Periodic Reporting for period 4 - TOPAS (Exploiting the Tumor Proteome Activity Status for Future Cancer Therapies)
Periodo di rendicontazione: 2024-03-01 al 2024-08-31
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.
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.
This project made significant progress in advancing precision oncology by help doctors recommend better therapies.
The first part of the project investigated how drugs called kinase inhibitors affect cancer cells. These drugs work by disrupting faulty signaling pathways that cancer cells use to grow. Scientists studied the effects of these drugs on sarcoma and other tumor cell types to uncover new insights into how exactly faulty signaling pathways are affected. To handle the complex data, the team created advanced software tools and automated laboratory processes, allowing them to analyze more than 8,000 proteins and generate 30 million data points for drug responses. They also developed software like “CurveCurator” to simplify data interpretation and “PTMnavigator” to visualize proteins in signaling pathways, making the findings more accessible to researchers and clinicians.
In the second part, the focus shifted to creating a tumor scoring system called TOPAS. This tool uses data about specific proteins to help identify faulty cellular pathways in patients. The scores can indicate how well certain drugs might work for a patient. By automating much of the data analysi, the team ensured that results were both reliable and easy to use. They also explored new biological insights based on earlier findings, thus advancing our understanding of cancer biology.
The third part of the project aimed to bring these discoveries into clinical practice. The researchers developed a fast and efficient system for analyzing cancer patient samples, examining up to 30,000 protein modifications per patient. This system delivers results in just 1-2 weeks—fast enough to assist doctors during weekly molecular tumor board meetings where treatment decisions are made. Over 1,800 tumor samples were analyzed, and the data has already helped refine treatments in more than 900 cases. The workflows are now being rolled out to more patients demonstrating how this approach can enhance patient care.
By combining these efforts, the project showed that integrating detailed protein and drug data into cancer treatment planning is feasible and valuable. The findings are now being tested in clinical trials, laying the groundwork for new, more effective ways to tailor treatments to individual patients, improving outcomes and advancing precision medicine.
The first part of the project investigated how drugs called kinase inhibitors affect cancer cells. These drugs work by disrupting faulty signaling pathways that cancer cells use to grow. Scientists studied the effects of these drugs on sarcoma and other tumor cell types to uncover new insights into how exactly faulty signaling pathways are affected. To handle the complex data, the team created advanced software tools and automated laboratory processes, allowing them to analyze more than 8,000 proteins and generate 30 million data points for drug responses. They also developed software like “CurveCurator” to simplify data interpretation and “PTMnavigator” to visualize proteins in signaling pathways, making the findings more accessible to researchers and clinicians.
In the second part, the focus shifted to creating a tumor scoring system called TOPAS. This tool uses data about specific proteins to help identify faulty cellular pathways in patients. The scores can indicate how well certain drugs might work for a patient. By automating much of the data analysi, the team ensured that results were both reliable and easy to use. They also explored new biological insights based on earlier findings, thus advancing our understanding of cancer biology.
The third part of the project aimed to bring these discoveries into clinical practice. The researchers developed a fast and efficient system for analyzing cancer patient samples, examining up to 30,000 protein modifications per patient. This system delivers results in just 1-2 weeks—fast enough to assist doctors during weekly molecular tumor board meetings where treatment decisions are made. Over 1,800 tumor samples were analyzed, and the data has already helped refine treatments in more than 900 cases. The workflows are now being rolled out to more patients demonstrating how this approach can enhance patient care.
By combining these efforts, the project showed that integrating detailed protein and drug data into cancer treatment planning is feasible and valuable. The findings are now being tested in clinical trials, laying the groundwork for new, more effective ways to tailor treatments to individual patients, improving outcomes and advancing precision medicine.
The work performed represents by far the most comprehensive molecular characterization of targeted cancer drugs and that will be of outstanding value for the scientific community, drug companies and physicians around the world. The systematic profiling of cancer patient phosphoproteomes in a molecular tumor board setting is a unique feature of the TOPAS project. Once this approach becomes better known, we anticipate to motivate many other to follow this example. At the end of the TOPAS project, we have analyzed over 1,800 pan-cancer specimen, much more than anticipated at the beginning. This is important as the proteomic data for these patients can be used in molecular tumor boards for their individual treatments. We are certain, that the TOPAS project will set an important example for how precision medicine can be improved and, eventually, be more broadly implemented into cancer patient care.