Work package 9: Clinical trials
Brief description and aims of work
The objectives of this WP are to validate the p-medicine environment by focusing on running clinical trials. The three selected diseases are Wilms Tumour, Breast Cancer and Acute Lymphoblastic Leukaemia (ALL). Trials for these diseases are selected by p-medicine in a way that they can address different aspects of the project. For all trials clinical relevant use cases are defined. Data coming from these trials will be stored in the data warehouse in a secure and anonymized way according to the legal and ethical framework of p-medicine. The Wilms tumour trial will be used to employ the newly developed and validated tools of p-medicine. The trial also provides data for the Oncosimulator testing a specific Wilms Tumour scenario (continuation of WP8 of ACGT with more molecular data and an increased number of patients).
Treatment for breast cancer has already been improved by discovery of reliable surrogate markers of response. The disease is now split into many subsets based on hormone receptor data, genomic signatures and imaging characteristics and the evidence base for validated therapeutic choices is more advanced than any other area of oncology. But there are five times more options emerging than are accredited due to the sheer speed of discovery of new markers, genomic, proteomic and metabolomic, and these need validation with new VPH tools designed by an amalgam of IT and clinical intelligence. Added to the astonishing database on breast cancer cell signatures are the emergence of cancer stem cell signatures, markers for drug and radioresistance, and the new insights in the essential role of the extracellular compartment in directing angiogenesis, typically corrupted in the breast cancer scenario. The primary aim of our studies is to maximize efficacy of therapy while minimizing side effects. We must answer several crucial questions: Are there surrogate markers of anti-angiogenic drug activity? Can gene array signatures of cancer cells, and cancer stem cells in particular, predict (non) responsiveness to biologic therapies, be they immunological, antiangiogenic or targeted small molecules? Do combinations of biological drugs with standard cytotoxic and/or hormonal therapies delay or overcome resistance? Which combinations of imaging technologies, PET, CT, MRI will most contribute to personalising therapy and monitoring its longer term effects? Electronic patient records interfaced with bio-banks, genetic databases, and medical imaging systems will be available for new methodologies of data analysis.
Treatment results in childhood ALL are one of the true success stories of clinical oncology, with current overall cure rates of approximately 80% in developed countries. Overall, the risk assessment procedures applied by different study groups mainly lead to therapy stratification into two or three risk groups (e.g., standard/low, intermediate, high) today. Poor microscopically assessed early morphological response to induction therapy is highly predictive of treatment failure. However, the majority of recurrences occur in the large group of patients with an adequate morphological response to treatment. Therefore, the value of molecularly assessed treatment response by investigating minimal residual disease (MRD) was assessed to allow sub-microscopic detection of leukemic clone-specific immunoglobulin and/or T-cell receptor gene rearrangements by polymerase chain reaction (PCR) with an approximately 1000 to 10,000-fold higher sensitivity than morphology and proven to be advantageous. In addition, there are numerous data including molecular biological findings available in individual patients with ALL (e.g. gene expression data, also genetic variation in drug metabolism). In contrast there is a the lack of models to correlate all these heterogeneous data with the outcome in single patients. The leukaemia trial in p-medicine will be used to develop such a model for the prediction of MRD and disease recurrence. By elucidating the functional role of differences in leukemia and predict targets for efficient intervention, this systems biology approach to identify novel predictive tools by integrating clinical and molecular data at different levels may lead to a model-based increased understanding of ALL which, hopefully, may be used in pilot projects for true translational research applications. More details about the trials will be found in Annex II (background information). The chosen topics and the tasks of this work package have considerable implications for public health as the VPH models and decision support tools developed here will be evaluated and validated by real data from patients in actual clinical trials and thus they will not only identify better treatment options for those patients but non trial patients too, and they will further serve as test of principle for other cancer types.