A comparison of prostate cancer cell transcriptomes in 2D monoculture vs 3D xenografts identify consistent gene expression alterations associated with tumor microenvironments

Background Prostate cancer (PC) research has relied heavily on patient-derived cell lines, which may be used for in vitro (two-dimensional [2D]) studies or cultivated as three-dimensional (3D) xenografts in mice. These approaches are likely to have differential impacts on cell phenotypes, with implications for experimental outcomes. Therefore, defining and comparing the transcriptional signatures associated with 2D and 3D approaches may be useful for designing experiments and interpreting research results. Methods In this study, LNCaP, VCaP, and 22Rv1 human PC cells were either cultivated in monolayers or as xenografts in NOD SCID mice, and their gene transcription profiles were quantitated and compared using microarray and real-time polymerase chain reaction techniques. Immunohistochemistry was used to evaluate protein expression in cancer cell xenografts. Results Comparisons of gene expression profiles of tumor cells grown in 2D vs 3D environments identified gene sets featuring similar expression patterns in all three cancer cell lines and unique transcriptional signatures associated with 3D vs 2D growth. Pathways related to cell-cell interactions, differentiation, and the extracellular matrix were enriched in 3D conditions. Immunohistochemical analyses confirmed that gene upregulation in xenografts occurred in implanted cancer cells and not in mouse stromal cells. Cultivating cells in vitro in the presence of mouse, rather than bovine serum failed to elicit the gene transcription profile observed in xenografts, further supporting the hypothesis that this profile reflects 3D growth and enhanced microenvironmental interactions, rather than exposure to species-specific serum factors. Conclusions Overall, these findings define the expression profiles observed in PC cells cultivated in 2D monolayers and in 3D xenografts, highlighting differentially regulated pathways in each setting and providing information for interpreting research results in model systems.