Successfully predicting how anticancer compounds will function clinically, based on preclinical studies, remains a significant challenge. High rates of phase II clinical trial failures indicate that many candidate compounds satisfy minimal safety requirements but lack efficacy in patients. Following the discovery of oncogenes and tumor suppressors, essentially the de facto demonstration that DNA mutations are at the heart of cancers, huge investments have been made in developing technologies to enable exploration of the total complexity of genomes and proteomes that are intrinsic to cells. One important, and wholly unexpected, outcome of those massive investments to understand cancer as a cell-intrinsic problem is the undeniable conclusion that mutations do not explain everything. Indeed, the fact that frankly malignant cells can be phenotypically normal, when held in check by a normal microenvironment, suggests that there is a dominant role of the microenvironment. Tumor microenvironments are known to modulate the malignant phenotype of cells and impact drug responses. Conventional 2-D plastic dishes are the substrate of choice for most drug screening, and rodent and other animals are used as in vivo models, but these modalities lack context in a way that is relevant to predicting drug activity. Alternatively, combinatorial microenvironment microarray platforms provide a high-throughput means of exploring cell-based functional responses in diverse microenvironmental milieus. Data from these techniques are single-cell resolution and encapsulate cell-cell heterogeneity, which provides direct linkages between cellular phenotypes, such as drug responses, and microenvironments. Here, we focus on the applications and analytic approaches used for functional cell-based exploration of combinatorial microenvironments using microarray technology.
Microenvironment Cancer MEMA Combinatorial microenvironment microarray Drug development Tissue architecture
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