Date of Graduation

12-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Biomedical Engineering

Advisor/Mentor

Rajaram, Narasimhan

Committee Member

Quinn, Kyle P.

Second Committee Member

Song, Young Hye

Third Committee Member

Dings, Ruud P.M.

Keywords

Collagen; Lung cancer; Metabolism; Multiphoton Microscopy; Optical Imaging; Recurrence

Abstract

Lung cancer remains the leading cause of cancer deaths, comprising nearly 25% of all cancer deaths (Sung et al., 2021a). The five-year survival rate of patients with non-small cell lung carcinoma (NSCLC) remains significantly low given that over half present with locally advanced or metastatic disease at time of diagnosis, and experience tumor recurrence following therapeutic intervention (Tamura et al., 2015; Uramoto & Tanaka, 2014). Current evaluation techniques to assess treatment response are lacking, given they are implemented several weeks after treatment completion and are solely based on anatomical changes in tumor size, forgoing other criteria such as functional or metabolic changes. There is a critical need to identify surrogate markers early on following diagnosis, that aid in distinguishing patients based on their long-term outcome. Multiphoton microscopy (MPM) techniques provide non-invasive high-resolution information on cell metabolism within tissue by utilizing an optical redox ratio (ORR) of FAD/[NADH+FAD] autofluorescence. The goal of this dissertation was to leverage MPM in the characterization of treatment-naïve NSCLC and identify measurable differences in optical endpoints of human NSCLC primary tumors that are indicative of their long-term outcome. Twenty-five treatment-naïve NSCLC specimens were classified into metastatic and non-metastatic groups according to subject-detail reports. The ORR and mean NADH lifetime were determined for each sample, revealing a significant increase in the ORR (p=0.02) and a decrease in the percent contribution of protein-bound NADH for the metastatic group (p=0.03) compared to non-metastatic. Kaplan-Meier survival curves indicated patient tumors with high ORRs had a significantly greater risk of metastatic recurrence (p=0.002). Proteomic analyses of the same NSCLC cohort revealed KEAP1 to be dysregulated, and we found a significant negative correlation between the mean redox ratio for NSCLC primary tumor samples and corresponding KEAP1 proteomic scores (p=0.04). MPM was also utilized to collect second harmonic generation signal (SHG) from the same samples. Quantitative analysis using two-photon excited fluorescence (TPEF) signal to SHG ratio (MAFSI index) revealed a significantly higher MAFSI index for the metastatic tumors compared to non-metastatic (p=0.04). Classification models based on logistic regression, support vector machines (SVM), and k-nearest neighbors (KNN) were developed by harnessing optical endpoints and clinicopathological features to classify patients according to metastatic risk. Models were tested separately with optical features and clinicopathological features, and then combined. Models with combined features performed best with logistic regression outperforming both SVM and KNN (AUC = 0.76). This work demonstrates the feasibility of using optical imaging to identify markers sensitive to both metabolic and structural changes within NSCLC that are indicative of long-term outcome. Moreover, this work demonstrates the prognostic value of optical markers in predicting metastatic risk in NSCLC patients, and the impact these markers could have on the management of early-stage NSCLC and patient 5-year survival.

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