Dissertation

Getting personal: Advancing personalized oncology through computational analysis of membrane proteins

Cancer is considered the silent pandemic of the 21st century and the second leading cause of death worldwide.

Author: M. Gorostiola González
Date: 24 January 2025
Links: Thesis in Leiden Repository

The significant heterogeneity of this disease, seen across various cancer types, individuals, and even tumor cells, makes it extremely challenging to treat effectively and safely in all patients. Personalized oncology has emerged as an efficient strategy to leverage the differences present in cancer for the selective targeting of tumor cells. This approach aims to reduce side effects while maintaining or enhancing therapeutic efficacy. However, the availability of personalized therapies is currently limited, leaving many cancer patients longing for more selective treatments. In this context, computational tools play a crucial role in exploring unresolved questions in cancer research and accelerating the discovery of new proteins that can be selectively targeted in anticancer therapies. One main advantage of using computational tools is the ability to investigate promising protein families that have been overlooked in cancer research due to experimental limitations or publication bias, such as membrane proteins. This thesis delves into the potential of computational tools in prioritizing novel targets, mutations, and drugs for use in personalized oncology, with a specific focus on membrane proteins.