and infectious diseases through studies focused on cancer genomics, immunotherapy, and COVID-19. In cancer genomics, a comparative analysis of variant calling tools revealed significant variability in their ability to identify cancer driver genes and clinically actionable variants, underscoring the need for tailored strategies across different cancer types. Combining mutations from multiple callers proved more effective in cancer driver gene detection, while MuTect2 identified more subclonal and actionable mutations linked to therapeutic outcomes. In the context of immunotherapy, we developed the EPICART signature, a DNA methylation-based classification model that successfully predicted complete clinical response in patients receiving CD19-targeted chimeric antigen receptor (CAR) T-cell therapy for relapsed or refractory B-cell malignancies. EPICART-positive CAR T- cell products, characterized by higher proportions of naïve and central memory T-cells, were associated with improved clinical outcomes. Importantly, the EPICART signature has since been licensed to a pharmaceutical company for validation in diverse patient cohorts, representing a key step toward potential clinical implementation. Extending the application of DNA methylation profiling to COVID-19, we identified the EPIMISC signature, which differentiated multisystem inflammatory syndrome in children (MIS-C) from pediatric COVID-19 cases without MIS-C. The presence of EPIMISC in Kawasaki disease further suggested shared immune mechanisms, likely triggered by viral infections such as SARS-CoV-2 in MIS-C. To deepen our understanding of COVID-19 pathology, we applied spatial transcriptomics to investigate diffuse alveolar damage in fatal cases, revealing key contributors to lung fibrosis, including aberrant myeloid activation, peribronchial fibroblast proliferation, and activation of the TGF-β/SMAD3 pathway. These findings highlight the critical role of bioinformatics in advancing precision medicine and emphasize the importance of multisectoral collaboration for clinical translation.