Inter-observer variability in the classification of ovarian cancer cell type using microscopy: a pilot study

2015 ◽  
Author(s):  
Marios A. Gavrielides ◽  
Brigitte M. Ronnett ◽  
Russell Vang ◽  
Jeffrey D. Seidman
Keyword(s):  
Ovarian Cancer ◽  
Pilot Study ◽  
Cancer Cell ◽  
Ovarian Cancer Cell ◽  
Observer Variability ◽  
Cell Type ◽  
Inter Observer Variability ◽  
Cancer Cell Type

scholarly journals Cell Type-Specific TGF-β Mediated EMT in 3D and 2D Models and Its Reversal by TGF-β Receptor Kinase Inhibitor in Ovarian Cancer Cell Lines

2019 ◽  
Vol 20 (14) ◽  
pp. 3568 ◽  
Author(s):  
Wafa Al Ameri ◽  
Ikhlak Ahmed ◽  
Fatima M. Al-Dasim ◽  
Yasmin Ali Mohamoud ◽  
Iman K. Al-Azwani ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Ovarian Cancer Cell ◽  
3D Models ◽  
Cancer Cell Lines ◽  
Cell Type ◽  
Ovarian Cancer Cell Lines ◽  
Cell Type Specific ◽  
Β Receptor

Transcriptome profiling of 3D models compared to 2D models in various cancer cell lines shows differential expression of TGF-β-mediated and cell adhesion pathways. Presence of TGF-β in these cell lines shows an increased invasion potential which is specific to cell type. In the present study, we identified exogenous addition of TGF-β can induce Epithelial to Mesenchymal Transition (EMT) in a few cancer cell lines. RNA sequencing and real time PCR were carried out in different ovarian cancer cell lines to identify molecular profiling and metabolic profiling. Since EMT induction by TGF-β is cell-type specific, we decided to select two promising ovarian cancer cell lines as model systems to study EMT. TGF-β modulation in EMT and cancer invasion were successfully depicted in both 2D and 3D models of SKOV3 and CAOV3 cell lines. Functional evaluation in 3D and 2D models demonstrates that the addition of the exogenous TGF-β can induce EMT and invasion in cancer cells by turning them into aggressive phenotypes. TGF-β receptor kinase I inhibitor (LY364947) can revert the TGF-β effect in these cells. In a nutshell, TGF-β can induce EMT and migration, increase aggressiveness, increase cell survival, alter cell characteristics, remodel the Extracellular Matrix (ECM) and increase cell metabolism favorable for tumor invasion and metastasis. We concluded that transcriptomic and phenotypic effect of TGF-β and its inhibitor is cell-type specific and not cancer specific.

Testing ovarian cancer cell lines to train dogs to detect ovarian cancer from blood plasma: A pilot study

2019 ◽  
Vol 32 ◽  
pp. 42-48 ◽  
Author(s):  
Monisha Murarka ◽  
Zoë I. Vesley-Gross ◽  
Jennifer L. Essler ◽  
Paige G. Smith ◽  
Jagmohan Hooda ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Pilot Study ◽  
Blood Plasma ◽  
Cell Lines ◽  
Cancer Cell ◽  
Ovarian Cancer Cell ◽  
Cancer Cell Lines ◽  
Ovarian Cancer Cell Lines

scholarly journals Role of Collagen Fiber Morphology on Ovarian Cancer Cell Migration Using Image-Based Models of the Extracellular Matrix

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1390 ◽  
Author(s):  
Samuel Alkmin ◽  
Rebecca Brodziski ◽  
Haleigh Simon ◽  
Daniel Hinton ◽  
Randall H. Goldsmith ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Extracellular Matrix ◽  
Cancer Cell ◽  
Collagen Fiber ◽  
Ovarian Cancer Cell ◽  
High Grade ◽  
Cancer Cell Migration ◽  
Cell Type ◽  
Fiber Morphology

Remodeling of the extracellular matrix (ECM) is an important part in the development and progression of many epithelial cancers. However, the biological significance of collagen alterations in ovarian cancer has not been well established. Here we investigated the role of collagen fiber morphology on cancer cell migration using tissue engineered scaffolds based on high-resolution Second-Harmonic Generation (SHG) images of ovarian tumors. The collagen-based scaffolds are fabricated by multiphoton excited (MPE) polymerization, which is a freeform 3D method affording submicron resolution feature sizes (~0.5 µm). This capability allows the replication of the collagen fiber architecture, where we constructed models representing normal stroma, high-risk tissue, benign tumors, and high-grade tumors. These were seeded with normal and ovarian cancer cell lines to investigate the separate roles of the cell type and matrix morphology on migration dynamics. The primary finding is that key cell–matrix interactions such as motility, cell spreading, f-actin alignment, focal adhesion, and cadherin expression are mainly determined by the collagen fiber morphology to a larger extent than the initial cell type. Moreover, we found these aspects were all enhanced for cells on the highly aligned, high-grade tumor model. Conversely, the weakest corresponding responses were observed on the more random mesh-like normal stromal matrix, with the partially aligned benign tumor and high-risk models demonstrating intermediate behavior. These results are all consistent with a contact guidance mechanism. These models cannot be synthesized by other conventional fabrication methods, and we suggest this approach will enable a variety of studies in cancer biology.

scholarly journals Classification of ovarian cancer cell lines using transcriptional profiles defines the five major pathological subtypes

2020 ◽  
Author(s):  
B. M. Barnes ◽  
L. Nelson ◽  
A. Tighe ◽  
R. D. Morgan ◽  
J. McGrail ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Ovarian Cancer Cell ◽  
Cancer Cell Lines ◽  
Low Grade ◽  
Transcriptional Profiles ◽  
Ovarian Cancer Cell Lines ◽  
Pathological Subtypes

AbstractEpithelial ovarian cancer (EOC) is a heterogenous disease consisting of five major pathologically distinct subtypes: High-grade serous ovarian carcinoma (HGSOC), low-grade serous (LGS), endometrioid, clear cell and mucinous carcinoma. Although HGSOC is the most prevalent subtype, representing approximately 75% of cases, a 2013 landmark study from Domcke et al., found that many frequently used ovarian cancer cell lines were not genetically representative of HGSOC tissue samples from The Cancer Genome Atlas. Although this work subsequently identified several rarely used cell lines to be highly suitable as HGSOC models, cell line selection for ovarian cancer research does not appear to have altered substantially in recent years. Here, we find that application of non-negative matrix factorisation (NMF) to the transcriptional profiles of 45 commonly used ovarian cancer cell lines exquisitely clusters them into five distinct classes, representative of the five main subtypes of EOC. This methodology was in strong agreement with Domcke et al., in identification of cell lines most representative of HGSOC. Furthermore, this robust classification of cell lines, including some previously not annotated or miss-annotated in the literature, now informs selection of the most appropriate models for all five pathological subtypes of ovarian cancer. Furthermore, using machine learning algorithms trained using the classification of the current cell lines, we are able provide a methodology for future classification of novel EOC cell lines.

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