Morphophenotypic classification of tumor organoids as an indicator of drug exposure and penetration potential

The dynamics of tumor progression is driven by multiple factors, which can be exogenous to the tumor (microenvironment) or intrinsic (genetic, epigenetic or due to intercellular interactions). While tumor heterogeneity has been extensively studied on the level of cell genetic profiles or cellular composition, tumor morphological diversity has not been given as much attention. The limited analysis of tumor morphophenotypes may be attributed to the lack of accurate models, both experimental and computational, capable of capturing changes in tumor morphology with fine levels of spatial detail. Using a three-dimensional, agent-based, lattice-free computational model, we generated a library of multicellular tumor organoids, the experimental analogues of in vivo tumors. By varying three biologically relevant parameters—cell radius, cell division age and cell sensitivity to contact inhibition, we showed that tumor organoids with similar growth dynamics can express distinct morphologies and possess diverse cellular compositions. Taking advantage of the high-resolution of computational modeling, we applied the quantitative measures of compactness and accessible surface area, concepts that originated from the structural biology of proteins. Based on these analyses, we demonstrated that tumor organoids with similar sizes may differ in features associated with drug effectiveness, such as potential exposure to the drug or the extent of drug penetration. Both these characteristics might lead to major differences in tumor organoid’s response to therapy. This indicates that therapeutic protocols should not be based solely on tumor size, but take into account additional tumor features, such as their morphology or cellular packing density.Author summary: Primary tumors and tumor metastases grow as three-dimensional (3D) masses of cells. Depending on the surrounding stroma, they may acquire various shapes, more or less irregular. Tumor organoids are the 3D experimental cultures that mimic growth of in vivo tumors, as well as their response to treatments. However, it is difficult to assess experimentally in a reproducible and quantitative way, how tumor morphology influences treatment efficacy. Here, we used mathematical modeling and computer simulations to analyze the structure of the simulated organoids and to classify them with regards to two quantitative features: the tumor accessible surface area (ASA) describing organoid exposure to the drug and the extent of drug penetration through the tumor tissue (organoid compactness). We showed that organoids of similar sizes and growth dynamics can, in fact, be characterized by distinct values of compactness and ASA, and thus may respond differently to the drug treatment. We suggest that these tumor features should be taken into consideration in addition to tumor size, when the therapeutic interventions are designed.