Advancing the discovery of predictive biomarkers in drug high-throughput screens and clinical trials for precision oncology

Ohnmacht, Alexander Joschua (2024): Advancing the discovery of predictive biomarkers in drug high-throughput screens and clinical trials for precision oncology. Dissertation, LMU München: Fakultät für Biologie

Abstract

Discovering a universal cure for all cancers, known as the one-drug-fits-them-all approach, is challenging due to the diverse and complex nature of the disease. Precision oncology is a paradigm in modern medicine which ought to overcome this approach by tailoring cancer treatments to tumour and patient characteristics for increased safety and efficacy. Carcinogenesis is driven by genetic alterations, which established themselves as suitable drug targets and predictive biomarkers in clinical practice. While these discoveries were previously limited to studying a few key cancer pathways or cancer genes, the contemporary accumulation of biomedical data, including molecularly profiled drug high-throughput screens and clinical trials, facilitates the discovery of biomarkers for predicting treatment success. Several computational models using data-driven methods were able to successfully predict responses to drugs in both preclinical and clinical settings based on molecular characteristics; however, the translation of the predicted biomarkers towards clinical utility has remained limited. In order to address this, this thesis presents a range of methods and analysis strategies that make sparse, interpretable and robust predictions of potential biomarkers for treatment efficacy. The chapters of this thesis include (1) an integrative method for identifying DNA methylation biomarkers associated with drug susceptibility using drug high-throughput screens and multi-omics characterisations in cancer cell lines, (2) an assessment of the epithelial-mesenchymal transition in cancer cell lines and its causal impact on drug susceptibility and (3) a framework for the exploration and identification of the molecular and biomarker landscapes of randomised controlled clinical trials in oncology. In summary, the presented work facilitates the discovery of predictive biomarkers by incorporating molecular data modalities into tailored modelling strategies to reflect cancer mechanisms in high-throughput screens and clinical trials. In future, these methods may become an indispensable part of a more integrated and data-driven drug discovery and development process to design more targeted and effective cancer treatment strategies.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Themengebiete: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
• Fakultäten: Fakultät für Biologie
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 12. Dezember 2024
• 1. Bericht­erstatter:in: Menden, Michael