Fusion-Based Digital Profiling of Extracellular Vesicles for Cancer Diagnosis

Abstract

Cancer is among the leading causes of death worldwide, accounting for approximately one in nine deaths in men and one in 12 deaths in women, according to the global cancer statistics (GLOBOCAN) 2022, with an estimate of 32.6 million incidence by the year 2045. Tissue biopsy has been held as the gold standard for cancer diagnosis; however, it presents various drawbacks, as it is invasive, painful, and poses infection risk. Tissue biopsy is also rather inappropriate for capturing tumor heterogeneity, which is considered as one of the key factors of treatment resistance, and inadequacy in detecting early-stage tumor, or residual lesions, constraining its effectiveness. In order to address these challenges that hamper the effectiveness of cancer diagnosis, a non-invasive alternative termed the liquid biopsy, has emerged. Liquid biopsy involves the sampling of body fluids such as urine and blood, to detect various biomarkers such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating tumor RNA (ctRNA) and tumor-derived extracellular vesicles (EVs). Extracellular vesicles (EVs) are considered as a promising liquid biopsy marker due to their stability and abundance, and due to the fact that they play roles in intercellular communication system, carrying various biomarkers such as DNA, RNA, protein, lipid, sugar structures, and metabolites. Various approaches in both EVs isolation and detection have been made to employ EVs as the target for liquid biopsies, and one approach that has been suggested for EVs detection is fusion. The process involves fusing the EVs with membrane vesicles containing targeting probe, allowing the detection of EV internal cargo such as nucleic acids. However, most fusion methods are done in bulk scale, compromising the system’s sensitivity. This thesis describes the combination of the two approaches of fusion and compartmentalization, aiming to provide an EV biomarker detection method in a digital manner, termed the EV-CLIP. EV-CLIP stands for EVs fusion with charged liposomes (CLIPs), which does not require the need for labor-intensive sample pre-processing, avoiding loss of the EVs, further simplifying the detection process and increasing the detection sensitivity. The EV-CLIP method described in this thesis allows a charge-based, protein-independent fusion method to incorporate molecular beacon (MB) into EVs. Through the adjustment of ratios between positive and negatively charged lipids, CLIP surface charge could be fine-tuned for efficient fusion with EVs, where variation of lipid composition yields different fusion efficiency ranging from 4.6% to 60.2%. Following the optimization of CLIP to EV ratio, coupling of EV-CLIP fusion with compartmentalization was achieved through microfluidic droplet reactor. The EV-CLIP platform could digitally profile the presence of miRNA and mRNAs within individual EVs, while preventing uncontrolled aggregation that would otherwise happen in the conventional bulk setting. This EV-CLIP approach simplifies RNA detection using a minimal sample volume of 20 µL, while eliminating the need for prior EV isolation or RNA preparation, further preserving the sample integrity. The thesis also discusses detailed aspects of the EV-CLIP system including the kinetics of the reaction, and other detailed characterization. Lastly, the thesis also highlights the clinical implementation of EV-CLIP. The approach was tested with 83 patient samples and detected EGFR L858R and T790M mutations with high AUC values of 1.0000 and 0.9784, respectively. Additionally, EV-CLIP’s performance in quantifying EV-derived mRNA for specific mutations in serial monitoring samples taken during course of chemotherapy highlights its potential for precise quantification of rare EV subpopulations, facilitating the exploration of single EV RNA content and enhancing understanding of diverse EV populations in various disease states. underlining the translational potential of EV-CLIP to be integrated into clinical systems. In conclusion, EV-CLIP method allows an unbiased comprehensive analysis of heterogeneous EV population, contributing to a more nuanced understanding of EV biology and biomarker applications and offering a reliable platform for the detection of EV-derived biomarkers, especially RNA–allowing a precise and informative liquid biopsy application.