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Investigating the heterogeneic biology of ovarian cancer metastases by high-resolution imaging to inform novel combination therapies
Author(s)
Date Issued
2025
Date Available
2025-10-23T11:26:32Z
Abstract
High-grade serous ovarian cancer (HGSOC) rarely shows symptoms, most patients present with metastases. Tumour heterogeneity between metastases may impede a patient’s response to therapy and impact survival. The aim of this work was to develop a methodology to decipher the heterobiology of HGSOC lesions using high-resolution imaging of patient derived explants (PDEs), to determine differential treatment response. Target proteins were selected, comprising of two panels, to characterise tumour cell population and activity, and characterise the tumour microenvironment (TME). Antibodies against each of the proteins were tested and optimised in HGSOC cell line monolayer and spheroid cultures. Panel 1 consisted of antibodies targeting PAX8, CK7, Geminin, γH2AX, RAD51, Ki67 and cPARP, and panel 2 consisted of antibodies targeting CD3, PD-1, PD-L1, TIGIT, and CD112. Primary and metastatic tumour samples were collected from five different patients and used to generate PDEs. Optimised antibodies were used to stain PDEs and in total four immunostaining protocols were tested for multiplex immunofluorescence (mIF) volumetric imaging. The aim was to volumetrically assess individual cells within primary and metastatic HGSOC PDEs to assess intrapatient heterobiology. Individual cell analysis was achieved for a primary tumour from one patient, when no more than two antibodies were utilised. However, individual cell analysis was not possible when four antibodies were incorporated, instead overall explant analysis was performed. To assess response to drug treatments in HGSOC PDEs single plane imaging of formalin fixed paraffin embedded slides was used. New antibodies, and antibody panels were established and optimised. Panel 1 focused on characterising cellular proliferation and consisted of one antibody targeting Ki67, panel 2 focused on characterising the TME and consisted of antibodies targeting AE1/AE3, CD3, CD8, PD-1, PD-L1 and TIGIT. Primary and metastatic tumour samples were collected from four different patients. These samples were cultured as PDEs and two were viable for mIF staining to assess response to therapy. This work was underpinned by public and patient involvement. A patient advisory committee of five women who had personal experience of ovarian cancer was established. This committee was engaged and involved with the research project over four years. Experimental design was impacted and improved by patient involvement. The committee was also expanded through the introduction of 18 new members, including representation from the Bangladeshi community In Ireland. The work conducted here showcases the issues that arise with commercially available antibodies. Progress was made in the development of immunostaining and image analysis methods in PDE material. A previously utilised immunostaining protocol established for spheroid cultures was successfully applied to HGSOC PDEs, and the detection and quantification of two antibodies and Hoechst 33342 was achieved through the development of a bespoke image analysis pipeline. The limitations of mIF volumetric imaging are highlighted, and steps established for the use of volumetric imaging with PDE samples. Assessment of cellular proliferation and characterisation of the TME was carried out for primary and metastatic HGSOC PDEs, via the use of mIF, ELISA and lactate dehydrogenase assays. Further study is warranted to improve upon the developments made in staining and volumetric imaging of PDEs
Type of Material
Doctoral Thesis
Qualification Name
Doctor of Philosophy (Ph.D.)
Publisher
University College Dublin. School of Biology and Environmental Science
Copyright (Published Version)
2025 the Author
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
File(s)
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Name
Connor2025.pdf
Size
19.66 MB
Format
Adobe PDF
Checksum (MD5)
ae860e2bea59caea82c8ab93b1205430
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