Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Transcriptomic Interrogation of the Drivers of High Grade Serous Ovarian Cancer Progression

Willis, John (2020) Transcriptomic Interrogation of the Drivers of High Grade Serous Ovarian Cancer Progression. Master's Thesis, University of Pittsburgh. (Unpublished)

Submitted Version

Download (3MB) | Preview


Ovarian cancer is the deadliest gynecologic malignancy, particularly the High Grade Serous subtype (HGSOC). Unlike other subtypes of ovarian cancer, the majority of initial HGSOC presentations respond well to the typical chemotherapy regimen, a platinum-based agent and a taxane. The majority of HGSOC deaths occur following an initial round of treatment, when the recurrent disease fails to respond to standard therapy. A better understanding of how HGSOC progresses from a treatable disease to a chemoresistant one may assist in the development of more effective therapies. However, determining the drivers of this evolution has been stymied by a lack of longitudinal sequencing data from HGSOC cases.
Recent advances in sequencing technology and an increased understanding of the value of long-term follow-up in cancer patients present an opportunity to improve our understanding of the mechanisms of HGSOC evolution. Dissemination of sequencing results through public databases like the Sequence Read Archive and European Nucleotide Archive offer researchers the opportunity to strengthen the conclusions drawn from their own sequencing studies by contextualizing their own results, or pooling results for more powerful meta-analyses. I have Investigated eight different RNA-Seq datasets (one generated in-house), four consisting of matched pairs of primary and recurrent HGSOC, two consisting of primary ovarian and metastatic samples from the same presentation, and two studies of ovarian cancer cell lines, offers insight into the evolution of HGSOC from multiple perspectives. Combining gene expression analysis and analysis of expressed gene fusions when possible suggests several common and potentially actionable pathways of HGSOC evolution.
My analysis of 118 pairs of HGSOC samples suggests genes involved in tumor-microenvironmental interactions, immune response, epigenetic factors, and regulators of epithelial to mesenchymal transition (EMT) are altered with HGSOC progression. Transcript level analysis further reveals differential transcript use with progression, including differential transcript use of LPCAT2 in HGSOC tumor associated macrophages. The chimeric transcript expression profile of 36 pairs of HGSOC samples also reveals preserved expression of the potentially disease-relevant gene fusion CCDC6-ANK3 in multiple patients, a fusion also detected in the cisplatin-resistant HGSOC cell lines OVCAR3 and ONCO-DG1. These results support the growing body of literature implicating altered tumor-TME interaction and epigenetic mechanisms in the evolution of HGSOC and supports the need for further detailed longitudinal studies of HGSOC cases, and particularly the use of single cell sequencing to parse the contributions of multiple interacting cell types to the evolution of this disease.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Willis, Johnjaw212pitt.edujaw212
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorLee,
Committee ChairHuckriede,
Committee MemberOesterreich,
Date: 29 July 2020
Date Type: Publication
Defense Date: 19 April 2020
Approval Date: 29 July 2020
Submission Date: 29 May 2020
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 70
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Integrative Systems Biology
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: ovary cancer genomics transcriptomics progression
Date Deposited: 29 Jul 2020 19:22
Last Modified: 29 Jul 2022 05:15


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item