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Virginia Tech

Metabolic Adaptations of Ovarian Cancer Metastases to Physiological Conditions and Disease Progression

Abstract

dc:description.abstract

Ovarian cancer is the fifth leading cause of all cancer deaths in women and the most lethal gynecologic cancer in the United States. During metastasis, cancer cells exfoliate from the primary tumor and aggregate to form spheroids, enhancing their survival within the peritoneal cavity during dissemination to a secondary outgrowth site. The inability of removal of these aggregates by traditional surgical interventions may contribute to the high recurrence and mortality rate of ovarian cancer diagnosed at late stages. Obesity, particularly abdominal obesity, has been shown to increase ovarian cancer risk and decrease survival. The recruitment of stromal vascular fraction (SVF) present in adipose tissue represents a growth and proliferation advantage to ovarian tumors, and endogenous sphingolipids like sphingosine-1-phosphate are increased in ovarian cancer patients. These conditions, combined with the physiological conditions within malignant ascites (hypoxia and low glucose), represent a physiological environment that can impact the metabolic responses of ovarian cancer spheroids. Here, we investigated the metabolic adaptations of serous ovarian cancer cells across the metastatic cycle and in conditions that mimic those of the peritoneal cavity and malignant ascites. We first investigated the different in metabolic responses between adherent monolayers and 3D spheroids. We confirmed that spheroids have a reduced metabolic rate and drug response that is affected by the incorporation of obese SVF into aggregates. To investigate these changes in the next stages of the metastatic cycle, we used time trials to observe how adherence of spheroids to a secondary site changes metabolic response and substrate utilization in physiological conditions. Adhesion of spheroids showed changes in energy metabolism and substrate utilization, switching from mainly glutamine oxidation to glucose oxidation that could support successful outgrowth. Spheroids also were resilient to culture conditions, even non-permissive conditions such as those found in the peritoneal cavity. Finally, we utilized human malignant ascites from ovarian cancer patients as a further investigation into conditions that imitate in vivo characteristics that could affect spheroid metabolism. Exposure to malignant ascites reduced spheroid viability as well as basal respiration and ATP synthesis. However, spare respiratory capacity was increased, and human spheroids changed their substrate utilization in response to ascites. Taken together, these studies provide an identification of metabolic switches across different stages of ovarian cancer metastasis that contribute to their survival, which represents an emerging target for prevention and treatment for individuals with ovarian cancer.

Degree

thesis:*
Name thesis:degree_name
Doctor of Philosophy
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Human Nutrition, Foods, and Exercise
Department dc:contributor.department
Human Nutrition, Foods and Exercise
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Compton, Stephanie Lynn Edwards
Chair dc:contributor.committeechair
  • Schmelz, Eva Maria
Committee members dc:contributor.committeemember
  • Craige, Siobhan
  • Drake, Joshua Chadwick
  • Gilbert, Elizabeth R.

Subjects

dc:subject × 7

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:34263
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/109640

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Compton, Stephanie Lynn Edwards. Metabolic Adaptations of Ovarian Cancer Metastases to Physiological Conditions and Disease Progression. doctoral thesis, Virginia Tech, 2022. http://hdl.handle.net/10919/109640