Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Area of Science:

• Computational oncology
• Mathematical modeling in medicine
• Bioinformatics and computational biology

Background:

• Precision medicine aims to tailor treatments to individual patients.
• Mathematical and computational oncology provides tools for predicting optimal drug, dose, and timing.
• Spatial tumor architecture is crucial for understanding tumor development and treatment response.

Purpose of the Study:

• To review mathematical models that incorporate the spatial architecture of 3D tumors.
• To demonstrate the application of these in silico models to patient-specific data for assessing therapeutic strategies.
• To introduce the concept of virtual clinical trials for personalized medicine.

Main Methods:

• Survey of mathematical models focusing on spatial tumor architecture (e.g., spheroids, organoids, multi-component tissues).
• Discussion of how these models simulate tumor development, progression, and response to treatment.
• Integration of patient-specific data, medical imaging, and organ-on-chip experiments with computational models.

Main Results:

• Mathematical models offer efficient and cost-effective simulations for evaluating numerous treatment schedules.
• In silico models can be adapted to patient-specific data to predict the most effective therapeutic strategies.
• Virtual clinical trials represent a novel approach to personalized treatment determination.

Conclusions:

• Mathematical modeling, particularly incorporating spatial tumor architecture, is vital for advancing precision medicine in oncology.
• In silico approaches enable the prediction of personalized treatment strategies, optimizing drug selection and dosage.
• The integration of computational models with experimental data and patient information facilitates the development of virtual clinical trials for personalized cancer care.