Periodic Reporting for period 2 - RESCUER (RESISTANCE UNDER COMBINATORIAL TREATMENT IN ER+ AND ER- BREAST CANCER.)
Berichtszeitraum: 2021-07-01 bis 2022-12-31
What is the problem/issue being addressed?
Some patients do and others do not benefit from combination therapies (chemotherapy combined with immune therapy or with targeted anti-angiogenesis treatment). We will study this by integrating multi-omics data from clinical trials (treatment resistance signatures) and use this information to predict response up front and to design effective combination therapies.
Why is it important for society?
Breast cancer is the most common type of cancer among women. A fraction of the breast cancers has either no good therapeutic offer (TNBC) or experience a late relapse often presenting with metastatic disease. This is a small fraction of all breast cancers, but on the overall a large number of cases. Our trial study the most common treatment modalities, chemotherapy and hormonal therapy. The revenue of Arimidex alone is millions of US dollars yearly. The most expensive drug is the one that does not work. Identifying the best combination and dosage for every woman will safe money and lives.
What are the overall objectives?
The general objective of this project is to discover effective treatment combinations for individual/subgroups of breast cancer patients through the assessment and validation in vivo, in vitro, and in silico of mechanisms of treatment resistance at cellular/sub-cellular level. RESCUER will integrate and analyze existing and newly generated clinical and multi-omic data and biological samples from ongoing clinical trials to identify the physiological characteristics of non-responders vs. responders and to suggest clinically effective personalized drugs combinations.
Identify which new combinations of existing drugs have a high probability to work on individual or thin strata of patients and propose them for further investigation.
Some patients do and others do not benefit from combination therapies (chemotherapy combined with immune therapy or with targeted anti-angiogenesis treatment). We will study this by integrating multi-omics data from clinical trials (treatment resistance signatures) and use this information to predict response up front and to design effective combination therapies.
Why is it important for society?
Breast cancer is the most common type of cancer among women. A fraction of the breast cancers has either no good therapeutic offer (TNBC) or experience a late relapse often presenting with metastatic disease. This is a small fraction of all breast cancers, but on the overall a large number of cases. Our trial study the most common treatment modalities, chemotherapy and hormonal therapy. The revenue of Arimidex alone is millions of US dollars yearly. The most expensive drug is the one that does not work. Identifying the best combination and dosage for every woman will safe money and lives.
What are the overall objectives?
The general objective of this project is to discover effective treatment combinations for individual/subgroups of breast cancer patients through the assessment and validation in vivo, in vitro, and in silico of mechanisms of treatment resistance at cellular/sub-cellular level. RESCUER will integrate and analyze existing and newly generated clinical and multi-omic data and biological samples from ongoing clinical trials to identify the physiological characteristics of non-responders vs. responders and to suggest clinically effective personalized drugs combinations.
Identify which new combinations of existing drugs have a high probability to work on individual or thin strata of patients and propose them for further investigation.
During the second reporting period we have worked on integrating the clinical data from the different studies and built a common database. The data management database is implemented and functional. Following the principles of open-source methodology we selected Open Microscope Environment Remote Objects (OMERO) software as the data management database infrastructure. Mass spectrometry data independent analysis (DIA) proteomics has been generated for some clinical trials. We have started and performed test experiments to set up a method called thermal protein profiling (TPP) to identify off-targets of the aromatase inhibitors from different clinical trials.
We have established a platform of hormone receptor (HR) positive or HR-negative PDX models showing different sensitivity towards standard therapies. For mechanistic studies we have established/got access to isogenic pairs of TNBC PDXs showing sensitivity or resistance, respectively, to commonly used chemotherapies. We have also adjusted the PDEC platform for clinical biopsy samples by utilizing a biopsy system (Semi-Automatic Biopsy System - Adria Medical healthcare products) for the primary breast cancer specimen in the lab.
An important progress is made on a modelling side where we developed and validated a mechanistic model for combined aromatase and cdkCDK4/6 inhibitor treatment of ER+ breast cancer. Furthermore, we have worked on extending the existing model to further improve the results and allow for the simulation of additional drug treatments. We also introduced bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. The custom drug libraries are designed and cell lines which will be tested are established.
All these achievements are either published in high rated journals or in a form of manuscript. This gives a good ground for the next reporting period.
We have established a platform of hormone receptor (HR) positive or HR-negative PDX models showing different sensitivity towards standard therapies. For mechanistic studies we have established/got access to isogenic pairs of TNBC PDXs showing sensitivity or resistance, respectively, to commonly used chemotherapies. We have also adjusted the PDEC platform for clinical biopsy samples by utilizing a biopsy system (Semi-Automatic Biopsy System - Adria Medical healthcare products) for the primary breast cancer specimen in the lab.
An important progress is made on a modelling side where we developed and validated a mechanistic model for combined aromatase and cdkCDK4/6 inhibitor treatment of ER+ breast cancer. Furthermore, we have worked on extending the existing model to further improve the results and allow for the simulation of additional drug treatments. We also introduced bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. The custom drug libraries are designed and cell lines which will be tested are established.
All these achievements are either published in high rated journals or in a form of manuscript. This gives a good ground for the next reporting period.
What we want to achieve ultimately is treatment decision based on response predictions based on mathematical modeling of tumor imaging and molecular measurements, which will come in addition to the current guidelines and are therefore beyond the current state of the art.