PROJECT SUMMARY/ABSTRACT
Ovarian cancer is a diverse group of malignancies that can vary greatly in molecular biology, etiology, and
presentation of symptoms. While it accounts for 2.5% of all cancers among women, it results in roughly 5%
of cancer-related deaths due to its high fatality rate. This is because 75% of patients are diagnosed with
advanced disease, largely attributed to its relatively late presentation of symptoms and a lack of reliable
detection and monitoring strategies. Extracellular vesicles (EVs) are released by all cells, including ovarian
cancer, and their cargo reflects their cells of origin. They have shown immense potential as stable
biomarkers, however their low abundance compared to EVs from healthy cells and a lack of sufficiently
sensitive characterization tools has limited their clinical translation. Surface-enhanced Raman spectroscopy
(SERS) is sensitive enough to biochemically fingerprint even single EVs, and the information-rich spectra
produced in EV SERS can be fed into machine learning (ML) algorithms to classify them based on their
latent spectral features. Despite early progress in EV SERS, the highly heterogeneous nature of EVs
indicates that their separation into distinct subpopulations prior to SERS analysis may help improve disease
diagnosis, classification, and monitoring. Microfluidic devices are uniquely capable of separating EV s into
subpopulations of interest while simultaneously enabling SERS spectral acquisition in a single device. In the
proposed research, during the mentored phase, ML-enabled inverse design will be combined with high
resolution nanofabrication techniques to improve the signal strength and spectral quality attainable from EV
SERS. Preliminary data indicates that the SERS enhancement is highly dependent on the substrate’s
nanoscale geometry, which is particularly important for EVs compared to conventional chemical analysis.
Towards the end of the mentored phase, once the improved substrates have been thoroughly tested using
bioreactor-produced EVs, they will be incorporated into two distinct microfluidic devices and tested
throughout the independent phase using both cell culture and patient EV samples. One device will capture
different subpopulations of EVs directly onto the microfluidic SERS substrates based on specific surface-
antigens for multiplexed characterization, while the other will separate EVs precisely by size and flow them
over the microfluidic SERS substrates to produce EV SERS barcodes. In parallel, ML algorithms tailored
specifically to these platforms will also be developed to process and classify the acquired spectra. This
proposal is multidisciplinary, utilizing advanced ML and micro-nanofabrication techniques as well as EV
production, isolation, and characterization strategies. These experiments are innovative and significant
because they will develop ML inverse design architectures specifically for EVs as well as microfluidic EV
SERS for characterizing and classifying ovarian cancer.
