Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26752
Title: Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models
Authors: Kerslake, R
Belay, B
Panfilov, S
Hall, M
Kyrou, I
Randeva, HS
Hyttinen, J
Karteris, E
Sisu, C
Keywords: ovarian cancer;high-grade serous ovarian cancer (HGSOC);monolayer;2D;3D;scaffold;tumour microenvironment (TME);extracellular matrix (ECM);collagen;Matrigel;agarose
Issue Date: 26-Jun-2023
Publisher: MDPI
Citation: Kerslake, R. et al. (2023) 'Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models', Cancers, 15 (13), 3350, pp. 1 - 21. doi: 10.3390/cancers15133350.
Abstract: Copyright © 2023 by the authors. Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012–2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial–mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.
Description: Data Availability Statement: RNAseq and array data can be found via the following NCBI accession codes: PRJNA472611, PRJNA530150, PRJNA564843, PRJNA564843, PRJNA232817, and PRJNA318768. A full list of samples can be viewed in Supplementary Table S1 available online at https://www.mdpi.com/2072-6694/15/13/3350#app1-cancers-15-03350 .
Simple Summary: Ovarian cancer is one of the most lethal female cancers. Numerous investigations into the development and progression of this disease have resulted in the creation of numerous three-dimensional culture models to better reflect the natural microenvironment of these tumours. In this study, we leverage the available transcriptomics and clinical and novel experimental data to evaluate the impact of the growth conditions on various cancer cells and examine whether they better approximate the behaviour of tumour cells compared to the classical two-dimensional models. Our results show that variability in the growth conditions can impact key genes and biological processes that are hallmarks of cancer, highlighting the need for future studies to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15133350/s1, Figure S1: Top enriched gene sets for 2D vs. 3D OVCAR8; Table S1: Cell line information and associated accession codes; Figure S2: Growth of SKOV3 cells from Day 2 to Day 9 showing clear spheroid-like structures; Table S2: (A) Top 150 differentially expressed genes in OVCAR8 in all three grown media: grown on agarose, collagen, Matrigel. The differential expression in each of the growth media is with respect to 2D controls of the OVCAR8; (B) Top 150 differentially expressed genes across the cell lines A1847, A2780, C30, C70, OVCAR3, OVCAR4, OVCAR5, OVCAR8, OVCAR10, PEO1, SKOV-3, UPN275 grown on agarose vs. 2D controls; (C) Top 150 differentially expressed genes in the cell lines Kuramochi, OVCAR4, and OVCAR8, grown on collagen vs. 2D controls or the respective cell lines.
URI: https://bura.brunel.ac.uk/handle/2438/26752
DOI: https://doi.org/10.3390/cancers15133350
Other Identifiers: ORCID iD: Marcia Hall https://orcid.org/0000-0003-0039-5041
ORCID iD: Ioannis Kyrou https://orcid.org/0000-0002-6997-3439
ORCID iD: Emmanouil Karteris https://orcid.org/0000-0003-3231-7267
ORCID iD: Cristina Sisu https://orcid.org/0000-0001-9371-0797
3350
Appears in Collections:Dept of Life Sciences Research Papers
Brunel Medical School Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf6.46 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons