Abstract
Screening for effective candidate drugs for breast cancer has shifted from two-dimensional (2D) to three-dimensional (3D) cultures. Here we systematically compared the transcriptomes of these different culture conditions by RNAseq of 14 BC cell lines cultured in both 2D and 3D conditions. All 3D BC cell cultures demonstrated increased mitochondrial metabolism and downregulated cell cycle programs. Luminal BC cells in 3D demonstrated overall limited reprogramming. 3D basal B BC cells showed increased expression of extracellular matrix (ECM) interaction genes, which coincides with an invasive phenotype not observed in other BC cells. Genes downregulated in 3D were associated with metastatic disease progression in BC patients, including cyclin dependent kinases and aurora kinases. Furthermore, the overall correlation of the cell line transcriptome to the BC patient transcriptome was increased in 3D cultures for all TNBC cell lines. To define the most optimal culture conditions to study the oncogenic pathway of interest, an open source bioinformatics strategy was established.
Highlights
Screening for effective candidate drugs for breast cancer has shifted from two-dimensional (2D) to three-dimensional (3D) cultures
To understand how cell culture systems affect the transcriptome of breast cancer (BC) cells, we performed RNA sequencing of 52 human breast cancer cell lines cultured on 2D tissue culture plastic and 14 cell lines cultured in a 3D matrigel-collagen environment
Cell lines separated consistently based on their subtype and, interestingly, the differences between cell lines were much larger than the differences between 2D/3D culture conditions; 2D and 3D samples of the same cell line clustered together (Fig. 1D, encircled)
Summary
Screening for effective candidate drugs for breast cancer has shifted from two-dimensional (2D) to three-dimensional (3D) cultures. To increase translation from in vitro findings to a clinical setting, different 3D culture systems are explored, such as organoid cultures, patient-derived xenograft models, reprogrammed stem cell like models, tumor-on-a-chip and 3D cultures of immortalized breast cancer cell lines[6]. Since the transcriptomic correlation of in vitro cultured cell models to patient tumor tissue was highly subtype and pathway dependent, we established a bioinformatics tool that can be used in future studies to select the most suitable cell type and culture conditions for the pathway of interest.
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