Sage Journals: Discover world-class research

Abstract

The vast majority of cancers are treatable when diagnosed early. However, due to the elusive trace and the limitation of traditional biopsies, most cancers have already spread widely and are at advanced stages when they are first diagnosed, causing ever-increasing mortality in the past decades. Hence, developing reliable methods for early detection and diagnosis of cancer is indispensable. Recently, extracellular vesicles (EVs), as circulating phospholipid vesicles secreted by cells, are found to play significant roles in the intercellular communication as well as the setup of tumor microenvironments and have been identified as one of the key factors in the next-generation technique for cancer diagnosis. However, EVs present in complex biofluids that contain various contaminations such as nonvesicle proteins and nonspecific EVs, resulting in the interference of screening for desired biomarkers. Therefore, applicable isolation and enrichment methods that guarantee scale-up of sample volume, purity, speed, yield, and tumor specificity are necessary. In this review, we introduce current technologies for EV separation and summarize biomarkers toward EV-based cancer liquid biopsy. In conclusion, a novel systematic isolation method that guarantees high purity, recovery rate, and tumor specificity is still missing. Besides that, a dual-model EV-based clinical trial system includes isolation and detection is a hot trend in the future due to efficient point-of-care needs. In addition, cancer-related biomarkers discovery and biomarker database establishment are essential objectives in the research field for diagnostic settings.

Keywords

extracellular vesicles isolation cancer diagnostic

Introduction

Extracellular vesicles (EVs), as phospholipid vesicles secreted by cells,¹ can be classified into 3 categories: apoptotic bodies, microvesicles (MVs), and exosomes.^1

–5 Among them, MVs originate from the buddings of the plasma membranes of tumors, neutrophils, and platelets¹ with the diameter larger than 200 nm,⁶ while exosomes are derived from the inward buddings of the plasma membranes⁷ with the size between 30 and 200 nm.⁸ Firstly, the inward buddings of the membrane inside the endosome produce the multivesicular bodies (MVBs),⁹ then exosomes are released because MVBs fuse with the plasma membranes of various cells.^10
–12 In virtue of appropriate isolation technologies, EVs can be easily extracted from blood,¹³ urine,¹⁴ breast milk,^15,16 saliva,¹⁷ and cerebral spinal fluid body fluids, and contain proteins,^18
–20 messenger RNAs (mRNAs), microRNAs (miRNAs),^21
–23 and DNA²⁴ with similar genetic characteristics of their parenting cells.^25,26 Hence, EVs can be used for diagnostic settings, especially for cancers.^27

–30 In addition to EVs, circulating tumor cells (CTCs) and circulating mRNA can be also used for liquid biopsy.^31,32 However, their undesirable maneuverability and high minimum amount for detection limit further application. In this review, we will highlight the EV-based liquid biopsy.

Comparing with traditional tissue biopsies that require complex sampling procedures and are invasive,³¹ EV-based liquid biopsy shows advantages of monitoring tumors with time evolution,^33

-36 long-term treatment response of the tumors, repeated sampling,^37,38 and easy sampling managements.^39,40 Moreover, traditional biopsies based on symptoms may fail to detect early-stage cancer because some symptoms only show at late-stage cancers; on the other hand, EV-based liquid biopsy, on genetic level, can waive the symptoms limitations and detect early-stage cancer. Extracellular vesicle–based liquid biopsy is also important for liver diseases,^41,42 immune diseases,^43,44 neuro diseases,⁴⁵ and Parkinson disease.⁴⁶ We only focus on cancer diagnostic in this review paper.

Extracellular vesicles show potential cancer diagnostic functions; however, EV-based liquid biopsy is limited in identifying EVs since they have size heterogeneities (30 nm to 1 mm)²⁵ and present in various human biofluids that contain outside vesicular small molecules (eg, RNAs and proteins).⁴⁷ Hence, efficient EV isolation methods are necessary before clinical analysis. In addition, normal EVs secreted by host cells influence the specificity and sensitivity of tumor EVs,⁴⁸ which can be possibly resolved through tumor biomarker immunoaffinity, so defining reliable cancer biomarkers is essential.

This review article presents recent EV isolation and characterization techniques and highlights the diagnosis progresses among various cancers. We compare the isolation methods by purity, recovery rate, processing time, and tumor specificity and evaluate the characterization techniques. Even though some reviews have published, we believe this review is helpful because of compiling latest diagnostic progresses for various cancers.

Extracellular Vesicle Isolation and Enrichment

Various isolation methods have been developed in the past decades and have been evaluated by recovery rate, purity, integrity, tumor specificity, and processing time.⁴⁹ In this review, we present bulk isolations that rely on size, density, coprecipitation, and affinity binding,⁴⁹ and subpopulation isolation methods that isolate and enrich subpopulation by targeting antibodies against EV surface biomarkers include common biomarkers (eg, CD9, CD63, and CD81)^50,51 and tumor-specific biomarkers (eg, HER2, EpCAM, EGFR, EGFRvIII, and GPC1).^22,52
–54

Size-Based Isolation

Ultracentrifugation

Ultracentrifugation (UC) is the gold standard protocol for isolating EVs.⁵⁵ Ultracentrifugation can scale-up sample volume and isolate EVs with high centrifugal force (100 000g),⁵⁶ while expensive equipment, skill dependent, time consuming, low purity, and yield are the drawbacks. Tangential flow filtration (TFF): Comparing with traditional dead-end filtration, TFF is a cross-flow filtration that avoids the filter cake and fractionates EVs through a module contains hollow fiber filters.⁵⁷ When the sample flows through the module, small molecules (permeate) pass through the hollow fiber filters, while EVs (retentate) are remained inside the module. Tangential flow filtration has been applied to isolate EVs from cell culture medium with 500 kDa hollow fiber filters, and enriched EVs from scalable sample volume at the meantime purified EVs with high recovery rate (5 times higher than UC) in a rapid, sterilized manner.⁵⁷ Size-exclusion chromatography: Size-exclusion chromatography (SEC) isolates EVs based on gel column with specific size pores.^58
–60 When the sample goes through the gel, smaller molecules are trapped into the pores, while EVs flow through directly that come faster than smaller molecules. Commercial columns (qEV; iZON, Boston, MA) have been applied to separate EVs from various biofluids such as plasma, serum, and cell culture media. Takov et al recently compared UC and qEV and showed qEV contained high EV number, EV protein, and stronger marker signal than UC; nevertheless, the qEV failed to remove all non-EV proteins.⁶¹ Based on SEC principle, there is a balance between purity and recovery rate. Moreover, several factors such as pore size, structure, length of column, and loading volume can contribute to the yield and purity to some extent.

Density-Based Isolation

Sucrose density gradient UC separates EVs in a continuous size-based UC mode; serial ultracentrifugations are applied to remove living cells and cell debris, then the EV and proteins are separated based on different flotation densities under UC.⁶² Despite the high purity, sucrose has lengthy problem due to multiple centrifugations, and the recovery rate is low due to multiple centrifugations.

Coprecipitation Isolation

Coprecipitation reduces the solubility of EVs by adding polymer or reagent (eg, ExoQuick) and causes precipitation, and EVs are isolated from precipitation with lower centrifugal forces later.^63,64 Comparing with UC, coprecipitation saves processing time but lacks scale-up and EV specificity due to additives.

Affinity Binding–Based Isolation

Membrane affinity binding

Recently, commercial kits (eg, exoEasy) use membrane-based affinity binding to isolate EVs,⁶⁵ which are selected based on generic, biochemical feature of vesicles and fixed on the affinity spin column that can be washed and eluted with buffers. The advantages of kits are easy handling, high purity, and extremely fast (25 minutes); however, low throughput and recovery rate are the shortcomings. Macías et al tested the kit and demonstrated it had low EV number and weak CD63 and CD9 Western blotting signal.⁶⁶ TiO₂ and lipid bilayer of EV binding: Gao et al utilized the advantage of the specific interaction between titanium oxide and EV phosphate lipid bilayer to separate EVs from serum.⁶⁷ Micron-sized TiO₂ particles enriched EVs through the bidentate binding with high recovery rate (93.4%) in a simple manner. They also tested the platform with patients with pancreatic cancer (PC) and found 29 novel proteins, which showed high potential diagnostic function. However, this method is limited by low throughput.

Immunoaffinity

One of the advantages of immunoaffinity compared to bulk isolation is the EV subpopulation isolation with high recovery rate and specificity. The immunoaffinity is the specific affinity between an antibody and antigen, which normally means the bindings between a capture antibody, a EV surface antigen, and a detection antibody.^68,69 Shao et al applied immunomagnetic beads to extract glioblastoma multiforme (GBM) EVs with 93% specificity, and they detected EPHA2, EGFR, and PDPN mRNA from patients with GBM successfully.⁷⁰ Recently, Sharma et al used the monoclonal antibody (mAb) with magnetic beads to capture tumor EVs from patients with melanoma that expresses CSPG4 epitope.⁷¹ Masud et al applied gold-loaded ferric oxide nanocubes functionalized with antibodies that work as “dispersible nanocarriers” on separating population of EVs.⁷² Bai et al have captured lung cancer EVs through queued beads functionalized with antibodies and combined with quantum dots in a microarray, which showed distinctive lung cancer marker detection level.⁷³ Immunoaffinity can isolate EV subpopulation, especially for tumor EVs, while limited sample processing volume is a big challenge for the immunoaffinity.

Comparison

Different isolation techniques are compared in Table 1 based on processing time, purity, recovery rate, sample scale, and tumor specificity. High recovery rate and purity are important because of clinical sensitivity and accuracy.⁴⁷ Flexible sample scale is another key point for clinical settings since various biofluids require different pretreatment, for example, urine must be in large scale to obtain enough EV number, while serum and plasma must be in small scale due to collecting limitation. It is worth to note that immunoaffinity isolation methods can be directly used for point-of-care diagnostics since tumor subpopulation capture.⁷⁴ However, immunoaffinity is highly dependent on the quality of antibodies. Until then, a platform that can guarantee high yield, purity, rapid time, and tumor specificity is still missing.

Table 1.

Comparison of EV Enrichment, Separation, and Purification Methods.

Platform	Principle	Advantages	Limitations
Ultracentrifuge	Size	Large scale, gold standard	Lengthy, low yield, low purity, expensive equipment, lack tumor specificity
Tangential flow filtration	Size	Large scale, high purity, yield, rapid, integrity	Lack tumor specificity
Size-exclusion chromatography	Size	User-friendly, relative high yield	Low purity, small scale, lack tumor specificity
Sucrose density gradient centrifuge	Density	User-friendly, gold standard, purity	Lengthy, expensive equipment, low yield, no tumor specificity
Coprecipitation	Charge	User-friendly	Lack tumor specificity, small scale
Membrane affinity binding	Surface	High yield, integrity	Lack tumor specificity, small scale
TiO₂ and lipid bilayer binding	Surface	High yield, integrity	Lack tumor specificity, small scale
Immunoaffinity	Surface	Tumor specificity	Small scale, low recovery rate, low purity

Extracellular Vesicle Detection and Characterization

Microscopy Quality Characterization

Scanning electron microscopy

Scanning electron microscopy (SEM) is a high-resolution technique in the EV field.^22,75,76 Extracellular vesicles are fixed by chemicals such as glutaraldehyde and dehydrated by critical point dry in ethanol; osmium tetroxide can be used to increase contrasts. Scanning electron microscopy scans EV surface with a focused beam of electrons, normally a thin sputter gold coating is required for focusing, and generates EV topography image due to the interaction between electrons and atoms in the EV sample. Majorities of EVs present spherical-shaped or cup-shaped morphologies under the SEM.⁷⁷ Transmission electron microscopy (TEM): TEM has higher resolution compared with SEM⁷⁷ and has similar procedures for fixation and contrast enhancement. The focused beam of electrons are transmitted through samples to generate images, and molecular contents on the EV surface can be characterized through TEM with immunolabeling.⁷⁸ Atomic force microscopy (AFM): AFM is another high-resolution microscopy in EV studies.^79,80 Extracellular vesicles are fixed on a mica substrate coated with 3-aminopropyltriethoxysilane and are air dried or nitrogen dried after extensive Deionized (DI) water washing steps. A metal probe is used for scanning EV surface to provide surface topography information under the amplitude modulation and local stiffness and adhesion information under phase modulation.⁸⁰ All 3 microscopies show outstanding images of EVs. Among them, SEM and TEM show higher resolution than AFM but require complex sample preparations. Moreover, TEM is able to characterize molecular content level, while AFM is professional in acquiring stiffness information.

Quantitative Characterization

Dynamic light scattering

Dynamic light scattering (DLS) shows EV size distribution based on the intensity of the scattered light.⁸¹ The suspended EVs are illuminated by a laser, and the intensity of the light fluctuates over time since EV goes through Brownian motion.^81
–83 The effective size of EVs is calculated by Strokes-Einstein equation based on the transformation between fluctuation rates and diffusivities of the EVs. However, the intensity of the scattered light is also associated with the size of the EV; larger EV size reflects higher intensity, which may influence accuracy. Nanoparticle tracking analyzer (NTA): Similar to DLS, a laser beam is used to illuminate EVs in the sample.^76,84 The path of every single EV under Brownian motion is recorded by a camera,³⁷ then a software such as NanoSight calculates the concentration and size distribution mathematically based on Strokes-Einstein equation that converts velocity and diffusivity of the EV. Unlike DLS that deals with bulk scattering intensity, NTA is capable of tracking a single EV that overcomes polydisperse problems. Both methods provide accurate and sensitive size distributions, while NTA shows concentration quantification. Tunable resistive pulse sensing (TRPS): TRPS is capable of measuring both EV concentration and size distribution.^85
–87 Suspended EV in electrolytes generates different voltage pulse when it passes through a nanopore, and the voltage pulse information can be transferred into size and concentration based on standard calibration sets. Comparing with NTA, TRPS represents higher sensitivity and accuracy since it can measure the narrow size distribution of EVs based on the nanopores, which cover specific size ranges (eg, NP 80, ranges from 40 to 225 nm). Dynamic light scattering, NTA, and TRPS are popular in the EV field for characterizing. Comparing with NTA and TRPS, DLS lacks quantification even though it can detect very small EVs. Both NTA and TRPS provide quantification information. Compared with NTA, TRPS overcomes the detection limit caused by low EV concentration and is more precise in size distribution. But notably, TRPS may need to take effort on nanopore maintenance to avoid EV clogging and instability issues.

Protein Analysis

Pierce bicinchoninic acid protein assay kit

Bicinchoninic acid is used to quantify total protein concentration including membrane proteins and intravesicular proteins.^88
–90 It is a highly sensitive and rapid method based on colorimetric solution but cannot characterize EV protein.⁹¹ SDS-PAGE and Western blotting: sodium dodecyl sulfate (SDS)-PAGE is a qualitative method to detect EV proteins⁹² that includes protein lysis, denature, and separation based on molecular weight through gel electrophoresis; protein with less mass moves faster than with greater mass. Once proteins are separated by electrophoresis, they can either use for downstream proteomics or can be transferred onto a membrane for Western blotting after staining. Western blotting is also a qualitative technique for specific protein analysis.⁹³ Primary antibody, secondary antibody, and detection reagents are used in sequence after membrane transferring. Even though Western blotting is time-consuming, it is a powerful technique to demonstrate target proteins that are associated with EVs, and it can process multiple proteins at the same time. Enzyme-linked immunosorbent assay (ELISA): ELISA is a plate assay for membrane protein detection.⁹⁴ Enzyme-linked immunosorbent assay applies a “sandwich” format, EVs with specific surface biomarkers are fixed between the support that is pretreated with antibodies that can bond with EV surface biomarkers and another detection antibody that is linked to an enzyme (horseradish peroxidase), which is incubated with a substrate to produce measurable products. Enzyme-linked immunosorbent assay is commercially available and relies on antibody–antigen interaction that can improve the specificity of detection, while it cannot quantify multiple proteins simultaneously. Flow cytometry: Flow cytometry is another technique that can quantify and characterize EV surface biomarker based on fluorescence intensity of the detection antibody.^95,96 A laser beam illuminate EVs, and the scattered light is converted to an intensity-associated voltage pulse that can be quantified later.⁹⁷ The advantage of flow cytometry is parallel multiple surface biomarker detection with different fluorescent antibodies; however, EV is too small for flow cytometry to capture the florescence signal since it is originally designed for cell surface expression analysis, so immunoconjugated beads are required to increase fluorescence signals since EVs can be mounted on the beads.

Nucleic Acids Analysis

Precipitation and spin columns

Both precipitation and spin columns complete total RNA extraction and are commercially available.^22,98 Precipitation relies on phase separation; RNA is suspended into the aqueous phase and is recovered through ethanol precipitation.⁹⁸ The spin column is based on solid-phase extraction that relied on silica and RNA binding with chaotropic agents. Amplification and sequencing: Amplification is used for detecting a target sequence by end-point electrophoresis or real-time fluorescence measurements through polymerase chain reaction.^22,52,99 Sequencing RNA profiling is able to generate a wider and deeper RNA characterization of the whole transcriptome, which is able to detect unknown RNAs.⁴⁷

Cancer Clinical Applications of EV

Conventional biopsies, such as tissue biopsy, are not only invasive¹⁰⁰ but have limitations to profile tumors due to tumor heterogeneous characteristics, and they cannot reflect the whole tumor information.^101,102 Hence, liquid biopsy shows advantages since it is noninvasive and on genetic level that can provide comprehensive information. Extracellular vesicles play an important role in the cancer liquid biopsy since they carry all types of genetic information from original tumors,^103,104 and they also obtain attention because of preinvasive and early state diagnosis.¹⁰⁵ Extracellular vesicle–based diagnostic relies on tumor exosomal biomarkers, so defining and discovering reliable biomarkers is vital. In this review, we will present the lasted EV diagnostic based on exosomal biomarkers among different cancers (Table 2).

Table 2.

Potential Biomarkers of EVs for Cancer Diagnostic Application.

Type	Protein Biomarker	Nucleic Acids	Long Noncoding RNA
Breast	HER2, CD82, ER, CD24, Ki67, TGF-β	miR-10b, miR-21, miR-145, miR-1246, miR-105, miR-222, and miR-200c	N/A
Lung (non-small cell lung cancer)	EFGR, MET, PIK2CA, ALK, KRAS, MAP2K1, HER2, BRAF, AKT1, CD151, CD171, and tetra-spannin 8, CD91, CD317, ECM1, LRG1	7b-5p, let-7e-5p, miR-23a-3p, miR-486-5p	lncRNA GAS5
Ovarian	HER2, TrKB, CD24, EpCAM	miR-375, miR-1307, miR-21, miR-200b, miR-100, miR-320, miR-141, miR-125b, miR-1246, and miR-93, miR-30a-5p, miR-145, miR25, miR148a, miR-101	N/A
Prostate	N/A	miR-1246, GATA2, miR-141, miR-375	SAP30L-AS1, SChLAP1
Pancreatic	GPC1, MIF, EGFR, Glypican-1, ZIP4, PD-L1, CD104, Epcam	miR-122-5p, miR-125b-5p, miR-1192-5p, miR-193b-3p, miR-221-3p and miR-27b-3p	N/A
Bladder	N/A	miR-148b-3p, miR-141, miR-27a-3p, miR-100, miR-92a, miR-99a, miR-93, miR-940, miR-375, miR-146a-5p	PCAT-1, SPRY4-IT1, UBC1 and SNHG16
Melanoma	PD-L1, CSPG4+	N/A	N/A
Brain (glioblastoma multiforme)	EGFR, EFGRvIII	miR-301a, miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p and miR-543, miR-22, miR-222	lncRNAs HOTAIR

Abbreviation: N/A, not applied.

Breast Cancer

Breast cancer (BC) plays the second position of cancer mortality in women.^106,107 Common breast X-ray screening is invasive and radioactive, thus EV, diagnostic setting is important. Both blood samples and breast milk contain reliable EV resources, while collecting milk requires specific time point even though it is simpler than serum.¹⁰⁸ There are several BC biomarkers; proteins such as HER2, ER, and Ki67 are highly expressed.^{106,109
–111} Yang et al said the expression of TGF-β in the breast milk increased the risk of BC.¹¹² van’t Veer et al claimed that CD24 was abundant in patients with late-stage BC,¹¹³ and Wang et al demonstrated CD82 expression level was negatively associated with patients with BC.¹¹⁴ As for miRNA, miR-10b and miR-145 are abundant in patients with BC.¹¹⁵ Hannafon et al demonstrated both miR-21 and miR-1246 expressed in patients were higher than healthy donors.¹⁰⁸ Recently, Zhai et al have used Au nanoflare probe to detect miR-1246 in plasma samples successfully (Figure 1A).¹¹⁶ Alba et al claimed miR-105 was higher in patients with metastasis BC than in healthy donors.¹¹⁷ Jong et al detected miR-21, miR-222, and miR-200c with high sensitivity with their surface-enhanced Ramen scattering sensor.¹¹⁸

Figure 1.

A, Schematic of Au nanoflare probe to detect miR-1246. Fluorescence-treated probes enter the exosomes and bind to the targets after incubation with exosomes from breast cancer cells. B, Comparison of miR-1246 expression level in patients with breast cancer (n = 46) and healthy controls (n = 28). Patients with breast cancer showed higher exosomal miR-1246. P < .0001. Reprinted with permission from Zhai et al.¹¹⁶ Copyright © 2019 American Chemical Society. C, Lung cancer liquid biopsy–related exosomal biomarkers. Reprinted with permission from Cui et al.¹²⁵ Copyright © 2019 from Elsevier Ltd.

Lung Cancer

Lung cancers (LCs) are the most common and high death leading type of cancer because the majority of LCs are at late stage and go through metastasis that cannot be cured when they are first found.¹¹⁹ Non-small cell lung cancer (NSCLC) is the most common type of LC and only shows symptoms at the late stage¹²⁰; hence, early-stage detection is essential. EFGR, MET, PIK2CA, ALK, KRAS, MAP2K1, HER2, BRAF, AKT1, CD151, CD171, and tetraspanin 8 were revealed to be highly associated with LC.¹²¹ Ueda et al found CD91 was a powerful surface biomarker in advanced stage LCs.⁹⁴ Jakobsen et al showed CD317 was able to distinguish patients with LC with 75% accuracy.¹²² Niu et al found patients with NSCLC expressed a high level of α-2-HS-glycoprotein (AHSG), the extracellular matrix protein 1 in the serum compared to healthy donors.¹²³ Li et al found α-2-glycoprotein (LRG1) was strongly expressed in urinary EVs from patients with NSCLC (Figure 1B).¹²⁴ Recently, Castellanos-Rizaldos et al improved the detection sensitivity and specificity of EGFR T790M from the plasma of the patients with NSCLC by combining exoRNA/DNA and circulating free tumor DNA.¹²⁵ Jin et al found let-7b-5p, let-7e-5p, miR-23a-3p, and miR-486-5p were related to early-stage NSCLC.¹²⁶ Xu et al demonstrated miR-21 and miR-155 were higher in patients with NSCLC with recurrence than without recurrence and healthy donors.¹²⁷ Moreover, Li et al found lncRNA GAS5 was downregulated in early-stage patients with NSCLC compared with healthy donors.¹²⁸

Ovarian Cancer

Ovarian cancer (OC) is difficult to be detected until it has spread within the pelvis and abdomen at the late stage, so early-stage detection is necessary.¹²⁹ Symptoms of early-stage OC are rare and nonspecific even in the advanced stage, so EV diagnostic setting on genetic levels has advantages. Protein biomarkers such as claudin-4, HSP70, HER2, and TrkB derived from exosomes from patients showed different expression compared with healthy controls (Figure 2A).^130

–133 CD24 and EpCAM are also possible biomarkers for OC.¹³⁷ Exosomal miRNAs are much more powerful for OC diagnosis. Overexpressed level of miR-21, miR-200b, miR-100, miR-320 miR-141, miR-125b, miR-1246, miR-375, and miR-93 differed between OC patients and healthy donors.¹³⁸ The miR-1290 also showed the possibility of diagnosis on high-grade OC.¹³⁹ The miR-30a-5p was highly expressed in the urine samples of patients with OC,¹⁴⁰ while miR-145, miR25, and miR148a were under expressed.¹⁴¹ Xu et al found miR-101 was expressed lesser in patients with OC than in healthy donors.¹³² Qiu et al found metastasis-associated lung adenocarcinoma transcript 1 (MA-LAT1) was positively associated with OC.¹⁴²

Figure 2.

Molecular components (long noncoding RNAs, microRNAs, and membrane proteins) in exosomes from patients with ovarian cancer. Reprinted with permission from Yang et al.¹³⁰ Copyright © 1999-2019 John Wiley & Sons, Inc. B, Histogram and boxplot of fluorescence intensity of exosomes (Target: Prostate-specific membrane antigen (PSMA) positive) from patients with prostate cancer and healthy donors detected by superparamagnetic conjunctions and molecular beacons (SMC-MB) platform. Reprinted with permission from Li et al.¹³⁴ Copyright © 2019 American Chemical Society. C, Comparison of glypican-1 expression level in patients with pancreatic cancer (n = 20), benign pancreatic disease (n = 7), and healthy controls (n = 11). Glypican-1 in patients with pancreatic cancer were elevated. Reprinted with permission from Lewis et al.¹³⁵ Copyright © 2019 American Chemical Society. D, Quantitative reverse transcription polymerase chain reaction analysis of exosomal H19 from patients with bladder cancer, benign disease, and healthy controls. P < .001. Reprinted with permission from Wang et al.¹³⁶ Copyright © International Scientific Information.

Prostate Cancer

Prostate cancer (PCa) is one of the most common types of cancer in men.¹⁴³ Some types of PCa grow slowly with minimum harmful effects, while some types are aggressive.¹⁴⁴ The early stage of the PCa that may be defined as the prostate gland is easy to cure, but they show no signs or symptoms. So the liquid biopsy based on exosomal molecular contents is important. Bhagirath et al used the nCounter technology and found miR-1246 was a promising aggressive PCa biomarker.¹⁴⁵ Donovan found PCA3 and ERG mRNAs predicted high-grade PCa.¹⁴⁶ Li et al used an ultrasensitive and reversible nanoplatform to detect PSA, PCA3, and mRNA successfully in urinary exosomes from patients with PCa (Figure 2B).¹³⁴ However, PSA has limitation because it may not differ cancer and benign prostatic hyperplasia (BPH).¹⁴⁶ Some miRNAs, such as miR-141 and miR-375, from serum EVs were associated with metastatic PCa, and the miR-19b distinguished PCa with 100% specificity and 93% sensitivity.¹⁴⁷ As for long noncoding RNAs, Wang et al found SAP30L-AS1 was related to tumor invasion, and SChLAP1 was expressed higher in PCa compared with BPH and healthy controls.¹⁴⁸

Pancreatic Cancer

Early-stage PC can only be detected in people with pancreatic cysts or family history of PCs,¹⁴⁹ but it can seldom be detected in other conditions.¹⁵⁰ Serum cancer antigen 19-9 (CA19-9) is a possible biomarker for PC¹⁵¹; however, it fails to show sensitivity and specificity of early-stage PC. Zhou et al compared 216 patients with PC with 220 healthy controls and found miR-122-5p, miR-125b-5p, miR-1192-5p, miR-193b-3p, miR-221-3p, and miR-27b-3p were significantly higher in patients with PC.¹⁵² Goto also found higher expression levels of miR-191, miR-21, miR-451a in PC.¹⁵³ Lewis et al developed an AC electrokinetic microarray chip that was capable of differing 20 patients with PC from healthy donors based on glypican-1 and CD63 expression levels with 99% sensitivity (Figure 2C).¹³⁵ Li et al designed an ultrasensitive polydopamine bifunctionalized Surface-Enhanced Raman Scattering (SERS) immunoassay with a detection limit of one exosome in 2 mL, and they discriminated patients with PC based on GPC1, MIF, and EGFR surface biomarkers successfully.¹⁵⁴ Jin et al claimed ZIP4 promoted PC growth and could be a novel diagnostic biomarker for PC.¹⁵⁵ Recently, Lux claimed the combination of CA19-9 and c-Met improved sensitivity test of patients with PC.¹⁵⁶ They found PD-L1-positive patients showed shorter postoperative survival time that can be used as a negative prognostic factor. Moreover, the combination of CD104, Epcam, Tspan8, and some miRNAs such as miRNA-1246 improved diagnostic sensitivity and specificity of patients with PC.¹⁵⁷

Bladder Cancer

Bladder cancer is usually in the bladder but can show in other parts that belong to the urinary tract drainage system.¹⁵⁸ Around 50% of the patients with muscle-invasive bladder cancer will go through metastasis and die in 2 to 3 years, even patients with non-muscle-invasive BC usually have recurrence rate.¹⁵⁹ The symptoms of bladder cancer include hematuria, pelvic pain, and urination pain,¹⁶⁰ while these symptoms normally show at the middle or late stage; hence, the early diagnosis of bladder cancer is very important. Cystoscopy is a gold standard diagnostic tool for non-muscle invasive bladder cancer,¹⁶¹ but it is expensive and time-consuming since subsequent cystoscopy is necessary once the result is negative,¹⁶¹ and it fails to provide sensitive surveillance information. Hence, EV diagnosis with novel biomarkers is essential for patients with bladder cancer and suspected individuals. Both blood and urine provide reliable EV source for bladder cancer diagnosis; miR-148b-3p, miR-141 were increased, but miR-27a-3p, miR-100, miR-92a, and miR-99a were decreased in serum and plasma.¹⁶² Compared with blood, urine shows advantages because urine contacts with bladder directly and can be collected with various time point easily to reflect different stages of the diseases, and EVs are able to cross the basement membrane into the urine with miRNAs. The miR-375 was decreased in high-grade bladder tumors, while miR-146a-5p was increased, especially in low-grade tumors.¹⁶² Wang et al also found serum exosomal H19 expression level was higher compared to healthy controls and benign disease patients (Figure 2D).¹³⁶ Zhan et al found PCAT-1 and SPRY4-IT1 were capable of the bladder cancer diagnosis.¹⁶³ Zhang et al claimed UBC1 and SNHG16 identified by multivariate logistic regression model also provided high diagnostic accuracy. Moreover, the high UBC1 expression level was associated with low recurrence-free survival.¹⁶⁴

Melanoma Cancer

Melanoma is the most serious type of skin cancers, but it can be treated successfully in the early stages.¹⁶⁵ Melanomas can occur in any areas of the skin that are exposed to excess UV light.¹⁶⁶ Sharma et al isolated melanoma tumor-derived exosomes successfully with an mAb 763.74 that captured the chondroitin sulfate peptidoglycan 4 (CSPG4+) that are expressed on the surface of exosomes.⁷¹ Chen et al claimed that exosomal PD-L1 was associated with anti-PD-1, which has shown promise in treating melanoma tumors. Hence, detection of PD-L1 on the exosomes is essential.¹⁶⁶

Glioblastoma Multiforme

Glioblastoma multiforme is an aggressive type of cancer that occurs in the brain or spinal cord, and it tends to occur in older adults.^167,168 Curing GBM is seldom possible but effective treatment can slow the progression of cancer and relief the symptoms.¹⁸ However, treating early-stage GBM guarantees minimum dissatisfactory effects. Normal diagnosis methods include the neurological examination, imaging tests (magnetic resonance imaging), and tissue tests. Nevertheless, compared with EV liquid biopsy, they are expensive and invasive. EGFRvIII was a well-known GBM-related biomarker, and it was highly expressed on the surface of EVs from GBM patients.²² Lan et al found miR-301a was a potential biomarker for GBM.¹⁶⁹ Tan et al also found serum lncRNAs HOTAIR was significantly higher than in the healthy controls.¹⁷⁰ Ebrahimkhani et al found miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p, and miR-543 were most likely stable for GBM classification after modeling and data comparisons among 26 relative microRNAs (Figure 3).¹⁷¹ Santangelo also claimed miR-22 and miR-222 were expressed higher in high-grade GBM patients.¹⁷²

Figure 3.

Hierarchical clustering of 26 microRNAs shows differences in GBM and healthy control exosomal profiles (fold change ≥2 or ≤0.5). Reprinted with permission from Ebrahimkhani et al.¹⁷⁰ Copyright Springer Nature Publishing AG.

Conclusion and Perspective

Extracellular vesicles play an important role in tumor microenvironment; besides carrying parenting genetic information, the specific tumor-related genetic information can be used for diagnosis and immunotherapy, while size and biomolecular heterogeneities of EVs bring problems on isolating EVs that challenge the EV diagnostic setting, which requires EV isolation in a pure and rapid manner. Hence, standard sample collection, storage procedures, and efficient isolation mechanisms are important. In this review, we summarized updated isolation methods, and majorities of them enriched the bulk EVs, and immunoaffinity can isolate tumor subpopulation EV based on tumor surface biomarkers. Successful isolation is the first step of the cancer diagnosis; characterizing EVs in qualitative and quantitative way, such as microscopy, DLS, NTA, and TRPS, is also necessary. The most important thing is the molecular content characterization such as protein classification and RNA sequencing, which is the fundamental of EV-based cancer liquid biopsy because clinical trials make decision based on expression level of reliable cancer-associated RNA and protein biomarkers. However, protein biomarkers lack tumor precision and show no superiority since the same type of biomarkers can be present in multiple cancers. For example, HER2 has high expression level in breast and LCs, while CD24 is abundant in both ovarian and BCs. In that, combination of different protein biomarkers to define a specific type of cancers is necessary. Comparing with proteins, RNAs may show high specifically toward single-type cancer, but it is relatively expensive. Overall, defining and exploring specific and reliable biomarkers and compiling a cancer biomarker database are big breakthroughs for cancer diagnosis. In this review paper, we introduce EV isolation and detection separately, and we believe in the future the combination of isolation and detection methods with high efficiency is desirable and plays key role since they fulfill the point-of-care cancer diagnostic need. Moreover, even though EV-based liquid biopsy is advantageous in many aspects such as noninvasive collection, early-stage detection compared with traditional biopsy, until now, gold standard operating procedures for collecting, isolating, and detecting EVs are still missing. Also, the universality and pricing are other concerns about liquid biopsy. Up to now, only HER2 and EGFR are qualified as biomarkers tested in clinical trials. Nevertheless, EV-based liquid biopsy is still a long road ahead, and we believe it will be more reliable and standard in the future since outstanding researchers make efforts on it.

Footnotes

Author Contribution

Chunyan Ma and Fan Jiang has contributed equally to this work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Zhejiang Medical Technology Program (2018KY918).

ORCID iD

Yifan Ma

Jingjing Zhang

References

Raposo

Stoorvogel

Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013;200(4):373–383. doi:10.1083/JCB.201211138.

Théry

Zitvogel

Amigorena

Exosomes: composition, biogenesis and function. Nat Rev Immunol. 2002;2(8):569–579.

Théry

Ostrowski

Segura

. Membrane vesicles as conveyors of immune responses. Nat Rev Immunol. 2009;9(8):581–593.

Heijnen

HFG

Schiel

Fijnheer

Geuze

Sixma

. Activated platelets release two types of membrane vesicles: microvesicles by surface shedding and exosomes derived from exocytosis of multivesicular bodies and α-granules. Blood. 1999;94(11):3791–3799.

Colombo

. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol. 2014;30:255–292. doi:10.1146/annurev-cellbio-101512-122326.

Yáñez-mó

Siljander

Andreu

, et al. Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles. 2015;4:27066. doi:10.3402/jev.v4.27066.

Johnstone

Adam

Hammond

Orr

Turbide

Vesicle formation during reticulocyte maturation. Association of plasma membrane activities with released vesicles (exosomes). J Biol Chem. 1987;262(19):9412–9420.

Hugel

Martínez

Kunzelmann

Freyssinet

. Membrane microparticles: two sides of the coin. Physiology. 2005;20:22–27.

Piper

Katzmann

. Biogenesis and function of multivesicular bodies. Annu Rev Cell Dev Biol. 2007;23:519–547.

10.

Hurley

Hanson

. Membrane budding and scission by the ESCRT machinery: it’s all in the neck. Nat Rev Mol Cell Biol. 2010;11(8):556–566.

11.

Luzio

Gray

Bright

. Endosome-lysosome fusion. Biochem Soc Trans. 2010;38:1413.

12.

Théry

Exosomes: secreted vesicles and intercellular communications. F1000 Biol Rep. 2011;3:15.

13.

Revenfeld

Bæk

Nielsen

Stensballe

Varming

Jørgensen

. Diagnostic and prognostic potential of extracellular vesicles in peripheral blood. Clin Ther. 2014;36(6):830–846.

14.

Salih

Zietse

Hoorn

. Urinary extracellular vesicles and the kidney: biomarkers and beyond. Am J Physiol Ren Physiol. 2014;306(11):F1251–F1259.

15.

Zonneveld

Brisson

van Herwijnen

, et al. Recovery of extracellular vesicles from human breast milk is influenced by sample collection and vesicle isolation procedures. J Extracell Vesicles. 2014;3:24215.

16.

Pieters

Arntz

Bennink

, et al. Commercial cow milk contains physically stable extracellular vesicles expressing immunoregulatory TGF-B. PLoS One. 2015;10(3):e0121123.

17.

Yang

Wei

Schafer

Wong

. Detection of tumor cell-specific mRNA and protein in exosome-like microvesicles from blood and saliva. PLoS One. 2014;9(11):e110641.

18.

Graner

Alzate

Dechkovskaia

, et al. Proteomic and immunologic analyses of brain tumor exosomes. FASEB J. 2009;23(5):1541–1557.

19.

Simpson

Lim

JWE

Moritz

. Exosomes: proteomic insights and diagnostic potential. 2009;6(3):267–283.

20.

Mathivanan

Fahner

Reid

Simpson

. ExoCarta 2012: database of exosomal proteins, RNA and lipids. Nucleic Acids Res. 2012;40:D1241–D1244.

21.

Valadi

Ekström

Bossios

Sjöstrand

Lee

Lötvall

. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9(6):654–659.

22.

Skog

Würdinger

van Rijn

, et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol. 2008;10(12):1470–1476.

23.

Rogers

Robbins

Dakhlallah

, et al. Attenuation of miR-17∼92 cluster in bronchopulmonary dysplasia. Ann Am Thorac Soc. 2015;12(10):1506–1513.

24.

Balaj

Lessard

Dai

, et al. Tumour microvesicles contain retrotransposon elements and amplified oncogene sequences. Nat Commun. 2011;2:180.

25.

Azmi

Bao

Sarkar

. Exosomes in cancer development, metastasis, and drug resistance: a comprehensive review. Cancer Metastasis Rev. 2013;32(3-4):623–642.

26.

Yang

Xie

Zhu

, et al. Functional exosome-mimic for delivery of siRNA to cancer: in vitro and in vivo evaluation. J Control Release. 2016;243:160–171. doi:10.1016/j.jconrel.2016.10.008.

27.

Logozzi

De Milito

Lugini

, et al. High levels of exosomes expressing CD63 and caveolin-1 in plasma of melanoma patients. PLoS One. 2009;4(4):e5219.

28.

Pisitkun

Shen

Knepper

. Identification and proteomic profiling of exosomes in human urine. Proc Natl Acad Sci U S A. 2004 ;101(36): 13368–13373.

29.

Keller

Rupp

Stoeck

, et al. CD24 is a marker of exosomes secreted into urine and amniotic fluid. Kidney Int. 2007; 72(9):1095–1102.

30.

Hosseini

Khatamianfar

Hassanian

, et al. Exosome-encapsulated microRNAs as potential circulating biomarkers in colon cancer. Curr Pharm Des. 2017 ;23(23): 1705–1709.

31.

Brock

Castellanos-rizaldos

Coticchia

Skog

. Liquid biopsy for cancer screening, patient stratification and monitoring. Transl Cancer Res. 2015 ; 4(3):280–290. doi:10.3978/j.issn.2218-676X.2015.06.05.

32.

Shi

Zhu

Chen

Sun

Yao

. A review on electroporation-based intracellular delivery. Molecules. 2018;23(11). doi:10.3390/molecules23113044.

33.

Alix-Panabières

Pantel

. Clinical prospects of liquid biopsies. Nat Biomed Eng. 2017 ;1(4):0065. doi:10.1038/s41551-017-0065.

34.

Gerlinger

Rowan

Horswell

, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–892.

35.

Wang

Kwak

Yang

, et al. Extracellular mRNA detected by molecular beacons in tethered lipoplex nanoparticles for diagnosis of human hepatocellular carcinoma. PLoS One. 2018;13(6):1–12. doi:10.1371/journal.pone.0198552.

36.

James Lee

Yang

Rahman

, et al. Extracellular mRNA detected by tethered lipoplex nanoparticle biochip for lung adenocarcinoma detection. Am J Respir Crit Care Med. 2016;193(12):1431–1433.

37.

Shao

Chung

Balaj

, et al. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat Med. 2012;18(12):1835–1840.

38.

Shao

Chung

Lee

, et al. Chip-based analysis of exosomal mRNA mediating drug resistance in glioblastoma. Nat Commun. 2015;6:6999.

39.

van der Pol

Böing

Harrison

Sturk

Nieuwland

. Classification, functions, and clinical relevance of extracellular vesicles. Pharmacol Rev. 2012;64(3):676–705.

40.

Raimondo

Morosi

Chinello

Magni

Pitto

. Advances in membranous vesicle and exosome proteomics improving biological understanding and biomarker discovery. Proteomics. 2011;11(4):709–720.

41.

Baig

Kothandaraman

Manikandan

, et al. Proteomic analysis of human placental syncytiotrophoblast microvesicles in preeclampsia. Clin Proteomics. 2014;11(1):40.

42.

Pillay

Maharaj

Moodley

Mackraj

. Placental exosomes and pre-eclampsia: maternal circulating levels in normal pregnancies and, early and late onset pre-eclamptic pregnancies. Placenta. 2016;46:18–25.

43.

Robbins

Morelli

. Regulation of immune responses by extracellular vesicles. Nat Publ Gr. 2014;14(3):195–208. doi:10.1038/nri3622.

44.

Durrani-Kolarik

Pool

Gray

, et al. miR-29b supplementation decreases expression of matrix proteins and improves alveolarization in mice exposed to maternal inflammation and neonatal hyperoxia. Am J Physiol Lung Cell Mol Physiol. 2017;313(2):L339–L349. doi:10.1152/ajplung.00273.2016.

45.

Bellingham

Coleman

Hill

. Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells. Nucleic Acids Res. 2012;40(21):10937–10949.

46.

Vella

Hill

Cheng

. Focus on extracellular vesicles: exosomes and their role in protein trafficking and biomarker potential in Alzheimer’s and Parkinson’s disease. Int J Mol Sci. 2016;17(2):173.

47.

Yang

Zhao

Yuan

Kim

Nanotechnology platforms for cancer immunotherapy. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2019. e1590. doi:10.1002/wnan.1590.

48.

György

Szabó

Pásztói

, et al. Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell Mol Life Sci. 2011;68(16):2667–2688.

49.

Ziaei

Berkman

Norton

. Review: isolation and detection of tumor-derived extracellular vesicles. ACS Appl Nano Mater. 2018;1(5):2004–2020. doi:10.1021/acsanm.8b00267.

50.

van Niel

Charrin

Simoes

, et al. The tetraspanin Cd63 regulates ESCRT-independent and -dependent endosomal sorting during melanogenesis. Dev Cell. 2011;21(4):708–721.

51.

Verweij

van Eijndhoven

Hopmans

, et al. Lmp1 association with Cd63 in endosomes and secretion via exosomes limits constitutive Nf-Kb activation. EMBO J. 2011;30(11):2115–2129.

52.

Conley

Minciacchi

Lee

, et al. High-throughput sequencing of two populations of extracellular vesicles provides an mRNA signature that can be detected in the circulation of breast cancer patients. RNA Biol. 2017;14(3):305–316.

53.

Jeong

Park

Pathania

Castro

Weissleder

Lee

. Integrated magneto-electrochemical sensor for exosome analysis. ACS Nano. 2016;10(2):1802–1809.

54.

Herlyn

Chen

, et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature. 2018;560(7718):382–386. doi:10.1038/s41586-018-0392-8.

55.

Gardiner

Di Vizio

Sahoo

, et al. Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey. J Extracell Vesicles. 2016;5(1):32945.

56.

Chen

Xing

. Isolation and visible detection of tumor-derived exosomes from plasma. Anal Chem. 2018;90(24):14207–14215. doi:10.1021/acs.analchem.8b03031.

57.

Busatto

Vilanilam

Ticer

, et al. Tangential flow filtration for highly efficient concentration of extracellular vesicles from large volumes of fluid. Cells. 2018;7(12). pii: E273. doi:10.3390/cells7120273.

58.

Koh

Almughlliq

Vaswani

Peiris

Murray

. Exosome enrichment by ultracentrifugation and size exclusion chromatography. Front Biosci (Landmark Ed). 2018;23:865–874.

59.

Böing

van der Pol

Grootemaat

Coumans

Sturk

Nieuwland

. Single-step isolation of extracellular vesicles by size-exclusion chromatography. J Extracell Vesicles. 2014;3(1):23430.

60.

Nordin

Lee

Vader

, et al. Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties. Nanomedicine. 2015;11(4):879–883.

61.

Takov

Yellon

Davidson

. Comparison of small extracellular vesicles isolated from plasma by ultracentrifugation or size-exclusion chromatography: yield, purity and functional potential. J Extracell Vesicles. 2019 ;8(1). doi:10.1080/20013078.2018.1560809.

62.

Théry

Amigorena

Raposo

Clayton

. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr Prot Cell Biol. 2006;30(1):3.22.1–3.22.29.

63.

Alvarez

Khosroheidari

Kanchi Ravi

DiStefano

. Comparison of protein, microRNA, and mRNA yields using different methods of urinary exosome isolation for the discovery of kidney disease biomarkers. Kidney Int. 2012;82(9):1024–1032.

64.

Rekker

Saare

Roost

, et al. Comparison of serum exosome isolation methods for microRNA profiling. Clin Biochem. 2014. doi:10.1016/j.clinbiochem.2013.10.020.

65.

Stranska

Gysbrechts

Wouters

, et al. Comparison of membrane affinity-based method with size-exclusion chromatography for isolation of exosome-like vesicles from human plasma. J Transl Med. 2018:1–9. doi:10.1186/s12967-017-1374-6.

66.

Macías

Rebmann

Mateos

Varo

Perez-gracia

. Comparison of six commercial serum exosome isolation methods suitable for clinical laboratories. Effect in cytokine analysis. Clin Chem Lab Med 2019;57(10):1539–1545.

67.

Gao

Jiao

Xia

, et al. A novel strategy for facile serum exosome isolation based on specific interactions between phospholipid bilayers and TiO₂ . Chem Sci. 2019;10(6). doi:10.1039/c8sc04197k.

68.

Yoo

Kim

, et al. A direct extraction method for microRNAs from exosomes captured by immunoaffinity beads. Anal Biochem. 2012;431(2):96–98.

69.

Chen

Skog

Hsu

, et al. Microfluidic isolation and transcriptome analysis of serum microvesicles. Lab Chip. 2010;10(4):505–511.

70.

Shao

Chung

Lee

, et al. Drug resistance in glioblastoma. Nat Commun. 2015;6:1–9. doi:10.1038/ncomms7999.

71.

Sharma

Ludwig

Muller

, et al. Immunoaffinity-based isolation of melanoma cell- derived exosomes from plasma of patients with melanoma. J Extracell Vesicles. 2018 ;7(1). doi:10.1080/20013078.2018.1435138.

72.

Masud

Yadav

Islam

Nguyen

Hossain

Shiddiky

MJA

. Gold-loaded nanoporous ferric oxide nanocubes with peroxidase-mimicking activity for electrocatalytic and colorimetric detection of autoantibody. Anal Chem. 2017;89(20):11005–11013. doi:10.1021/acs.analchem.7b02880.

73.

Bai

Wang

, et al. Rapid isolation and multiplexed detection of exosome tumor markers via queued beads combined with quantum dots in a microarray. Nano-Micro Lett. 2019;11:59. doi:10.1007/s40820-019-0285-x.

74.

Tauro

Greening

Mathias

, et al. Comparison of ultracentrifugation, density gradient separation, and immunoaffinity capture methods for isolating human colon cancer cell line LIM1863-derived exosomes. Methods. 2012;56(2):293–304.

75.

Shao

Min

Issadore

, et al. Magnetic nanoparticles and microNMR for diagnostic applications. Theranostics. 2012;2(1):55–65.

76.

Sokolova

Ludwig

Hornung

, et al. Characterisation of exosomes derived from human cells by nanoparticle tracking analysis and scanning electron microscopy. Colloids Surf, B. 2011;87(1):146–150.

77.

Théry

Aled

Sebastian

Graça

. Isolation and characterization of exosomes from cell culture supernatants. Curr Protoc Cell Biol. 2006;30(1):3.22.1–3.22.29.

78.

Hakulinen

Sankkila

Sugiyama

Lehti

Keski-Oja

. Secretion of active membrane type 1 matrix metalloproteinase (MMP-14) into extracellular space in microvesicular exosomes. J Cell Biochem. 2008;105(5):1211–1218.

79.

Tatischeff

Larquet

Falcón-Pérez

Turpin

Kruglik

. Fast characterisation of cell-derived extracellular vesicles by nanoparticles tracking analysis, cryo-electron microscopy, and Raman tweezers microspectroscopy. J Extracell Vesicles. 2012;1:19179.

80.

Sharma

Rasool

Palanisamy

, et al. Structural-mechanical characterization of nanoparticles exosomes in human saliva, using correlative AFM, FESEM and force spectroscopy. ACS Nano. 2010;4(4):1921–1926.

81.

Lawrie

Albanyan

Cardigan

Mackie

Harrison

. Microparticle sizing by dynamic light scattering in fresh-frozen plasma. Vox Sang. 2009;96(3):206–212.

82.

Nakane

Maurer-Spurej

. Novel test for microparticles in platelet-rich plasma and platelet concentrates using dynamic light scattering. Transfusion. 2011;51(2):363–370.

83.

Sitar

Kejžar

Pahovnik

, et al. Size characterization and quantification of exosomes by asymmetrical-flow field-flow fractionation. Anal Chem. 2015;87(18):9225–9233.

84.

Gardiner

Ferreira

Dragovic

Redman

Sargent

. Extracellular vesicle sizing and enumeration by nanoparticle tracking analysis. J Extracell Vesicles. 2013;2(1):19671.

85.

Coumans

van der Pol

Böing

, et al. Reproducible extracellular vesicle size and concentration determination with tunable resistive pulse sensing. J Extracell Vesicles. 2014;3(1):25922.

86.

Vogel

Coumans

Maltesen

, et al. A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing. J Extracell Vesicles. 2016;5:31242.

87.

Akers

Ramakrishnan

Nolan

, et al. Comparative analysis of technologies for quantifying extracellular vesicles (EVs) in clinical cerebrospinal fluids (CSF). PLoS One. 2016;11(2):e0149866.

88.

Ziaei

Geruntho

Marin-Flores

Berkman

Grant Norton

. Silica nanostructured platform for affinity capture of tumor-derived exosomes. J Mater Sci. 2017;52(12):6907–6916.

89.

Crow

Roth

Zeng

Godwin

. Integrated immunoisolation and protein analysis of circulating exosomes using microfluidic technology. Lab Chip. 2014;14(19):3773–3780.

90.

Davies

Kim

Jang

Choi

E-J

Gho

Park

. Microfluidic filtration system to isolate extracellular vesicles from blood. Lab Chip. 2012;12(24):5202–5210.

91.

Krieg

Dong

Schwamborn

Knuechel

. Protein quantification and its tolerance for different interfering reagents using the BCA-method with regard to 2D SDS PAGE. 2005;65:13–19. doi:10.1016/j.jbbm.2005.08.005.

92.

Rath

Glibowicka

Nadeau

Chen

Deber

. Detergent binding explains anomalous SDS-PAGE. Proc Natl Acad Sci U S A. 2009;106(6):1760–1765.

93.

Lobb

Becker

Wen

, et al. Optimized exosome isolation protocol for cell culture supernatant and human plasma. J Extracell Vesicles. 2015;4(1):27031.

94.

Ueda

Ishikawa

Tatsuguchi

Saichi

Fujii

Nakagawa

. Antibody-coupled monolithic silica microtips for high throughput molecular profiling of circulating exosomes. Sci Rep. 2015;4:6232.

95.

Pospichalova

Svoboda

Dave

, et al. Simplified protocol for flow cytometry analysis of fluorescently labeled exosomes and microvesicles using dedicated flow cytometer. J Extracell Vesicles. 2015;4(1):25530.

96.

Arraud

Gounou

Turpin

Brisson

. Fluorescence triggering: a general strategy for enumerating and phenotyping extracellular vesicles by flow cytometry. Cytom Part A. 2016;89(2):184–195.

97.

Orozco

Lewis

. Flow cytometric analysis of circulating microparticles in plasma. Cytom Part A. 2010;77(6):502–514.

98.

Enderle

Spiel

Coticchia

, et al. Characterization of RNA from exosomes and other extracellular vesicles isolated by a novel spin column-based method. PLoS One. 2015;10(8):e0136133.

99.

Huang

Yuan

Tschannen

, et al. Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics. 2013;14:319.

100.

Mueller

Fusenig

. Friends or foes—bipolar effects of the tumour stroma in cancer. Nat Rev Cancer. 2004;4(11):839–849.

101.

van der Meel

Krawczyk-Durka

van Solinge

Schiffelers

. Toward routine detection of extracellular vesicles in clinical samples. Int J Lab Hematol. 2014;36(3):244–253.

102.

Pucci

Garris

Lai

, et al. SCS macrophages suppress melanoma by restricting tumor-derived vesicle-b cell interactions. Science. 2016;352(6282):242–246.

103.

Pietras

Ostman

. Hallmarks of cancer: interactions with the tumor stroma. Exp Cell Res. 2010;316(8):1324–1331.

104.

Turley

Cremasco

Astarita

. Immunological hallmarks of stromal cells in the tumour microenvironment. Nat Rev Immunol. 2015;15(11):669–682.

105.

D’Souza-Schorey

Clancy

. Tumor-derived microvesicles: shedding light on novel microenvironment modulators and prospective cancer biomarkers. Genes Dev. 2012;26(12):1287–1299.

106.

Grayson

. Breast cancer. Nature. 2012;485:S49.

107.

Duffy

Evoy

McDermott

. Ca 15–3: uses and limitation as a biomarker for breast cancer. Clin Chim Acta. 2010;411(23-24):1869–1874.

108.

Hannafon

Trigoso

Calloway

, et al. Plasma exosome microRNAs are indicative of breast cancer. Breast Cancer Res. 2016;18(1):90.

109.

Admyre

Johansson

Qazi

, et al. Exosomes with immune modulatory features are present in human breast milk. J Immunol. 2007;179(3):1969–1978.

110.

Hamed

Zahra

Arash

, et al. Breast cancer diagnosis: imaging techniques and biochemical markers. J Cell Physiol. 2018;233(7):5200–5213. doi:10.1002/jcp.26379.

111.

Halvaei

Daryani

Eslami-s

, et al. Exosomes in cancer liquid biopsy: a focus on breast cancer. Mol Ther Nucleic Acid. 2018;10:131–141. doi:10.1016/j.omtn.2017.11.014.

112.

Yang

Schneider

Chisholm

, et al. Association of TGF-β2 levels in breast milk with severity of breast biopsy diagnosis. Cancer Causes Control. 2015;26(3):345–354. doi:10.1007/s10552-014-0498-8.

113.

van’t Veer

Dai

van de Vijver

, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–536.

114.

Wang

Zhong

, et al. Exosomal protein CD82 as a diagnostic biomarker for precision medicine for breast cancer. Mol Carcinog. 2019;58(5):674–685. doi:10.1002/mc.22960.

115.

Zhang

Yang

Zhang

Sun

Zhang

. MicroRNA-10b expression in breast cancer and its clinical association. PLoS One. 2018:13(2):e0192509.

116.

Zhai

Pan

, et al. In situ detection of plasma exosomal MicroRNA-1246 for breast cancer diagnostics by a Au nanoflare probe. ACS Appl Mater Interfaces. 2018;10(46):39478–39486. doi:10.1021/acsami.8b12725.

117.

Rodríguez-Martínez

Miguel-pérez

De Ortega

, et al. Exosomal miRNA profile as complementary tool in the diagnostic and prediction of treatment response in localized breast cancer under neoadjuvant chemotherapy. Breast Cancer Res. 2019;21(1):210.

118.

Lee

Kim

Lee

Park

Sim

. Quantitative and specific detection of exosomal miRNAs for accurate diagnosis of breast cancer using a surface-enhanced Raman scattering sensor based on plasmonic head-flocked gold nanopillars. Small. 2019;15(17):1–10. doi:10.1002/smll.201804968.

119.

Siegel

Zou

Jemal

. Cancer statistics, 2014. CA Cancer J Clin. 2014;64(1):9–29.

120.

Shen

Zhao

, et al. Specific capture and release of circulating tumor cells using aptamer-modified nanosubstrates. Adv Mater. 2013;25(16):2368–2373. doi:10.1002/adma.201300082.

121.

Flores

Kindelberger

Ligon

, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495–1502. doi:10.1038/sj.bjc.6605676.

122.

Sandfeld-Paulsen

Jakobsen

Bæk

, et al. Exosomal proteins as diagnostic biomarkers in lung cancer. J Thorac Oncol. 2016;11(10):1701–1710.

123.

Niu

Song

Wang

Xue

Song

Xie

. Tumor-derived exosomal proteins as diagnostic biomarkers in non-small cell lung cancer. Cancer Sci. 2019;110(1):433–442. doi:10.1111/cas.13862.

124.

Cui

Cheng

Qin

Jiang

. Lung cancer exosomes as a liquid biopsy for lung cancer. Lung Cancer. 2018;116(25):46–54. doi:10.1016/j.lungcan.2017.12.012.

125.

Castellanos-Rizaldos

Grimm

Tadigotla

, et al. Exosome-based detection of EGFR T790M in plasma from non-small cell lung cancer patients. Clin Cancer Res. 2018;24(12):2944–2950. doi:10.1158/1078-0432.CCR-17-3369.

126.

Lin

Huang

, et al. Exosomes: novel biomarkers for clinical diagnosis. ScientificWorldJournal. 2015;2015:657086. doi:10.1155/2015/657086.

127.

Shi

. High expression of miR-155 and miR-21 in the recurrence or metastasis of non-small cell lung cancer. Oncol Lett. 2019;18(1):758–763. doi:10.3892/ol.2019.10337.

128.

Fan

Chen

. Tumor-derived exosomal lncRNA GAS5 as a biomarker for early-stage non-small-cell lung cancer diagnosis. J Cell Physiol. 2019;234(11):20721–20727. doi:10.1002/jcp.28678.

129.

Graves

Ariztia

E V

Navari

Matzel

Stack

Fishman

. Proinvasive properties of ovarian cancer ascites-derived membrane vesicles. Cancer Res. 2004;64(19):7045–7049.

130.

Yang

Kim

. The potential role of exosomes derived from ovarian cancer cells for diagnostic and therapeutic approaches. J Cell Physiol. 2019;234(12):21493–21503. doi:10.1002/jcp.28905.

131.

Beach

Zhang

Ratajczak

Kakar

. Exosomes: an overview of biogenesis, composition and role in ovarian cancer. J Ovarian Res. 2014;7:14.

132.

Zhang

Liu

. MicroRNA profiling of plasma exosomes from patients with ovarian cancer using high-throughput sequencing. Oncol Lett. 2019;17(6):5601–5607. doi:10.3892/ol.2019.10220.

133.

Press

. Advances of exosome in the development of ovarian cancer and its diagnostic and therapeutic prospect. Onco Targets Ther 2018;11:2831–2841.

134.

Han

, et al. Ultrasensitive and reversible nanoplatform of urinary exosomes for prostate cancer diagnosis. ACS Sensors. 2019;4:1433–1441. doi:10.1021/acssensors.9b00621.

135.

Lewis

Vyas

Qiu

Messer

White

Heller

. Integrated analysis of exosomal protein biomarkers on alternating current electrokinetic chips enables rapid detection of pancreatic cancer in patient blood. ACS Nano. 2018;12(4):3311–3320. doi:10.1021/acsnano.7b08199.

136.

Wang

Yuan

Gao

. Determination of serum exosomal H19 as a noninvasive biomarker for bladder cancer diagnosis and prognosis. Med Sci Monit. 2018;24:9307–9316. doi:10.12659/MSM.912018.

137.

Runz

Keller

Rupp

, et al. Malignant ascites-derived exosomes of ovarian carcinoma patients contain CD24 and EpCAM. Gynecol Oncol. 2007;107(3):563–571. doi:10.1016/j.ygyno.2007.08.064.

138.

Giannopoulou

Zavridou

Kasimir-bauer

Lianidou

. Liquid biopsy in ovarian cancer: the potential. Transl Res. 2019;205:77–91. doi:10.1016/j.trsl.2018.10.003.

139.

Kobayashi

Sawada

Nakamura

, et al. Exosomal miR-1290 is a potential biomarker of high-grade serous ovarian carcinoma and can discriminate patients from those with malignancies of other histological types. J Ovarian Res 2018;11(1):81.

140.

Zhou

Gong

Tan

, et al. Urinary microRNA-30a-5p is a potential biomarker for ovarian serous adenocarcinoma. Oncol Rep. 2015;33(6):2915–2923. doi:10.3892/or.2015.3937.

141.

Kim

Choi

Jeong

Hwang

Jung

Joo

. Serum exosomal miRNA-145 and miRNA-200c as promising biomarkers for preoperative diagnosis of ovarian carcinomas. J Cancer. 2019;10(9):1958–1967. doi:10.7150/jca.30231.

142.

Qiu

Lin

Tang

Zheng

Lin

. Exosomal metastasis-associated lung adenocarcinoma transcript 1 promotes angiogenesis and predicts poor prognosis in epithelial ovarian cancer. Int J Biol Sci. 2018;14(14):1960–1973. doi:10.7150/ijbs.28048.

143.

Loeb

Bjurlin

Nicholson

, et al. Overdiagnosis and overtreatment of prostate cancer. Eur Urol. 2014;65(6):1046–1055.

144.

Lee

Mallin

Graves

, et al. Recent changes in prostate cancer screening practices and epidemiology. J Urol. 2017;198(6):1230–1240.

145.

Bhagirath

Yang

Bucay

Sekhon

Majid

. microRNA-1246 is an exosomal biomarker for aggressive prostate cancer. Cancer Res. 2018;78(7):1833–1844. doi:10.1158/0008-5472.CAN-17-2069.

146.

McKiernan

Donovan

O’Neill

, et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol. 2016;2(7):882–889.

147.

Bryant

Pawlowski

Catto

JWF

, et al. Changes in circulating microRNA levels associated with prostate cancer. Br J Cancer. 2012;106(4):768–774. doi:10.1038/bjc.2011.595.

148.

Physiology

. Tumor-derived exosomal long noncoding RNAs as promising diagnostic biomarkers for prostate cancer. Cell Physiol Biochem. 2018;430071(169):532–545. doi:10.1159/000488620.

149.

Allenson

Castillo

San Lucas

, et al. High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann Oncol. 2017;28(4):741–747.

150.

Nuzhat

Kinhal

Sharma

Rice

Joshi

Salomon

. Tumour-derived exosomes as a signature of pancreatic cancer-liquid biopsies as indicators of tumour progression. Oncotarget. 2017;8(10):17279–17291.

151.

Ballehaninna

Chamberlain

. The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: an evidence based appraisal. J Gastrointest Oncol. 2012;3(2):105–119. doi:10.3978/j.issn.2078-6891.2011.021.

152.

Zhou

Wang

Huang

Zhu

Miao

Plasma miRNAs in diagnosis and prognosis of pancreatic cancer: a miRNA expression analysis. Gene. 2018;673:181–193. doi:10.1016/j.gene.2018.06.037.

153.

Goto

Fujiya

Konishi

, et al. An elevated expression of serum exosomal neoplasm is considered to be efficient diagnostic marker. BMC Cancer. 2018;18:116. doi:10.1186/s12885-018-4006-5.

154.

Zhang

Chen

, et al. An ultrasensitive polydopamine bi-functionalized SERS immunoassay for exosome-based diagnosis and classification of pancreatic cancer. Chem Sci. 2018;9(24):5372–5382. doi:10.1039/c8sc01611a.

155.

Jin

Liu

, et al. Exosomal zinc transporter ZIP4 promotes cancer growth and is a novel diagnostic biomarker for pancreatic cancer. Cancer Sci. 2018;109(9):2946–2956. doi:10.1111/cas.13737.

156.

Lux

Kahlert

Grützmann

Pilarsky

. c-Met and PD-L1 on circulating exosomes as diagnostic and prognostic markers for pancreatic cancer. Int J Mol Sci 2019;20(13). doi:10.3390/ijms20133305.

157.

Jimenez-luna

Torres

Ortiz

, et al. Proteomic biomarkers in body fluids associated with pancreatic cancer. Oncotarget 2018;9(23):16573–16587.

158.

Andreu

Otta Oshiro

Redruello

, et al. Extracellular vesicles as a source for non-invasive biomarkers in bladder cancer progression. Eur J Pharm Sci. 2017;98:70–79.

159.

Welton

Khanna

Giles

, et al. Proteomics analysis of bladder cancer exosomes. Mol Cell Proteomics. 2010;9(6):1324–1338.

160.

Maji

Matsuda

Yan

Parasramka

Patel

. Extracellular vesicles in liver diseases. Am J Physiol Gastrointest Liver Physiol. 2017;312(3):G194–G200.

161.

Witjes

Lebret

Compérat

, et al. Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer. Eur Urol. 2017;71:462–475. doi:10.1016/j.eururo.2016.06.020.

162.

Blobel

Jung

. Circulating miRNAs in blood and urine as diagnostic and prognostic biomarkers for bladder cancer: an update in 2017. Biomark Med. 2018;12(6):667–676.

163.

Zhan

Wang

, et al. Expression signatures of exosomal long non-coding RNAs in urine serve as novel non-invasive biomarkers for diagnosis and recurrence prediction of bladder cancer. Mol Cancer. 2018;17(1):142.

164.

Zhang

Wang

, et al. Evaluation of serum exosomal LncRNA-based biomarker panel for diagnosis and recurrence prediction of bladder cancer. J Cell Mol Med. 2019;23(2):1396–1405. doi:10.1111/jcmm.14042.

165.

Sun

Fang

Chen

, et al. Delivery system of CpG oligodeoxynucleotides through eliciting an effective T cell immune response against melanoma in mice. J Cancer. 2016;7(3):241–250. doi:10.7150/jca.12899.

166.

Chen

Huang

Zhang

, et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature. 2018;560(7718):382–386. doi:10.1038/s41586-018-0392-8.

167.

Touat

Duran-Peña

Alentorn

Lacroix

Massard

Idbaih

Emerging circulating biomarkers in glioblastoma: promises and challenges. Expert Rev Mol Diagn. 2015;15(10):1311–1323.

168.

Kang

Sun

Zhu

, et al. Delivery of nanoparticles for treatment of brain tumor. Curr Drug Metab. 2016;17:745–754. doi:10.2174/1389200217666160728152939.

169.

Lan

Qing

Pan

Yue

. Serum exosomal miR-301a as a potential diagnostic and prognostic biomarker for human glioma. Cell Oncol (Dordr). 2018;41(1):25–33. doi:10.1007/s13402-017-0355-3.

170.

Tan

Pastori

Penas

, et al. Serum long noncoding RNA HOTAIR as a novel diagnostic and prognostic biomarker in glioblastoma multiforme. Mol Cancer 2018;17(1):74.

171.

Ebrahimkhani

Vafaee

Hallal

, et al. Deep sequencing of circulating exosomal microRNA allows non-invasive glioblastoma diagnosis. NPJ Precis Oncol. 2018;2:28. doi:10.1038/s41698-018-0071-0.

172.

Santangelo

Imbrucè

Gardenghi

, et al. A microRNA signature from serum exosomes of patients with glioma as complementary diagnostic biomarker. J Neurooncol. 2018;136(1):51–62. doi:10.1007/s11060-017-2639-x.

Isolation and Detection Technologies of Extracellular Vesicles and Application on Cancer Diagnostic

Abstract

Keywords

Introduction

Extracellular Vesicle Isolation and Enrichment

Size-Based Isolation

Ultracentrifugation

Density-Based Isolation

Coprecipitation Isolation

Affinity Binding–Based Isolation

Membrane affinity binding

Immunoaffinity

Comparison

Extracellular Vesicle Detection and Characterization

Microscopy Quality Characterization

Scanning electron microscopy

Quantitative Characterization

Dynamic light scattering

Protein Analysis

Pierce bicinchoninic acid protein assay kit

Nucleic Acids Analysis

Precipitation and spin columns

Cancer Clinical Applications of EV

Breast Cancer

Lung Cancer

Ovarian Cancer

Prostate Cancer

Pancreatic Cancer

Bladder Cancer

Melanoma Cancer

Glioblastoma Multiforme

Conclusion and Perspective

Footnotes

Author Contribution

Declaration of Conflicting Interests

Funding

ORCID iD

References