More than two-thirds of all women diagnosed with epithelial ovarian cancer (EOC) will die from the disease (>14,000 deaths annually), a fact that has not changed considerably in the last three decades. Although the 5-year survival rates for most other solid tumors have improved steadily, ovarian cancer remains an exception, making it the deadliest of all gynecologic cancers and five times deadlier than breast cancer. When diagnosed early, treatment is more effective, with a 5-year survival rate of up to 90%. Unfortunately, most cases are not detected until after the cancer has spread, resulting in a dismal 5-year survival rate of less than 30%. Current screening methods for ovarian cancer typically use a combination of a pelvic examination, transvaginal ultrasonography, and serum cancer antigen 125 (CA125), but these have made minimal impact on improving mortality. Thus, there is a compelling unmet need to develop new molecular tools that can be used to diagnose early-stage EOC and/or assist in the clinical management of the disease after a diagnosis, given that more than 220,000 women are living with ovarian cancer in the United States and are at risk of recurrence. Here, we discuss the state of advancing liquid-based approaches for improving the early detection of ovarian cancer.

See all articles in this Special Collection Honoring Paul F. Engstrom, MD, Champion of Cancer Prevention

Ovarian cancers are a heterogeneous group of malignancies arising from or involving the ovary and/or fallopian tubes (1). Ovarian cancers are broadly classified into nonepithelial ovarian cancer and epithelial ovarian cancer (EOC) and that the latter is attributed to the majority of ovarian cancer–related deaths (2). Overall, EOC is the fifth leading cause of cancer-related death among American women, making it the deadliest cancer of the reproductive system (3). EOC is a heterogeneous disease, comprising several histotypes (serous, ∼70%; endometrioid, ∼10%; clear cell, ∼10%; and mucinous, ∼3%–10%) with distinct epidemiologic, molecular, and clinical features. High-grade serious carcinomas (HGSC) are estimated to be 50% to 60% of all ovarian malignancies and account for half of all EOCs.

EOCs are diagnosed predominantly at an advanced stage with widespread metastases throughout the peritoneal cavity (4–6). Manifestations of the disease are typically vague and do not become apparent until the disease is advanced and difficult to treat. Thus, ovarian cancer is caught at an early stage only in about one-fifth of all cases according to the National Ovarian Cancer Coalition. Furthermore, no reliable screening tests are currently available for ovarian cancer, and current diagnostic tools for early detection remain inadequate. Early detection of ovarian cancer at a localized stage (stages IA and IB) results in far better disease prognosis: according to the American Cancer Society (2019), the projected 5-year survival rate for these patients is about 92%. However, only 15% of all ovarian cancers are successfully diagnosed at this stage. Patients with advanced-stage HGSC have a worst prognosis (∼32% and ∼15% for 5- and 10-year survival, respectively) compared with patients diagnosed with early-stage HGSC [∼71% and ∼53% (5- and 10-year survival, respectively); ref. 7]. Moreover, 30 years of statistics gathered by the NIH Surveillance, Epidemiology, and End Results (SEER) Program indicate that approximately 70% of patients diagnosed with ovarian cancer will experience disease recurrence after initial benefit from chemotherapy (8). These statistics clearly emphasize the need to develop tools to detect ovarian cancer at an earlier stage.

The two most commonly used screening tests in the clinic are transvaginal ultrasound (TVUS) and blood tests for elevated cancer antigen-125 (CA125) protein levels. It is difficult to distinguish benign from malignant tumors by TVUS and, hence, the need for more invasive biopsies. On the other hand, CA125 protein levels are typically not elevated in the serum of up to 50% of patients with stage I ovarian cancer; thereby, it is not as reliable for early detection (9). Also, blood tests quantifying CA125 protein level are not specific to ovarian cancer, as CA125 elevation can be potentiated by many factors (10). Unlike breast cancer in which a biopsy can be performed for diagnosis, EOC screening requires invasive surgery to make a definitive diagnosis (11, 12) and therefore false-positive surgeries must be limited to at most 10 per screen-detected cancer, that is, a positive predictive value (PPV) exceeding 10%, which for the patient is quite unacceptable. Thus, the low prevalence of this cancer in the general postmenopausal population (1 in 2,500) requires an effective screening strategy to have a high sensitivity for early-stage disease (>75%) and a very high combined specificity (99.6%) to achieve a PPV exceeding 10% (11–19). When CA125 is interpreted longitudinally with the risk of ovarian cancer algorithm (ROCA) referring 2% annually at highest risk to transvaginal ultrasound (TVUS), this “2-stage screening strategy” achieved a combined specificity of 99.8%, a 22% PPV—greater than the 10% lower limit, and detected an increased but still modest proportion of EOCs in early stage (11, 20, 21). In a 2017 landmark study, the United Kingdom Collaborative Trial of Ovarian Cancer Screening compared several screening approaches in more than 200,000 postmenopausal women (median follow-up of 11.1 years): (i) serum CA125 level evaluated using ROCA (50,640 women), (ii) annual TVUS screening (50,639 women), or (iii) no screening (101,359 women). Unfortunately, like other ovarian cancer screening studies primarily focused on measuring serum CA125 levels (22–27), this study did not show a significant decrease in ovarian cancer mortality (28). This clinical problem highlights the urgent need to expand the class of liquid-based biomarkers to detect a substantially greater proportion of cancers in early stage, while maintaining a 98% annual specificity for referral to imaging (e.g., TVUS).

Pathologic analysis of tumor tissue biopsies has been the gold standard in the initial diagnosis of cancer, but liquid biopsies, which analyze tumor-derived material circulating in the bloodstream and other bodily fluids, are rapidly gaining traction in the clinic. Liquid biopsy is a minimal or noninvasive technology that detects molecular biomarkers using liquid sample without the need for costly or invasive procedures. These tests have considerable potential in oncology, e.g., for early detection of cancer, treatment and recurrence monitoring, and as surrogates for traditional biopsies with the purpose of predicting response to treatments and the development of acquired resistance. A liquid biopsy can provide information about the genetic landscape of all cancerous lesions (primary and metastases) as well as offer the opportunity to systematically track genomic evolution. The liquid biopsy biomarker types are primarily segmented into circulating tumor cells (CTCs), cell-free DNA (cfDNA), and extracellular vesicles (EV; Fig. 1). The discovery of CTCs dates back to the 1860s, when cells that were morphologically identical to the tumor were identified in the blood of a patient with metastatic cancer (29). Their potential significance was not fully realized until the late 2000s, when the number of CTCs in the bloodstream were shown to have prognostic significance in various different tumor types, for example, metastatic breast, metastatic castration-resistant prostate, and metastatic colorectal cancer (30). Another way that tumors release biomarker information is through tumor cell necrosis and the release of dead cells or cell fragments. These cells are engulfed by phagocytes that process the tumor cell DNA into small fragments (160–180 base pairs in length) of nucleic acids, which is then released into the bloodstream (31, 32). Although levels of tumor DNA have been shown to mirror tumor burden, interpretation is often complicated by the presence of other cfDNA derived from nontumor cells. Therefore, a number of highly sensitive methods have been developed to detect aberrations found in circulating tumor DNA (ctDNA), including mutation, amplification, chromosomal rearrangement, and hypermethylation. A third biomarker, which has been known for years, has reemerged and is showing great promise. This member of the EV family, known as exosomes or small EVs, are nano-sized vesicles (30–150 nm) of endocytic origin, which are produced and released by most cell types under normal physiologic and in diseased states (33, 34). Once considered little more than garbage cans whose job was to discard unwanted cellular components, recent discoveries have sparked considerable interest in exosomes as circulating biomarkers. Exosomes are informative molecules that carry cargo representative of their originating cell including nucleic acids, cytokines, membrane-bound receptors, and a wide assortment of other, biologically active lipids and proteins (35–38). This cargo remains functional upon entry or fusion with a recipient cell, thus exosomal transfer is now considered an important form of cell–cell communication in normal and pathologic states, such as cancer. Because exosomes can travel systemically throughout the body, efforts are underway to exploit them as potential biomarkers to detect and monitor disease states. In this review, we will focus on the current methods and advancements using liquid-based biopsies that have shown promising potential in the early detection of ovarian cancer.

Figure 1.

A graphical representation of a variety of approaches ovarian cancer researchers can take to evaluate blood-based liquid biopsies for cancer-associated biomarkers to aid in early detection of malignancy.

Figure 1.

A graphical representation of a variety of approaches ovarian cancer researchers can take to evaluate blood-based liquid biopsies for cancer-associated biomarkers to aid in early detection of malignancy.

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First observed in 1869 by Thomas Ashworth in the blood of an individual with metastatic disease, CTCs have long been thought to be an important component of tumor dissemination (29, 39, 40). CTCs are cancer cells that have detached from the tumor and are found at extremely low levels in the bloodstream. Modern research on CTCs has focused on CTC enumeration and characterization. Some speculate that tumor cell shedding is an early event in tumorigenesis and might be useful for early detection, but few studies have shown utility in this clinical setting. As a blood-based, FDA-approved biomarker, CTCs were thought useful for determining the prognosis of patients with metastatic breast, colorectal, and prostate cancers (41–45). As the only FDA-cleared device for the enumeration of CTCs in whole blood, CellSearch (Menarini Silicon Biosystems, Inc.) was originally used for the evaluation of CTC numbers in patients with cancer to help predict prognosis and overall survival (30). However, clinical enumeration of these circulating cells has become less useful and costly, due to the fact CTC tests are rarely reimbursed by insurance. Regardless, the interest in CTCs continues and many different platforms for CTC isolation have been developed for research as well as clinical use in recent years (46). Nevertheless, CTC isolation and characterization remain technically challenging and progress has been hampered by the difficulty in rapidly and effectively isolating pure populations of CTCs using liquid biopsy.

The three main categories of isolation techniques are immune-affinity–based methods, size-/density-based methods, and microfluidic device–based methods (ref. 47; Fig. 2). The earliest approaches utilized positive and negative selection for common CTC surface antigens or organ-specific markers to differentiate CTCs from other cell types. In these studies, researchers used antibody-coated beads to positively select CTCs from the cellular milieu or deplete the milieu of other cell types with a negative selection marker. EpCAM, a cell adhesion protein found on epithelial cells, is often used as a positive selection marker for CTCs (48). The most common negative selection marker is CD45 because it is found only on cells of hematopoietic origin. First reported in 2000, the ISET (isolation by size of epithelial tumor cells) system is based on cell size filtration and has been shown to effectively recover CTCs for diagnostic cytopathologic analysis (49). However, smaller CTCs less than 8 μm in diameter can be lost using this method, due to the selective pore size. Microfluidic devices offer several attractive advantages for CTC-based studies, such as continuous sample processing using a small volume to reduce loss and integration of downstream analyses on the chip itself. These devices can also reduce hands-on time and increase consistency of heterogeneous CTC profiles (50). Some microfluidic devices integrate existing CTC isolation techniques within their design, such as the size-dictated immunocapture chip (SDI-Chip; ref. 51). The SDI-chip is composed of two mirrored anti-EpCAM antibody-coated micropillar arrays that selectively enhance the interaction of CTCs with the selection antibodies, resulting in efficient capture of CTCs based on the principle of deterministic lateral displacement (52). The disadvantage to the EpCAM-based capture is that not all CTCs express EpCAM (53). In an effort to move away from the bias introduced by affinity-based isolation, researchers have begun to focus on label-free surface expression–independent microfluidic devices to isolate CTCs. A label-free approach is optimal because CTCs are very heterogeneous and fluid in their gene expression, making any affinity-based approach biased toward cells expressing a certain marker. Several label-free CTC separation techniques have arisen in recent years including a microfluidic flow fractionation approach, acoustic-based separation, and deterministic lateral displacement separation (52, 54, 55). The Labyrinth device developed by Lin and colleagues in 2017 is a label-free circular microfluidic chamber that uses a hydrodynamic maze to isolate CTCs by size (56). The device functions by flowing blood through a labyrinth (hence the name) of 56 sharp turns, which results in size-based enrichment of CTCs depleted of hematopoietic cells. Improvements can be made on the purity of resultant CTCs by double Labyrinth separation. Labyrinth output can even be used with various downstream single-cell analysis tools including BioMark Real-Time PCR System (Fluidigm), DEPArray (Silicon Biosystems), and droplet digital PCR (Rain-dance).

Figure 2.

Examples of methods to capture circulating tumor cell from whole blood.

Figure 2.

Examples of methods to capture circulating tumor cell from whole blood.

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CTC enumeration data can provide only limited insight into the underlying biology of malignant disease. More recent research efforts have been focused on the molecular characterization and functional analysis of CTCs. Using less than 1 mL of blood, Racila and colleagues demonstrated that 12 of 14 patients with clinically organ-confined breast cancers and 3 of 3 patients with organ-confined prostate cancers had excess epithelial cells detected in their blood (57). However, the diagnostic utility of CTCs in ovarian cancer has been decidedly underwhelming. In the first studies led by us, CTCs (as circulating endothelial cells/circulating endothelial progenitors) were found to be rare in patients with ovarian cancer and were not overly informative when evaluated along with activity of molecular targeted therapies (58, 59). In a 2018 study, CTCs were detected in only 5 out of 29 patients (17.2%) with proven primary ovarian cancer (Table 1). CTCs were shown to be more prevalent in patients with metastases to the ovary, rather than in primary ovarian cancer (43). Although CTCs may not be useful as early detection biomarkers, CTC status in ovarian cancer is associated with overall survival and progression-free survival according to a recent meta-analysis of 11 publications and 1,129 patients (60).

Table 1.

Examples of liquid-based biopsy studies to detect ovarian cancer using ctDNA and CTCs.

Platform usedSelection parametersCase sizeControl sizeSensitivityaSpecificityaCI (95%)References
CTCs CellSearch® EpCAM Histologic diagnosis of ovarian cancer n = 29 n = 17a Benign disease 25.7% 100% Se = 12.5% ± 43.3% Sp = 76.8% ± 100% 43 
   Cancer with metastases to the ovary n = 5      
Circulating tumor DNA (ctDNA) Microarray (MethDet 56) Promoters: RASSF1A, CALCA, EP300 Benign disease n = 30 n = 30 90% 86.7% Se = 80% ± 100% Sp = 66.7% ± 96.7% 75 
   Ovarian cancer n = 30      
 Microfluidic digital PCR TP53-mutant allele fraction Ovarian n = 32 n = 5 71% 88% Se = 42% ± 92% Sp = 64% ± 99% 71 
   Primary peritoneal n = 5      
   Fallopian tube n = 3      
 Ultra-high coverage bi-sulfite sequencing Unspecified 3 DNAme marker panel Ovarian cancer n = 41 n = 21 41.4% 90.7% Se = 24.1% ± 60.9% Sp = 84.3% ± 94.8% 76 
   Other cancer n = 37      
   Nonepithelial tumors n = 5      
   Borderline n = 27      
   Benign tumors n = 119      
Platform usedSelection parametersCase sizeControl sizeSensitivityaSpecificityaCI (95%)References
CTCs CellSearch® EpCAM Histologic diagnosis of ovarian cancer n = 29 n = 17a Benign disease 25.7% 100% Se = 12.5% ± 43.3% Sp = 76.8% ± 100% 43 
   Cancer with metastases to the ovary n = 5      
Circulating tumor DNA (ctDNA) Microarray (MethDet 56) Promoters: RASSF1A, CALCA, EP300 Benign disease n = 30 n = 30 90% 86.7% Se = 80% ± 100% Sp = 66.7% ± 96.7% 75 
   Ovarian cancer n = 30      
 Microfluidic digital PCR TP53-mutant allele fraction Ovarian n = 32 n = 5 71% 88% Se = 42% ± 92% Sp = 64% ± 99% 71 
   Primary peritoneal n = 5      
   Fallopian tube n = 3      
 Ultra-high coverage bi-sulfite sequencing Unspecified 3 DNAme marker panel Ovarian cancer n = 41 n = 21 41.4% 90.7% Se = 24.1% ± 60.9% Sp = 84.3% ± 94.8% 76 
   Other cancer n = 37      
   Nonepithelial tumors n = 5      
   Borderline n = 27      
   Benign tumors n = 119      

Note: Pairs of sensitivity and specificity in this table are not directly comparable with each other because they are calculated on the basis of different cutoffs throughout.

aSensitivity (Se) and specificity (Sp) pair values listed correspond to a specific decision cutoff utilized in each study.

It was reported for the first time in 1977 by Leon and colleagues that patients with cancer have increased levels of free DNA in their blood serum (61). Cell-free DNA broadly refers to all circulating DNA, whereas ctDNA is specifically tumor-derived as the name suggests. Unfortunately, typical DNA isolation methods do not distinguish DNA by its origin, so scientists have developed several strategies to increase the proportion of ctDNA isolated from serum or plasma. Enrichment for ctDNA is optimal because it increases the likelihood of uncovering biomarkers specific to the cellular heterogeneity of the tumor. Apoptotic DNA fragmentation is thought to be the main source of ctDNA due to its average size of 70 to 200 base pairs, which corresponds to the length of DNA around a nucleosome (62). The amount of ctDNA can be highly variable. ctDNA can comprise approximately 0.01% ± 90% of total circulating DNA (63–65). Even so, ctDNA has been used to detect tumor-specific mutations, loss of heterozygosity, DNA integrity, microsatellite alterations, and epigenetic alterations (62, 66). Importantly, ctDNA can be used to quantify the level of disease burden and reveal the genomic landscape of the tumor. Researchers have estimated that the half-life of ctDNA ranges from 16 minutes to 2.5 hours, based on the degradation kinetics of DNA in circulation (67–69). This makes ctDNA a temporal snapshot of tumor status. Even with this short half-life, promising results have been shown in colorectal cancer, in which >60% of early-stage tumors can be detected through sensitive mutational analysis of DNA fragments in plasma (64). Figure 3 depicts typical analysis methods used to evaluate ctDNA and its required sensitivity as well as various applications.

Figure 3.

Sensitivity, methods, and applications for circulating tumor DNA analysis. Overview of the most common applications and techniques for ctDNA analysis. ctDNA represented by red DNA strands in blood collection tubes (adapted from ref. 102).

Figure 3.

Sensitivity, methods, and applications for circulating tumor DNA analysis. Overview of the most common applications and techniques for ctDNA analysis. ctDNA represented by red DNA strands in blood collection tubes (adapted from ref. 102).

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As we move toward the future, more and more advancements are taking place to increase the sensitivity of current DNA-based analysis methods such as real-time PCR, microfluidic digital-drop PCR, BEAMing, Safe-SeqS, CAPP-Seq, and other next-generation sequencing techniques (70). In a recent retrospective study, researchers determined that detection of mutations in TP53 using high-grade serous ovarian carcinoma patient plasma has the potential to assess clinical prognosis and response. This study also demonstrated that the amount of ctDNA correlated with tumor volume as determined by three-dimensional (3D) volume reconstruction from CT images (71). Overall, all patients with cancer evaluated in this study with a tumor volume greater than 20 cm3 had reliably detectable TP53 mutations; however, the role of ctDNA mutation screening for early detection remains unproven. Using a mathematical model, researchers postulate that tumors in the millimeter diameter range can only be detected using secreted blood biomarkers under ideal conditions of extremely high rates of biomarker secretion and essentially zero background from healthy cells (72, 73). Thus, very sensitive technology is necessary to detect ctDNA from early-stage ovarian cancer.

Epigenetic changes can also be reliably detected in ctDNA. Aberrant methylation in circulating DNA has been shown in many cancer types (74). Cancer cells use promoter methylation to deregulate gene expression and can be informative as a noninvasive biomarker. The hypermethylation of important DNA repair genes is often an early step in carcinogenesis. In 2011, Liggett and colleagues determined that the methylation of three promoters differentiated patients with ovarian cancer from healthy controls with a sensitivity of 90% and a specificity of 86.7% (75). These methylated genes, RASSF1A, CALCA, and EP300, are associated with tumor suppression, calcium regulation, and histone acetylation, respectively. In addition, promoter methylation of RASSF1A and PGR-PROX was informative for distinguishing ovarian cancer from benign ovary disease (sensitivity 80%, specificity 73.3%; ref. 75). Methylation patterns found in ctDNA have even shown the potential to diagnose a subset of ovarian cancers up to 2 years in advance of elevated CA-125 levels (Table 1; ref. 76).

It is thought that solid malignant neoplasms shed DNA into the circulatory system most often by necrosis, rather than apoptosis as in normal tissue (77). Necrosis typically results in larger and less uniform DNA fragments than apoptosis. Because of this, DNA integrity is increased in cancer and can be analyzed using ctDNA. Using the most common mobile element in the human genome, Alu repeats, researchers can derive a DNA integrity index that can differentiate patients with cancer from healthy individuals by isolating circulating DNA from plasma. This approach was used by Wang and colleagues to establish a cutoff index value of 0.59, meaning all samples above this index are likely malignant (77). This study demonstrated inadequacies in sensitivity of the assay (62%); however, specificity was 100% (Table 1). DNA integrity analysis represents a less-expensive and simpler alternative to DNA sequencing.

Extracellular vesicles compared with CTCs and cfDNA are considered a relatively new class of cancer biomarker. EVs are secreted by cells as a form of intercellular communication. These EVs can shuttle nucleic acids, lipids, and proteins from their cell of origin to surrounding cells to regulate the function of other cells (78). EVs are classified according to size (from a few nanometers to a few micrometers) and subcellular origin (79). A subtype of EVs, termed exosomes, is endocytic in origin and includes 60- to 80-nm small exosomes (Exo-S), as well as 90- to 120-nm large exosomes (Exo-L; ref. 80). In general, the term “exosomes” is broadly used to refer to a heterogenous mixture of small EVs (sEVs) that are less than 200 nm in size; this is due to the fact that widely used purification methods (such as differential ultracentrifugation) cannot definitively isolate EV class based on subcellular origin (81).

The role of exosomes in cancer metastasis has been demonstrated in various cancer types (82–85), establishing a rationale for exploring exosome-based diagnostics. In the ovarian cancer context, our laboratory has shown that tumor-derived exosomes from CP30 and CP70, which are both platinum-resistant ovarian cancer cell lines, imparted platinum resistance to a platinum-sensitive cell line (A2780; ref. 86). In another in vitro study, EOC cells were shown to secrete exosomes that affected the phenotype of mesothelial cells of the peritoneum via transfer of CD44, a cell surface glycoprotein that plays a role in cell adhesion and migration. The mesothelial cells, in turn, increased CD44 surface and MMP-9 secretion, resulting in the degradation of the extracellular matrix and promoting ovarian cell invasion (87). Collectively, these studies suggest that exosomes represent a general mechanism by which one cell type can modulate the phenotype and characteristics of neighboring cells.

Ovarian tumor–derived EVs are not only rich in proteins but these contain miRNAs. miRNAs are small noncoding RNAs that can target messenger RNA thereby altering gene expression. miRNA profiling of EpCAM-captured exosomes from ovarian cancer patients' serum identified miR-21, miR-141, miR200a, miR200b, miR-200c, miR-203, miR-205, and miR-214 to be elevated compared with controls. A high expression of let-7 has been correlated to ovarian cancer cell invasiveness in SKOV3 (88). It has been demonstrated that miR-1246 expression in ovarian tumor–derived exosomes can aid tumor growth by downregulating Cav1 expression, thereby increasing P-glycoprotein expression in infiltrating pro-tumorigenic immune cells (M2-type macrophages; ref. 89). This makes the tumor microenvironment favorable because this confers drug resistance to paclitaxel (89). In addition, other studies have shown that miRNAs from ovarian cancer tumor–derived EVs can confer drug resistance through other pathways (82, 90–92).

Because there are various evidence of tumor-derived EVs contributing to the progression of ovarian cancer (87, 93, 94), it is logical to assess the potential of EVs for diagnostics. Recently, several technologies have been developed to capture exosomes from minute volumes of starting material to surpass the problem of requiring larger sample volumes when using conventional methods such as ELISA and immunoblotting (Table 2). Technologies such as microfluidic chips and the nano-plasmonic exosome (nPLEX) platform can be conjugated to antibodies to allow for capture and profiling of exosomes. One study has assessed exosomes derived from ascites of patients with ovarian cancer using the nPLEX platform (95). Initially, molecular profiling of antibodies was used to coat the surface of nanoholes using selected ovarian cancer markers based from literature. Ovarian cell lines and exosomes derived from the same cell lines were then profiled. The authors found that the combination of EpCAM and CD24 can distinguish ovarian tumor–derived exosomes with a detection accuracy of 97%. Furthermore, it was found that these two markers are highly expressed in the ascites of patients with ovarian cancer (n = 20) compared with the control population (n = 10), which consisted of ascites from nonmalignant conditions (95). These findings correspond with a previous report that identified that EpCAM and CD24 are found in exosomes of cultured cell lines and malignant ascites (96). Our laboratory was the first to demonstrate the capture of exosomes directly from the plasma of patients with ovarian cancer using a microfluidic chip (97). This was followed by the development of a microfluidic chip called ExoSearch (Fig. 4) by Zhao and colleagues (2016), wherein exosomes from plasma of healthy controls and patients with ovarian cancer were captured using anti-CA-125, anti-EpCAM (Epithelial Cell Adhesion Molecule), and anti-CD24 (cluster of differentiation 24 or heat stable antigen CD24; ref. 98). In this study, it was observed that exosomal CA125 and EpCAM may be used to discriminate patients with ovarian cancer from the healthy controls (98). Most recently, our group together with Zhang and colleagues (2019) has reported the development of an ultrasensitive analysis of exosomes using a 3D nanostructured herringbone chip (nano-HB; ref. 99). In this small case–control study, we report that exo-folate receptor alpha as well as exo-EpCAM and exo-CD24 could be detected in small volumes of plasma samples (as little as 2 μL) and could significantly distinguish patients with ovarian cancer from cancer-free controls (area under the curve = 1; Fig. 5; ref. 99). While these types of devices have yet to be assessed under the rigor of clinical trials or in longitudinal samples from asymptotic patients who later develop cancer, these devices clearly hold promise.

Table 2.

Comparison of studies using circulating EVs to detect ovarian cancer.

Platform usedSelection parametersCase sizeControl sizeAUCSensitivityaSpecificityaReferences
nPLEX EpCAM 20 10 0.968 90%b 100%b 95 
 CD24   0.900 80%b 100%b  
 EpCAM + CD24   0.995 95%b 100%b  
 CD63   0.670 75%b 60%b  
Microfluidic chip (ExoSearch) EpCAM 15 1.00 93%a 100%a 98 
 CA-125   1.00 93%a 100%a  
 CD24   0.91 93%a 80%a  
Microfluidic chip (nanoHB) EpCAM 20 10 1.00 100%a 100%a 99 
 CD24   1.00 100%a 100%a  
 FR-α   0.995 95%a 100%a  
 NTA   1.00 69%a 86%a  
Platform usedSelection parametersCase sizeControl sizeAUCSensitivityaSpecificityaReferences
nPLEX EpCAM 20 10 0.968 90%b 100%b 95 
 CD24   0.900 80%b 100%b  
 EpCAM + CD24   0.995 95%b 100%b  
 CD63   0.670 75%b 60%b  
Microfluidic chip (ExoSearch) EpCAM 15 1.00 93%a 100%a 98 
 CA-125   1.00 93%a 100%a  
 CD24   0.91 93%a 80%a  
Microfluidic chip (nanoHB) EpCAM 20 10 1.00 100%a 100%a 99 
 CD24   1.00 100%a 100%a  
 FR-α   0.995 95%a 100%a  
 NTA   1.00 69%a 86%a  

Note: Pairs of sensitivity and specificity in this table are not directly comparable with each other because they are calculated on the basis of different cutoffs throughout.

aSensitivity and specificity pair values listed correspond to a specific decision cutoff utilized in each study. The sensitivity and specificity correspond to the maximized Youden index.

bAs reported by each study.

Figure 4.

A, Schematic of the ExoSearch chip. Bright-field microscopy images of Y-shaped injector (B), mixer (C), immunomagnetic bead (D), and TEM of exosomes (E) captured on the surface of the immunomagnetic bead (reprinted with permission from ref. 98).

Figure 4.

A, Schematic of the ExoSearch chip. Bright-field microscopy images of Y-shaped injector (B), mixer (C), immunomagnetic bead (D), and TEM of exosomes (E) captured on the surface of the immunomagnetic bead (reprinted with permission from ref. 98).

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Figure 5.

A, Schematic of the 3D nanostructured herringbone chip (nano-HB), method of nano-HB chip fabrication (B), a nano-HB chip with magnified SEM images in the middle (C), and testing clinical samples on the nano-HB chip (D, left). CD24, EpCAM, and FRα on exosomes derived from the plasma of patients with ovarian cancer (n = 20) and control population (n = 10) were quantified. Error bars, SD (n = 3). E, Plot of the exosomal protein concentration captured on the nano-HB chip versus ovarian cancer staging, with stage I/II considered as early and stage III/IV as advanced (reprinted with permission from ref. 99). BKG, Background; CSA, colloidal self-assembly; SEM, scanning electron microscopy.

Figure 5.

A, Schematic of the 3D nanostructured herringbone chip (nano-HB), method of nano-HB chip fabrication (B), a nano-HB chip with magnified SEM images in the middle (C), and testing clinical samples on the nano-HB chip (D, left). CD24, EpCAM, and FRα on exosomes derived from the plasma of patients with ovarian cancer (n = 20) and control population (n = 10) were quantified. Error bars, SD (n = 3). E, Plot of the exosomal protein concentration captured on the nano-HB chip versus ovarian cancer staging, with stage I/II considered as early and stage III/IV as advanced (reprinted with permission from ref. 99). BKG, Background; CSA, colloidal self-assembly; SEM, scanning electron microscopy.

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Liquid-based approaches to detect cancer, although gaining traction, are not new. Complete blood cell counts, including white blood cells, red blood cells, and platelets, are used to help diagnose blood disorders, including leukemia and lymphoma. Prostate-specific antigen is used to help diagnosis prostate cancer, whereas calcitonin, α-fetoprotein, and human chorionic gonadotropin are used to diagnosis medullary thyroid cancer, liver cancer, and germ cell tumors, such as testicular cancer and ovarian cancer, respectively. Liquid-based biopsies are likely here to stay and as technologies improve so will their clinical utility. These types of assays, which are considerably less invasive when compared with the tissue biopsy procedure, will help further advance personalized targeted therapy and immunotherapy by offering a source of easily obtainable material for mutation analysis. Liquid biopsies also have the potential to be useful in efficacy assessment, especially if imaging cannot be used or the interpretation is problematic, and in real-time monitoring of molecular profiles and clonal evolution in patients undergoing cancer therapy to detect metastatic relapse or metastatic progression as well as mechanisms of resistance.

Even with the growing excitement surrounding this area of translational research, significant improvements in liquid biopsy platforms and techniques will be required to advance the promises of precision cancer medicine. At present, a liquid biopsy test in itself cannot simply replace the gold standard tissue biopsy–based test; they primarily serve as a complimentary test. As such, there are several remaining challenges impeding the wider adoption of liquid biopsy–based clinical tests. First, it is important to determine the level of sensitivity of a given liquid-based assay. It is well established that CTCs, ctDNA, and tumor-derived EVs are relatively rare compared with the number of “other molecules” found in a blood or bodily fluid sample; therefore, major hurdles remain to improve the test's detection ability. Furthermore, it is not clear if a given test can accurately dissect the heterogeneity of a tumor and identify the “bad actors” among the other tumor subclones. Second, the majority of liquid biopsy assays lack extensive clinical validation resulting in limited reimbursements and utilization of these next-generation tests within the medical community (100). It is, therefore, essential to rigorously validate and demonstrate to both providers and payers the value of liquid biopsies in the clinical setting before they impact clinical practice and are routinely reimbursed. As we move toward the future, the technologies supporting the development of liquid biopsy assays will continue to improve and in turn the sensitivity and specificity of the test. Currently, strides are being made in improving single CTC capture approaches and platforms. With the adoption of digital drop PCR, researchers are able to increase the sensitivity of detection of low-abundance mutations in the ctDNA (101). Microfluidic technologies are also proving suitable for isolation of low-abundance cancer-associated EVs/exosomes (99).

In summary, developing effective screening tests for early detection of ovarian cancer remains one of the most significant unmet needs in the diagnosis and treatment of this disease. It is vital to identify new liquid-based tests and to develop an effective strategy to identify ovarian cancer at its earliest stages so that women stand the best chance for successful treatment and improved survival. To implement a population-wide ovarian cancer screening strategy, there must be evidence that the test is both sensitive enough to detect the cancer early and specific enough not to cause harm to healthy people. It is clear that when cancer, any cancer, is detected late, that is, after it has metastasized to other parts of the body, the outcomes are generally grim. These new liquid-based platforms represent potentially valuable clinical tools to evaluate various circulating biomarkers at a specificity and sensitivity necessary to detect ovarian cancer while it is still localized. In the next decade, liquid-based biopsies will become important clinical tools for cancer screening, diagnosis, prognosis evaluation, drug response predictions, and disease monitoring. However, the analytical and clinical utility of such tools must be rigorously established before this can be applied. Successful “bench to bedside” transition will require extensive clinical validation, which demonstrates a significant decrease in the mortality of women diagnosed with ovarian cancer.

No potential conflicts of interest were disclosed.

This report was supported in part by The Kansas Institute for Precision Medicine (GM130423, to A.K. Godwin), a graduate student award from the OVERRUN Ovarian Cancer Foundation (to C. Trinidad) and the Honorable Tina Brozman Foundation, Inc. (Tina's wish to A.K. Godwin). A.K. Godwin is the Chancellors Distinguished Chair in Biomedical Sciences.

1.
Cho
KR
,
Shih Ie
M
. 
Ovarian cancer
.
Annu Rev Pathol
2009
;
4
:
287
313
.
2.
Kurman
RJ
,
Shih Ie
M
. 
The dualistic model of ovarian carcinogenesis: revisited, revised, and expanded
.
Am J Pathol
2016
;
186
:
733
47
.
3.
Siegel
RL
,
Miller
KD
,
Jemal
A
. 
Cancer statistics, 2018
.
CA Cancer J Clin
2018
;
68
:
7
30
.
4.
Sahdev
A
,
Hughes
JH
,
Barwick
T
,
Rockall
AG
,
Gallagher
CJ
,
Reznek
RH
. 
Computed tomography features of recurrent ovarian carcinoma according to time to relapse
.
Acta Radiol
2007
;
48
:
1038
44
.
5.
Hirose
S
,
Tanabe
H
,
Nagayoshi
Y
,
Hirata
Y
,
Narui
C
,
Ochiai
K
, et al
Retrospective analysis of sites of recurrence in stage I epithelial ovarian cancer
.
J Gynecol Oncol
2018
;
29
:
e37
.
6.
Guth
U
,
Arndt
V
,
Stadlmann
S
,
Huang
DJ
,
Singer
G
. 
Epidemiology in ovarian carcinoma: lessons from autopsy
.
Gynecol Oncol
2015
;
138
:
417
20
.
7.
Kim
J
,
Park
EY
,
Kim
O
,
Schilder
JM
,
Coffey
DM
,
Cho
CH
, et al
Cell origins of high-grade serous ovarian cancer
.
Cancers
2018
;
10
(11):
pii: E433
.
8.
Giornelli
GH.
Management of relapsed ovarian cancer: a review
.
Springerplus
2016
;
5
:
1197
.
9.
Scholler
N
,
Urban
N.
CA125 in ovarian cancer
.
Biomark Med
2007
;
1
:
513
23
.
10.
Sevinc
A
,
Adli
M
,
Kalender
ME
,
Camci
C
. 
Benign causes of increased serum CA-125 concentration
.
Lancet Oncol
2007
;
8
:
1054
5
.
11.
Lu
KH
,
Skates
S
,
Hernandez
MA
,
Bedi
D
,
Bevers
T
,
Leeds
L
, et al
A 2-stage ovarian cancer screening strategy using the Risk of Ovarian Cancer Algorithm (ROCA) identifies early-stage incident cancers and demonstrates high positive predictive value
.
Cancer
2013
;
119
:
3454
61
.
12.
Jelovac
D
,
Armstrong
DK.
Recent progress in the diagnosis and treatment of ovarian cancer
.
CA Cancer J Clin
2011
;
61
:
183
203
.
13.
Rein
BJ
,
Gupta
S
,
Dada
R
,
Safi
J
,
Michener
C
,
Agarwal
A
. 
Potential markers for detection and monitoring of ovarian cancer
.
J Oncol
2011
;
2011
:
475983
.
14.
Urban
N
,
Drescher
C.
Potential and limitations in early diagnosis of ovarian cancer
.
Adv Exp Med Biol
2008
;
6222008
:
3
14
.
15.
Bast
RC
 Jr
,
Brewer
M
,
Zou
C
,
Hernandez
MA
,
Daley
M
,
Ozols
R
, et al
Prevention and early detection of ovarian cancer: mission impossible?
Recent Results Cancer Res
2007
;
174
:
91
100
.
16.
Cohen
JG
,
White
M
,
Cruz
A
,
Farias-Eisner
R
. 
In 2014, can we do better than CA125 in the early detection of ovarian cancer?
World J Biol Chem
2014
;
5
:
286
300
.
17.
Jacobs
I
,
Bast
RC
 Jr.
The CA 125 tumour-associated antigen: a review of the literature
.
Hum Reprod
1989
;
4
:
1
12
.
18.
Jacobs
I
,
Oram
D.
Prevention of ovarian cancer: a survey of the practice of prophylactic oophorectomy by fellows and members of the Royal College of Obstetricians and Gynaecologists
.
Br J Obstet Gynaecol
1989
;
96
:
510
5
.
19.
Skates
SJ
,
Xu
FJ
,
Yu
YH
,
Sjovall
K
,
Einhorn
N
,
Chang
Y
, et al
Toward an optimal algorithm for ovarian cancer screening with longitudinal tumor markers
.
Cancer
1995
;
76
:
2004
10
.
20.
Pinsky
PF
,
Zhu
C
,
Skates
SJ
,
Black
A
,
Partridge
E
,
Buys
SS
, et al
Potential effect of the risk of ovarian cancer algorithm (ROCA) on the mortality outcome of the Prostate, Lung, Colorectal and Ovarian (PLCO) trial
.
Int J Cancer
2013
;
132
:
2127
33
.
21.
Menon
U
,
Ryan
A
,
Kalsi
J
,
Gentry-Maharaj
A
,
Dawnay
A
,
Habib
M
, et al
Risk algorithm using serial biomarker measurements doubles the number of screen-detected cancers compared with a single-threshold rule in the United Kingdom Collaborative Trial of Ovarian Cancer Screening
.
J Clin Oncol
2015
;
33
:
2062
71
.
22.
Hogg
R
,
Friedlander
M.
Biology of epithelial ovarian cancer: implications for screening women at high genetic risk
.
J Clin Oncol
2004
;
22
:
1315
27
.
23.
Chan
A
,
Gilks
B
,
Kwon
J
,
Tinker
AV
. 
New insights into the pathogenesis of ovarian carcinoma: time to rethink ovarian cancer screening
.
Obstet Gynecol
2012
;
120
:
935
40
.
24.
Buys
SS
,
Partridge
E
,
Black
A
,
Johnson
CC
,
Lamerato
L
,
Isaacs
C
, et al
Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial
.
JAMA
2011
;
305
:
2295
303
.
25.
Menon
U.
Ovarian cancer screening has no effect on disease-specific mortality
.
Evid Based Med
2012
;
17
:
47
8
.
26.
Henderson
JT
,
Webber
EM
,
Sawaya
GF
. 
Screening for ovarian cancer: updated evidence report and systematic review for the US Preventive Services Task Force
.
JAMA
2018
;
319
:
595
606
.
27.
Kobayashi
H
,
Yamada
Y
,
Sado
T
,
Sakata
M
,
Yoshida
S
,
Kawaguchi
R
, et al
A randomized study of screening for ovarian cancer: a multicenter study in Japan
.
Int J Gynecol Cancer
2008
;
18
:
414
20
.
28.
Jacobs
IJ
,
Menon
U
,
Ryan
A
,
Gentry-Maharaj
A
,
Burnell
M
,
Kalsi
JK
, et al
Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial
.
Lancet
2016
;
387
:
945
56
.
29.
Ashworth
T.
A case of cancer in which cells similar to those in the tumours were seen in the blood after death
.
Australasian Med J
1869
;
14
:
146
7
.
30.
Miller
MC
,
Doyle
GV
,
Terstappen
LW
. 
Significance of circulating tumor cells detected by the cell search system in patients with metastatic breast colorectal and prostate cancer
.
J Oncol
2010
;
2010
:
617421
.
31.
Thierry
AR
,
El Messaoudi
S
,
Gahan
PB
,
Anker
P
,
Stroun
M
. 
Origins, structures, and functions of circulating DNA in oncology
.
Cancer Metastasis Rev
2016
;
35
:
347
76
.
32.
Jahr
S
,
Hentze
H
,
Englisch
S
,
Hardt
D
,
Fackelmayer
FO
,
Hesch
RD
, et al
DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells
.
Cancer Res
2001
;
61
:
1659
65
.
33.
Raposo
G
,
Stoorvogel
W
. 
Extracellular vesicles: exosomes, microvesicles, and friends
.
J Cell Biol
2013
;
200
:
373
83
.
34.
Simons
M
,
Raposo
G.
Exosomes–vesicular carriers for intercellular communication
.
Curr Opin Cell Biol
2009
;
21
:
575
81
.
35.
Wubbolts
R
,
Leckie
RS
,
Veenhuizen
PT
,
Schwarzmann
G
,
Mobius
W
,
Hoernschemeyer
J
, et al
Proteomic and biochemical analyses of human B cell-derived exosomes. Potential implications for their function and multivesicular body formation
.
J Biol Chem
2003
;
278
:
10963
72
.
36.
Hemler
ME.
Targeting of tetraspanin proteins—potential benefits and strategies
.
Nat Rev Drug Discov
2008
;
7
:
747
58
.
37.
Hemler
ME
. 
Tetraspanin proteins mediate cellular penetration, invasion, and fusion events and define a novel type of membrane microdomain
.
Annu Rev Cell Dev Biol
2003
;
19
:
397
422
.
38.
Théry
C
,
Regnault
A
,
Garin
J
,
Wolfers
J
,
Zitvogel
L
,
Ricciardi-Castagnoli
P
, et al
Molecular characterization of dendritic cell-derived exosomes
.
J Cell Biol
1999
;
147
:
599
610
.
39.
Meropol
NJ.
The significance of circulating tumor cells as prognostic markers for colon cancer
.
Clin Adv Hematol Oncol
2009
;
7
:
247
8
.
40.
Dotan
E
,
Cohen
SJ
,
Alpaugh
KR
,
Meropol
NJ
. 
Circulating tumor cells: evolving evidence and future challenges
.
Oncologist
2009
;
14
:
1070
82
.
41.
Cohen
SJ
,
Punt
CJ
,
Iannotti
N
,
Saidman
BH
,
Sabbath
KD
,
Gabrail
NY
, et al
Prognostic significance of circulating tumor cells in patients with metastatic colorectal cancer
.
Ann Oncol
2009
;
20
:
1223
9
.
42.
Cristofanilli
M
,
Budd
GT
,
Ellis
MJ
,
Stopeck
A
,
Matera
J
,
Miller
MC
, et al
Circulating tumor cells, disease progression, and survival in metastatic breast cancer
.
N Engl J Med
2004
;
351
:
781
91
.
43.
Lou
E
,
Vogel
RI
,
Teoh
D
,
Hoostal
S
,
Grad
A
,
Gerber
M
, et al
Assessment of circulating tumor cells as a predictive biomarker of histology in women with suspected ovarian cancer
.
Lab Med
2018
;
49
:
134
9
.
44.
Cohen
SJ
,
Alpaugh
RK
,
Gross
S
,
O’Hara
SM
,
Smirnov
DA
,
Terstappen
LW
, et al
Isolation and characterization of circulating tumor cells in patients with metastatic colorectal cancer
.
Clin Colorectal Cancer
2006
;
6
:
125
32
.
45.
Cohen
SJ
,
Punt
CJ
,
Iannotti
N
,
Saidman
BH
,
Sabbath
KD
,
Gabrail
NY
, et al
Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer
.
J Clin Oncol
2008
;
26
:
3213
21
.
46.
Zhang
J
,
Chen
K
,
Fan
ZH
. 
Circulating tumor cell isolation and analysis
.
Adv Clin Chem
2016
;
75
:
1
31
.
47.
Gwak
H
,
Kim
J
,
Kashefi-Kheyrabadi
L
,
Kwak
B
,
Hyun
KA
,
Jung
HI
. 
Progress in circulating tumor cell research using microfluidic devices
.
Micromachines
2018
;
9
:
pii: E353
.
48.
de Wit
S
,
van Dalum
G
,
Lenferink
AT
,
Tibbe
AG
,
Hiltermann
TJ
,
Groen
HJ
, et al
The detection of EpCAM(+) and EpCAM(−) circulating tumor cells
.
Sci Rep
2015
;
5
:
12270
.
49.
Vona
G
,
Sabile
A
,
Louha
M
,
Sitruk
V
,
Romana
S
,
Schütze
K
, et al
Isolation by size of epithelial tumor cells
.
Am J Pathol
2000
;
156
:
57
63
.
50.
Bulfoni
M
,
Turetta
M
,
Del Ben
F
,
Di Loreto
C
,
Beltrami
AP
,
Cesselli
D
. 
Dissecting the heterogeneity of circulating tumor cells in metastatic breast cancer: going far beyond the needle in the haystack
.
Int J Mol Sci
2016
;
17
:
pii: E1775
.
51.
Ahmed
MG
,
Abate
MF
,
Song
Y
,
Zhu
Z
,
Yan
F
,
Xu
Y
, et al
Isolation, detection, and antigen-based profiling of circulating tumor cells using a size-dictated immunocapture chip
.
Angew Chem Int Ed
2017
;
56
:
10681
5
.
52.
McGrath
J
,
Jimenez
M
,
Bridle
H
. 
Deterministic lateral displacement for particle separation: a review
.
Lab Chip
2014
;
14
:
4139
58
.
53.
Pantel
K
,
Speicher
MR.
The biology of circulating tumor cells
.
Oncogene
2016
;
35
:
1216
24
.
54.
Li
P
,
Mao
Z
,
Peng
Z
,
Zhou
L
,
Chen
Y
,
Huang
PH
, et al
Acoustic separation of circulating tumor cells
.
Proc Natl Acad Sci U S A
2015
;
112
:
4970
5
.
55.
Hyun
KA
,
Kwon
K
,
Han
H
,
Kim
SI
,
Jung
HI
. 
Microfluidic flow fractionation device for label-free isolation of circulating tumor cells (CTCs) from breast cancer patients
.
Biosens Bioelectron
2013
;
40
:
206
12
.
56.
Lin
E
,
Rivera-Baez
L
,
Fouladdel
S
,
Yoon
HJ
,
Guthrie
S
,
Wieger
J
, et al
High-throughput microfluidic labyrinth for the label-free isolation of circulating tumor cells
.
Cell Syst
2017
;
5
:
295
304
.
57.
Racila
E
,
Euhus
D
,
Weiss
AJ
,
Rao
C
,
McConnell
J
,
Terstappen
LW
, et al
Detection and characterization of carcinoma cells in the blood
.
Proc Natl Acad Sci U S A
1998
;
95
:
4589
94
.
58.
Behbakht
K
,
Sill
MW
,
Darcy
KM
,
Rubin
SC
,
Mannel
RS
,
Waggoner
S
, et al
Phase II trial of the mTOR inhibitor, temsirolimus and evaluation of circulating tumor cells and tumor biomarkers in persistent and recurrent epithelial ovarian and primary peritoneal malignancies: a Gynecologic Oncology Group study
.
Gynecol Oncol
2011
;
123
:
19
26
.
59.
Schilder
RJ
,
Sill
MW
,
Lankes
HA
,
Gold
MA
,
Mannel
RS
,
Modesitt
SC
, et al
A phase II evaluation of motesanib (AMG 706) in the treatment of persistent or recurrent ovarian, fallopian tube and primary peritoneal carcinomas: a Gynecologic Oncology Group study
.
Gynecol Oncol
2013
;
129
:
86
91
.
60.
Zhou
Y
,
Bian
B
,
Yuan
X
,
Xie
G
,
Ma
Y
,
Shen
L
. 
Prognostic value of circulating tumor cells in ovarian cancer: a meta-analysis
.
PLoS One
2015
;
10
:
e0130873
.
61.
Leon
SA
,
Shapiro
B
,
Sklaroff
DM
,
Yaros
MJ
. 
Free DNA in the serum of cancer patients and the effect of therapy
.
Cancer Res
1977
;
37
:
646
50
.
62.
Schwarzenbach
H
,
Hoon
DS
,
Pantel
K
. 
Cell-free nucleic acids as biomarkers in cancer patients
.
Nat Rev Cancer
2011
;
11
:
426
37
.
63.
Diaz
LA
 Jr
,
Bardelli
A
. 
Liquid biopsies: genotyping circulating tumor DNA
.
J Clin Oncol
2014
;
32
:
579
86
.
64.
Diehl
F
,
Li
M
,
Dressman
D
,
He
Y
,
Shen
D
,
Szabo
S
, et al
Detection and quantification of mutations in the plasma of patients with colorectal tumors
.
Proc Natl Acad Sci U S A
2005
;
102
:
16368
73
.
65.
Forshew
T
,
Murtaza
M
,
Parkinson
C
,
Gale
D
,
Tsui
DW
,
Kaper
F
, et al
Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA
.
Sci Transl Med
2012
;
4
:
136ra68
.
66.
Giannopoulou
L
,
Kasimir-Bauer
S
,
Lianidou
ES
. 
Liquid biopsy in ovarian cancer: recent advances on circulating tumor cells and circulating tumor DNA
.
Clin Chem Lab Med
2018
;
56
:
186
97
.
67.
Lo
YM
,
Zhang
J
,
Leung
TN
,
Lau
TK
,
Chang
AM
,
Hjelm
NM
. 
Rapid clearance of fetal DNA from maternal plasma
.
Am J Hum Genet
1999
;
64
:
218
24
.
68.
Yao
W
,
Mei
C
,
Nan
X
,
Hui
L
. 
Evaluation and comparison of in vitro degradation kinetics of DNA in serum, urine and saliva: a qualitative study
.
Gene
2016
;
590
:
142
8
.
69.
To
EW
,
Chan
KC
,
Leung
SF
,
Chan
LY
,
To
KF
,
Chan
AT
, et al
Rapid clearance of plasma Epstein-Barr virus DNA after surgical treatment of nasopharyngeal carcinoma
.
Clin Cancer Res
2003
;
9
:
3254
9
.
70.
Elazezy
M
,
Joosse
SA.
Techniques of using circulating tumor DNA as a liquid biopsy component in cancer management
.
Comput Struct Biotechnol J
2018
;
16
:
370
8
.
71.
Parkinson
CA
,
Gale
D
,
Piskorz
AM
,
Biggs
H
,
Hodgkin
C
,
Addley
H
, et al
Exploratory analysis of TP53 mutations in circulating tumour DNA as biomarkers of treatment response for patients with relapsed high-grade serous ovarian carcinoma: a retrospective study
.
PLoS Med
2016
;
13
:
e1002198
.
72.
Brown
PO
,
Palmer
C.
The preclinical natural history of serous ovarian cancer: defining the target for early detection
.
PLoS Med
2009
;
6
:
e1000114
.
73.
Lutz
AM
,
Willmann
JK
,
Cochran
FV
,
Ray
P
,
Gambhir
SS
. 
Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes
.
PLoS Med
2008
;
5
:
e170
.
74.
Warton
K
,
Samimi
G.
Methylation of cell-free circulating DNA in the diagnosis of cancer
.
Front Mol Biosci
2015
;
2
:
13
.
75.
Liggett
TE
,
Melnikov
A
,
Yi
Q
,
Replogle
C
,
Hu
W
,
Rotmensch
J
, et al
Distinctive DNA methylation patterns of cell-free plasma DNA in women with malignant ovarian tumors
.
Gynecol Oncol
2011
;
120
:
113
20
.
76.
Widschwendter
M
,
Zikan
M
,
Wahl
B
,
Lempiainen
H
,
Paprotka
T
,
Evans
I
, et al
The potential of circulating tumor DNA methylation analysis for the early detection and management of ovarian cancer
.
Genome Med
2017
;
9
:
116
.
77.
Wang
BG
,
Huang
HY
,
Chen
YC
,
Bristow
RE
,
Kassauei
K
,
Cheng
CC
, et al
Increased plasma DNA integrity in cancer patients
.
Cancer Res
2003
;
63
:
3966
8
.
78.
Yanez-Mo
M
,
Siljander
PR
,
Andreu
Z
,
Zavec
AB
,
Borras
FE
,
Buzas
EI
, et al
Biological properties of extracellular vesicles and their physiological functions
.
J Extracell Vesicles
2015
;
4
:
27066
.
79.
Colombo
M
,
Raposo
G
,
Thery
C
. 
Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles
.
Annu Rev Cell Dev Biol
2014
;
30
:
255
89
.
80.
Zijlstra
A
,
Di Vizio
D
. 
Size matters in nanoscale communication
.
Nat Cell Biol
2018
;
20
:
228
30
.
81.
Tkach
M
,
Thery
C.
Communication by extracellular vesicles: where we are and where we need to go
.
Cell
2016
;
164
:
1226
32
.
82.
Atay
S
,
Banskota
S
,
Crow
J
,
Sethi
G
,
Rink
L
,
Godwin
AK
. 
Oncogenic KIT-containing exosomes increase gastrointestinal stromal tumor cell invasion
.
Proc Natl Acad Sci U S A
2014
;
111
:
711
6
.
83.
Fu
Q
,
Zhang
Q
,
Lou
Y
,
Yang
J
,
Nie
G
,
Chen
Q
, et al
Primary tumor-derived exosomes facilitate metastasis by regulating adhesion of circulating tumor cells via SMAD3 in liver cancer
.
Oncogene
2018
;
37
:
6105
18
.
84.
Hoshino
A
,
Costa-Silva
B
,
Shen
TL
,
Rodrigues
G
,
Hashimoto
A
,
Tesic Mark
M
, et al
Tumour exosome integrins determine organotropic metastasis
.
Nature
2015
;
527
:
329
35
.
85.
Peinado
H
,
Aleckovic
M
,
Lavotshkin
S
,
Matei
I
,
Costa-Silva
B
,
Moreno-Bueno
G
, et al
Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET
.
Nat Med
2012
;
18
:
883
91
.
86.
Crow
J
,
Atay
S
,
Banskota
S
,
Artale
B
,
Schmitt
S
,
Godwin
AK
. 
Exosomes as mediators of platinum resistance in ovarian cancer
.
Oncotarget
2017
;
8
:
11917
36
.
87.
Nakamura
K
,
Sawada
K
,
Kinose
Y
,
Yoshimura
A
,
Toda
A
,
Nakatsuka
E
, et al
Exosomes promote ovarian cancer cell invasion through transfer of CD44 to peritoneal mesothelial cells
.
Mol Cancer Res
2017
;
15
:
78
92
.
88.
Kobayashi
M
,
Salomon
C
,
Tapia
J
,
Illanes
SE
,
Mitchell
MD
,
Rice
GE
. 
Ovarian cancer cell invasiveness is associated with discordant exosomal sequestration of Let-7 miRNA and miR-200
.
J Transl Med
2014
;
12
:
4
.
89.
Kanlikilicer
P
,
Bayraktar
R
,
Denizli
M
,
Rashed
MH
,
Ivan
C
,
Aslan
B
, et al
Exosomal miRNA confers chemo resistance via targeting Cav1/p-gp/M2-type macrophage axis in ovarian cancer
.
EBioMedicine
2018
;
38
:
100
12
.
90.
Pink
RC
,
Samuel
P
,
Massa
D
,
Caley
DP
,
Brooks
SA
,
Carter
DR
. 
The passenger strand, miR-21-3p, plays a role in mediating cisplatin resistance in ovarian cancer cells
.
Gynecol Oncol
2015
;
137
:
143
51
.
91.
Weiner-Gorzel
K
,
Dempsey
E
,
Milewska
M
,
McGoldrick
A
,
Toh
V
,
Walsh
A
, et al
Overexpression of the microRNA miR-433 promotes resistance to paclitaxel through the induction of cellular senescence in ovarian cancer cells
.
Cancer Med
2015
;
4
:
745
58
.
92.
Au Yeung
CL
,
Co
NN
,
Tsuruga
T
,
Yeung
TL
,
Kwan
SY
,
Leung
CS
, et al
Exosomal transfer of stroma-derived miR21 confers paclitaxel resistance in ovarian cancer cells through targeting APAF1
.
Nat Commun
2016
;
7
:
11150
.
93.
Cho
JA
,
Park
H
,
Lim
EH
,
Kim
KH
,
Choi
JS
,
Lee
JH
, et al
Exosomes from ovarian cancer cells induce adipose tissue-derived mesenchymal stem cells to acquire the physical and functional characteristics of tumor-supporting myofibroblasts
.
Gynecol Oncol
2011
;
123
:
379
86
.
94.
Ying
X
,
Wu
Q
,
Wu
X
,
Zhu
Q
,
Wang
X
,
Jiang
L
, et al
Epithelial ovarian cancer-secreted exosomal miR-222–3p induces polarization of tumor-associated macrophages
.
Oncotarget
2016
;
7
:
43076
87
.
95.
Im
H
,
Shao
H
,
Park
YI
,
Peterson
VM
,
Castro
CM
,
Weissleder
R
, et al
Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor
.
Nat Biotechnol
2014
;
32
:
490
5
.
96.
Runz
S
,
Keller
S
,
Rupp
C
,
Stoeck
A
,
Issa
Y
,
Koensgen
D
, et al
Malignant ascites-derived exosomes of ovarian carcinoma patients contain CD24 and EpCAM
.
Gynecol Oncol
2007
;
107
:
563
71
.
97.
He
M
,
Crow
J
,
Roth
M
,
Zeng
Y
,
Godwin
AK
. 
Integrated immunoisolation and protein analysis of circulating exosomes using microfluidic technology
.
Lab Chip
2014
;
14
:
3773
80
.
98.
Zhao
Z
,
Yang
Y
,
Zeng
Y
,
He
M
. 
A microfluidic ExoSearch chip for multiplexed exosome detection towards blood-based ovarian cancer diagnosis
.
Lab Chip
2016
;
16
:
489
96
.
99.
Zhang
P
,
Zhou
X
,
He
M
,
Shang
Y
,
Tetlow
AL
,
Godwin
AK
, et al
Ultrasensitive detection of circulating exosomes with a 3D-nanopatterned microfluidic chip
.
Nat Biomed Eng
2019
;
3
:
438
451
.
100.
Heitzer
E
,
Haque
IS
,
Roberts
CES
,
Speicher
MR
. 
Current and future perspectives of liquid biopsies in genomics-driven oncology
.
Nat Rev Genet
2019
;
20
:
71
88
.
101.
Ramalingam
N
,
Jeffrey
SS.
Future of liquid biopsies with growing technological and bioinformatics studies: opportunities and challenges in discovering tumor heterogeneity with single-cell level analysis
.
Cancer J
2018
;
24
:
104
8
.
102.
Butler
TM
,
Spellman
PT
,
Gray
J
. 
Circulating-tumor DNA as an early detection and diagnostic tool
.
Curr Opin Genet Dev
2017
;
42
:
14
21
.