UC Davis

For more effective early-stage cancer diagnostics, there is a need to develop sensitive and specific, non- or minimally invasive, and cost-effective methods for identifying circulating tumor-associated biomolecules, including extracellular vesicles (EVs). As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. While many studies have demonstrated the promise of Raman spectroscopy to provide value for clinical diagnostics, the sensitivity and specificity of such platforms typically drops when applied to larger patient cohorts. Additionally, Raman can suffer from low signal due to the number of inelastically scattered photons (~1 in a million) produced after a sample is interrogated with a laser. Surface enhanced Raman scattering (SERS) is a powerful extension of this technique, providing orders of magnitude increase in chemical sensitivity compared to spontaneous Raman scattering. Yet it remains a challenge to synthesize robust, uniform SERS substrates quickly and easily. Raman technology has not been successfully moved into the clinic and is hindered by the need to develop more miniaturized and automated systems that are integrated with inexpensive and useful SERS materials. Thus, the objective of this dissertation work is three-fold: 1) to carry out experiments on large clinical datasets to validate Raman and SERS diagnostics; 2) to examine the value of spectroscopic analysis of EVs; and 3) to develop novel SERS materials that are robust, biocompatible, and inexpensive.To address these objectives, we carried out Raman analysis of plasma, serum, and saliva from hundreds of cancer patients and benign controls (from patients undergoing similar procedures or screenings without cancer), including patients diagnosed with head and neck, ovarian, and endometrial cancers. Several notable findings were reported arising from this analysis, ranging from optimization of Raman data collection and data analysis, discovery and application of new plasmonic materials, and applied clinical testing of EVs. First, we showed that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics. Next, we reported the utilization of a simple plasmonic scaffold composed of a microscale biosilicate substrate embedded with silver nanoparticles for SERS analysis of ovarian and endometrial cancer EVs. We observed a major loss of sensitivity for ovarian and endometrial cancer following enzymatic cleavage of EVs’ extraluminal domain, suggesting its critical significance for diagnostic platforms. Using SERS, we also confirmed that three common EV isolation methods (differential ultracentrifugation, density gradient ultracentrifugation, and size exclusion chromatography) yield variable lipoprotein content. However, in combining SERS analysis with machine learning assisted classification, we showed that the disease state is the main driver of distinction between EV samples, and largely unaffected by choice of isolation. Finally, we demonstrated the synthesis and characterization of a new homogeneous gold nanofoam (AuNF) substrate produced by a rapid, one-pot, four-ingredient synthetic approach. These novel AuNFs were rapidly nucleated with macroscale porosity and then chemically roughened to produce nanoscale features that confer homogeneous and high signal enhancement (~10^9) across large areas, a comparable performance to lithographically produced substrates, with high utility for application in low-resource settings The work presented below comprehensively shows the promise of Raman as a clinical diagnostic tool and takes measured steps toward validating the technology in the context of cancer disease states. The technique has high disease discrimination in whole biofluids and isolated EV populations, and the addition of novel nanomaterials increases the sensitivity and specificity to reach clinically necessary levels. This work is foundational in promoting the continued emphasis on translating Raman to be a clinically relevant diagnostic tool.