Date of Award

8-2021

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biology/Molecular, Cellular, and Organismal Biology

First Advisor

Jill A. Macoska

Second Advisor

Changmeng Cai

Third Advisor

Jonathan Celli

Abstract

Renal Cell Carcinoma (RCC) is diagnosed in approximately 65,000 people annually in the United States, accounting for ~3% of all human cancers. The death rate for patients diagnosed with ccRCC is about 22% and accounts for about 80% of all renal masses. Additionally, recurrence-free survival of patients deemed high-risk using conventional tumor stage and grade criteria is only 44%, and the molecular components that drive high grade, metastatic disease, remain largely unknown. Since tumor histology has limited value in determining patient outcome, there is a clear need for better predictive tools that can guide treatment strategies. The ability to predict risk of tumor recurrence at the time of diagnosis would improve RCC treatment strategies including more aggressive approaches for high-risk patients. A personalized approach would help to further stratify patients where traditional histological diagnoses fall short. Urinary cell free supernatant provides suitable quality RNA for next gen sequencing and can provide biomarkers at the transcript level capable of identifying metastatic and non-metastatic patients. Urine was collected at the time of diagnosis from both patients whose tumors did not ultimately recur and patients who experienced metastatic disease. Cells were spun out of the sample, and the RNA was purified from the resulting supernatant. Sequencing of the RNA was carried out on the Illumina HiSeq 2500. Following successful sequencing and identification of a panel of transcripts, validation was conducted using the NanoString nCounter platform. A custom panel of RNA probesets specifically designed to bind to regions conserved within the degraded RNA seen in the sequencing data, and to work with NanoString’s low input/single cell method was created. Efforts were also made to remove genomic DNA and normalize input into the NanoString assay using a qPCR based standard curve to reduce unwanted noise. Logistic regression revealed a subset of transcripts with predictive power to classify metastatic and non-metastatic samples. Our results were further supported using publicly available data. The results of these studies show that urine can be a suitable biospecimen for biomarker discovery. The validation of such a panel of biomarkers could have significant clinical utility in understanding the prognosis of a patent at a critical early junction when treatment plans are established.

Comments

Free and open access to this Campus Access Dissertation is made available to the UMass Boston community by ScholarWorks at UMass Boston. Those not on campus and those without a UMass Boston campus username and password may gain access to this dissertation through resources like Proquest Dissertations & Theses Global or through Interlibrary Loan. If you have a UMass Boston campus username and password and would like to download this work from off-campus, click on the "Off-Campus UMass Boston Users" link above.

Download
Off-Campus UMass Boston Users

Share

COinS