Phenotypic heterogeneity in high-grade serous ovarian cancer: evidence for a stochastic model of proliferation
Creators: name: Hall, Douglas
Publisher: name: Apollo - University of Cambridge Repository
Year: 2019
Subjects: ovarian cancer
HGSOC
high-grade serous
stochastic
proliferation
cancer stem cells
stem cells
PDX
xenografts
subcutaneous
EpCAM
slow-cycling
apoptosis
lineage tracing

description: High-grade serous ovarian cancer (HGSOC) represents the largest and most lethal subtype of ovarian cancers. Chemotherapy is eﬀective at depleting these tumours, sometimes eliminating all visible disease, however the tumour invariably regrows from surviving tumour cells. Improving treatment depends on understanding and targeting these residual cells. The bulk of HGSOC tumour cells strongly express the epithelial surface marker EpCAM. However, previous work in the Brenton and Stingl Laboratories identiﬁed a distinct subpopulation of cells with low levels of EpCAM. These cells are uniquely able to regrow tumours in the lab, and are enriched by chemotherapy. The objective of this project was to understand the dynamics of these cells, not during transplantation or initiation, but under conditions similar to an established tumour in the patient. To this end, tumours were engrafted into immune-deﬁcient mice and allowed to grow. These were then studied only once they had become established. Nucleoside tracing studies were used to assay proliferation within each of the subpopulations. These were performed both over the short-term (to determine subpopulation proliferation rate), and over the long-term (to determine resulting expansion of the subpopulations over time). The results show that the subpopulation deﬁcient in EpCAM is seen to divide more slowly in all cases. Both subpopulations divide stochastically, and their rates of proliferation match the expansion of each subpopulation. This indicates that both subpopulations are self-supporting. Inducible lineage tracing tools were validated to investigate hierarchy and subpopulation interconversion. Unfortunately, the tools generated for this purpose did not prove reliable and solutions were not available within the scope of this project. An alternative approach was also tested, assessing the symmetry of cell division by immunoﬂuorescence, but is not yet optimised. To provide a deeper understanding of these phenotypes, single-cell-mRNAseq was performed. Sequencing is only half complete, but initial analysis already appear to indicate distinct gene expression patterns between cell clusters corresponding to observed phenotypic subpopulations. This provides a basis for future work into targeting the resistant subpopulation. In conclusion, this work refutes the proposed cancer stem cell model in HGSOC and presents evidence of stochastic division and self-supporting dynamics in distinct phenotypic subpopulations. It provides evidence of a distinct subpopulation, low in EpCAM, which is slow-cycling. This further supports the idea that these cells are the origin of relapse. In addition, this work begins the process of assessing (at single-cell level) differential expression in this fraction, elucidating the molecular nature of this subpopulation, and guiding future work to target these cells.
description: Funded by Cancer Research UK