Abstrait

Modelling approaches in tumor microenvironment.

Isaac Crespo, George Coukos, Marie-Agnes Doucey and Ioannis Xenarios

For decades, cancer research has been focused on understanding the neoplastic transformation of normal cells into cancerous ones from a cell-centric perspective. However, it is increasingly evident that the surrounding tumor microenvironment (TME) is equally important for tumor growth, progression and dissemination. The TME is a complex and heterogeneous system of interplaying elements strongly intertwined with normal processes of the surrounding hosting tissue. Cancerous cells and stromal cells, including different types of infiltrating immune cells and resident tissue cells, interact with each other and with extracellular matrix components in a very convoluted way. In addition, all of these cells may have phenotypically distinct variants exhibiting variability in cell traits, such as cell-cell adhesion, migration capability, proliferation rate and responsiveness to specific treatments; the composition of the cell population can differ between different regions of the tumor and between different tumors of the same or different patients, which result in both intratumor and across tumor heterogeneity. Altogether, the complexity and heterogeneity of the TME hinder the elucidation of cancer driving mechanisms and biomarkers and render the tumor behavior difficult to anticipate. Ultimately, that slows down the development of novel cancer therapies and makes difficult the choice of suitable treatments for specific patients. Mathematical and computational models may help on describing, explaining and predicting cancer in a new generation of experimental design assisted by computer simulations. These novel experimental and computational approaches face new challenges in the era of precision medicine and personalized cancer therapies, such as capturing the spatiotemporal structure of the TME, vertical and horizontal integration of multiple-omics data and dealing with heterogeneity at both intratumor and patient population level.