Epithelial to Mesenchymal Transition As A Predictor of Response to Polo-Like Kinase 1 Inhibition-Induced Apoptosis In Non-Small Cell Lung Carcinoma

<p>Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Outcomes are poor for patients with recurrent, advanced or metastatic NSCLC. Polo-like kinase 1 (PLK1), involved in the regulation of mitotic processes and the response to DNA damage, is overexpressed in NSCLC. Inhibiting PLK1 may be an effective treatment for NSCLC patients as it is involved in the mechanisms of resistance to several chemotherapy drugs. PLK1 inhibition or knock-down has various effects in cancer cells, including mitotic arrest, apoptosis, and senescence. Predictive biomarkers have not been identified to select those patients who are likely to respond to PLK1 inhibitors although a small subset of NSCLC patients respond well to single agent therapy.</p> <p>Our lab found that mesenchymal NSCLC cell lines were more sensitive to PLK1 inhibitors than the epithelial cell lines <em>in vitro</em>. The induction of an epithelial phenotype using miR-200 expression increased resistance to PLK1 inhibition, whereas the induction of a mesenchymal phenotype using ZEB1 expression or TGF-β increased PLK1 inhibition–induced apoptosis. To elucidate the mechanisms of resistance to PLK1 inhibition, our lab compared gene and protein expression in sensitive and resistant NSCLC cell lines and we identified β-Catenin, SMAD4 and PDK1 to be differentially regulated between epithelial and mesenchymal NSCLC cell lines after PLK1 inhibition. We tested the role of β-Catenin, SMAD4 and PDK1 in PLK1 inhibition induced apoptosis in NSCLC.</p> <p>Here, we demonstrate that mesenchymal NSCLC tumors are more sensitive to PLK1 inhibition compared to epithelial NSCLC <em>in vivo</em> in patient derived-xenograft (PDX) models as well as orthotopic mouse models in which the EMT properties are manipulable by modulating the miR200/ZEB1 axis. To facilitate analysis of these <em>in vivo</em> studies, we developed a novel semi-automated method of metastatic lung tumor burden calculation from computed tomography images by the calculation of the mass of the thoracic cavity. This method takes into account the aggregate tumor metastases in the thoracic cavity which significantly accounts for tumor burden in lung adenocarcinoma and provides details about the dynamic processes that occur <em>in vivo</em> over time.</p>

Name thesis:degree_name

Masters of Science (MS)

Level thesis:degree_level

Thesis (MS)

Year dc:date.available

2018

Authors dc:creator

• Viswanath, Pavitra
• <p><a href="http://www.orcid.org/0000-0002-5647-3283" target="_blank">http://www.orcid.org/0000-0002-5647-3283</a></p>

Contributors dc:contributor

• Faye M. Johnson, M.D., Ph.D.
• Don L. Gibbons, M.D., Ph.D.
• Joya Chandra, Ph.D.

• PLK1
• lung cancer
• patient-derived xenograft
• PDX
• orthotopic
• EMT
• epithelial to mesenchymal transition
• Cancer Biology
• Medicine and Health Sciences

Repository record dc:identifier

https://digitalcommons.library.tmc.edu/utgsbs_dissertations/856

OAI identifier oai:identifier

oai:digitalcommons.library.tmc.edu:utgsbs_dissertations-1902