Despite substantial improvements in cancer treatment over the past decades, cancer incidence continues to rise, and the prognosis remains poor for many patients. With an aging population increasing cancer risk and societal challenges in providing adequate care will grow. Therefore, continuous advancements in cancer research are needed to identify new vulnerabilities that can be targeted by existing or novel drugs.
Precision oncology offers a promising approach by tailoring treatments based on an individual patient's tumor biology. Currently, it relies on genomic profiling, with molecular tumor boards evaluating genetic abnormalities for druggability. However, most genomic aberrations lack functional understanding, limiting their use as treatment biomarkers. Additionally, genomic information poorly reflect protein activity, despite proteins driving malignant cancer behavior and serving as targets for most cancer drugs, particularly those affecting post-translational modifications like phosphorylation. Adding proteomics to molecular tumor profiling could address these gaps. The goal of this project was to establish the tumor proteome activity status (TOPAS) as an evidence-based criterion for treatment recommendations in molecular tumor boards. This requires understanding patient proteomes, drug-induced proteome changes, and their integration into treatment recommendation-making. Accordingly, the objectives of the TOPAS project were: 1) to show that modulating the TOPAS of cancer models with kinase inhibitors tell us more about cancer biology and reveals drug mechanisms of action; 2) to develop and validate TOPAS scores to make treatment suggestions based on proteomes and phosphoproteomes; and 3) to demonstrate that TOPAS scoring complements genomic profiling in aiding treatment recommendations by molecular tumor boards.
One the one hand, a key challenge is to foster our understanding of how therapeutic drugs function and modulate the tumor proteome in a cancer background. Kinase inhibitors, a major drug class, reprogram aberrant signaling pathways and are especially relevant for precision oncology. This project sought to better understand their molecular effects and context-dependency, providing a stronger foundation for evidence-based treatment recommendations. On the other hand, this project aimed at a better establishing the profiling of the tumor proteome activity status in cancer patients, and most importantly to include this information in the decision-making process of molecular tumor boards. To streamline data for the time-sensitive molecular tumor board process, the project developed bioinformatic tools for simplified data validation and analysis. These include visualization tools to contextualize individual patients within cohorts and scoring systems to identify molecular vulnerabilities and optimal treatments.
In conclusion, the project significantly advanced phosphoproteomics by developing methodologies, tools, and workflows that integrate proteomic insights into clinical practice. This establishes a foundation for oncology diagnostics and clinical trials, offering new opportunities to improve patient outcomes in precision medicine.
