Work package 12: VPH modelling and the integrated Oncosimulator

Brief description and aims of work

The goal of this workpackage is to develop an integrated multiscale Oncosimulator able to simulate the response of clinical tumours to several treatment schemes and/or schedules in the patient individualized context as well as a number of mutually compatible detailed models of specific tumour biomechanisms aiming at enhancing our understanding of the natural phenomenon of cancer. The ultimate target of both actions is to optimize individualized cancer treatment. The model development will comply with the VPH toolkit directives so that the modules to be developed will satisfy major VPH compatibility requirements. The developed Oncosimulator will consist of 7 steps as graphically outlined in section 1.1.1.3:

• First step: Obtain patient’s individual multiscale and inhomogeneous data.
  Data sets to be collected for each patient include: clinical data (age, sex, weight etc.), eventual previous anti-tumour treatment history, imaging data (e.g. MRI, CT, PET etc images), histopathological data (e.g. detailed identification of the tumour type, grade and stage, histopathology slide images whenever biopsy is allowed and feasible etc.), molecular data (DNA array data, selected molecular marker values or statuses, serum markers etc.). It is noted that the last two data categories are extracted from biopsy material and/or body fluids.
  
• Second step: Preprocess patient’s data.
  The data collected are pre-processed in order to take an adequate form allowing its introduction into the “Tumour
  and Normal Tissue Response Simulation” module of the Oncosimulator. For example the imaging data are segmented, interpolated, eventually fused and subsequently the anatomic entity/-ies of interest is/are three dimensionally reconstructed. This reconstruction will provide the framework for the integration of the rest of data and the execution of the simulation. In parallel the molecular data is processed via molecular interaction networks so as to perturb and individualize the average pharmacodynamic or radiobiological cell survival parameters.
  
• Third step: Describe one or more candidate therapeutic scheme(s) and/or schedule(s).
  The clinician describes a number of candidate therapeutic schemes and/or schedules and/or no treatment (obviously leading to free i.e. non-inhibited tumour growth), to be simulated in silico i.e. on the computer.
  
• Fourth step: Run the simulation.
  The computer code of tumour growth and treatment response is massively executed on distributed grid or cluster computing resources so that several candidate treatment schemes and/or schedules are simulated for numerous combinations of possible tumour parameter values in parallel. Predictions concerning the toxicological compatibility of each candidate treatment scheme are also produced.
  
• Fifth step: Visualize the predictions.
  The expected reaction of the tumour as well as toxicologically relevant side effect estimates for all scenarios simulated are visualized using several techniques ranging from simple graph plotting to four dimensional virtual reality rendering.
  
• Sixth step: Evaluate the predictions and decide on the optimal scheme or schedule to be administered to the patient.
  The Oncosimulator’s predictions are carefully evaluated by the clinician by making use of their logic, medical education and even qualitative experience. If no serious discrepancies are detected, the predictions support the clinician in taking their final and expectedly optimal decision regarding the actual treatment to be administered to the patient.
  
• Seventh step: Apply the theoretically optimal therapeutic scheme or schedule and further optimize the Oncosimulator.

The expectedly optimal therapeutic scheme or schedule is administered to the patient. Subsequently, the predictions regarding the finally adopted and applied scheme or schedule are compared with the actual tumour course and a negative feedback signal is generated and used in order to optimize the Oncosimulator.

The concrete objectives of the workpackage are the following:

1. to develop three exemplary multiscale simulation models of clinical tumour response to treatment: one for nephroblastoma, one for breast cancer and one for acute lymphoblastic leukaemia (ALL) based on the principles
   that have been shown to be most appropriate for the clinical trial context. These three models will constitute the simulation core of the “p-medicine Oncosimulator”.
2. to clinically adapt, optimize and validate the three Oncosimulator models using the data generated by one clinical trial per tumour type. Especially for the breast cancer type two complementary breast cancer trials will be
   considered which will be jointly viewed as the “Oncosimulator breast cancer branch clinical trial”.
3. to develop the p-medicine integrated Oncosimulator as a treatment support system.
4. to develop a number of separate mutually compatible models focused on various biological mechanisms that determine tumour dynamics at various combinations of biocomplexity levels/scales in order to gain insight into
   the complex phenomenon of cancer and suggest treatment strategies by studying these mechanisms in silico.
   
5. to utilize the tumour biomechanism focused models in order to explore the dynamics of the corresponding mechanisms in silico.
   
6. to utilize high performance computing resources such as DEISA/PRACE petascale facilities in order to increase the accuracy and the speed of numerical calculations.
   
7. to evaluate the p-medicine Oncosimulator and the tumour mechanism focused models.

Work package leader

•  WP 11
• WP 13