Periodic Reporting for period 5 - MESI-STRAT (Systems Medicine of Metabolic-Signaling Networks: A New Concept for Breast Cancer Patient Stratification)
Período documentado: 2023-01-01 hasta 2023-09-30
Breast cancer (BC) is a complex disease with high prevalence in the EU. BC claims the lives of 92,000 Europeans per year and accounts for the highest costs of cancer-related healthcare. Each year 332,000 new cases are registered. 75 ̶ 80% of them are estrogen receptor-positive (ER+), and are treated with endocrine therapies (ET). ET block ER-driven tumor growth and show high efficacy. Yet, a significant proportion of the patients eventually relapses with metastatic breast cancer, and the recurrence rates remain almost constant for up to 20 years. To tackle this challenge, the MESI-STRAT consortium has developed new models for knowledge-based stratification of patients into subgroups with different ET resistance mechanisms. We have established predictive pipelines for (1) patient stratification prior and during ET; (2) recurrence risk assessment when ending ET; (3) marker panels to guide established targeted therapies for ET-resistant patients; (4) novel ET resistance mechanism-based therapy design. The unique collection of matched BC tissue, serum, and >10 years follow-up from the patient organization and MESI-STRAT co-coordinator PATH as well as the MUI biobank and prospective clinical trials within MESI-STRAT have enabled the longitudinal analysis of ET resistance and relapse.
WP1 provided biological samples, preclinical models, clinical samples, and data for model development and to derive and validate marker panels for clinical decision making in ER+BC. The main effort was to provide a comprehensive overview of available resources in the consortium and to set up and update the MESI-Repository. WP1 also collaborated with WP2 to develop and update the data management plan (DMP).
WP2 supported MESI-STRAT with tailored consulting and services. This included data storage resources; assistance and advice on structuring and annotating data; and optimizing data handling pipelines to facilitate data sharing according to the principles of FAIR Data Management and participation in the open data pilot. The DMP was established and regularly updated. This set a strong framework for data sharing within the network and with external partners in a secure fashion.
WP3 investigated signaling networks relevant for ER+BC progression and therapy response to ER inhibition. To enable mathematical modeling of these signaling networks, WP3 performed analyses of ER and oncogenic signaling in BC cell lines. These data enabled parametrization of mathematical models. A standard protocol to measure oncogenic signaling and metabolism in the same experimental setup has been established. The generated data improved our mathematical models by integrating signaling and metabolic changes during ET. Beyond this general approach, WP3 also specifically addressed the connection between key enzymes of metabolism and signaling nodes as well as their functional interaction in the context of ET and targeted drug treatments.
WP4 conducted quantitative analyses of key molecules in metabolic networks important in ER+BC using established and active high-resolution experimental techniques but in addition also developing new analytical tools. WP4 collaborated with WP3 to gather matched metabolic and signaling data for mathematical modeling in WP5. Major advances have been made in establishing comprehensive and reliable techniques to obtain omics data from model systems and patient samples. This enabled important mechanistic insights and high precision analyses. Pathways and the molecular mechanisms of tryptophan, NAD and energy metabolism have been scrutinized to provide high-quality data for improved model parametrization. In close collaboration with WP3, we provided a comprehensive perspective on the situation in the patient by integrating signaling and metabolic changes during ET.
WP5 developed computational models of metabolism and cell signaling capable of simulating differences observed in patient groups. These models have delivered four major outputs:
1) Computational models informed by knowledge and data from a broad range of BC cell lines, representative of different patient groups and their distinct metabolic responses.
2) Integration of patient data and prediction of patient subgroups that differ in signaling and metabolism behavior and most importantly prognosis. This required development of a novel analysis pipeline based on machine learning clustering methods.
3) In selected subgroups, we have successfully incorporated molecular mechanisms, which resulted in the observed behavior. These molecular mechanisms are necessary to simulate patient group specific treatment strategies.
4) We implemented a novel approach to improve the predictive capacity of genome scale metabolic models.
WP6 ran preclinical and clinical trials without drug treatment to derive MESI marker panels to identify clinically relevant ER+BC patient subgroups. All trials and data analyses have been finalized. Importantly, they validated the model-based predictions from WP5 on predictive marker panels.
WP7 validated the predictive power of the MESI models and marker panels in preclinical and clinical intervention trials. Specifically, the MESI-STRAT-WOO2 prospective and interventional window of opportunity trial of 3 weeks neoadjuvant anastrozole in postmenopausal women with ER+BC, and the MESI-STRAT Endocrine Therapy Termination Trial have delivered important clinical validation of the model predictions and preclinical results from WPs 3-5.
WP8 implemented the management plan to coordinate all MESI-STRAT actions. Regular project meetings between the partners ensured the data and information flow across the consortium. The consequences of the COVID-19 pandemic have influenced all parts of the project including substantial delays of the clinical trials. WP8 installed measures to mitigate delays as much as possible and all project tasks were achieved.
WP9 coordinated measures to maximize impact, by implementing the (1) Dissemination and exploitation plan, (2) Plan for open access and open data, and (3) Communication plan. The partnering SMEs acted in concert to maximize the project impact towards exploitation of the results.
WP10 ensured compliance with the ethics requirements applicable to H2020 projects as defined by the European Commission. A report of the MESI-STRAT ethics advisor was included in every periodic report.
WP2 supported MESI-STRAT with tailored consulting and services. This included data storage resources; assistance and advice on structuring and annotating data; and optimizing data handling pipelines to facilitate data sharing according to the principles of FAIR Data Management and participation in the open data pilot. The DMP was established and regularly updated. This set a strong framework for data sharing within the network and with external partners in a secure fashion.
WP3 investigated signaling networks relevant for ER+BC progression and therapy response to ER inhibition. To enable mathematical modeling of these signaling networks, WP3 performed analyses of ER and oncogenic signaling in BC cell lines. These data enabled parametrization of mathematical models. A standard protocol to measure oncogenic signaling and metabolism in the same experimental setup has been established. The generated data improved our mathematical models by integrating signaling and metabolic changes during ET. Beyond this general approach, WP3 also specifically addressed the connection between key enzymes of metabolism and signaling nodes as well as their functional interaction in the context of ET and targeted drug treatments.
WP4 conducted quantitative analyses of key molecules in metabolic networks important in ER+BC using established and active high-resolution experimental techniques but in addition also developing new analytical tools. WP4 collaborated with WP3 to gather matched metabolic and signaling data for mathematical modeling in WP5. Major advances have been made in establishing comprehensive and reliable techniques to obtain omics data from model systems and patient samples. This enabled important mechanistic insights and high precision analyses. Pathways and the molecular mechanisms of tryptophan, NAD and energy metabolism have been scrutinized to provide high-quality data for improved model parametrization. In close collaboration with WP3, we provided a comprehensive perspective on the situation in the patient by integrating signaling and metabolic changes during ET.
WP5 developed computational models of metabolism and cell signaling capable of simulating differences observed in patient groups. These models have delivered four major outputs:
1) Computational models informed by knowledge and data from a broad range of BC cell lines, representative of different patient groups and their distinct metabolic responses.
2) Integration of patient data and prediction of patient subgroups that differ in signaling and metabolism behavior and most importantly prognosis. This required development of a novel analysis pipeline based on machine learning clustering methods.
3) In selected subgroups, we have successfully incorporated molecular mechanisms, which resulted in the observed behavior. These molecular mechanisms are necessary to simulate patient group specific treatment strategies.
4) We implemented a novel approach to improve the predictive capacity of genome scale metabolic models.
WP6 ran preclinical and clinical trials without drug treatment to derive MESI marker panels to identify clinically relevant ER+BC patient subgroups. All trials and data analyses have been finalized. Importantly, they validated the model-based predictions from WP5 on predictive marker panels.
WP7 validated the predictive power of the MESI models and marker panels in preclinical and clinical intervention trials. Specifically, the MESI-STRAT-WOO2 prospective and interventional window of opportunity trial of 3 weeks neoadjuvant anastrozole in postmenopausal women with ER+BC, and the MESI-STRAT Endocrine Therapy Termination Trial have delivered important clinical validation of the model predictions and preclinical results from WPs 3-5.
WP8 implemented the management plan to coordinate all MESI-STRAT actions. Regular project meetings between the partners ensured the data and information flow across the consortium. The consequences of the COVID-19 pandemic have influenced all parts of the project including substantial delays of the clinical trials. WP8 installed measures to mitigate delays as much as possible and all project tasks were achieved.
WP9 coordinated measures to maximize impact, by implementing the (1) Dissemination and exploitation plan, (2) Plan for open access and open data, and (3) Communication plan. The partnering SMEs acted in concert to maximize the project impact towards exploitation of the results.
WP10 ensured compliance with the ethics requirements applicable to H2020 projects as defined by the European Commission. A report of the MESI-STRAT ethics advisor was included in every periodic report.
Our team of oncologists, modelers, bioinformaticians and experimentalists has developed new computational models in combination with network analyses and pharmacogenomics, to integrate multi-omics data and explore metabolic and signaling (MESI) networks driving ET resistance. Clinically validated metabolite marker panels measured in biological fluids enable patient stratification, resistance monitoring and clinical decision-making. BC metabolism has been hitherto poorly explored for diagnostics and therapy. MESI-STRAT has fostered a new precision oncology concept for endocrine therapy response monitoring. Based on the successful validation of the marker panels in preclinical models and clinical trials, our clinical and industrial partners will continue to develop the results towards improved diagnostics and therapy concepts.