Introduction and description of the scenario. The most important question to be answered by the hypermodel for nephroblastoma is the following: Will the nephroblastoma of a patient shrink in response to preoperative chemotherapy? The answer to this question should be ‘yes’ or ‘no’. Shrinkage is determined by using imaging studies to measure the change in tumor volume. If the tumor volume is less than 75% of the initial tumor volume then the answer to the question is ‘yes’. If there is an increase of more than 25 % in tumor volume after preoperative chemotherapy then the answer will be ‘no’. If the tumor volume is between 75% and 125% of the volume before preoperative chemotherapy then the tumor volume is regarded as unchanged and therefore the answer is ‘no’ as well. This question is of importance as all patients diagnosed with a kidney tumor between the ages of 6 months and 16 years will receive preoperative chemotherapy within the SIOP (International Society of Paediatric Oncology) protocol, if imaging studies suggest nephroblastoma as the most probable diagnosis. Chemotherapeutic treatment is currently based solely on imaging studies without histologically proven diagnosis. The reason to start with preoperative chemotherapy is the fact that 90% of tumors do shrink, making surgical removal of the nephroblastoma easier and resulting in downstaging of the tumor with less postoperative treatment. However in about 10% of patients the tumor will not shrink but increase in size. Such behaviour results in a worse situation for the patient that should be avoided. At present it is not possible to predict which tumors will shrink and which will not. By collecting all available data of a patient with nephroblastoma at the time of diagnosis the response to preoperative chemotherapy can hopefully be simulated with these data. If the developed model will predict the correct answer to the above described question patients will benefit from such an approach by applying them the best treatment right from the time of diagnosis. Physicians will only believe such a model if the results of the simulations are validated. Therefore this model needs to be developed with retrospective data and validated with prospective data in an iterative process. The hypermodel for nephroblastoma will be an advanced oncosimulator simulating the response of chemotherapy on nephroblastoma in the computer (in silico).
Introduction and description of the scenario. The first question that needs to be answered by the Glioblastoma Multiforme (GBM) hypermodel is: Will a specific patient benefit from adding Dendritic Cell vaccination (DC vaccination) to the standard treatment for GBM? The answer should be ‘YES’ or ‘NO’ and will be answered by measuring Progression Free Survival (PFS) after 6 months (the confirmatory primary end-point of the phase IIb prospective double blind placebo-controlled randomized clinical trial HGG-2010 trial, EurdraCT 2009- 018228-14). In the case of ‘YES’ there is a benefit of DC vaccination in terms of higher probability of reaching the 6 months PFS point (PFS 6m). Since there is a more complex design in the study (cfr. below), patients in the placebo group receive vaccination after ±6 months in case relapse did not occur. A second question will hence be: Will a patient benefit more from early (within the first 6 months of standard treatment) or late (after 6 months of standard treatment) vaccination for overall survival.
Introduction and description of the scenario. Tumor-specific pathways detected very early after tumor diagnosis will be used to choose the most promising therapy as first line therapy, maintenance or adjuvant therapy. For that purpose a system biology model will be developed based on the transcriptome analysis of up to 100 tumour specimen to get new insights in the biology of NSCLC. This data will form the basis for the bottom-up approach of the in silico model for NSCLC and thus improving the accuracy of the developed in silico Hyper- Multiscale Models. All relevant clinical data, tumor typing according to the current adenocarcinoma classification as well as radiological, macroscopic, quantitative microscopic data, proliferation data and angiogenesis data were retrieved or collected prospectively from lung cancer resection specimens of NSCLC. Genetic profiles of the relevant pathways, xxXXX data, and deep sequencing data of at least a limited number of well-defined NSCLCs, will be added for comprehensive analysis. Small biopsy samples of lung cancer, which are often the only tumor tissue available from patients with advanced NSCLC, manual dissection, laser-microdissection, quantitative few cell PCR approaches, DNA sequencing, biochip reverse-phase hybridization, mRNA preamplification and whole genome amplification are available as well as epidemiological and follow-up parameters from the Saarland Tumor Center and will be integrated into the Models for In Silico Oncology in order facilitate the therapy-related clinical decisions.
Introduction and description of the scenario. The main question of our investigation is related to the management of the biochemical recurrences that sometimes follow the surgical/radiotherapy radical approach of the prostate cancer (see Fig.
8.1). As a matter of fact, this scenario is the most dramatic one both for the patient and for the clinician, who faces the problem with salvage therapies and/or hormonal therapy (Androgen Deprivation Therapy) but often doesn’t know enough about timing, dosage and success probability of such actions. Modelling the natural evolution of the pathology and/or the effects of the therapies maybe extremely useful provided the models are properly validated on robust clinical data.
