WO2018213141A1

WO2018213141A1 - Methods for detecting ovarian cancer using extracellular vesicles for molecular analysis

Info

Publication number: WO2018213141A1
Application number: PCT/US2018/032419
Authority: WO
Inventors: Cindy Yamamoto; Shu-Wing NG
Original assignee: Hitachi Chemical Co. America, Ltd.; Hitachi Chemical., Ltd.; The Brigham & Women's Hospital, Inc.
Priority date: 2017-05-16
Filing date: 2018-05-11
Publication date: 2018-11-22

Abstract

The present disclosure relates to methods of collecting exosomes and microvesicles (EMV) from urine, isolating corresponding mRNA, and analyzing expression patterns in order to diagnose and treat various post-kidney transplant complications. In particular, annexinl mRNA expression patterns are analyzed through a unique diagnostic formula.

Description

METHODS FOR DETECTING OVARIAN CANCER USING EXTRACELLULAR VESICLES FOR MOLECULAR ANALYSIS

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

[0001] Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are hereby incorporated by reference and made part of the present disclosure.

BACKGROUND

Field

[0002] The present disclosure relates to methods for using biological fluids to detect and/or treat ovarian cancer. Several embodiments relate to an ex vivo assay that evaluates mRNA and miRNA profiles in extracelluar vesicles isolated from the biological fluid.

Description of the Related Art

[0003] Ovarian cancer results in over 14,000 deaths annually in the U.S. and is the fifth-leading cause of cancer deaths in women. Due to the lack of early obvious symptoms, most women present with advanced stage disease. Approximately 60% of women are diagnosed at stage 3 or higher where the 5-year survival rate is below 30%. In contrast, only 15% of cases are diagnosed at stage 1, when the tumor is localized to the primary site and patients have a 5-year survival rate of about 92%. Identification of specific and sensitive biomarkers for early detection of ovarian cancer may improve clinical outcome and survival by increasing the numbers of cases diagnosed at this early stage.

SUMMARY

[0004] There are provided herein, in several embodiments, methods and systems for identifying such biomarkers, and using such biomarkers to diagnose, monitor, prognose, or direct treatment of ovarian cancer in a patient. In certain aspects, various RNA can be used in the methods, including, but not limited to screen a patient for early ovarian cancer. [0005] In several embodiments, the method includes detecting mRNA expression levels of the markers in extracellular vesicle samples. Vesicle samples can be obtained from blood, urine, or any other biological sample. In certain variants, the method includes quantifying mRNA expression of an mRNA selected from the group consisting of CAl l, MEDAG, LAMA4, NANOG, SPINT2, let7b, miR23b, miR29a, miR30d, miR205, and miR720.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Various embodiments are depicted in the accompanying drawings for illustrative purposes, and should in no way be interpreted as limiting the scope of the embodiments. Furthermore, various features of different disclosed embodiments can be combined to form additional embodiments, which are part of this disclosure.

[0007] FIGURE 1A shows LAMA4 mRNA expression in ovarian cancer ascites and non-malignant peritoneal fluid.

[0008] FIGURE IB shows CAl l mRNA expression in ovarian cancer ascites and non-malignant peritoneal fluid.

[0009] FIGURE 1C shows MEDAG mRNA expression in ovarian cancer ascites and non-malignant peritoneal fluid.

[0010] FIGURE ID shows SPINT2 mRNA expression in ovarian cancer ascites and non-malignant peritoneal fluid.

[0011] FIGURE IE shows NANOG mRNA expression in ovarian cancer ascites and non-malignant peritoneal fluid.

[0012] FIGURE 2 shows a plot of micro-RNA expression levels in extracellular vesicle samples from non-malignant peritoneal fluids and ovarian cancer ascites.

DETAILED DESCRIPTION

[0013] Certain aspects of the present disclosure are generally directed to a minimally-invasive method for screening a patient for ovarian cancer. Each and every feature described herein, and each and every combination of two or more of such features, is included within the scope of the present disclosure provided that the features included in such a combination are not mutually inconsistent.

[0014] Screening tests currently used for ovarian cancer detection include pelvic examination, transvaginal ultrasound, and cancer antigen 125 (CA125) detection. If an adnexal mass is detected by palpation and/or ultrasound, surgery is ultimately needed for the confirmed ovarian cancer diagnosis and staging. CA125 is more routinely used as an early marker for disease recurrence and treatment response. CA125 has been shown to be elevated in 80% of epithelial ovarian carcinomas, but its increase in other conditions such as endometrial, pancreatic and breast cancer and benign conditions such as inflammatory bowel disease and hepatitis have limited the use of CA125 as an early screening marker. In addition, annual screening with both CA125 and transvaginal ultrasound has not been shown to reduce ovarian cancer mortality compared with usual care.

[0015] An additional protein marker that has been FDA approved for monitoring women with ovarian cancer is the human epididymis protein 4 (HE4). HE4 is a small, secreted protein (25 kDa) found in various biofluids such as serum, urine, vaginal fluid and saliva and has been detected in normal, benign and malignant tissues throughout the body including the female genital tract. HE4 is encoded by the whey-acidic -protein four-disulfide core domain protein 2 (WFDC2) gene, which has been shown to be overexpressed in patients with ovarian carcinoma. Comparable sensitivity for CA125 and HE4 in the serum from postmenopausal women with ovarian cancer has been shown using an ELISA immunoassay. Subsequent studies have confirmed the sensitivity of HE4. The diagnostic performance of algorithms that consider HE4 and CA125 together suggest that the utility of these markers in differentiating epithelial ovarian cancer (EOC) and benign diseases may be enhanced when the markers are considered together compared to when the markers are considered individually. A need still exists to identify biomarkers that can be effective in early ovarian cancer screening.

[0016] Extracellular vesicles (EVs) may be a useful source for early disease biomarkers of ovarian cancer. EVs mainly consist of exosomes (30-100nm) and microvesicles (100-1000 nm) which are either actively released from cells by fusion of multivesicular bodies to plasma membrane or formed by direct budding of the cell membrane into the extracellular space, respectively. Exosomes and microvesicles contain proteins, lipids and nucleic acids such as mRNA and miRNA from their cell of origin. The possible roles of EV in cell-to-cell communication, their high abundance in plasma (10 12 per mL), and their highly stable nature, suggests that EV-based biomarkers may provide useful markers for early ovarian cancer screening. In some embodiments, the methods of the present disclosure evaluate EV mRNA and miRNA in ovarian cancer ascites, plasma and non-malignant peritoneal fluids. The present disclosure identifies mRNA and miRNA that are differentially expressed between ovarian cancer ascites and non-malignant peritoneal fluids. At least one of the mRNA is detected in plasma from ovarian cancer patients.

[0017] Extracellular vesicles (EVs) can be isolated from various biological fluids such as urine, blood, ascites and saliva. The RNA enclosed within these biological components is protected from degradation by nucleases and could be used as potential noninvasive sources of biomarkers. In some embodiments, EV mRNA are isolated from ovarian cancer ascites and non-malignant peritoneal fluids and analyzed for differential expression levels. In some embodiments, ascites from advanced stage ovarian cancer patients and peritoneal fluids from patients with non-malignant conditions were collected from peritoneal cavity using Yankauer suction connected to a drainage bag, or bulb suction from volumes smaller than 500 mL. Fluids were transferred into 50 cc tubes and centrifuged at 2000 x g for 15 min at 4°C to remove cell debris. The supernatant was then stored at -80°C until further use.

[0018] A filterplate developed to capture EVs and an oligo (dT) microplate was used as an integrated platform to isolate EV mRNA from these biofluids. Messenger RNA was quantitated using conventional qPCR analysis. MicroRNAs were also examined in each of these biofluids using conventional miRNA isolation and qPCR analysis. Differential gene expression analysis was performed using the comparative Ct method. Genes modulated more than 2-fold with p-value less than 0.05 were considered to be differentially expressed.

[0019] The biofluids were initially centrifuged at 2000 x g for 10 minutes at 4°C. Supernatant (300-400 μί) was applied to a 96-well EV capture filterplate and then centrifuged at 2000 x g for 5 minutes at 4°C to capture EVs onto the filter membrane. Lysis buffer is applied to the EVs captured on the filter and incubated at 37°C for 10 minutes. Centrifugation of the filterplate was performed at 2000 x g for 5 minutes at 4°C to transfer lysate to the mRNA Capture Plate for hybridization of mRNA to the oligo(dT)-covalently linked wells. After wash steps, on-plate random-primed cDNA synthesis using MMLV was performed at 37°C for 2 hours. For qPCR analysis, 2 μL· of cDNA was used with Sso Advanced SYBR mix and gene-specific primers (see Table 3). Real-time PCR was performed on a ViiA7 (Life Technologies, Carlsbad, CA) instrument using the following profile: initial denaturation at 95°C for 10 min, 40 cycles of 95°C for 30 sec and 60°C for 1 min, melting curve analysis. Ct values greater than 36 were set to 36 cycles for data analysis. Real-time PCR data was processed by Data Assist v3.01 (Thermo Fisher Scientific, Waltham, MA) and analyzed by Excel using comparative Ct method for differential gene expression analysis. ACTB was used for normalization. Genes modulated more than 2-fold with p-value less than 0.05 were considered to be differentially expressed.

[0020] A list of 35 genes (see Table 3) were selected for a qPCR screening of ovarian cancer ascites (n=8) and peritoneal fluids (n=10). Of the selected mRNA for qPCR validation, five were found to be significantly (p < 0.05) differentially expressed in ovarian cancer ascites and peritoneal fluid (Figure 1). Three mRNAs (CA11, LAMA4, MED AG) were .01 -.28-fold lower expressed. Two mRNAs (SPINT2 and NANOG) were 3.2-5.8-fold higher expressed in ovarian cancer ascites versus non-malignant peritoneal fluid EVs.

[0021] Figures 1A-E show differentially-expressed mRNA in ovarian cancer ascites compared to non-malignant peritoneal fluid. The relative gene expression in EV samples from peritoneal fluids (n=10, black column) and ovarian cancer ascites (n=8, white column) was evaluated for the markers LAMA4 (A), CA11 (B), MED AG (C), SPINT2 (D), and NANOG (E). Messenger RNA (mRNA) expression of each biomarker was normalized to ACTB and is shown as dCT gene expression levels with average and SD indicated by lines. Statistical significance between non-malignant peritoneal fluids and ovarian cancer ascites for A-C is p < 0.005 and D and E is p < 0.05 using Student's t-test.

[0022] Figure 2 shows differentially-expressed miRNA in ovarian cancer ascites compared to non-malignant peritoneal fluid. Micro-RNA (miRNA) expression levels that had been normalized by SNORD61 levels were evaluated in EV samples from non-malignant peritoneal fluids (n=10) and ovarian cancer ascites (n=8). Average and SD 2^A-dCT values are shown as a column graph. Statistically significant miRNA expression levels between non-malignant peritoneal fluid and ovarian cancer ascites samples are indicated by asterisk (*) for p < 0.01 and double asterisks (**) for p < 0.05 by Student's t-test.

[0023] Additional modifications to the above procedure may include a pre- amplification step to improve sensitivity of real-time PCR detection. Pre-amplification of cDNA prior to conventional qPCR has been shown to improve the detection of low copy number targets. Pre-amplification has been shown to be optimal between 15-18 pre- amplification cycles and 5-20 ng transcribed total RNA diluted either 20x or 40x after pre- amplification for the BioMark System from Fluidigm. An alternative or additional method to improve sensitivity may include selective isolation of extracellular vesicles using antibody- coated magnetic beads. Epithelial ovarian cancer stem cells may have surface markers such as CD24, CD44 and CD133 while other epithelial tumorigenic cells may have surface markers such as EpCAM, cancer antigen 125 and cytokeratin 7. These stem cells and tumorigenic cells may release extracellular vesicles that also present with those surface markers. Selectively isolating ovarian cancer-specific stem and tumorigenic extracellular vesicles using antibody-coated magnetic beads may enrich for mRNA specific in this application.

[0024] MicroRNAs were also examined in each of these biofluids using conventional miRNA isolation and qPCR analysis. The specific miRNAs selected were from previous literature indicating their involvement in ovarian cancer and include let-7b, miR134, miR141, miR181a, miR183, miR200a, miR200b, miR200c, miR205, miR21, miR23b, miR29a, miR30b, miR30d, miR429, miR451, miR720, and reference miRNAs RNU6-2 and SNORD61. Biofluids were initially centrifuged at 2000 x g for 10 minutes at 4°C. Supernatant (300-400 μί) was applied to a 96-well EV capture filterplate and then centrifuged at 2000 x g for 5 minutes at 4°C to capture EVs onto the filter membrane. Lysis buffer from miRNeasy (Qiagen), was applied to the wells in the filterplate. Total RNA was isolated per manufacturer's protocol. Synthesis of cDNA was performed using miScript RT kit (Qiagen). The cDNA was diluted 1:4 and 1 μL· was used with the miScript PCR assay (Qiagen) for qPCR screening. For miRNA analysis, Excel and DataAssist v3.01 was used with the following parameters: cut-off value of Ct = 36, SNORD61 selected as endogenous reference RNA, and p-values adjusted using Benjamini-Hochberg False Discovery Rate.

[0025] Small RNAs are abundant within extracellular vesicles. Six miRNAs, let7b, miR23b, miR29a, miR30d, miR205 and miR720, were found to be significantly differentially expressed between ovarian cancer ascites and non-malignant peritoneal fluid (Figure 2). All six miRNAs were found in elevated levels in peritoneal fluid EVs compared to ascites samples.

[0026] Biomarker signatures comprising multiple mRNA or miRNA were investigated to develop algorithms to diagnose, monitor, predict or prognose ovarian cancer. In some embodiments, multivariate discriminant analysis with Minitabl7 software was used to analyze the combination of mRNA and miRNA for their classification capabilities. In these cases, only raw Ct values were used and not the normalized reference gene values (delta CT). In certain embodiments, the raw mRNA and miRNA qPCR results were used for multivariate discriminant analysis to obtain a squared distance between the ascites group and the peritoneal fluid group. In at least one embodiment, the squared distance between the two groups was 30.8119. In some embodiments, the linear discriminant function for groups and classification results were determined (see, e.g., Table 1). In some embodiments, a combination of the mRNA markers, LAMA4, CA11, MED AG, NANOG and SPINT2 and miRNA markers, let7b, miR23b and miR29a were found to classify 87.5% and 100% of the ovarian cancer and benign control subjects, respectively (see e.g., Table 2).

Table Linear discriminant function for ascites and disease control groups.

miR29a 65.6 69.5

Table 2. Classification results for mRNA markers, LAMA4, CA11, MEDAG, NANOG and SPINT2 and miRNA markers, let7b, miR23b and miR29a.

[0027] The mRNA markers found to be differentially expressed between ovarian cancer ascites and non-malignant peritoneal fluid includes CA11, MEDAG, LAMA4, NANOG, and SPINT2. CA11, MEDAG and LAMA4 are mRNA identified by differential gene expression analysis of next generation mRNA sequencing of ascites (n=2) and peritoneal fluid (n=3) samples. These three mRNA do not yet have any previously published literature linking their expression with ovarian cancer. CA11 is a gene which encodes a carbonic anhydrase, a family of zinc metalloenzymes that catalyse the hydration of carbon dioxide. The carbonic anhydrase family of enzymes is involved in various biological processes, such as producing aqueous humor, cerebrospinal fluid and saliva. They also are described as being involved in respiration, calcification, acid-base balance and bone resorption. MEDAG is a protein expressed mainly in adipose tissue and has been found to be more abundant in omental fat than subcutaneous depot in obese patients. LAMA4 is a member of a family of extracellular matrix glycoproteins that comprise a portion of basement membranes. Laminins have been shown to be involved in various processes including cell adhesion, differentiation and migration. Despite the diversity of biological functions observed for the laminin family, the true function of LAMA4 is not yet known. NANOG is a transcription factor involved in self-renewal of embryonic stem cells. It has been found to be involved in epithelial-mesenchymal transition in ovarian cancer as well as gastric cancers. SPINT2 is a Kunitz-type protease inhibitor which is a putative tumor suppressor. CA11, MEDAG, LAMA4, NANOG and SPINT2 mRNA have not previously been found to be present in ovarian cancer ascites and non-malignant peritoneal fluid extracellular vesicles or plasma from healthy and ovarian cancer subjects. The methods described in this application indicate an increased mRNA expression level of NANOG and/or SPINT2 in ovarian cancer ascites compared to non-malignant peritoneal fluids. The presence of plasma extracellular vesicle NANOG mRNA is also detected by real-time PCR. In contrast to NANOG and SPINT2, there is a decreased expression of CA11, MEDAG, and/or LAMA4 in ovarian cancer ascites compared to non-malignant peritoneal fluid.

[0028] Selected mRNA biomarkers were measured in plasma obtained from healthy subjects. Blood was collected from subjects using EDTA or ACD blood collection tubes. Plasma was isolated by centrifugation of the blood for at least 15 minutes at 2500 RPM. The plasma was aliquotted into sterile plastic screw-cap vials and stored at -80°C until further use. Plasmas were thawed at room temperature and centrifuged at 2000 x g for 10 minutes in eppendorf microfuge at 4°C. 350 μΐ_^ plasma supernatant was aliquoted into a well of a EV capture filterplate placed on top of a 96-well deep well plate. The EV capture filterplate was then centrifuged at 2000 x g for 5 minutes at 4°C. After EVs were captured on filterplate, 80 μΐ_^ lysis buffer was applied to each well and incubated at 37°C for 10 minutes. The EV filterplate was then placed on the mRNA capture plate and centrifuged at 2000 x g for 5 minutes to transfer the EV mRNA lysate to the mRNA capture plate. Hybridization takes place overnight at 4°C. Following hybridization and 3x wash steps with 100 μΐ_^ Wash Buffer A and 150 μΐ_^ Wash Buffer B, random-primed cDNA synthesis is performed in- well for 2 hours at 37°C. Following cDNA synthesis, 96-well mRNA capture plate is sealed with aluminum tape and covered with lid and placed in 4°C if it will be used within 2 days or at - 20°C if it will be used after 2 days. 4 μL· of cDNA is then used for qPCR analysis. For qPCR analysis, Sso Advance SYBR PCR master mix is used with gene-specific primers for detection of CA11, LAMA4, MEDAG, NANOG, SPINT2, ZEB2, ACTB, GAPDH. Primer sequences are listed in Table 1. Real-time PCR was performed on a ViiA7 (Life Technologies, Carlsbad, CA) instrument using the following profile: initial denaturation at 95°C for 10 min, 40 cycles of 95°C for 30 sec and 60°C for 1 min, melting curve analysis. The threshold for Ct determination was manually set to 0.1. Ct values greater than 36 were set to 36 cycles for data analysis. Real-time PCR data was processed by Data Assist v3.01 (Thermo Fisher Scientific, Waltham, MA) and analyzed by Excel using comparative Ct method for differential gene expression analysis. ACTB was used for normalization.

[0029] The sense and anti-sense primers used for the PCR analysis are presented in Table 3 below. Alternative primers derived from publicly available sequences for the genes above may also be used in several embodiments.

Table 3. Primer sequences used in quantitative real-time PCR analysis.

Gene Forward (5 '-3') SEQ.ID.NO. Reverse ( 5 ' -3 ' ) SEQ. ID

Human

NANOG GCCAGGATGGTCTCGATCTC 1 GGTGGCTCACGCCTGTAAAT 2

ZEB2 AAGATAGGTGGCGCGTGTTT 3 CTTTCGGCCACTCCAGGAA 4

SPINT2 TCCCACGCTGGTACTTTGAC 5 AACCACCACCTTTGAGCCAA 6

AZGP1 TGCAGGGAAGGTTTGGTTGT 7 TTGGTTATCTGGGCTGCTGG 8

LGALS IB GGAGGTGGTCTTCAACAGCA 9 TCGTCTGACGCGATGATGAG 10

LINC00251 GGTGAGCAGCTGACTCAGTT 11 TTGGAGAGGAGGACTAGCCC 12

LCE1F CTCCTGTCTCTTCCTGCTGC 13 CTGCAGCAGTCAGAGCTCTG 14

HNF1A-AS1 CTGGGTTTGAGCCTCGTTCT 15 GGGATTGCAGGTGTGATCCA 16

OR8D2 CCACAGTCCATACTACCCGC 17 AAGGACCGCCAGTGTAGTTG 18

PSCA TGCTGTGCTACTCCTGCAAA 19 TCATCCACGCAGTTCAAGCT 20

TFF3 CTGTCTGCAAACCAGTGTGC 21 TCCTGGAGTCAAAGCAGCAG 22

MTRNR2L1 AGGACATCCCAATGGTGCAG 23 TGAAGTGGGCCCCATTTCTC 24

LAMA4 GGAAGTGCACTCGAGAACCA 25 TTGGCGTTTTTGCTTCCGAG 26

RBP1 AATGTGGCCTTGCGCAAAAT 27 TGCCTGTCAGATCCTCCTCA 28

MMP2 TGATGGCATCGCTCAGATCC 29 GGCCTCGTATACCGCATCAA 30

CA11 TGTTCCCTGAATCCTTCGGC 31 GAGGTGATATTGAGGGCCCG 32

GZMA GGTGGAAGAGACTCGTGCAA 33 TATAGACACCAGGCCCACGA 34

IGFBP1 GGCACAGGAGACATCAGGAG 35 AGACCCAGGGATCCTCTTCC 36

MTRNR2L10 CTCCGCAAATTTTACCCCGC 37 CTGCGGCCATTGAACGTATG 38

KLK5 CAAAGTGCTTGGTGTCTGGC 39 GTCTCGGGTAAGCATCCTCG 40

GPX3 AGTATGTCCGACCAGGTGGA 41 AAAGTTCCAGCGGATGTCGT 42

IL8 CTCCAAACCTTTCCACCCCA 43 TTCCTTGGGGTCCAGACAGA 44

SERPING1 CTCCTACCCAGCCCACTACT 45 TTGCTGAGAAGGCGTGGTAG 46

RNASE7 CTCAAGCATGCAACTCAGCC 47 GCAGGCTATTTTGGGGGTCT 48

0DF2L AGCCAAGTGGAACCTGCAAT 49 TCAGACAACTTGGCTTCCTGA 50

MEDAG GCAAGGGATGGACATGGTCA 51 ACCACTTCATTTCCTGGGGG 52

LUM GTGGTACCAGTGGCCAGTAC 53 ATTCCAGGAGGCACCATTGG 54 LYPD3 TCTGACCTCCGCAACAAGAC 55 CTCCCTGTCTCGGAGTCTGA 56

CSTA ACGGAAAATTGGAAGCTGTGC 57 CGTCAGCTCGTCATCCTTGT 58

UPK1B ACCAAAACAACAGCCCTCCA 59 GGCCAGGGATAGTCAGCATC 60

S1 00A1 6 GCAGTCATTGTCCTGGTGGA 61 GATGAGCTTATCCGCAGCCT 62

SPARC CAAGAAGCCCTGCCTGATGA 63 TCTTCGGTTTCCTCTGCACC 64

GAPDH CCCACTCCTCCACCTTTGAC 65 CATACCAGGAAATGAGCTTGACAA 66

ACTB TTTTTCCTGGCACCCAGCACAAT 6 7 TTTTTGCCGATCCACACGGAGTACT 68

B2M TGACTTTGTCACAGCCCAAGATA 69 AATGCGGCATCTTCAAACCT 70

Conclusion

[0030] In conclusion, the methods of the present application can provide a promising diagnostic and prognostic assay that is non-invasive and identifies ovarian cancer and other complications with greater accuracy than the biomarkers used in current standard practice (e.g., CA125 and HE4).

Claims

WHAT IS CLAIMED IS:

1. A method of collecting ascites fluids or peritoneal fluids and identifying biomarkers for ovarian cancer, comprising:

(I) obtaining ascites or peritoneal fluids by suction;

(II) isolating one or more of membrane particles, exosomes, exosome-like vesicles, and microvesicles from said fluid; and

(ΙΠ) detecting expression of a biomarker selected from the group consisting of CAl l, MED AG, LAMA4, NANOG, SPINT2, let7b, miR23b, miR29a, miR30d, miR205, and miR720, wherein detecting expression of a biomarker comprises:

(a) releasing RNA from the isolated membrane particles, exosomes, exosome-like vesicles, and/or microvesicles;

(b) contacting the liberated RNA with a reverse transcriptase to generate complementary DNA (cDNA); and

(c) contacting said cDNA with sense and antisense primers that are specific for the biomarker of ovarian cancer and a DNA polymerase in order to generate amplified DNA.

2. A method of collecting blood and identifying biomarkers for ovarian cancer, comprising:

(I) obtaining blood using blood collection tube;

(II) isolating plasma or serum from centrifugation of blood;

(ΙΠ) isolating one or more of membrane particles, exosomes, exosome-like vesicles, and microvesicles from said fluid; and

(IV) detecting expression of a biomarker selected from the group consisting of CAl l, MED AG, LAMA4, NANOG, SPINT2, let7b, miR23b, miR29a, miR30d, miR205, and miR720, wherein detecting expression of a biomarker comprises:

(b) contacting the liberated RNA with a reverse transcriptase to generate complementary DNA (cDNA); and (c) contacting said cDNA with sense and antisense primers that are specific for the biomarker of ovarian cancer and a DNA polymerase in order to generate amplified DNA.

3. The method of claim 1 in which mRNA is normalized to ACTB and miRNA is normalized to SNORD61.

4. The method of claim 2 in which mRNA is normalized to ACTB and miRNA is normalized to SNORD61.

5. A method of identifying a human subject at a higher risk for ovarian cancer, the method comprising:

(I) capturing, from a biofluid sample obtained from said human subject, at least a portion of membrane particles, exosomes, exosome-like vesicles, and microvesicles, thereby generating a vesicle sample;

(Π) detecting an expression level of a biomarker in the vesicle sample, wherein said biomarker is selected from the group consisting of CAl l, MED AG, LAMA4, NANOG, SPINT2, let7b, miR23b, miR29a, miR30d, miR205, and miR720; and

(ΠΙ) determining that said vesicle sample expresses said one or more biomarkers at a level significantly different from a control expression level of the biomarker in a vesicle sample of a healthy human control subject not suffering from ovarian cancer, thereby diagnosing the human subject as at a higher risk for ovarian cancer.

6. The method of claim 5, wherein the biomarker is selected from the group consisting of SPINT2 and NANOG, and the level significantly different is higher than the control expression level.

7. The method of claim 5, wherein the biomarker is selected from the group consisting of CAl l, LAMA4, MAD AG, let7b, miR23b, miR29a, miR30d, miR205, and miR720, and the level significantly different is lower than the control expression level.

8. The method of claim 5, wherein detecting comprises quantifying a combined expression level of at least three of said biomarkers.

9. The method of claim 8, wherein detecting comprises quantifying a combined expression level of CA11, LAMA4, MED AG, SPINT2, and NANOG.

10. The method of claim 8, wherein detecting comprises quantifying a combined expression level of let7b, miR23b, and miR29a.

11. A method of identifying and treating a human patient displaying an indication of ovarian cancer, the method comprising:

(A) having a vesicle-containing sample obtained from said human patient sent to a laboratory for the laboratory to perform an assay comprising the following steps (D-(3):

(1) capturing, from a biofluid sample obtained from said human patient, at least a portion of membrane particles, exosomes, exosome-like vesicles, and microvesicles, thereby generating a vesicle sample;

(2) detecting an expression level of a biomarker in the vesicle sample, wherein said biomarker is selected from the group consisting of CA11, MEDAG, LAMA4, NANOG, SPINT2, let7b, miR23b, miR29a, miR30d, miR205, and miR720; and

(3) determining said vesicle sample expresses said one or more biomarkers at a level significantly different from a control expression level of the biomarker in a vesicle sample of a healthy human control subject not suffering from ovarian cancer, thereby diagnosing the human patient as displaying an indication of ovarian cancer; and

(B) administering an effective amount of an ovarian cancer medication to the human patient displaying an indication of ovarian cancer, wherein said ovarian cancer medication is selected from the group consisting of chemotherapy, radiation, surgery, and immunotherapy.

12. The method of claim 11, wherein the biomarker is selected from the group consisting of SPINT2 and NANOG, and the level significantly different is higher than the control expression level.

13. The method of claim 11, wherein the biomarker is selected from the group consisting of CAl l, LAMA4, MAD AG, let7b, miR23b, miR29a, miR30d, miR205, and miR720, and the level significantly different is lower than the control expression level.

14. The method of claim 11, wherein detecting comprises quantifying a combined expression level of at least three of said biomarkers.

15. The method of claim 14, wherein detecting comprises quantifying a combined expression level of CAl l, LAMA4, MED AG, SPINT2, and NANOG.

16. The method of claim 14, wherein detecting comprises quantifying a combined expression level of let7b, miR23b, and miR29a.