Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models

Rachel Kerslake; Birhanu Belay; Suzana Panfilov; Marcia Hall; Ioannis Kyrou; Harpal S Randeva; Jari Hyttinen; Emmanouil Karteris; Cristina Sisu

doi:10.3390/cancers15133350

Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models

Cancers (Basel). 2023 Jun 26;15(13):3350. doi: 10.3390/cancers15133350.

Authors

Rachel Kerslake¹, Birhanu Belay², Suzana Panfilov¹, Marcia Hall^{1

3}, Ioannis Kyrou^{4

5

6

7

8}, Harpal S Randeva^{4

5}, Jari Hyttinen², Emmanouil Karteris¹, Cristina Sisu¹

Affiliations

¹ Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK.
² Computational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland.
³ Mount Vernon Cancer Centre, Rickmansworth Road, Northwood HA6 2RN, UK.
⁴ Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK.
⁵ Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.
⁶ Research Institute for Health & Wellbeing, Coventry University, Coventry CV1 5FB, UK.
⁷ Aston Medical School, College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK.
⁸ Laboratory of Dietetics and Quality of Life, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece.

Abstract

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.

Keywords: 2D; 3D; Matrigel; agarose; collagen; extracellular matrix (ECM); high-grade serous ovarian cancer (HGSOC); monolayer; ovarian cancer; scaffold; tumour microenvironment (TME).

Grants and funding

12899/University Hospitals Coventry and Warwickshire NHS Trust