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University of Cambridge

Imaging Metabolic Signatures in High Grade Serous Ovarian Cancer

Abstract

dc:description.abstract

<p>High Grade Serous Ovarian Cancer (HGSOC) can be classified by gene copy number signatures into 7 subtypes that have differing prognoses and treatment sensitivities and that show differences in the activities of various signalling pathways. An ongoing clinical study has demonstrated the feasibility of imaging hyperpolarized <sup>13</sup>C pyruvate metabolism in ovarian cancer and has demonstrated inter and intra-tumoural metabolic heterogeneity, where metabolic differences were observed between patients and between different tumour deposits within the same patient. Here I compared the use of <sup>13</sup>C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized [1-<sup>13</sup>C]pyruvate metabolism and positron emission tomography (PET) measurements of 2-Deoxy-2-[<sup>18</sup>F]fluoroglucose ([<sup>18</sup>F]FDG) uptake for detecting metabolic heterogeneity between HGSOC patient-derived xenografts (PDXs) that had different copy number signatures.</p> <p>I showed that differences in glycolytic metabolism between the subtypes, as defined by their copy number signatures, could be detected using hyperpolarized [1-<sup>13</sup>C]pyruvate but not with [<sup>18</sup>F]FDG PET. Dynamic Contrast Enhanced MRI measurements showed that the metabolic differences between the subtypes were not due to differences in tumour perfusion. I also investigated whether differences in tumour metabolism could help to predict and detect early treatment response. I compared the use of metabolic imaging techniques (hyperpolarized [1-<sup>13</sup>C]pyruvate imaging, [<sup>18</sup>F]FDG PET/CT) with cell death imaging techniques (diffusion-weighted <sup>1</sup>H MRI (DWI) and <sup>2</sup>H MRSI measurements of [2,3-<sup>2</sup>H<sub>2</sub>]fumarate metabolism, measurement of circulating tumour DNA (ctDNA)) to detect early evidence of response to standard-of-care chemotherapy (Carboplatin). Both hyperpolarized [1-<sup>13</sup>C]pyruvate and [<sup>18</sup>F]FDG-PET detected response to treatment with Carboplatin, in a Carboplatin-sensitive tumour, before there was a change in tumour volume. Both metabolic imaging techniques were successful in discriminating responding from non-responding tumours. The techniques for detecting cell death were not as sensitive for detecting treatment response, which may reflect a slow accumulation of dead cells post treatment, a lack of knowledge of when the rate of cell death increases post treatment and also because of immune clearance of dead cells. These studies have shown that imaging with hyperpolarized [1-<sup>13</sup>C]pyruvate has the potential to be used in the clinic to detect the early response of HGSOC patients to treatment.</p> <p>Finally, I explored the potential of imaging glyoxalase-1 (Glo-1) activity with [2-<sup>13</sup>C]Methylglyoxal for detecting metabolic heterogeneity between breast and ovarian cancer PDXs. However, Glo-1 activity did not differ between ovarian or breast cancer subtypes and therefore while <sup>13</sup>C MRSI with [2-<sup>13</sup>C]Methylglyoxal has the potential to detect the presence of disease it may not be useful for differentiating between different HGSOC or breast cancer subtypes.</p>

Degree

thesis:*
Name dc:type.qualificationname
Doctor of Philosophy (PhD)
Level dc:type.qualificationlevel
Doctoral
Grantor dc:publisher.institution
University of Cambridge
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Chia, Ming Li
Advisor dc:contributor.advisor
  • Brindle, Kevin

Subjects

dc:subject × 3

Rights

dc:rights
Language dc:language
eng

Identifiers

dc:identifier.*
DOI dc:identifier.doi
https://doi.org/10.17863/CAM.105465
OAI identifier oai:identifier
oai:www.repository.cam.ac.uk:1810/363358

Chain of custody

source
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Cambridge University
Base URL
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Last updated
2026-07-22
Source record
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citation

Chia, Ming Li. Imaging Metabolic Signatures in High Grade Serous Ovarian Cancer. Doctoral thesis, University of Cambridge, 2023. https://doi.org/10.17863/CAM.105465