Invasive nature and pain caused to patients inhibit the routine use of tissue biopsy-based procedures for cancer diagnosis and surveillance. The analysis of extracellular vesicles (EVs) from biofluids have recently gained significant traction in the liquid biopsy field. EVs offer an essential "snapshot" of their precursor cells in real time and contain information-rich collection of nucleic acids, proteins, lipids, etc.The analysis of protein phosphorylation, as a direct marker of cellular signaling and disease progression, could be an important stepstone to successful liquid biopsy applications. Here, we introduce a rapid EV isolation method based on chemical affinity called EVtrap (Extracellular Vesicles Total Recovery and Purification) for EV phosphoproteomics analysis of human plasma. Incorporating EVtrap with high performance mass spectrometry (MS), we were able to identify over 16,000 unique peptides representing 2,238 unique EV proteins from just 5 μL plasma sample, including most known EV markers, with substantially higher recovery levels compared to ultracentrifugation. Most importantly, more than 5,500 unique phosphopeptides representing almost 1,600 phosphoproteins in EVs were identified using only 1 mL of plasma. Finally, we carried out quantitative EV phosphoproteomics analysis of plasma samples from patients diagnosed with chronic kidney disease or kidney cancer, identifying dozens of phosphoproteins capable of distinguishing disease states from healthy controls. The study demonstrates the potential feasibility of our robust analytical pipeline for cancer signaling monitoring by tracking plasma EV phosphorylation.

Background Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson’s disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease. Methods Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs). Results After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation. Conclusions These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for both familial and sporadic forms of Parkinson's disease (PD). However, compared to cancer, overall lower genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease. Utilizing 141 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic and prognostic biomarkers for PD from urinary extracellular vesicles (EVs). After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identified and quantified 4,480 unique proteins and 2,682 unique phosphoproteins. We detected multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways such as neuronal cell death, neuroinflammation, autophagy, and formation of amyloid fibrils. We established two panels of proteins and phosphoproteins as novel candidates for disease and risk biomarkers, and substantiated using ROC, machine learning, and in-depth network analysis. Several disease biomarkers were further validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation. These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Background Pancreatic cancer is one of the most difficult cancers to detect early and most patients die from complications arising due to distant organ metastases. The lack of bona fide early biomarkers is one of the primary reasons for late diagnosis of pancreatic cancer. It is a multifactorial disease and warrants a novel approach to identify early biomarkers. Methods In order to characterize the proteome, Extracellular vesicles (EVs) isolated from different in vitro conditions mimicking tumor-microenvironment interactions between pancreatic cancer epithelial and stromal cells were analyzed using high throughput mass spectrometry. The biological activity of the secreted EVome was analyzed by investigating changes in distant organ metastases and associated early changes in the microbiome. Candidate biomarkers (KIF5B, SFRP2, LOXL2, and MMP3) were selected and validated on a mouse-human hybrid Tissue Microarray (TMA) that was specifically generated for this study. Additionally, a human TMA was used to analyze the expression of KIF5B and SFRP2 in progressive stages of pancreatic cancer. Results The EVome of co-cultured epithelial and stromal cells is different from individual cells with distinct protein compositions. EVs secreted from stromal and cancer cells cultures could not induce significant changes in Pre-Metastatic Niche (PMN) modulation, which was assessed by changes in the distant organ metastases. However, they did induce significant changes in the early microbiome, as indicated by differences in α and β-diversities. KIF5B and SFRP2 show promise for early detection and investigation in progressive pancreatic cancer. These markers are expressed in all stages of pancreatic cancer such as low grade PanINs, advanced cancer, and in liver and soft tissue metastases. Conclusions Proteomic characterization of EVs derived from mimicking conditions of epithelial and stromal cells in the tumor-microenvironment resulted in the identification of several proteins, some for the first time in EVs. These secreted EVs cannot induce changes in distant organ metastases in in vivo models of EV education, but modulate changes in the early murine microbiome. Among all the proteins that were analyzed (MMP3, KIF5B, SFRP2, and LOXL2), KIF5B and SFRP2 show promise as bona fide early pancreatic cancer biomarkers expressed in progressive stages of pancreatic cancer.

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