The Oncosimulator is at the same time a concept of multiscale integrative cancer biology, a complex algorithmic construct, a biomedical engineering system and eventually in the future a clinical tool which primarily aims at supporting the clinician in the process of optimizing cancer treatment in the patient individualized context through conducting experiments in silico i.e. on the computer. Additionally, it is a platform for simulating, investigating better understanding and exploring the natural phenomenon of cancer, supporting the design and interpretation of clinicogenomic trials and finally training doctors, researchers and interested patients alike.
A synoptic outline of the clinical utilization of a specific version of the Oncosimulator, as envisaged to take place following an eventually successful completion of its clinical adaptation, optimization and validation process is provided below.
- Step 1: Obtain patient’s individual multiscale and inhomogeneous data.
Data sets to be collected for each patient include: clinical data (age, sex, weight etc.), previous anti-tumour treatment history, if any, 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.).
- Step 2: 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 the anatomic entity/-ies of interest is/are reconstructed subsequently in a three–dimensional manner. This reconstruction will form the framework for the integration of the rest of the 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.
- Step 3: 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.
- Step 4: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.
- Step 5: 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 virtualreality rendering.
- Step 6: 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 clinicians in taking their final and expectedly optimal decision regarding the actual treatment to be administered to the patient.
- Step 7: Apply the theoretically optimal therapeutic scheme or schedule and further optimize the Oncosimulator.
The specific p-medicine Oncosimulator includes three multiscale simulation models corresponding to the three tumour types addressed in p-medicine, i.e. nephroblastoma (WT), breast cancer and acute lymphoblastic leukaemia (ALL). The models make use of multiscale (imaging, histological, molecular, clinical, treatment) data of the patient and focus on tumour response to treatment (chemotherapy, targeted therapy, radiotherapy and combinations). The three aforementioned models are quantitatively adapted to clinical reality by exploiting sets of real multiscale data. Clinical trial data is used in order to optimize and validate the simulation codes.