Assessing Spatial Distribution of Multicellular Self-Assembly Enables the Prediction of Phenotypic Heterogeneity in Glioblastoma

Abstract

Simple Summary The heterogeneity of tumors is one of the primary obstacles to successful treatment. Although the examination of tumor heterogeneity is essential for comprehending tumor characteristics and planning therapeutic strategies, it remains challenging to assess and evaluate heterogeneous tumor populations. Here, we propose the self-assembly-based evaluation method, which is capable of predicting inter/intracellular heterogeneity in glioblastoma. Depending on their self-assembly pattern, heterotypic multicellular aggregates (hMA) are formed by mixed populations of glioblastoma cells. The cells located at the outermost hMA exhibit a diminished temozolomide response, and are related with poor patient survival. Our findings imply that the multicellular self-assembly pattern is indicative of the intertumoral and intra-patient heterogeneity of glioblastomas, and is also prognostic of the therapeutic response. Phenotypic heterogeneity of glioblastomas is a leading determinant of therapeutic resistance and treatment failure. However, functional assessment of the heterogeneity of glioblastomas is lacking. We developed a self-assembly-based assessment system that predicts inter/intracellular heterogeneity and phenotype associations, such as cell proliferation, invasiveness, drug responses, and gene expression profiles. Under physical constraints for cellular interactions, mixed populations of glioblastoma cells are sorted to form a segregated architecture, depending on their preference for binding to cells of the same phenotype. Cells distributed at the periphery exhibit a reduced temozolomide (TMZ) response and are associated with poor patient survival, whereas cells in the core of the aggregates exhibit a significant response to TMZ. Our results suggest that the multicellular self-assembly pattern is indicative of the intertumoral and intra-patient heterogeneity of glioblastomas, and is predictive of the therapeutic response.