Date of Award

Spring 5-6-2022

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Biological Engineering

Advisor

Leif Oxburgh

Second Committee Member

Peter Brooks

Third Committee Member

Lisa Beaule

Additional Committee Members

Clifford Rosen

Sunder Sims-lucas

Abstract

Renal cell carcinoma (RCC) is the 8th most common cancer in the United States, with the clear cell variant (ccRCC) being the most prevalent. Over 14,000 people die every year to RCC, with rates continuing to increase with an aging general population. Patients suffering from metastatic RCC (mRCC) have extremely poor prognoses, with a 5-year survival of only 11.2%. Current treatment options include resection of primary lesions, tyrosine kinase inhibition (Sunitinib, Pazopanib), mTOR inhibition (Temsirolimus, Everolimus), and immune checkpoint inhibition (Nivolumab, Atezolizumab). Recent attention has been drawn to inhibition of transcription factors like HIF2α (Belzutifan). There is a need to discover new targets, as well as gain better insight into the molecular signatures for patient specific drug response prediction. The work presented herein presents our investigations into the transcription factor FOXD1, and the role it plays in regulating ccRCC growth. We knocked out FOXD1 in a classic model ccRCC cell line, the 786-O (786-OFOXD1null). Loss of FOXD1 led to growth inhibition and reduced tumor forming capacity in vivo. We uncovered a cell-cycle specific role of FOXD1 in promoting progression through the G2/M phase and phosphorylation of Histone H3. Due to the limitations in vivo using 786-OFOXD1null, we designed an in vitro tumor model meant to recapitulate the human tumor microenvironment. This model was based on extracellular matrix profile of patient ccRCC tissue through proteomic analysis. Using this model, we were able to create tumor avatars containing heterogenous cell populations. In addition to this, the model system could be used to analyze 3D tumor growth of cell lines in response to drug treatments. We used this model system to examine how FOXD1 regulates the cell cycle and 3D tumor growth. An analysis pipeline was devised to delineate targets downstream of FOXD1 that utilized RNA sequencing, predicted transcription factor binding, toxicity screening, and cell cycle analysis. Using our newly devised 3D tumor model allowed us to confirm the applicability of this approach to a broader population of primary tumor cells. This pipeline uncovered novel signaling pathways regulating cell cycle progression in RCC lines and may have therapeutic implications.

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