Method for detecting extracellular vesicles and circulating microRNAs enriched with tumor-derived microRNAs

Abstract

This invention provides methods, kits, and systems for isolating extracellular vesicles (EVs) rich in cancer-related miRNAs. It also provides methods, kits, and systems for detecting cancer-related miRNA biomarkers from such EVs. These methods, systems, and kits can be used for the diagnosis, prognosis, and prediction of recurrence risk of specific cancers.

Description

[Technical Field]

[0001] Cross-references to related applications This application claims priority and interest to U.S. Provisional Patent Application No. 63 / 487,144, filed on 27 February 2023, the contents of which are incorporated herein by reference as if they were fully described herein.

[0002] Statement regarding rights to inventions made under federal government-supported research and development. This invention was developed with government support under contract number CA270508 issued by the National Institutes of Health (NIH). The government has certain rights to this invention. [Background technology]

[0003] Circulating microRNAs (miRNAs) have attracted significant interest as candidate biomarkers for cancer diagnosis, prognosis, and recurrence detection. However, trace amounts of miRNAs from small tumors may not be detectable even after isolation from body fluids using conventional free miRNA extraction methods. Extracellular vesicles (EVs) contain miRNAs and are ideal for fluid biopsies. Because the cargo (nucleic acids, lipids, proteins, etc.) of EVs often reflects the genetic and biological state of the parent cell, the components of EV cargo have been studied as potential cancer biomarkers. However, circulating EVs are highly heterogeneous, and their cargo is also diverse. Distinguishing subpopulations of EVs containing biomarkers in body fluids has been difficult. Furthermore, since almost all cell types release EVs, it is unclear what percentage of EVs in the body fluids of cancer patients originate from cancer cells. Cancer cell-derived EVs may constitute only a small fraction of the EVs in the body fluids of cancer patients with comorbidities and / or microtumors. Therefore, detecting biomarkers in these EVs can be extremely difficult. Currently, there is no clearly defined method for identifying cancer cell-derived extracellular assets (EVs) in bodily fluids.

[0004] Therefore, there is a need for novel methods to isolate cancer-derived extracellular genes (EVs) so that cancer-related miRNA biomarkers contained within them can be detected. This disclosure addresses this need and also provides other advantages. [Overview of the project]

[0005] This summary is provided to introduce some of the concepts that will be explained in more detail in the detailed description. This summary is not intended to identify the main or essential features of the subject matter described in the claims, nor is it intended to be used as an aid to limit the scope of the subject matter described in the claims.

[0006] In one embodiment, the present disclosure provides a method for isolating a population of extracellular vesicles rich in microRNA (miRNA) derived from cancer cells, the method comprising: (i) providing a biological sample derived from a target; (ii) contacting the biological sample with an antibody that specifically binds to CD147 for a certain period of time; and (iii) a CD147-positive (CD147-positive) vesicle containing CD147 on its surface. + ) Extracellular vesicles, CD147 + Based on the binding of antibodies to CD147 on the surface of extracellular vesicles, CD147 rich in miRNAs derived from cancer cells is separated from other components of the biological sample. + This includes providing an isolated population of extracellular vesicles.

[0007] In another embodiment, the Disclosure provides a method for isolating a population of miRNA-rich extracellular vesicles derived from cancer cells, the method comprising: (i) providing a biological sample derived from a subject; (ii) optionally obtaining a sample containing extracellular vesicles from the biological sample; (iii) contacting the biological sample from step (i) or the sample from step (ii) with an antibody that specifically binds to CD147 for a certain period of time; and (iv) a CD147 containing CD147 on its surface. + Extracellular vesicles, CD147 +Based on the binding of an antibody to CD147 on the surface of extracellular vesicles, it is separated from the biological sample of step (i) or other components of the sample of step (ii), thereby obtaining CD147 enriched in cancer cell-derived miRNA + It includes providing an isolated population of extracellular vesicles. In some embodiments, obtaining a sample containing extracellular vesicles from a biological sample is carried out.

[0008] In some embodiments, the biological sample is obtained from a subject known or suspected to have cancer. In certain examples, the biological sample is a blood sample. In some embodiments, the blood sample is a plasma sample. In some embodiments, the biological sample is a population of extracellular vesicles isolated from a sample obtained from a subject.

[0009] In some embodiments, CD147 + The isolated population of extracellular vesicles is rich in miRNA compared to the population of tetraspanin-positive (tetraspanin + ) extracellular vesicles isolated from the same biological sample. In certain embodiments, CD147 + The isolated population of extracellular vesicles has a miRNA content that is at least 8-fold higher than that of the population of tetraspanin + extracellular vesicles.

[0010] In some embodiments, the subject has symptoms of one or more cancers. In some examples, the cancer is ovarian cancer. In other examples, the cancer is kidney cancer.

[0011] In some embodiments, obtaining extracellular vesicles from a biological sample includes culturing a plurality of cells derived from the biological sample in a medium for a time sufficient for extracellular vesicles to be released from the plurality of cells to generate a conditioned medium containing extracellular vesicles. In certain embodiments, obtaining extracellular vesicles from a biological sample further includes removing cells, cell debris, and microparticles from the conditioned medium and performing one or more centrifugation steps.

[0012] In some embodiments, this method involves immunocapture of CD147 + The method further includes isolating extracellular vesicles from other extracellular vesicles. In some examples, an antibody that specifically binds to CD147 is conjugated to a solid support. In certain embodiments, the solid support is magnetic beads.

[0013] In another embodiment, the present disclosure provides a method for isolating a population of extracellular vesicles rich in miRNA derived from cancer cells, the method comprising: (i) providing a biological sample; (ii) contacting the biological sample with an antibody that specifically binds to CD147 for a certain period of time; and (iii) CD147 containing CD147 on its surface. + Extracellular vesicles were separated from other components of the biological sample based on the binding of antibodies to CD147 on the surface of the extracellular vesicles, thereby identifying CD147-rich miRNAs derived from cancer cells. + This includes providing an isolated population of extracellular vesicles. In some embodiments, the biological sample is a population of isolated extracellular vesicles.

[0014] In another embodiment, the Disclosure provides a method for detecting cancer-related miRNAs in a subject, the method comprising (i) a CD147 rich in cancer cell-derived miRNAs. + (ii) Isolating a population of extracellular vesicles from a biological sample derived from a subject known to or suspected to have cancer, according to any one of the methods described herein, and (ii) CD147 + This involves detecting one or more cancer-associated miRNAs in an isolated population of extracellular vesicles. In some embodiments, the detection of one or more cancer-associated miRNAs is CD147 +The method includes isolating RNA from an isolated population of extracellular vesicles. In certain embodiments, the detection of one or more cancer-related miRNAs further includes reverse transcription of the isolated RNA and polymerase chain reaction (PCR) amplification of one or more miRNA sequences. In certain embodiments, the method includes determining the amount of one or more cancer-related miRNAs. In some embodiments, the method includes isolating CD147 from a biological sample derived from the subject. + The amount of one or more cancer-associated miRNAs in a population of extracellular vesicles is compared to CD147 isolated from biological samples derived from healthy individuals without cancer. + This further includes comparing the amount of one or more cancer-associated miRNAs in extracellular vesicles. In some embodiments, the detection of cancer-associated miRNAs in a subject is used for diagnosing cancer, determining prognosis, and / or determining the risk of recurrence in the subject.

[0015] In another embodiment, the Disclosure provides a method for treating a subject having cancer, the method comprising (i) detecting one or more cancer-related miRNAs in the subject in accordance with the method described herein, and (ii) administering one or more cancer therapies to the subject based on at least one of the type of cancer or stage of cancer identified in the subject.

[0016] In another embodiment, the present disclosure provides a method for generating a report containing information regarding the detection results of cancer-related miRNA biomarkers, wherein the method involves CD147 according to the method described herein. + This involves detecting one or more cancer-associated miRNA biomarkers in an isolated population of extracellular vesicles and generating a report, which is useful for diagnosing cancer in the subject.

[0017] In another embodiment, the Disclosure provides a method for identifying cancer-related miRNAs in a subject, the method comprising (i) a CD147 rich in cancer cell-derived miRNAs. +(ii) Isolating a population of extracellular vesicles from a biological sample derived from at least one subject known to have cancer, according to one or more of the methods described above; (ii) CD147 from a biological sample derived from at least one subject known to have cancer. + (iii) Identifying one or more miRNAs in an isolated population of extracellular vesicles; (iii) CD147 isolated from a biological sample derived from at least one subject known to have cancer. + One or more miRNAs in the extracellular vesicle population were isolated from CD147 from a control biological sample. + (iv) CD147 isolated from a biological sample derived from at least one subject known to have cancer, compared with a population of one or more miRNAs in an extracellular vesicle population, and + CD147 is present or abundant in extracellular vesicle populations, but isolated from control biological samples. + This involves identifying one or more cancer-associated miRNAs that are absent or unenriched in a population of one or more miRNAs in an extracellular vesicle population. In some embodiments, CD147 isolated from a biological sample derived from at least one subject known to have cancer. + Identifying one or more miRNAs in an extracellular vesicle population is possible with CD147 + The method includes isolating RNA from an isolated population of extracellular vesicles. In some embodiments, identifying one or more miRNAs includes reverse transcription and sequencing of the isolated RNA. In some embodiments, the method involves isolating CD147 from a biological sample derived from at least one subject. + This includes determining the quantity and / or identity of one or more miRNAs in an extracellular vesicle population.

[0018] In another embodiment, the Disclosure provides a method for generating a report containing information regarding the identification of cancer-related miRNA biomarkers, wherein the method involves isolated CD147 +This involves identifying one or more cancer-related miRNA biomarkers in an extracellular vesicle population according to one or more of the methods provided above, and generating a report, wherein the report is useful for diagnosing cancer in the subject. [Brief explanation of the drawing]

[0019] This application includes the following figures. These figures are intended to illustrate specific embodiments and / or features of the compositions and methods and to supplement the description of the compositions and methods. The figures do not limit the scope of the compositions and methods unless expressly indicated so in the specification.

[0020] [Figure 1A] Figures 1A and 1B show the size distribution of purified EVs according to specific embodiments of this disclosure. Figure 1A shows purified EVs from 293T cells, HeLa cells, SKOV3 cells, and 786-O cells visualized by transmission electron microscopy. Figure 1B shows the evaluation of the size distribution of purified EVs by nanoparticle tracking analysis. Each plot shows the combined results of 10 repeated measurements. The median diameter of the EVs is shown. [Figure 1B] Same as above.

[0021] [Figure 2A] Figures 2A–2C show the detection of surface markers in EVs by flow cytometry according to a particular aspect of this disclosure. The flow cytometry setup was optimized for EV detection by acquiring microbeads of different diameters (100 nm, 200 nm, 500 nm, 760 nm). Figures 2A–2B show representative forward scatter versus side scatter plots of the bead population and EVs derived from 293T cells acquired using the same setup. Estimates of the EV size distribution based on size marker bead gates are shown. Figure 2C shows the percentage of EVs from 293T cells, HeLa cells, SKOV3 cells, and 786-O cells expressing the indicated surface markers. Mean ± SD is shown for n=3 independent experiments, each using a different batch of EVs. [Figure 2B] Same as above. [Figure 2C] Same as above.

[0022] [Figure 3A] Figures 3A–3B show the results of detecting surface markers in EVs by immunoblotting according to a specific aspect of this disclosure. From media adapted with 293T cells and HeLa cells, fractions with the indicated suspension densities were isolated by density gradient ultracentrifugation and measured for CD63, CD81, CD9, CD147, and CD98 by immunoblotting. As a positive control, the fractions were measured for tumor susceptibility gene 101 (TSG101), which encodes an exosome cargo protein. EV-containing fractions with suspension densities of 1.09–1.14 g / mL are indicated by asterisks. Immunoblot data were validated in three independent experiments. [Figure 3B] Same as above.

[0023] [Figure 4A] Figures 4A–4C show the expression of CD147 and CD98 in CD63, CD81, and CD9-negative EVs according to a particular aspect of this disclosure. Figure 4B shows the expression of CD147 and CD98 in CD63, CD81, and CD9-negative EVs. Figure 4A is a schematic diagram of the analysis of depletion of EVs expressing a given surface marker and the expression of another given surface marker in the remaining marker-negative EVs. Figures 4B–4C show the proportion of EVs expressing any of CD63, CD81, CD9, CD147, or CD98 in the overall EV population and the indicated subpopulation of marker-negative EVs derived from 293T cells. Figures 4B–4C show the mean ± SD of n=3 independent experiments. ns, no significant difference, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, one-way ANOVA with Bonferroni correction. [Figure 4B] Same as above. [Figure 4C] Same as above.

[0024] [Figure 5A]Figures 5A–5E show the size distributions of CD147+ and CD98+ EVs compared with CD63+, CD81+, and CD9+ EVs, according to a particular aspect of this disclosure. Figures 5A–5D show the size distributions of EVs derived from 293T cells expressing CD63, CD81, CD9, CD147, or CD98 as green fluorescent protein (GFP) fusion proteins. Representative forward and side scatter plots of gated GFP+ EVs, and estimated size distributions based on size marker bead gates (Figure 2A) are shown in Figures 5A–5B. The size distributions of GFP+ EVs evaluated by fluorescent nanoparticle tracking analysis are shown in Figures 5C–5D. Each plot shows the result of combining 10 repeated measurements. Immunogin labeling of markers in EVs derived from HeLa cells is shown in Figure 5E. [Figure 5B] Same as above. [Figure 5C] Same as above. [Figure 5D] Same as above. [Figure 5E] Same as above.

[0025] [Figure 6A]Figures 6A–6D compare the biosynthesis of CD147+ and CD98+ extracellular vesicles (EVs) based on specific aspects of this disclosure with that of CD63+, CD81+, and CD9+ extracellular vesicles (EVs). Figure 6A shows the intracellular localization of CD63, CD81, CD9, CD147, and CD98 detected by immunofluorescence staining and visualized by confocal microscopy in HeLa cells. The nucleus was visualized by 4',6-diamidino-2-phenylindole staining. Enlarged insets show the presence of CD147 and CD98 in outwardly swollen or pinched-off (indicated by arrows) cell membrane regions. Figures 6B–6D show 293T cells stably transfected with tetracycline-regulating short hairpin RNA (shRNA) or 293T cells transfected with the TRIPZ vector. Figure 6B shows the levels of HGS, TSG101, and the indicated surface markers detected by immunoblotting in equal volumes of cell lysates (20 μg). Figures 6C–6D show the number of CD63+, CD81+, CD9+, CD147+, and CD98+ EVs produced by an equal number of 293 T cells (approximately 5 x 10⁶ cells) with and without doxycycline-inducible HGS and TSG101 knockdown. Mean ± SD of n=5 independent experiments are shown. ns, no significant difference, ****P<0.0001, by unpaired two-tailed Student's t-test. [Figure 6B] Same as above. [Figure 6C] Same as above. [Figure 6D] Same as above.

[0026] [Figure 7A]Figures 7A–7F illustrate an example of CD147+EV transporting biologically active miRNA to receptor cells according to a particular aspect of this disclosure. Figure 7A is a schematic diagram of an assay system for miR-302 transport via EV. Figure 7B shows the copy numbers of miR-302a and miR-302c in untreated and doxycycline-treated 293T, HeLa, and SKOV3 donor cells. Figures 7C–7D show the fluorescence levels of mKate2 in 293T receptor cells expressing mKate2 with a 3' miR-302a target sequence. After stimulating receptor cells with EV, the fluorescence of mKate2 was measured 48 hours later and expressed relative to the fluorescence intensity of unstimulated receptor cells. Figures 7C–7D show the fluorescence intensity of mKate2 relative to the fluorescence intensity of unstimulated receptor cells. In Figure 7C, recipient cells were stimulated with an equal number (approximately 2 x 10⁶) of EVs derived from parental cells and miR-302-overexpressing donor cells. As a positive control, recipient cells were transfected with a miR-302a mimetic. In Figure 7D, from an equal number (approximately 2 x 10⁶) of EVs derived from miR-302-overexpressing HeLa donor cells, EVs expressing either CD63, CD81, CD9, CD147, or CD98 were removed, or not removed, and then used to stimulate recipient cells. Figures 7B–7D show the mean ± SD of n=3 independent experiments. EVs (approximately 2 x 10⁶) derived from 293T cells stably expressing CD63, CD81, CD9, CD147, or CD98 as GFP fusion proteins were added to parental 293T cells, and EV uptake was evaluated (Figure 7E). EV uptake was expressed as GFP fluorescence intensity at each time point, relative to the GFP fluorescence intensity 24 hours after EV addition. The mean ± SD of n=4 independent experiments is shown. For the relative copy number of miR-302a, EVs (approximately 2 x 10⁷) from CD63+, CD81+, CD9+, CD147+, and CD98+ donor cells were evaluated equally (Figure 7F). For normalization, the copy number of the cel-miR-39 spike-in control was used. The mean ± SD of n=3 independent experiments is shown.ns, no significant difference, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, unpaired two-tailed Student's t-test in Figure 7B; one-way ANOVA with Bonferroni correction in Figures 7C, 7D and 7F. [Figure 7B] Same as above. [Figure 7C] Same as above. [Figure 7D] Same as above. [Figure 7E] Same as above. [Figure 7F] Same as above.

[0027] [Figure 8A]Figures 8A–8E show the evaluation of miRNA content in an equal number of EVs according to a particular aspect of this disclosure. The miRNA content of an equal number of CD63+, CD81+, CD9+, CD147+, and CD98+ EVs derived from 293T cells, HeLa cells, and SKOV3 cells was evaluated (Figures 8A–8B). In Figures 8A–8B, in Figure 8A, the number of marker-positive EVs was determined from the difference between the initial total EV input and the number of EVs in the supernatant after immunocapture of EVs expressing specific markers. Figure 8B shows the total miRNA concentration. Figures 8A–8B show the mean ± SD of n=3 independent experiments. Small RNAs isolated from cells and an equal number of marker-positive EVs (approximately 2 x 10⁷) were evaluated using the Agilent 2100 Bioanalyzer® (Figure 8C). Figure 8C shows the electrophoretic profiles of small RNAs isolated from HeLa cells and marker-positive HeLa cell-derived EVs. Small RNAs detected between the two dotted lines were considered miRNAs. The 4nt peak corresponds to the loading control. To confirm that CD147+EVs and CD98+EVs were not contaminated with high-density lipoprotein, total EVs were isolated from the culture supernatants of 293T cells, SKOV3 cells, and HeLa cells and immunocaptured (IC) using antibodies against CD147, CD98, or Ig isotype controls (Figure 8D). Lysates of the immunocaptured materials were measured for ApoA1, the major protein component of high-density lipoprotein, by immunoblotting. Culture supernatant (CM) of 293T cells was used as a positive control for ApoA1. Figure 8E shows the total miRNA concentrations in marker-positive EVs isolated from ascites fluid of ovarian cancer (OVCA) patients (n=5) and plasma of OVCA patients (n=2) or renal cell carcinoma (RCC) patients (n=3). ns, no significant difference, *P<0.05, **P<0.01, ****P<0.0001. One-way ANOVA with Bonferroni correction was performed in Figures 8A, 8B, and 8E, and paired samples were analyzed in Figure 8E. [Figure 8B] Same as above. [Figure 8C] Same as above. [Figure 8D] Same as above. [Figure 8E] Same as above.

[0028] [Figure 9A] Figures 9A–9F illustrate the interaction between CD147 and heterologous ribonucleoprotein A2 / B1 (hnRNP A2 / B1) according to specific aspects of this disclosure. Figure 9A shows an immunoblot of hnRNP A2 / B1 levels in marker-positive extracellular vesicles (EVs) derived from parental 293T cells. Figure 9B shows an immunoblot of hnRNP A2 / B1 levels in equivolute cell lysates (20 μg) of parental 293T cells and 293T cells in which the HNRNPA2B1 gene was deleted by CRISPR / Cas9 gene editing (hnRNP A2 / B1-KO). The cell lysates were also evaluated for the indicated surface markers to confirm that the cellular expression levels of these markers were not affected by hnRNP A2 / B1 knockout. Figure 9C shows an immunoblot of hnRNP A2 / B1 levels in marker-positive extracellular vesicles (EVs) derived from parental 293T cells. Figure 9C shows the proportions of CD63+, CD81+, CD9+, CD147+, and CD98+ EVs from parental 293T cells and hnRNP A2 / B1-KO 293T cells. Mean ± SD is shown for n=3 independent experiments. Figure 9D shows the interaction between CD147 and hnRNP A2 / B1 in 293T cells as detected by immunoprecipitation (IP). Figures 9E-9F show the total miRNA concentration (Figure 9E) and small RNA content (Figure 9F) in equal numbers of CD147+ EVs from parental 293T cells and hnRNP A2 / B1-KO 293T cells. Mean ± SD is shown in Figure 9E for n=3 independent experiments. ns, no significant difference, **P<0.01, by unpaired two-tailed Student's t-test in Figures 9C and 9E. [Figure 9B] Same as above. [Figure 9C] Same as above. [Figure 9D] Same as above. [Figure 9E] Same as above. [Figure 9F] Same as above.

[0029] [Figure 10A]Figures 10A–10D show CD147+ EVs primarily derived from cancer cells, according to a particular aspect of this disclosure. Nude mice subcutaneously inoculated with HeLa cells or 786-O cells. Figure 10A shows tumor size and time points at which peripheral blood was collected from the mice. Figures 10B–10D show plasma EVs derived from human cancer cells and non-cancerous mouse host cells, distinguished by staining with antibodies specific to human and mouse markers. The percentages of human cancer cell-derived CD9+ EVs (Figure 10B) and CD147+ EVs (Figure 10C) at each time point, as well as the percentages of human cancer cell-derived (light gray column) and mouse host cell-derived (dark gray column) CD9+ EVs and CD147+ EVs at the final time point (Figure 10D) are shown. Mean ± SD of results for n=5 mice are shown. ns, no significant difference, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. Figures 10B-10C show one-way ANOVA with Bonferroni correction; Figure 10D shows two-way ANOVA with Bonferroni correction for paired samples. [Figure 10B] Same as above. [Figure 10C] Same as above. [Figure 10D] Same as above.

[0030] [Figure 11A]Figures 11A–11G show that the prevalence of CD147+ EVs increases from the early stages of the disease in cancer patients, according to certain aspects of this disclosure. Analysis of EVs isolated from equal volumes (200 μL) of plasma from healthy adult volunteers (vol) and patients with benign gynecological (gyn) disease, stage I / II OVCA, or stage III / IV OVCA (Figures 11A–11C), and from healthy adult volunteers and patients with stage I / II RCC or stage IV RCC (Figures 11D–11F). The total number of EVs in the plasma samples is shown in Figures 11A and 11D. The percentage of EVs expressing any of CD63, CD81, CD9, CD147, or CD98 is shown in Figures 11B, 11C, 11E, and 11F. Data from healthy volunteers are shown redundantly in Figures 11A–11G. Figures 11A, 11D, and 11B, 11C, 11E, and 11F are shown. Figure 11G shows the CA125 concentrations in plasma of healthy adult volunteers and patients with benign gynecological conditions (stage I / II OVCA or stage III / IV OVCA), as evaluated by ELISA. Figures 11A-11G show the mean ± SD for n=10 cases per group. ns, no significant difference, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. Figures 11A-11G were analyzed using one-way ANOVA with Bonferroni correction. [Figure 11B] Same as above. [Figure 11C] Same as above. [Figure 11D] Same as above. [Figure 11E] Same as above. [Figure 11F] Same as above. [Figure 11G] Same as above.

[0031] [Figure 12A]Figures 12A–12G demonstrate that CD147 immunocapture enhances the detection of cancer-derived circulating miRNAs, in accordance with certain aspects of this disclosure. Figures 12A–12B show experimental results using plasma collected from mice with tumors derived from miR-302-expressing HeLa cells (day 50) or parental 786-O cells (day 49) (described in Figure 10A). miRNAs were isolated from equal volumes of plasma (100 μL) by either direct lysis of total plasma, precipitation using ExoQuick® reagent, or immunocapture (IC) using CD147 antibody. Figure 12A shows the concentrations of total miRNA isolated by each of the three methods. Figure 12B shows the copy numbers of miR-302a (HeLa model) and miR-1233 (786-O model) detected in equal volumes of total miRNA. The mean ± SD of n=5 independent samples is shown in Figures 12A and 12B. Figures 12C and 12D show miRNAs isolated from plasma (200 μL) of healthy volunteers and RCC patients by direct lysis and CD147 immunocapsulation. Figure 12C shows the copy number of miR-210 detected in miRNA samples isolated by direct lysis from the plasma of healthy volunteers and patients with stage I / II RCC or stage IV RCC. Figure 12D shows the copy number of miR-210 detected in miRNA samples isolated by direct lysis and CD147 immunocapsulation from the same plasma samples of healthy volunteers and patients with stage I / II RCC. Data from the direct lysis samples are shown redundantly in Figures 12C and 12D. Figures 12C and 12D show the mean ± SD for each group of n=10 cases. Figures 12E-12G show experimental results using miRNAs isolated from body fluids (ascites or plasma, 200 μL) of OVCA or RCC patients using three different methods (OVCA#21 ascites, n=74 miRNAs (Figure 12E), OVCA#22 plasma (Figure 12F), n=81 miRNAs, RCC#21 plasma, n=76 miRNAs (Figure 12G)). The expression levels of 84 cancer-related miRNAs in each of the three body fluid samples from each case were evaluated using the miRCURY LNA® miRNA cancer-focused PCR panel (Qiagen).In each case, only miRNAs detected in at least one fluid-derived sample were included in the correlation analysis. Figure 12E shows the Spearman rank correlation between miRNA levels in ascites-derived samples and the corresponding miRNA levels in tumor tissue. Figures 12F-12G show the Spearman rank correlation between miRNA levels in each plasma-derived sample and the corresponding miRNA levels in tumor tissue for each case. The dotted lines in Figures 12E-12G indicate the 95% confidence interval. The number of miRNAs analyzed for each case is shown. ns, no significant difference, *P<0.05, ***P<0.001, ****P<0.0001. Figures 12A-12C show one-way ANOVA with Bonferroni correction, Figures 12A and 12B show paired samples analyzed; Figure 12D shows two-way ANOVA with Bonferroni correction. [Figure 12B] Same as above. [Figure 12C] Same as above. [Figure 12D] Same as above. [Figure 12E] Same as above. [Figure 12F] Same as above. [Figure 12G] Same as above. [Modes for carrying out the invention]

[0032] Detailed explanation The following description enumerates various aspects and embodiments of the composition and method. No particular embodiment is intended to define the scope of the composition and method. Rather, the embodiments merely provide non-limiting examples of various compositions and methods that fall within the scope of the disclosed composition and method. The description herein should be interpreted from the perspective of those skilled in the art, and therefore does not necessarily contain information that is well known to those skilled in the art.

[0033] This specification discloses materials, compositions, and methods that can be used for, in combination with, or in preparation for the disclosed embodiments. These and other materials are disclosed herein, and where combinations, subsets, interactions, groups, etc., of these materials are disclosed, specific references to various individual and collective combinations and permutations of these compositions may not be expressly disclosed, but each is understood to be specifically assumed and described herein. For example, where a method is disclosed and discussed, and several modifications that can be made to several molecules contained in that method are discussed, all combinations and permutations of that method, and possible modifications, are specifically assumed unless otherwise indicated. Similarly, these subsets or combinations are also specifically assumed and disclosed. This concept applies to all aspects of this disclosure, including but not limited to the steps of a method using the disclosed compositions. Therefore, where various additional steps are possible, each of these additional steps can be carried out using any particular step or combination of steps of the disclosed method, and each of such combinations or subsets of combinations should be considered specifically assumed and disclosed.

[0034] Publications cited herein and materials from which they are cited are incorporated herein by reference in their entirety. The following description provides further non-limiting examples of the disclosed compositions and methods.

[0035] I. Introduction Extracellular vesicles (EVs) are detected in body fluids and their contents can be protected from degradation, making them highly promising for fluid biopsies (Maas et al. 2017; Xu et al. 2018). Because EV cargo often reflects the genetic and biological state of the parent cell, the components of EV cargo have been widely studied as potential cancer biomarkers (Hannafon et al. 2016; Shin et al. 2021; Wang et al. 2022; Xu et al. 2018; Zhou et al. 2014). However, since almost all types of cells release EVs, it is unclear what percentage of EVs in the body fluids of cancer patients originate from cancer cells. Circulating EVs are also elevated in other conditions such as diabetes and hypertension (Li et al. 2016; Preston et al. 2003), which are common complications in cancer patients (Roy et al. 2018). Cancer cells often secrete more extracellular viable cells (EVs) than normal cells (Xu et al. 2018), but the origin of EVs in the bodily fluids of cancer patients remains unclear. In the bodily fluids of cancer patients with comorbidities or microtumors, cancer cell-derived EVs may constitute only a small fraction of the total EVs. Distinguishing between the extremely diverse group of EVs in bodily fluids in terms of origin and content remains difficult. Therefore, detecting biomarkers from these EVs may be extremely challenging.

[0036] Currently, there is no established method for identifying cancer cell-derived extracellular vesicles (EVs) in body fluids. While the tetraspanins CD63, CD81, and CD9 are commonly used as surface markers for EVs, they are universally expressed (Maecker et al. 1997), making it impossible to distinguish between EVs released from cancer cells and those released from normal cells. Glypican-1, a cell surface proteoglycan, has been reported to be abundant in circulating EVs from pancreatic cancer patients (Melo et al. 2015). However, glypican-1 is expressed not only in pancreatic cancer cells but also in the stroma, and has been detected in EVs released from stromal fibroblasts (Nigri et al. 2022; Tsujii et al. 2021). Therefore, developing methods to identify cancer cell-derived EVs in body fluids is crucial for improving the detection of biomarkers contained within these EVs.

[0037] Another important consideration when evaluating biomarkers contained in extracellular vesicles (EVs) is that individual cell types, including cancer cells, may release multiple EV subpopulations with different compositions (Kowal et al. 2016). EVs have been captured from body fluids using antibodies against CD63, CD81, or CD9 (Campos-Silva et al. 2019; Duijvesz et al. 2015; Logozzi et al. 2009). However, these tetraspanins are heterogeneously distributed among EVs (Han et al. 2021; Mathieu et al. 2021; Tian et al. 2018), and tetraspanins +Numerous studies have demonstrated biochemical and biophysical heterogeneity among extracellular organisms (EVs) (Kowal et al. 2016; Mathieu et al. 2021; Temoche-Diaz et al. 2019). Furthermore, not all EVs are tetraspanin-positive. EVs expressing at least one tetraspanin have been shown to account for less than 60% of all EVs in cancer cell culture supernatants and body fluids (Mizenko et al. 2021; Tian et al. 2018). The characteristics of tetraspanin-negative EVs are not fully understood. However, glioblastoma cells have been shown to release epidermal growth factor receptor variant III in large EVs lacking CD63 and CD81 (Yekula et al. 2019), and platelet-derived growth factor receptor α in EVs lacking all three tetraspanins (Lee et al. 2018). These studies highlight the importance of investigating tetraspanin-negative EVs as a source of cancer biomarkers.

[0038] MicroRNAs (miRNAs) contained in extracellular matrix (EVs) have attracted considerable attention as candidate biomarkers for cancer diagnosis, prognosis, and recurrence, given that miRNA expression patterns are often dysregulated in cancer (Hannafon et al. 2016; Shin et al. 2021; Wang et al. 2022; Xu et al. 2018; Zhou et al. 2014). However, the miRNA content in EVs remains a subject of ongoing debate. The Vesiclepedia database, a comprehensive collection of biomolecules identified in EVs, currently contains over 10,000 EV-related miRNA entries (Kalra et al. 2012). However, stoichiometric analyses and functional studies of miRNA content in EVs have revealed that the vast majority of EVs do not contain a biologically significant number of miRNA copies (Albanese et al. 2021; Chevillet et al. 2014; Zhang et al. 2021). These studies suggest the possibility of subpopulations of extracellular vehicles (EVs) that are selectively rich in miRNAs, but these subpopulations had not been identified until now.

[0039] As described herein, the inventors have found that glycoprotein CD147 is tetraspanin in its biosynthesis, content, and cellular origin. + We discovered that we can define a subpopulation of extracellular vesicles (EVs) distinct from tetraspanin. + In contrast to the EV, the CD147 + EVs are not generated by the endosomal sorting complex (ESCRT) mechanism required for endosomal transport, and tetraspanin + It's larger than an EV. CD147 + Although the intracellular origin of EVs cannot be clearly identified, CD147 is mainly localized to the cell membrane, particularly to the region where it sprouts and is fragmented on the outside, suggesting that CD147 + EVs are likely to be microvesicles. Also, the glycoprotein CD98 is tetraspanin +It was also discovered that a subpopulation of extracellular vesicles (EVs) distinct from EVs can be defined. Similar to CD147, CD98 is also primarily localized to the cell membrane. + EV is tetraspanin + Larger in size than EVs, not generated by the ESCRT mechanism, and likely microvesicles. However, despite the fact that CD147 and CD98 have been shown to interact on the cell surface (Cho et al. 2001), the distribution of these proteins on EVs was almost mutually exclusive. Taken together, these findings suggest that the clustering of CD147 and CD98 on the cell membrane is dynamically rearranged during microvesicle formation. A key finding is that CD147 interacts with the miRNA-binding protein hnRNP A2 / B1. + EV is tetraspanin + The miRNA content is significantly higher than that of EVs. (CD98) + EV is CD147 + It was found that the miRNA content was lower than that of EVs, indicating that miRNA enrichment is not common in other microvesicles. Furthermore, tetraspanin + In contrast to the EV, the CD147 + The inventors revealed that EVs primarily originate from cancer cells and their prevalence increases from the early stages of the disease in cancer patients (e.g., ovarian and kidney cancer patients, as well as xenograft models). In contrast, tetraspanin + There was no significant difference in the occurrence rate of extracellular viable cells (EVs) between cancer patients and healthy individuals, and tetraspanin was the most abundant EV in body fluids. + CD9 is a subgroup of EVs. + The inventors also revealed that the majority of EVs originate from non-cancer cells. + By enriching EVs, circulating miRNAs can be isolated using conventional methods (e.g., tetraspanin + Compared to EV or total EV, the sensitivity of detecting cancer cell-specific miRNAs is improved, and it was revealed that miRNAs that more accurately reflect the characteristics (signature) of tumor miRNAs can be obtained. Since CD147 is overexpressed in many types of cancer, CD147+ Evaluation of EV-derived miRNAs can be used to detect circulating cancer-derived miRNAs in multiple disease sites, and to detect early-stage or small tumors.

[0040] Based on these findings, this specification refers to CD147 + Isolation of EVs, such as CD147 + Various methods, systems, and kits are provided for the detection of cancer cell-derived microRNAs in extracellular viable cells (EVs), for providing diagnosis or prognosis of subjects based on the detection of cancer cell-derived microRNAs in biological samples from the subject, and for the treatment of subjects with cancer.

[0041] II. Terminology Unless otherwise defined, all technical terms, notations, and other scientific or medical terms used herein are intended to have meanings commonly understood by those skilled in the art. In some cases, terms having commonly understood meanings are defined herein for clarity and / or ease of reference, and the inclusion of such definitions herein should not be construed as substantially different from the definitions of terms commonly understood in the art.

[0042] In this specification, the articles "a" and "an" are used to refer to one or more (i.e., at least one) of the grammatical objects of the articles. For example, "an element" means at least one element and can include two or more elements.

[0043] In this specification, the terms “including,” “comprising,” or “having,” and their variations, are intended to encompass the elements and their equivalents listed thereafter, as well as any additional elements. Embodiments that “including,” “comprising,” or “having” a particular element are also intended to “essentially consist of” and “consist of those particular elements.” In this specification, “and / or” means and encompasses all possible combinations of one or more of the related enumerated items, and, if interpreted alternatively ("or"), also encompasses the absence of any combination.

[0044] In this specification, the transitional phrase “essentially consisting of” (and its grammatical variations) is interpreted to encompass the materials or processes described and “that do not substantially affect the basic and novel characteristics of the claimed invention.” See, for example, In re Herz, 537 F.2d 549, 551-52, 190 USPQ 461, 463 (CCPA 1976) (emphasis as in the original); also see MPEP §2111.03. Therefore, the term “essentially consisting of” as used herein should not be interpreted as synonymous with “containing.”

[0045] Unless otherwise specified herein, the descriptions of value ranges are intended solely as a convenient way to refer individually to each individual value within that range, and each individual value is incorporated herein as if it were individually stated herein. For example, if the concentration range is stated as 1% to 50%, then values ​​such as 2% to 40%, 10% to 30%, or 1% to 3% are intended to be explicitly listed herein. These are merely examples of what is specifically intended, and all possible combinations of numbers between (and including) the listed minimum and maximum values ​​are deemed to be expressly listed herein.

[0046] As used herein, the terms “about” and “approximately” generally mean the degree of acceptable error with respect to a measured quantity, taking into account the nature or precision of the measurement. An exemplary degree of error is within 20%, preferably within 10%, and more preferably within 5%, of a given value or range of values. A reference to “about X” or “approximately X” specifically indicates values ​​of at least X, 0.95X, 0.96X, 0.97X, 0.98X, 0.99X, 1.01X, 1.02X, 1.03X, 1.04X, and 1.05X. Thus, the expression “about X” or “approximately X” is intended to teach and provide written support for a limitation of a claim, such as “0.98X.” The numerical values ​​presented herein are approximate unless otherwise specified, and the terms “about” or “approximately” mean that they can be inferred, even if not explicitly stated. When “about” is used at the beginning of a numerical range, it applies to both ends of the range.

[0047] The terms “protein,” “peptide,” and “polypeptide” are used interchangeably to refer to polymers of amino acid residues. These terms apply to naturally occurring amino acid polymers, unnatural amino acid polymers, and amino acid polymers in which one (or more) amino acid residues are artificial chemical mimics of corresponding naturally occurring amino acids. These terms encompass amino acid chains of any length, including full-length proteins, in which amino acid residues are linked by covalent peptide bonds.

[0048] The term “antibody” and related terms refer to immunoglobulins or fragments thereof that bind to specific spatial and polar structures of other molecules. Immunoglobulins include various classes and isotypes, such as IgA, IgD, IgE, IgG1, IgG2a, IgG2b, IgG3, IgG4, and IgM. Antibodies are monoclonal or recombinant antibodies and can be prepared by laboratory techniques, such as creating serial hybrid cell lines to collect secreted proteins, or cloning and expressing nucleotide sequences or variants thereof that encode at least the amino acid sequence required for binding. Antibodies referred to herein may have sequences derived from non-human antibodies, human sequences, chimeric sequences, or fully synthetic sequences. The term “antibody” includes natural, artificially modified, and artificially produced antibody forms, such as humanized antibodies, human antibodies, single-chain antibodies, chimeric antibodies, synthetic antibodies, recombinant antibodies, hybrid antibodies, mutant antibodies, transplanted antibodies, and in vitro-generated antibodies, as well as fragments thereof. The term “antibody” also includes complex forms, including but not limited to fusion proteins containing an immunoglobulin portion. The term "antibody" also refers to non-quaternary antibody structures (such as antibodies from camelids and antibodies derived from camelids), antibody antigen-binding fragments, minibodies, bispecific antibodies, nanobodies (also called VHH fragments), and diabodies. See Siontorou CG. 2013, “Nanobodies as novel agents for disease diagnosis and therapy,” Int J Nanomedicine 8:4215-4227. Antibody fragments include Fab, Fv, F(ab')2, Fab', scFv, dsFv, ds-scFv, Fd, dAb, and Fc. Papain-digested native antibodies produce three fragments: two Fab fragments and one Fc fragment. The Fc fragment is a dimer containing two CH2 heavy-chain domains and two CH3 heavy-chain domains. The CH3 domains interact to form a homodimer.See Yang et al., 2018, “Engineering of Fc Fragments with Optimized Physicochemical Properties Implying Improvement of Clinical Potentials for Fc-Based Therapeutics,” Frontiers in Immunology 8:1860. Furthermore, aggregates, polymers, and conjugates of immunoglobulins or their fragments may also be used as needed. Details of antibodies useful in the context of this disclosure are given below.

[0049] In this specification, the term “antibody” encompasses, but is not limited to, all classes of immunoglobulins (i.e., complete antibodies). A natural immunoglobulin G (IgG) antibody molecule is a tetramer containing two identical light chains (L chains) and two identical heavy chains (H chains). Typically, each light chain is attached to a heavy chain by one covalent disulfide bond, although the number of disulfide bonds varies between heavy chains of different immunoglobulin isotypes. Each heavy and light chain also has intrachain disulfide bridges at regular intervals. Each heavy chain has a variable domain (VH) at one end, followed by several constant domains. Each light chain has a variable domain (VL) at one end and a constant domain at the other. The constant domain of the light chain aligns with the first constant domain of the heavy chain, and the variable domain of the light chain aligns with the variable domain of the heavy chain. Within the light and heavy chains, the variable and constant regions are linked by a "J" region consisting of approximately 12 or more amino acids, and the heavy chain also contains a "D" region consisting of approximately 10 or more amino acids. Generally, see Fundamental Immunology, Paul, W., ed., 3rd ed. Raven Press, NY, 1993, SH. 9 (the entire text is incorporated by reference for all purposes). Antibody sequence and structural information is widely available. See, for example, Lima et al., 2020, “The ABCD database: a repository for chemically defined antibodies” Nucleic Acids Research 48:D261-D264. The light chains of antibodies from any vertebrate species can be assigned to one of two distinct types, called kappa (κ) and lambda (λ), based on the amino acid sequence of the constant domain. Immunoglobulins are classified into different classes based on the amino acid sequence of the heavy chain constant domain. Immunoglobulins have five main classes: IgA, IgD, IgE, IgG, and IgM. Some of these are further classified into subclasses (isotypes) such as IgG-1, IgG-2, IgG-3, IgG-4, IgA-1, and IgA-2.The heavy chain constant domains corresponding to different classes of immunoglobulins are called alpha, delta, epsilon, gamma, and mu, respectively. In this specification, the term “antibody” also includes antibody fragments, such as antigen-binding fragments. An antigen-binding fragment contains at least one antigen-binding domain. An example of an antigen-binding domain is one formed by a VH-VL dimer. Antibodies and antigen-binding fragments can be described by the antigens to which they specifically bind.

[0050] The "Fc fragment" contains two heavy chain fragments, each containing the CH2 and CH3 domains of the antibody. The two heavy chain fragments are linked by two or more disulfide bonds and hydrophobic interactions of the CH3 domain. The Fc domain introduced into the fusion protein may promote dimerization.

[0051] The "Fab fragment" contains a light chain and a heavy chain with a CH1 region and a variable region, and can specifically recognize target epitopes such as spike protein epitopes. When the Fab domain is introduced into a fusion protein, the fusion protein binds to the target.

[0052] A "single-chain variable fragment" or "scFv fragment" is a fusion protein containing variable regions of the heavy and light chains derived from an antibody. The heavy and light chain portions may be linked by a linker peptide. The scFv fragment may retain the binding specificity of the antibody from which it was derived.

[0053] The term "immune capture" refers to an experimental method that uses specific capture reagents (e.g., antibodies) to extract a target analyte from a biological sample.

[0054] In this specification, the term “solid support” refers to any surface suitable for immobilizing antibodies. Solid supports include beads (e.g., magnetic beads), plates, slides, tips, filters, membranes (e.g., nylon, nitrocellulose, polynibilidene fluoride (PVDF)), and columns.

[0055] In this specification, the terms “nucleic acid” or “nucleotide” refer to deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) in either single-stranded or double-stranded forms, and polymers thereof. When RNA is described, the corresponding cDNA is also described, and uridine is understood to be represented as thymidine. Unless otherwise specified, this term encompasses nucleic acids containing known analogs of natural nucleotides that have similar properties to the reference nucleic acid and are metabolized in the same way as natural nucleotides. Nucleic acid sequences may include combinations of deoxyribonucleic acid and ribonucleic acid. Such deoxyribonucleic acid and ribonucleic acid include both natural molecules and synthetic analogs. The polynucleotides of the present invention also include all forms of sequences, including but not limited to single-stranded, double-stranded, hairpin, and stem-loop structures.

[0056] Unless otherwise stated, a given nucleic acid sequence implicitly includes, in addition to the explicitly stated sequence, its conserved variants (e.g., degenerate codon substitutions), alleles, homologous genes, SNPs, and complementary sequences. Specifically, degenerate codon substitutions can be achieved by generating sequences in which the third position of one or more (or all) selected codons is replaced with a mixed base and / or deoxyinosine residue (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)).

[0057] When used in the context of polynucleotide or polypeptide sequences as described herein, the terms “identity” or “substantial identity” mean a sequence having at least 60% sequence identity with a reference sequence. Alternatively, the identity percentage may be any integer from 60% to 100%. Exemplary embodiments include at least 60%, 65%, 70%, 75%, 80%, 85%, 88%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% when compared with a reference sequence using the program described herein, preferably BLAST with the standard parameters described below. Those skilled in the art will understand that these values ​​can be appropriately adjusted by considering codon degeneracy, amino acid similarity, reading frame position, etc., to determine the corresponding identity of proteins encoded by two nucleotide sequences.

[0058] Tetraspanins, also known as transmembrane 4 superfamily (TM4SF) proteins, have four transmembrane α-helices and two extracellular domains. One is short (called the small extracellular domain or loop, SED / SEL or EC1), and the other is longer, typically consisting of 100 amino acid residues (called the large extracellular domain / loop, LED / LEL or EC2). Tetraspanins are defined by a conserved amino acid sequence in which the EC2 domain contains four or more cysteine ​​residues, two of which contain a highly conserved "CCG" motif. Examples of tetraspanins include the well-known extracellular vesicle surface markers CD63, CD81, and CD9. In this disclosure, "tetraspanin-positive" or "tetraspanin" are used in the context of cells, exosomes, and extracellular vesicles. + The term "exosome" refers to cells, exosomes, and extracellular vesicles having at least one of CD63, CD81, or CD9 (for example, one or more of CD63, CD81, and CD9, e.g., CD63 only, CD81 only, CD9 only, CD63 and CD81, CD63 and CD9, CD81 and CD9, or CD63, CD81, and CD9).

[0059] III. Subjects and Samples This method can be used to detect cancer in subjects, for example, subjects exhibiting symptoms of one or more cancers. Throughout this specification, “subject” means an individual. Subjects may be adult subjects or pediatric subjects. Pediatric subjects include subjects from birth to 18 years of age. Preferably, subjects are animals, such as mammals, including primates, and more preferably humans. Non-human primates are also subjects. The term “subject” includes livestock such as cats and dogs, domestic animals (e.g., cattle, horses, pigs, sheep, goats, etc.), and laboratory animals (e.g., ferrets, chinchillas, mice, rabbits, rats, gerbils, guinea pigs, etc.). Thus, veterinary uses and pharmaceutical formulations are envisioned herein.

[0060] In some embodiments, the subjects referred to in the methods provided in this disclosure are subjects having cancer. In certain embodiments, the subjects have cancer that has been detected early. In certain embodiments, the subjects have advanced cancer, such as metastatic cancer. In some embodiments, the subjects may be receiving or continuing treatment for cancer. In some embodiments, the subjects have not responded to previous treatment with one or more cancer therapies.

[0061] In some embodiments, the patient has never been diagnosed with cancer before, or the stage of the cancer in the subject has not yet been determined. In some embodiments, the subject referred to in the methods provided herein is a subject suspected of having cancer. In some embodiments, the subject may have one or more stages of cancer, such as ovarian cancer or kidney cancer.

[0062] In some embodiments, subjects may be considered at high risk of developing cancer even if they are asymptomatic. For example, subjects may have one or more risk factors for ovarian cancer, such as advanced age, being overweight or obese, smoking, receiving postmenopausal hormone replacement therapy, undergoing fertility treatment (in vitro fertilization), endometriosis, age at onset and end of menstruation, pregnancy after age 35 or never having been pregnant, having had breast cancer, hereditary genetic changes (e.g., hereditary breast and ovarian cancer syndrome associated with BRCA1 / BRCA2 gene mutations, hereditary nonpolyposis colorectal cancer, Peutz-Jeghers syndrome, MUTHY-associated polyposis, and / or mutations in other genes associated with ovarian cancer (e.g., ATM, BRIP1, RAD51C, RAD51D, and PALB2)), and / or a family history of breast and / or ovarian cancer. In another example, a subject may have one or more risk factors for kidney cancer, such as being older, overweight or obese, having high blood pressure, undergoing long-term dialysis, and having a family history of kidney cancer.

[0063] In some embodiments, the cancerous status and / or type of cancer a subject has can be determined by measuring the levels of one or more cancer biomarkers in a sample from the subject. In some cases, these cancer biomarkers are tumor-associated antigens. Cancer biomarkers used to date are described, for example, in Liu (2019) Cancer biomarkers for targeted therapy. Biomark Res. 7:25.

[0064] In this specification, the term “cancer” refers to or describes a physiological condition in mammals typically characterized by uncontrolled cell proliferation. In some embodiments, cancer is a carcinoma or sarcoma. In some embodiments, cancer is a blood cancer. In some embodiments, cancer is breast cancer, prostate cancer, testicular cancer, renal cell carcinoma, bladder cancer, ovarian cancer, cervical cancer, endometrial cancer, lung cancer, colorectal cancer, anal cancer, pancreatic cancer, stomach cancer, esophageal cancer, hepatocellular carcinoma, head and neck cancer, brain cancer (e.g., glioblastoma, melanoma, or bone or soft tissue sarcoma). In one embodiment, cancer is acral melanoma. In some embodiments, cancer is acute lymphoblastic leukemia, acute myeloid leukemia, adrenocortical carcinoma, astrocytoma, basal cell carcinoma, cholangiocarcinoma, bone tumor, brainstem glioma, cerebellar astrocytoma, cerebral astrocytoma, ependymnomas, medulloblastoma, supratentorial primordial neuroectodermal tumor, visual tract and hypothalamic glioma, bronchial adenoma, Burkitt lymphoma, central nervous system lymphoma, cerebellar astrocytoma, chondrosarcoma, chronic lymphocytic leukemia, chronic Myeloid leukemia, chronic myeloproliferative disorders, colorectal cancer, cutaneous T-cell lymphoma, fibrinogenic round cell tumor, endometrial cancer, ependymoma, epithelioid sarcoma, episarcoma, hemangioendothelioma (EHE), esophageal cancer, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic cholangiocarcinoma, eye cancer, intraocular melanoma, retinoblastoma, gallbladder cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), germ cell tumor, gestational trophoblastoma Surgery, gastric cancer, hairy cell leukemia, hepatocellular carcinoma, Hodgkin lymphoma, hypopharyngeal cancer, glioma of the hypothalamus and visual pathway, childhood cancer, intraocular melanoma, islet cell carcinoma, Kaposi's sarcoma, kidney cancer, laryngeal cancer, leukemia, lip cancer and oral cancer, liposarcoma, liver cancer, non-small cell lung cancer, small cell lung cancer, lymphoma, macroglobulinemia, male breast cancer, malignant fibrous histiocytoma of bone, medulloblastoma, Merkel cell carcinoma, mesothelioma, metastasis Transmissible squamous cell carcinoma of the neck, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, mycosis fungoides, myelodysplastic syndrome, myeloid leukemia, myeloid leukemia, acute adult myeloproliferative disorders, chronic myxoma, nasal and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin lymphoma, oligodendroglioma, oral cancer, oropharyngeal cancer, osteosarcoma, ovarian epithelial carcinoma, ovarian germ cell tumor, low-grade ovarian tumor, paranasal and nasal sinus cancer, parathyroid cancer,Penile cancer, pharyngeal cancer, pheochromocytoma, pineal astrocytoma, pineal germ cell tumor, pineal blastoma, supratentorial primitive neuroectodermal tumor, pituitary adenoma, plasma cell tumor, pleuroblastoma, primary central nervous system lymphoma, rectal cancer, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, Ewing's sarcoma, Kaposi's sarcoma, soft tissue sarcoma, uterine sarcoma, Sézary syndrome, non-melanoma skin cancer, melanoma, small intestine cancer, squamous cell carcinoma, squamous cell carcinoma of the neck, gastric cancer, cutaneous T-cell lymphoma, pharyngeal cancer, thymoma, thyroid cancer, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, pregnancy tumor, urethral cancer, uterine cancer, vaginal cancer, vulvar cancer, Waldenström macroglobulinemia, Wilms' tumor, and / or any of the cancers listed in Table 1.

[0065] In this specification, “cancer” includes, but is not limited to, tumor-related phenomena such as abnormal or uncontrolled cell proliferation, metastasis, disruption of the normal function of adjacent cells, release of abnormal levels of cytokines or other secretory products, suppression or exacerbation of inflammatory or immune responses, tumor formation, precancerous conditions, malignancies, and invasion into surrounding or distant tissues or organs, such as lymph nodes. A non-exclusive list of cancer symptoms includes unexplained weight loss, fever, fatigue, pain, skin changes, changes in bowel habits or bladder function, or abnormal bleeding or discharge. Symptoms may be mild, moderate, or severe.

[0066] In some embodiments, the cancer is ovarian cancer, for example, ovarian epithelial carcinoma, ovarian germ cell tumor, or low-grade ovarian tumor. Ovarian cancer causes nonspecific symptoms. A non-limiting list of symptoms of ovarian cancer includes abdominal pain or discomfort, abdominal mass, bloating, back pain, urinary urgency, constipation, fatigue, pelvic pain, abnormal vaginal bleeding, involuntary weight loss, and a variety of other symptoms.

[0067] In some embodiments, the cancer is renal cancer, for example, renal cell carcinoma. Renal cancer causes nonspecific symptoms. A non-limiting list of symptoms of renal cancer includes abdominal pain or discomfort, abdominal mass, blood in the urine, weight loss, and loss of appetite.

[0068] The symptoms of cancer (e.g., ovarian cancer and kidney cancer) can be mild, moderate, or severe. The cancer may be in any of the following stages, for example, I, II, III, or IV. In some embodiments, subjects may be considered at risk of developing cancer even if they are asymptomatic. For example, a subject may have one or more risk factors for cancer.

[0069] In some embodiments, cancer is localized within the subject. In some embodiments, cancer is widespread within the subject (e.g., metastatic cancer). Cancer staging is the process of determining how localized or widespread the cancer is. This represents the extent to which the cancer has spread within the subject's body. Treatment and prognosis may depend on the stage of the cancer in the subject. Cancer staging can be performed using tests such as computed tomography (CT), magnetic resonance imaging (MRI), scans, bone marrow biopsy, mediastinoscopy, and blood tests.

[0070] Diagnosing certain cancers can be invasive and expensive. For example, the diagnosis of ovarian cancer may include a physical examination (e.g., pelvic exam), blood tests (CA-125 and / or other markers), and transvaginal ultrasound. The diagnosis must be confirmed by abdominal examination, biopsy, and surgery to search for cancer cells in the ascites fluid. The diagnosis of kidney cancer may include a physical examination (e.g., abdominal examination), CT scan, and MRI scan. The diagnosis may be confirmed by percutaneous biopsy to search for cancer cells in the biopsy tissue. In some embodiments, the disclosed methods include non-invasive techniques (e.g., fluid biopsy).

[0071] In certain embodiments, the disclosed method can be used to assess the state of cancer in a subject. In some cases, a biological sample is taken from the subject. In some embodiments, the biological sample is a blood sample. In certain embodiments, the blood sample is plasma. In other embodiments, the blood sample is serum. In yet another embodiment, the blood sample is whole blood. In some embodiments, the biological sample is at least one of tumor tissue, ascites, or plasma. Other suitable biological samples include urine, ascites, semen, vaginal secretions, cerebrospinal fluid (CSF), synovial fluid, pleural fluid, pericardial fluid, ascites, amniotic fluid, saliva, nasal secretions, ear secretions, gastric juice, breast milk, nipple aspirate, bone marrow aspirate, amniotic fluid, bile, gastric juice, lymph, mucus, pus, saliva, sebum, serous fluid, sputum, sweat, tears, peritoneal lavage fluid, pleural lavage fluid, bronchoalveolar lavage fluid, and / or other types of biological samples. In general, any biological sample, including EV, can be used in the method of the present disclosure. The sample can be collected from the subject using prior art known in the relevant field.

[0072] IV. Extracellular Vesicles Extracellular vesicles (EVs) are lipid-bound particles released into the extracellular space from almost all types of cells. Based on their intracellular origin, extracellular vesicles are classified into three main subtypes: microvesicles (MVs), exosomes, and apoptotic bodies. Because EV cargo (nucleic acids, lipids, proteins, etc.) may reflect the genetic and biological state of the parent cell, components of EV cargo may function as cancer biomarkers (Hannafon et al. 2016; Shin et al. 2021; Wang et al. 2022; Xu et al. 2018; Zhou et al. 2014). EVs can be detected in bodily fluids and are suitable for non-invasive analysis (e.g., liquid biopsy). Furthermore, because EVs protect transporters from degradation (Maas et al. 2017; Xu et al. 2018), transporters (nucleic acids, proteins, etc.) are preserved for analysis.

[0073] Individual cell types, including cancer cells, can release multiple subpopulations of extracellular genes (EVs) with different transporters (Kowal et al. 2016). Since EVs are produced by all cell types, isolating EVs from cancer cells is crucial for the development of EV-based cancer diagnosis, prognosis, and recurrence risk models. EVs are commonly isolated from body fluids using antibodies against tetraspanins CD63, CD81, or CD9, as described, for example, by Campos-Silva et al. 2019, Duijvesz et al. 2015, and Logozzi et al. 2009. These tetraspanins are universally expressed (Maecker et al. 1997), making it impossible to distinguish between EVs released from cancer cells and those released from normal cells. Furthermore, there is strong evidence that these tetraspanins are heterogeneously distributed among EVs (Han et al. 2021; Mathieu et al. 2021; Tian et al. 2018). Therefore, isolation of EVs using antibodies against tetraspanin does not yield a subpopulation of EVs derived from cancer cells. On the other hand, a method for isolating a subpopulation of EVs derived from cancer cells is provided.

[0074] The inventors' findings are in CD147 + The increased miRNA content in EVs is due to interaction with CD147. +This suggests that the selective enrichment of hnRNP A2 / B1 in extracellular organisms (EVs) is the cause. It is unclear whether CD147 directly binds to hnRNP A2 / B1. Without being constrained by any particular theory, CD147 may interact with hnRNP A2 / B1 via caveolin-1. This possibility is supported by reports that hnRNP A2 / B1 interacts with caveolin-1 to sort miRNAs into microvesicles (Lee et al. 2019), and that caveolin-1 interacts with CD147 (Tang & Hemler 2004). hnRNP A2 / B1 has been reported to control the sorting of miRNAs into exosomes (Villarroya-Beltri et al. 2013), and our findings support similar research results that hnRNP A2 / B1 was not detected in tetraspanin-positive extracellular vesicles (mainly exosomes) (Jeppesen et al. 2019). Supermares, a type of extracellular nanoparticle rich in miRNAs containing hnRNP A2 / B1, have recently been identified (Zhang et al. 2021). In contrast to EVs, supermares lack a surrounding membrane and CD147 + It is significantly smaller than the EV (less than 30 nm in diameter) (Zhang et al. 2021). Furthermore, the supermare is rich in the RNA-binding protein Argonaut 2 (Zhang et al. 2021), and CD147 + CD147 was not detected in EV (data not included, see Figure 5D in Ko et al. 2023). Furthermore, while supermares contain fragmented extracellular domains of membrane proteins (Zhang et al. 2021), only the full-length form of CD147 was detected in EV in this study. Considering these findings together, CD147 + I support the idea that EVs are different from supermare vehicles.

[0075] V.CD147 + Isolation of EVs The glycoprotein CD147 (also known as basidine or emmprin) promotes cancer progression through several mechanisms, the most studied being stimulation of matrix metalloproteinase secretion (Xiong et al. 2014). Elevated circulating blood CD147 concentrations have been detected in cancer patients (Lacina et al. 2022; Lee et al. 2016), and both the extracellular domain cleaved by proteolysis of CD147 and its full-length form are shed from cancer cells (Egawa et al. 2006). CD147 is overexpressed in various solid tumors, but is also expressed in endothelial cells, fibroblasts, platelets, and leukocytes (Xin et al. 2016; Xiong et al. 2014). Prior to our research, it was not thought that CD147-positive extracellular genes (EVs) originated from cancer cells because CD147 is also expressed in several types of normal cells, including endothelial cells, fibroblasts, platelets, and leukocytes. In some embodiments, the disclosed method uses the presence of CD147 as a basis for isolating subpopulations of EVs derived from cancer cells.

[0076] In certain embodiments, extravasation plasma (EVs) are isolated from a biological sample obtained from a subject. For example, in some embodiments, the biological sample is plasma obtained from a subject. Plasma can be obtained from a subject using conventional techniques known in the art. For example, in some embodiments, peripheral whole blood can be collected in a blood collection tube containing an anticoagulant (e.g., an EDTA-treated tube), and the tube can be centrifuged at 200 × g for 10 minutes at room temperature to remove the plasma layer from the sample. EVs can be isolated from freshly collected samples or from frozen or refrigerated samples.

[0077] In some embodiments, this method is used with CD147 +This involves isolating a population of EVs from a biological sample before isolating a subpopulation of EVs. In other embodiments, cancer-derived EVs are isolated directly from the biological sample without first isolating a population of EVs. Although not essential, clarifying the biological sample before purification to remove debris in the sample can yield a higher purity of the EV population. Clarification methods include centrifugation, ultracentrifugation, filtration, or ultrafiltration. In certain embodiments, a method for isolating EVs from a biological sample includes ultrafiltration to remove soluble non-EV proteins and particulate matter, followed by fractionation based on suspension density (Ko et al. 2019). Various other methods for isolating EVs are also known in the art (e.g., Konoshenko et al. 2018; Liangsupree et al. 2021).

[0078] In some embodiments, cellular debris is removed from the sample. In specific examples, soluble proteins and microparticles smaller than 100 kD are removed from the sample. For example, in some embodiments, the sample is centrifuged at 2,400 x g at 4°C for 10 minutes to remove cells and cellular residue. The centrifuged sample can then be enriched using a CENTRICON® Plus-70 centrifugal filter unit and an ULTRACEL® 100 kDa cutoff filter (Millipore) to remove soluble proteins and particles smaller than 100 kD to prepare a purified sample.

[0079] In certain embodiments, extracellular viable (EV) is isolated from a purified sample fractionated based on suspension density, such as in the method described in Ko et al. 2019. For example, the purified sample (i.e., enriched supernatant) can be mixed with 1.5 mL of OPTIPREP® stock solution (60% (w / v) iodixanol aqueous solution, Axis-Shield PoC) and placed at the bottom of a 14x95 mm polyalomer ultracentrifuge tube (Beckman Coulter). The iodixanol solution for the discontinuous gradient can be prepared by diluting the OPTIPREP® stock solution with a buffer containing 0.25 M sucrose, 10 mM Tris-HCl (pH 7.4), and 1 mM EDTA. The gradient can be formed by layering the iodixanol solutions in the following order: 3.0 mL of 40% solution, 2.5 mL of 20% solution, 2.5 mL of 10% solution, and 2.0 mL of 5% solution. Centrifuge can be performed at 200,000xg for 18 hours at 4°C using an SW40 Ti rotor (Beckman Coulter). Ten 1.0 mL gradient fractions can be collected from top to bottom. The density of each fraction can be determined from absorbance measurements at 244 nm using a standard curve of serial dilutions of iodixanol solution (Schroder et al. 1997). Individual fractions are washed with phosphate-buffered saline (PBS), enriched using an AMICON® Ultra-4 centrifuge filter unit equipped with an ULTRACEL® 100kDa cutoff filter (Millipore), and then suspended in PBS for further analysis or purification.

[0080] In some embodiments, the method includes isolating a subpopulation of cancer-derived extracellular viable cells (EVs) from a biological sample using an antibody that specifically binds to CD147. In some cases, the EV population is isolated from the biological sample prior to this step. CD147 +Isolating subpopulations of EVs involves contacting a biological sample containing EVs with an antibody that specifically binds to CD147 for a certain period of time. In some embodiments, the sample containing EVs and the CD147 antibody are contacted for at least 4, 8, 16, 20, 24, 28, 32, 36, 40, 44, or 48 hours. In certain embodiments, the CD147 antibody is then used. + Separate the EV from the rest of the sample. In some cases, CD147 + Extracellular vesicles (EVs) can be separated by immunocapture. In some cases, EVs can be separated using CD147 antibody-conjugated microparticles or collected on the surface of a CD147 antibody-conjugated microarray slide or microtiter plate. When microparticles are used, they can be separated from the bulk fluid using a magnetic field or centrifugal force. For example, in some embodiments, a biotinylated antibody against CD147 is incubated with streptavidin-conjugated magnetic beads at 4°C for 16 hours and washed with PBS to produce CD147 antibody-conjugated beads. Next, a sample containing extracellular vesicles (EVs) is brought into contact with the CD147 antibody-conjugated beads at 4°C for 16 hours. Subsequently, CD147 is separated using magnetic separation. + Subpopulations of EVs can be isolated.

[0081] In some embodiments, the CD147 of the EV + The subpopulation is EV tetraspanin + It has a higher miRNA content than the subpopulation. In some embodiments, EV's CD147 + The miRNA content of the subpopulation is EV tetraspanin + The miRNA content of the subpopulation is at least 5, 10, 15, 20, 25, 30, 35, 40, or 50 times (e.g., 5-10 times, 5-20 times, 5-30 times, 10-20 times, 20-30 times, 20-50 times, 30-50 times, 40-50 times). In some embodiments, EV's CD147 + The miRNA content of the subpopulation is EV tetraspanin + The miRNA content is at least 8 to 26 times that of the subpopulation. Therefore, in some embodiments, this method is used to obtain miRNA-rich CD147 derived from cancer cells. +This includes isolating subpopulations of EVs. CD147 is overexpressed in approximately 20 types of cancer (Xin et al. 2016; Xiong et al. 2014), therefore CD147 + The isolation and evaluation of EVs can be used to detect circulating cancer-derived miRNAs in multiple disease sites. It cannot be assumed that cells expressing a specific surface marker will produce EVs containing the same marker on their EV surface. In fact, there is evidence that some proteins expressed on the cell surface are undetectable on the EV surface (Del Conde et al. 2005; Cvjetkovic et al. 2016). However, as described herein, we have shown that EVs can be isolated from cancer cells using CD147, and that these CD147 + We demonstrated that EV contains cancer-derived miRNAs.

[0082] VI. MicroRNA MicroRNAs (miRNAs) are a family of small non-coding RNAs that regulate various biological processes. Extracellular viable cells (EVs) are known to contain miRNAs, and because miRNA expression patterns are often dysregulated in cancer, they have attracted considerable attention as candidate biomarkers for cancer diagnosis, prognosis, and recurrence (Hannafon et al. 2016; Shin et al. 2021; Wang et al. 2022; Xu et al. 2018; Zhou et al. 2014). The miRNA content in EVs is not yet fully understood. Currently, the Vesiclepedia database, a comprehensive database of biomolecules identified in EVs, contains entries for over 10,000 EV-related miRNAs (Kalra et al. 2012). However, stoichiometric analysis of miRNA content in extracellular viable cells (EVs) and functional studies have revealed that the vast majority of EVs do not contain biologically important miRNA copy numbers (Albanese et al. 2021; Chevillet et al. 2014; Zhang et al. 2021). Therefore, there is a need for methods to isolate EVs derived from cancer cells that are selectively enriched with miRNA. In another aspect, CD147 + A method for analyzing miRNA from EVs is provided. In some embodiments, the method involves CD147 + This includes detecting and / or determining the amount of cancer-related miRNAs in subpopulations of extracellular viable (EVs).

[0083] VII. Identification and Detection of Cancer Biomarkers Circulating miRNAs are attracting significant interest as candidate biomarkers for cancer diagnosis, prognosis, and recurrence (Dias et al. 2017; Hannafon et al. 2016; Shin et al. 2021; Wang et al. 2022; Zhou et al. 2014). While a vast number of miRNAs are detected in extracellular vein (EV) echocardiography, several studies have shown that the majority of EVs contain only biologically insignificant amounts of miRNA (Albanese et al. 2021; Chevillet et al. 2014; Zhang et al. 2021). CD147 + EVs are mainly derived from cancer cells and are rich in miRNAs. Therefore, CD147 + The detection of cancer-related miRNAs and / or their quantity from extracellular viable cells (EVs) can be used for cancer diagnosis, prognosis, and recurrence assessment.

[0084] Therefore, in another form, CD147 + A method is provided for detecting the presence and / or amount of miRNA cancer biomarkers in an EV population. In some embodiments, the method includes isolating cancer-derived EVs using an antibody that specifically binds to CD147 (e.g., by CD147 immunocapture), as disclosed herein. In some embodiments, compared to conventional methods, CD147 + Isolating circulating miRNAs from extracellular viable cells (EVs) improves the detection sensitivity of cancer cell-specific miRNAs and yields miRNAs that more accurately reflect the characteristics of tumor miRNAs. Since CD147 is overexpressed in many types of cancer (Table 1), CD147 + By using miRNA evaluation from extracellular viable cells (EVs), it is possible to detect circulating cancer-derived miRNAs in multiple disease sites. [Table 1]

[0085] It is known to those skilled in the art that various miRNAs are associated with specific cancers. These miRNA cancer biomarkers can be used to diagnose, prognose, or determine the risk of recurrence of specific cancers. The method may include measuring the amount of one or more cancer-associated miRNAs in a sample obtained from a subject, and diagnosing, prognosing, and / or determining the risk of recurrence in a subject with cancer based on the amount of detected cancer-associated miRNAs. Thus, in some embodiments, a non-invasive method for diagnosing, prognosing, and / or determining the risk of cancer recurrence is provided.

[0086] To diagnose cancer in an individual (i.e., a subject or patient), the amount of cancer-related miRNA measured in a sample obtained from the individual can be compared to a reference level, for example, the amount taken from a healthy individual without cancer. The reference level is CD147 + The amount of miRNA isolated from EVs can be measured simultaneously, or it can be a value determined based on previous measurements. Therefore, in one embodiment, CD147 in a sample derived from the target + A method for diagnosing, prognosing, and / or determining the risk of cancer recurrence is provided, which includes differentially detecting the levels of one or more cancer-related miRNAs in extravasation cells (EVs) compared to their levels in a control group.

[0087] When measuring the levels of multiple cancer-related miRNAs, it is not necessary for all miRNAs in a sample from a specific target to be increased or decreased compared to the control level in order to determine cancer. For example, there may be some overlap in the levels of a particular cancer-related miRNA among individuals belonging to different probability categories. However, the combined total amount of all miRNAs included in the assay may, for example, indicate a high probability of cancer presence.

[0088] The provided method can determine the amount of miRNA in a sample. In some embodiments, this method can determine CD147 +This method includes determining the total amount of miRNA isolated from EVs. In some embodiments, this method involves CD147 + This involves determining the amount of one or more specific miRNAs isolated from EVs. In some cases, CD147 + The amount of miRNA isolated from EVs represents the amount of one or more miRNAs present in the cells of the biological sample from which the subject originated. In some cases, CD147 + The amount of miRNA isolated from extracellular viable cells (EVs) represents the amount of one or more miRNAs present in the cancer of interest. In certain cases, a spike-in control (i.e., a known amount of miRNA) is used to normalize measurements of miRNA content in EVs and / or body fluids. For example, during miRNA isolation, cel-miR-39 (Qiagen) can be added to the TRIZOL® reagent as a spike-in control. In some embodiments, an endogenous control (e.g., RNU-48) is added to the sample to normalize intracellular miRNA levels.

[0089] In some embodiments, the measurement of specific miRNA amounts from biological samples can be performed using a variety of techniques, reagents, and methods. Typical methods for detecting and quantifying miRNA include Northern blotting, reverse transcription quantitative polymerase chain reaction (RT-qPCR), next-generation sequencing, and microarray / chip analysis. Exemplary methods are described, for example, in Precazzini et al. 2021, Mestdagh et al. 2014, and Ouyang et al. 2019. For example, RT-qPCR of miRNA can be performed using the Taqman® MicroRNA Reverse Transcription Kit (Applied Biosystems) and sequence-specific stem-loop primers for cancer cell-specific miRNAs. As an example, qPCR can be performed using the miRCURY LNA® miRNA Cancer Focus PCR Panel (Qiagen), which contains a set of primers for 84 known cancer-related miRNAs. The reaction can be carried out using, for example, a STEPONE PLUS® real-time PCR system with a 1X master mix and a 1X probe (TaqMan® microRNA Expression Assay, Applied Biosystems). The relative level of miRNA in EV can be calculated, for example, using the comparative CT method (2-ΔΔCT), and a control (e.g., cel-miR-39 spike-in control) can be used for normalization. An endogenous control (e.g., RNU-48) can be used for normalizing intracellular miRNA levels. The absolute copy number of miRNA can be calculated from a standard curve created using miRNA mimes.

[0090] In some embodiments, the measured miRNA amount is adjusted (``normalized'') relative to one or more standard amounts. As is well known in the art, normalization is performed to remove platform-specific technical variability and obtain an amount or relative amount. miRNA amounts are typically normalized after detection and quantification according to a particular platform using methods routinely practiced by those of ordinary skill in the art.

[0091] In some embodiments, the amount of a selected miRNA is quantified and compared to one or more pre-selected levels or thresholds. A threshold can be selected that provides the ability to predict the presence or absence of cancer. Such a threshold can be set, for example, by calculating a receiver operating characteristic (ROC) curve using a first population of subjects with cancer and a second population of subjects without cancer. The threshold or cut-off value can be adjusted to alter test performance, such as test sensitivity and specificity. For example, if desired, the cancer threshold can be intentionally lowered to increase the sensitivity of the cancer test. In some embodiments, the first population can be a plurality of subjects having the same type of cancer. For example, the subject can have the same type of cancer as the cancer that the subject being tested has, had, or is suspected of having.

[0092] In certain embodiments, the method includes isolating a subpopulation of EVs from a biological sample using an antibody that specifically binds to CD147 (e.g., a CD147 immunocapture antibody). In some embodiments, CD147 + miRNA is isolated from a sample containing EVs. In some embodiments, CD147 + The sample containing EVs is purified prior to miRNA isolation. In certain embodiments, CD147 + Extracellular RNA is removed from the sample containing EVs. For example, the isolated sample of CD147 + EVs can be treated with RNase A for 30 minutes at 37 °C to remove extracellular RNA.

[0093] miRNA can be isolated from CD147 + EVs using conventional techniques for miRNA isolation known in the art. Exemplary methods are described, for example, in Wright et al. 2020, Brown et al. 2018, Roest et al. 2021. For example, miRNA can be isolated using a 2-column PURELINK® miRNA Isolation Kit (Invitrogen) according to the manufacturer's instructions.

[0094] In some embodiments, + Isolation of circulating miRNA from CD147 EVs improves the detection sensitivity of cancer cell-specific miRNAs and provides results that more accurately reflect the stage of cancer compared to conventional methods. As described in the examples, methods that include determining the copy number value of miRNA isolated from CD147 EVs according to the present disclosure + Distinguished early cancer samples from reference control samples.

[0095] In another aspect, a method is provided for generating a report containing information regarding the detection results of cancer-related miRNA biomarkers. The method includes detecting one or more cancer-related miRNA biomarkers in a CD147 + EV population and generating a report, where the CD147 + EVs are isolated from a sample obtained from a subject, and the report is useful for diagnosing cancer in the subject.

[0096] In another aspect, the present disclosure provides a method for identifying a novel cancer-related biomarker not previously known or for verifying the association between a known miRNA and cancer. In some embodiments, biological samples are obtained from a population of subjects having a specific type of cancer, and CD147 + EVs are isolated from these biological samples, and CD147 +The identity of one or more miRNAs in EV can be determined (e.g., by sequencing). In some embodiments, CD147 isolated from biological samples of cancer patients + miRNAs present in or abundant in EVs were found in CD147 isolated from biological samples of healthy, non-cancerous control patients. + In contrast to EVs, which are either absent or unabundant, such miRNAs have not been previously known to be associated with cancer or a specific type of cancer, and can be identified as cancer-related biomarkers for cancer or a specific type of cancer. In some cases, CD147 isolated from biological samples of cancer patients of different types, subtypes, and / or stages. + By comparing the identity of miRNAs present in extracellular viable cells (EVs), it is possible to identify miRNAs associated with specific types, subtypes, and / or stages of cancer.

[0097] VIII. Treatment If one or more cancer-related miRNAs are detected in a subject using this method, it is possible to indicate the provision of medical care appropriate to the stage, morphology, or other characteristics of the detected cancer. In some embodiments, the subject receives treatment such as drug therapy, radiotherapy, and / or surgical procedures. If the subject is currently undergoing cancer treatment, after cancer-related miRNAs are detected in a patient-derived sample, the patient may receive or continue to receive cancer treatment such as drug therapy, radiotherapy, and / or surgical procedures. Details of each of these treatments are described below.

[0098] Accordingly, in one embodiment, the Specified provides a method for treating cancer in a subject, the method comprising administering an effective amount of a cancer treatment agent to a subject having one or more cancer-related miRNAs in a sample derived from the subject (e.g., a blood sample) compared to a control, which essentially consists of, or consists of, the one or more cancer-related miRNAs being detected using the method of the Disclosure.

[0099] In some embodiments, cancer treatment includes, but is not limited to, chemotherapy, targeted drug therapy, immunotherapy, radiotherapy, or surgery. Examples of anticancer agents that may be administered include, but are not limited to, chemotherapeutic agents (e.g., carboplatin, paclitaxel, pemetrexed, etc.), tyrosine kinase inhibitors (e.g., erlotinib, crizotinib, osimertinib, etc.), and immunotherapeutic agents (e.g., pembrolizumab, nivolumab, durvalumab, atezolizumab, etc.).

[0100] In some embodiments, cancer treatment includes immunotherapy. A key part of the immune system is its ability to suppress attacks on normal cells in the body. To do this, the immune system utilizes “checkpoints,” i.e., proteins on immune cells that need to be turned on or off to initiate an immune response. Cancer cells may utilize these checkpoints to evade attack by the immune system. In some embodiments, immunotherapy includes immune checkpoint inhibitor (ICB) therapy, such as therapies that disrupt T cell suppression signals, including cytotoxic lymphocyte antigen 4 (CTLA-4), programmed cell death protein 1 (PD-1), or programmed cell death ligand 1 (PD-L1). Thus, in certain embodiments, immunotherapy includes immune checkpoint inhibitor conjugates (or “checkpoint inhibitors”) such as anti-CTLA4 antibodies, anti-PD1 antibodies, anti-PD-L1 antibodies, anti-LAG-3 antibodies, anti-TIM-3 antibodies, anti-TIGIT antibodies, anti-CD47 antibodies, or anti-VISTA antibodies.

[0101] In some embodiments, cancer treatment includes surgical treatment of cancer. For example, a patient may undergo surgical resection (removal of the tumor by surgery). Smaller tumors can also be treated with other types of treatment, such as ablation or radiation therapy. Ablation is a treatment that destroys the tumor without removing it. These techniques can be used in patients with a few small tumors or when surgery is not a suitable option. While less likely to cure cancer than surgery, it can still be very useful for some patients. Ablation is most commonly used for tumors less than 3 cm in diameter. For slightly larger tumors (1-2 inches or 3-5 cm in diameter), it can be used in combination with embolization. Because ablation often destroys some of the normal tissue surrounding the tumor, it may not be suitable for treating tumors near major blood vessels, the diaphragm, or major bile ducts. In some embodiments, ablation is radiofrequency ablation (RFA). In some embodiments, ablation is microwave ablation (MWA). In some embodiments, ablation is cryoablation (cryotherapy). In some embodiments, the ablation is ethanol (alcohol) ablation, such as transdermal ethanol injection (PEI).

[0102] In some embodiments, cancer treatment includes radiotherapy. Radiotherapy destroys cancer cells using high-energy radiation or particles. Radiation may be useful, for example, for cancers that cannot be surgically removed, cancers that cannot be treated by ablation or that have not responded well to such treatment, cancers that have metastasized to areas such as the brain or bones, patients experiencing severe pain due to large tumors, and patients with tumor thrombi.

[0103] In some embodiments, cancer treatment includes drug therapy, such as targeted drug therapy or chemotherapy. Targeted drugs exhibit different mechanisms of action from standard chemotherapy agents and include, for example, kinase inhibitors such as sorafenib (Nexavar), lenvatinib (Lenvima), regorafenib (Stivarga), and cabozantinib (Cabometyx). Immunotherapy involves the administration of monoclonal antibodies. Monoclonal antibodies are designed to bind to specific targets. Monoclonal antibodies used to treat liver cancer affect the tumor's ability to form new blood vessels, i.e., angiogenesis. These treatment agents are often called angiogenesis inhibitors and include bevacizumab (Avastin) and ramucirumab (Cyramza), which can be used in combination with the immunotherapy agent atezolizumab (Tecentriq).

[0104] Commonly used chemotherapy drugs for cancer treatment include gemcitabine (Gemzar), oxaliplatin (Eloxatin), cisplatin, doxorubicin (pegylated liposomal doxorubicin), 5-fluorouracil (5-FU), capecitabine (Xeloda), mitoxantrone (Novantrone), or combinations thereof. Chemotherapy can be administered locally by injecting the drug into the artery leading to the tumor site. This concentrates the chemotherapy on the cancer cells in that area of ​​the body, reducing side effects by limiting the amount of drug that reaches other parts of the body.

[0105] In some embodiments, the methods for detecting cancer-related biomarker levels described herein are performed multiple times on individual subjects. For example, in some embodiments, subjects are undergoing cancer treatment (e.g., drug treatment, radiation treatment, and / or surgical treatment), and samples are taken at different time points during the treatment to assess the effectiveness of the treatment. In some embodiments, subjects are known or suspected to be at risk of cancer, and samples are taken at different time points to detect potential changes in cancer risk and / or to detect cancer as early as possible.

[0106] IX. Kits and Systems In one embodiment, a kit for detecting cancer in a subject is provided, which can be used to detect the miRNA biomarkers described herein. The kit may, for example, detect CD147 + The kit may include one or more agents for the isolation and separation of EVs (e.g., one or more antibodies that specifically bind to CD147, magnetic beads), one or more agents for the detection and quantification of cancer-specific miRNAs (e.g., primers for known cancer-associated miRNAs), a container for holding a biological sample (e.g., a plasma sample isolated from a human subject suspected of having cancer), and instructions for reacting the biological sample or a portion of the biological sample with the agents to detect the presence or amount of at least one miRNA biomarker in the biological sample. These agents may be packaged in separate containers. The kit may further include one or more control reference samples and reagents for carrying out the methods described herein. The kit may also include one or more devices or instruments for carrying out any of the methods described.

[0107] The kit may include one or more containers for the compositions contained within the kit. The compositions may be in liquid form or lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and tubes. The containers may be formed from a variety of materials, including glass or plastic. The kit may also include accompanying documentation for methods of diagnosing or detecting various cancers.

[0108] In one embodiment, a system, such as a measurement system, is provided. Such a system is, for example, CD147 +This enables the detection of the presence and / or levels of cancer-related miRNA biomarkers in extravasation cells (EVs), as well as the recording of data obtained from the detection. The stored data can then be analyzed to determine the state of the cancer in question. Such a system includes, for example, an assay system (e.g., comprising an assay device and a detector), the assay system of which data can be transmitted to a logic system (such as a computer or other system or device for capturing, transforming, analyzing, or otherwise processing data from the detector). The logic system may have one or more functions, including controlling elements of the entire system such as the assay system, transmitting data or other information to a storage device or external memory, and / or issuing commands to a treatment device.

[0109] A system for detecting various cancers in a sample is also provided. This system detects one or more cancer-related miRNA biomarkers in a sample using a station for analyzing miRNAs by RT-qPCR. For example, biomarkers for ovarian cancer include miR-200a, miR-200b, and miR-200c (Kim et al. 2019; Meng et al. 2016), and biomarkers for renal cell carcinoma include miR-210 and miR-1233 (Dias et al. 2017; Iwamoto et al. 2014; Zhang et al. 2018). In some embodiments, the sample is a blood sample, e.g., a plasma sample, taken from a subject, and the report is useful for diagnosing cancer in the subject, e.g., ovarian cancer or renal cancer. Optionally, a station for generating a report containing information about the analytical results is further included.

[0110] Furthermore, a method is provided for generating a report containing information on the detection results of cancer biomarkers (e.g., miRNA biomarkers for ovarian cancer or kidney cancer) in a sample, and a method for generating a report, wherein one or more cancer biomarkers are one or more cancer-related miRNAs, and the one or more cancer-related miRNAs are CD147 of a biological sample isolated from a subject. + It can be detected by EVs.

[0111] Also provided is a method for generating a report containing information on the identification results of cancer biomarkers in a sample (e.g., miRNA biomarkers for ovarian cancer or kidney cancer), and a method for generating such a report, wherein one or more cancer biomarkers are one or more cancer-related miRNAs, and the one or more cancer-related miRNAs are CD147 in a biological sample isolated from the subject. + It is detectable in EVs.

[0112] Certain aspects of the methods described herein may be performed in whole or in part by a computer system comprising one or more processors configurable to perform the steps. Thus, embodiments relate to a computer system configured to perform the steps of the methods described herein, with potentially different components performing each step or each group of steps. The computer system of this disclosure may be part of the measurement system described above, or it may be independent of any measurement system. In some embodiments, this disclosure provides a computer system that uses input biomarker expression data (and optionally other data) to determine the state of a cancer of interest.

[0113] Computer systems may include desktop computers, laptop computers, tablets, mobile phones, and other mobile devices. Systems may include various elements such as printers, keyboards, storage devices, monitors (e.g., LED displays), peripherals, devices for connecting computer systems to wide-area networks such as the Internet, mouse input devices, scanners, storage devices, computer-readable media, cameras, microphones, and accelerometers. Any data referred to herein can be output from one component to another and to the user.

[0114] In one embodiment, the Disclosure provides a computer implementation method for determining the presence or absence of cancer (e.g., ovarian cancer or kidney cancer) in a patient. The computer performs steps including, for example, receiving input patient data which includes values ​​of levels of one or more biomarkers in a biological sample from the patient. It analyzes the levels of one or more biomarkers, optionally compares them to their respective reference values, optionally compares the levels of the biomarkers to one or more thresholds to determine the cancer status, and displays information regarding the patient's cancer status or probability. In a particular embodiment, the input patient data includes values ​​of levels of multiple biomarkers in a biological sample derived from the patient, for example, one or more biomarkers.

[0115] In a further embodiment, a diagnostic system for performing the method implemented in a computer as described is included. The diagnostic system may include a computer containing a processor, a storage component (i.e., memory), a display component, and other components typically found in a general-purpose computer. The storage component stores processor-accessible information, including instructions that can be executed by the processor and data that can be retrieved, manipulated, or stored by the processor.

[0116] The storage component includes instructions for determining the state of the target cancer. For example, the storage component includes instructions for determining the state of cancer based on biomarker levels, as described herein. The computer processor is coupled to the storage component and configured to receive patient data and execute the instructions stored in the storage component to analyze the patient data according to one or more algorithms. The display component displays information related to the patient's diagnosis. The storage component may be any type that can store information accessible by the processor, such as a hard drive, memory card, ROM, RAM, DVD, CD-ROM, USB flash drive, writable memory, and read-only memory.

[0117] Instructions may be any set of instructions that are executed directly (such as in machine code) or indirectly (such as in a script) by the processor. In this regard, the terms “instructions,” “process,” and “program” may be used interchangeably herein. Instructions may be stored in object code format for direct processing by the processor, or they may be stored in scripts or other computer languages, including sets of independent source code modules that are interpreted on demand or pre-compiled.

[0118] Data may be retrieved, stored, or modified by the processor according to instructions. For example, a diagnostic system is not limited to a specific data structure, but data may be stored in computer registers, relational databases as tables with multiple different fields and records, XML documents, or flat files. Data may also be formatted in any computer-readable format, including but not limited to binary values, ASCII, or Unicode. Furthermore, data may include any information sufficient to identify relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memory (including other network locations), or information used by functions to compute the relevant data. In certain embodiments, the processor and storage components may include multiple processor and storage components, which may or may not be stored in the same physical housing. For example, some instructions and data may be stored on a removable CD-ROM, and others on a read-only computer chip. Some or all of the instructions and data may be stored in a location physically separate from the processor, but in a location accessible from the processor. Similarly, in practice, the processor may include a collection of processors, which may or may not operate in parallel. In one embodiment, the computer is a server that communicates with one or more client computers. Each client computer may be configured similarly to a server, containing a processor, storage components, and instructions. While client computers may include full-sized personal computers, many aspects of the system and method are particularly advantageous when used in conjunction with mobile devices that can wirelessly exchange data with a server over a network such as the internet. [Examples]

[0119] The following examples are provided to illustrate the invention as described in the claims and are not intended to limit the invention. The experiments described in the following examples are also described in Ko, SY, et al., J Extracell Vesicles. 2023 April;12(4):e12318. Doi: 10.1002 / jev2.12318.

[0120] Example 1. Materials and Methods This specification discloses the materials and methods used in the embodiments described below.

[0121] Antibodies and plasmids. The antibodies used are listed in Table 2. The plasmids for expressing CD63-GFP, CD81-GFP, CD9-GFP, CD147-GFP, and CD98-GFP fusion proteins are as follows: CD63-pEGFP C2 (donated by Paul Luzio, University of Cambridge; Addgene#62964), mMerald-CD81-10 (He et al. 2013), mMerald-CD9-10 (donated by Michael Davidson, Florida State University; Addgene#54031, #54029), pCMV3-CD147-GFPSpark, and pCMV3-CD98-GFPSpark (purchased from Sino Biological Inc.; HG10186-ACG, HG16415-ACG). Other plasmids include: tetracycline-regulated miR-302 cluster expression plasmid pCW57-GFP-miR-302 (Peskova et al. 2019) (donated by Tomas Barta of Masaryk University; Addgene#132549), LSB-hsa-miR-302a-3p and LSB-hsa-miR-302c-3p reporter plasmids (Gam et al. 2018) (donated by Ron Weiss of MIT; Addgene#103400, #103403), TRIPZ HGS shRNA, TRIPZ TSG101 shRNA, and TRIPZ vector (purchased from Horizon Discovery Biosciences; RHS4740-EG9146, RHS4740-EG7251, RHS4750). [Table 2]

[0122] Cell lines. The parent cell lines 293T, HeLa, SKOV3, and 786-O were purchased from the American Type Culture Collection. The ID8 cell line was provided by Katherine Roby (University of Kansas). Cell lines that stably express tetracycline repressor protein were obtained from T-Rex®-293T, T-Rex®-HeLa (Invitrogen), and T-Rex®-SKOV3 (Applied Biological Materials Inc.). The 293T cell line, in which the HNRNPA2B1 gene was deleted by CRISPR / Cas9 gene editing, was purchased from Abcam. All cell lines were confirmed to be free from mycoplasma contamination and their authenticity was confirmed by short tandem repeat analysis. Culture media were purchased from Corning. Cell lines were cultured in Dulbecco's Modified Eagle Medium (293T, 786-O, ID8), RPMI 1640 (HeLa), or McCoy's 5A Medium (SKOV3). These media were supplemented with 10% fetal bovine serum (for parental lines) or Tet-approved fetal bovine serum (for T-Rex® strains), 100 units / mL penicillin, and 100 μg / mL streptomycin. Stably transfected cell lines were prepared by transfecting cells using Lipofectamine® 3000 reagent (Invitrogen) as follows: Parental 293T cells were transfected with a marker GFP fusion expression plasmid, and GFP-expressing cells were sorted using a BD FACSAria® II cell sorter. To generate HGS knockdown cells and TSG101 knockdown cells, T-Rex®-293T cells were introduced with TRIPZ vector, TRIPZ HGS shRNA, or TRIPZ TSG101 shRNA expression plasmids and selected with 0.5 μg / mL puromycin (Sigma-Aldrich). shRNA expression was induced by adding 1 μg / mL doxycycline (Sigma-Aldrich) to the culture medium.Donor cells for assaying miR-302 introduction via EV were generated by introducing a miR-302 cluster expression plasmid into a T-Rex® cell line and selecting GFP-expressing cells. miR-302 expression in donor cells was induced by adding 1 μg / mL doxycycline to the culture medium. Receptor cells were generated by transfecting parental 293T cells with a miR-302a or miR-302c reporter construct and selecting mKate2-expressing cells.

[0123] Clinical specimens. Studies using human tissue specimens were reviewed and approved by the institutional research committees of the University of Texas MD Anderson Cancer Center and the University of Chicago. All tissue specimens are available, and full informed consent for research use was obtained from all subjects. Tumor tissue, ascites, and plasma from ovarian cancer patients, as well as plasma from patients with benign gynecological diseases, were obtained from the University of Chicago Ovarian Cancer Tumor Bank and the Southern Branch of the Collaborative Human Tissue Network (CHTN), supported by the National Cancer Institute at Duke University. CA125 concentrations in plasma specimens were measured using the CA125 Quantikine ELISA kit (R&D Systems). Tumor tissue and plasma specimens from renal cell carcinoma patients were obtained from the Echstein Tissue Collection Laboratory at the University of Texas MD Anderson Cancer Center and CHTN. The clinicopathological features of the cases are shown in Table 3. Plasma was separated from independent batches using EDTA-treated tubing from peripheral blood of healthy adult volunteers obtained from the University of Texas MD Anderson Cancer Center Blood Bank. Each batch contained pooled blood from a different donor. EV analysis of bodily fluid samples was performed with clinical data blinded. [Table 3-1] [Table 3-2]

[0124] Animal experiment. The animal experiment was reviewed and approved by the Animal Experimentation Committee of the University of Texas MD Anderson Cancer Center. Four-week-old female nude mice (purchased from Envigo) were given 2x10 T-Rex®-HeLa cells expressing the miR-302 cluster. 6 Individual or parent cells of 786-O cells 5x10 6 The cells were subcutaneously inoculated. To induce miR-302 in T-Rex®-HeLa cells, mice were fed pellets containing doxycycline (200 mg per kg of feed) (Bio-Serv). Tumor diameter was measured twice a week with calipers, and tumor volume was calculated from two vertical measurements. Blood samples (200 μL) were collected retroorbitally using an EDTA-treated tube before injecting cancer cells when the tumor first became palpable, and then the tumor volume was approximately 1000 mm³. 3 Samples were collected every two weeks until a certain threshold was reached. The animals were then euthanized by CO2 asphyxiation. Platelet-free plasma samples were collected by centrifugation at 2,000xg for 15 minutes. Only mice that did not develop tumors were excluded from the analysis.

[0125] Isolation of extracellular vesicles (EVs). EVs were isolated from culture supernatant, plasma, and ascites fluid, as in previous reports (Ko et al. 2019). Detailed information is provided in accordance with the latest guidelines of the International Society for Extracellular Vesicles (Thery et al. 2018). Cells were cultured for 48 hours in a medium containing 2% fetal bovine serum to prepare the culture supernatant. The prepared medium and body fluids were centrifuged at 2,400xg for 10 minutes at 4°C to remove cells and cellular residue, and then enriched using a Centricon® Plus-70 centrifuge filter unit and an Ultracel® 100kDa cutoff filter (Millipore) to remove soluble proteins and microparticles less than 100kD. Each enriched supernatant was mixed with 1.5 mL of OptiPrep® stock solution (60% (w / v) iodixanol aqueous solution, Axis-Shield PoC) and placed at the bottom of a 14x95 mm polyalomer ultracentrifuge tube (Beckman Coulter). Iodixanol solutions for the discontinuous gradient were prepared by diluting OptiPrep® stock solution with a buffer containing 0.25 M sucrose, 10 mM Tris-HCl (pH 7.4), and 1 mM EDTA. The gradient was formed by layering the iodixanol solutions in the following order: 3.0 mL of 40% solution, 2.5 mL of 20% solution, 2.5 mL of 10% solution, and 2.0 mL of 5% solution. The samples were centrifuged at 200,000 xg at 4°C for 18 hours using an SW 40 Ti rotor (Beckman Coulter). Ten 1.0 mL fractions were collected from top to bottom. The density of each fraction was determined from absorbance measurements at 244 nm using the standard curve for serial dilution of iodixanol solution (Schroder et al. 1997). Each fraction was washed with phosphate-buffered saline (PBS), enriched using an Amicon® Ultra-4 centrifuge filter unit equipped with an Ultracel® 100 kDa cutoff filter (Millipore), and then suspended in PBS for further analysis or purification.To purify extracellular viable cells (EVs) expressing a specific surface marker, 10 μg of biotinylated antibody against the marker was incubated with 100 μL of streptavidin-conjugated magnetic beads (Invitrogen) at 4°C for 16 hours and washed with PBS. The antibody-conjugated magnetic beads were then used to purify the EVs (approximately 1 x 10⁻¹). 8 The cells were incubated with the following at 4°C for 16 hours. After magnetic separation, the supernatant containing marker-negative EVs was collected for further analysis. The pellet containing marker-positive EVs was washed three times with PBS, and proteins or RNA were isolated by processing as follows.

[0126] Flow cytometry. Flow cytometry data acquisition and analysis were performed using a BD FACSCanto® II cytometer equipped with FACS Diva® software (BD Biosciences). The antibody concentrations used are shown in Table 2. To detect cell surface proteins, cells were suspended in PBS containing 1% bovine serum albumin (BSA) and incubated with FITC-labeled antibody or isotype control at 4°C for 30 minutes. Subsequently, cells were washed with PBS containing 1% BSA, fixed with 4% paraformaldehyde, and acquired. Staining was evaluated using a gated live cell population. At least 10,000 events were analyzed for each sample. Three independent experiments were performed to confirm the expression of specific surface proteins in each cell type. EV detection settings were optimized using a bead calibration kit (100 nm, 200 nm, 500 nm, and 760 nm diameter beads, Bangs Laboratories). Unless otherwise specified, the absolute number of EVs in the sample was calculated by using 760 nm beads as counting beads and multiplying the ratio of EV events to bead events by the number of beads in the sample. To detect EV surface proteins, 100 μL of EV sample (approximately 2 x 10⁶) was used. 6EVs were incubated with FITC-labeled antibody or isotype control at room temperature (RT) for 30 minutes. After incubation, samples were diluted in PBS to a final volume of 500 μL and acquired. Staining was evaluated in a gated singlet EV population. At least 10,000 events were analyzed for each sample. Three independent experiments were conducted to confirm the expression of specific surface proteins in EVs from each cell type. Different batches of EVs were used in each experiment. Contour plots and histogram plots were created using FlowJo® software (FlowJo, LLC).

[0127] Particle size analysis and immunogold labeling. The particle size distribution of purified extracellular viable cells (EVs) was analyzed using an Alpha Nano Tech LLC ZETAVIEW® QUATT instrument (Particle Metrix). For each batch of purified EVs, the average particle size was 2 x 10⁶. 11 Nine vesicles were isolated and subjected to 10 replicate measurements. EV markers were detected by immunogold labeling, as previously reported (Ko et al. 2019). Briefly, carbon-coated and formaldehyde-coated nickel grids (200 mesh) were treated with poly-L-lysine for 30 minutes. EVs were fixed with 2% paraformaldehyde and placed on the grids for 1 hour of absorption at room temperature. The grids were then placed in PBS containing 2% BSA and 0.1% saponin for 20 minutes, followed by incubation with primary antibody at 4°C for 16 hours. Control grids were incubated without primary antibody. The grids were washed with PBS and suspended on droplets of 10 nm gold particle-labeled secondary antibody at room temperature for 2 hours. The antibody concentrations used are shown in Table 2. After incubation, the grids were washed with PBS, fixed with 1% glutaraldehyde for 5 minutes, and washed with water. The grids were stained with 1% uranyl acetate for 1 minute and dried. Samples were evaluated using a JEM 1010 transmission electron microscope (JEOL USA, Inc.) at an acceleration voltage of 80 kV. Images were acquired using an AMT imaging system (Advanced Microscopy Techniques Corp.).

[0128] Immunoprecipitation. Cells were lysed in an immunoprecipitation buffer containing a protease inhibitor cocktail (Thermo Fisher Scientific) (1% Triton X-100, 25 mM HEPES, 150 mM NaCl, 5 mM MgCl2). 500 μg of cell lysate was incubated with 10 μg of antibody-conjugated agarose beads at 4°C for 16 hours. The beads were then washed twice with immunoprecipitation buffer and three times with PBS. The pellet was dissolved in a 2-fold diluted tris-glycine dodecyl sulfate sodium (SDS) sample buffer (Thermo Fisher Scientific) and analyzed by immunoblotting.

[0129] Immunoblotting was performed. Cells and extracellular proteins (EVs) were lysed in M-PER buffer (Thermo Fisher Scientific), and proteins were extracted. The protein concentration of the lysate was measured by the Bradford assay (BioRad). The lysates were electrophoresed on an SDS-polyacrylamide gel and transferred to a polyvinylidene fluoride membrane (GE Healthcare). The membrane was blocked at room temperature for 1 hour with 5% skim milk in Tris-buffered saline (TBS-T) containing 0.1% Tween-20, and then incubated with the primary antibody at 4°C for 16 hours. After washing with TBS-T buffer, the membrane was incubated with HRP-labeled secondary antibody at room temperature for 45 minutes, washed, and visualized with ECL detection reagent (Millipore). The antibody concentrations used are shown in Table 2. Immunoblotting data were validated in three independent experiments.

[0130] Immunocytochemistry. Cells were seeded and adhered to chamber slides under subconfluence. The cells were then fixed with 4% formaldehyde on ice for 20 minutes, followed by permeabilization with PBS containing 0.1% Triton X-100 on ice for 20 minutes. The cells were washed three times with PBS, blocked with PBS containing 1% goat serum for 30 minutes, and then incubated with FITC-labeled antibody for 16 hours. The antibody concentrations used are shown in Table 2. After washing with PBS, the cells were stained with 4,6-diamidino-2-phenylindole (Sigma-Aldrich). Cells were observed and photographed using an LSM 710 confocal microscope (Zeiss) and ZEN® software (Zeiss).

[0131] Analysis of miRNA introduction via extracellular matrix (EV). EVs were isolated from parental cell lines and donor cells expressing the miR-302 cluster as described above, and external RNA was removed by treatment with 0.2 μg / mL of Rnase A (Thermo Fisher Scientific) at 37°C for 30 minutes. Recipient 293T cells expressing miR-302a and miR-302c reporter constructs were placed in black 96-well optical bottom plates (Thermo Fisher Scientific) at a rate of 2 x 10⁶ cells per well. 4 Individual seeds were seeded. The recipient cells were EV (approximately 2 x 10⁻¹⁰). 6 Cells were incubated at 37°C for 48 hours with and without the addition of miR-302a or miR-302c mimetic (Sigma-Aldrich) (copy number 1 x 10) using Lipofectamine 3000 reagent as a positive control. 4 A fluorescent molecule was introduced. After incubation, the receptor cells were washed with PBS. The fluorescence intensity of the far-red fluorescent protein mKate2 and the blue fluorescent protein EBFP2 in the receptor cells was measured using a SPARK® microplate reader (Tecan). The activity of each miRNA was calculated as the relative value of the mKate2 intensity to the EBFP2 intensity. Three independent experiments were performed for each assay, using different batches of EV in each experiment.

[0132] Analysis of EV uptake. EVs were isolated from 293T cells expressing the marker GFP fusion protein as described above. Parental 293T cells were plated in 96-well plates (2 x 10⁶). 4 Sow seeds in EV (approximately 2 x 10) per well. 6 The cells were incubated with EVs at 37°C for 3, 6, 12, 18, and 24 hours. The cells were washed three times with PBS, and EV uptake was evaluated by measuring GFP fluorescence intensity using a SPARK® microplate reader. Untreated 293T cells were used as a blank. Marker-positive EV uptake was evaluated by GFP fluorescence at each time point, with GFP fluorescence at 24 hours after EV addition as the baseline. Four independent experiments were performed for each assay, with each experiment using a different batch of EVs.

[0133] Isolation and quantification of miRNAs. Before isolating miRNAs, all EV batches were treated with 0.2 μg / mL RNase A at 37°C for 30 minutes to remove extranucleotides. miRNAs were isolated using a 2-column PureLink® miRNA isolation kit (Invitrogen) according to the manufacturer's instructions. Briefly, EVs or cells were lysed with Trizol® reagent, chloroform was added, and the mixture was centrifuged at 4°C for 15 minutes. The aqueous phase was collected, mixed with an equal volume of 100% ethanol, and loaded onto the first column to retain large RNAs. The column was centrifuged at 12,000xg for 1 minute, the permeate was collected, mixed with twice the volume of 100% ethanol, and loaded onto the second column to retain small RNAs. The second column was centrifuged at 12,000xg for 1 minute and washed twice with the wash buffer provided in the kit. Small RNAs were eluted from the second column by adding RNase-free water (50 μL) and centrifugation at 12,000 x g for 1 minute. miRNAs were isolated from equal volumes of body fluid samples (100 μL for mouse plasma, 200 μL for human plasma or ascites) using three different methods. In the direct lysis method, 600 μL of TRIZOL® reagent was added directly to the body fluid sample, and miRNAs were isolated as described above. In the precipitation method, the body fluid sample was diluted with 300 μL of PBS, incubated with 126 μL of EXOQUICK® reagent (System Biosciences) at 4°C for 16 hours, and then centrifuged at 1,500 x g for 30 minutes. The pellet was dissolved with TRIZOL® reagent, and miRNAs were isolated as described above. In the CD147 immunocapsulation method described in these examples, the body fluid sample was diluted with 800 μL of PBS and incubated with CD147 antibody-conjugated magnetic beads at 4°C for 16 hours. The beads were then washed three times with PBS. TRIZOL® reagent was added directly to the beads, and miRNA was isolated using the method described above. The miRNA concentration in the sample was measured using the QUANT-IT® microRNA assay kit (Invitrogen) according to the manufacturer's instructions.The size distribution of RNA molecules was evaluated using a 2100 BIOANALYZER® equipped with a small RNA chip (Agilent), according to the manufacturer's instructions.

[0134] RT-qPCR of miRNAs. To standardize the amount of miRNAs in extracellular vesicles (EVs) and body fluids, cel-miR-39 spike-in control (Qiagen) was added to the TRIZOL® reagent during miRNA isolation. RT-qPCR of miRNAs was performed using the TAQMAN® MicroRNA Reverse Transcription kit (Applied Biosystems) and sequence-specific stem-loop primers for hsa-miR-302a-3p, hsa-miR-302c-3p, hsa-miR-1233-3p, hsa-miR-210-3p, cel-miR-39-3p, and RNU-48 (Applied Biosystems). The reaction was performed using the StepOne Plus® Real-Time PCR system with 1X Master Mix and 1X probe (TAQMAN® microRNA Expression Assay, Applied Biosystems). The relative levels of miRNAs in extracellular viable cells (EVs) were calculated and standardized using the comparative CT method (2-ΔΔCT) and a cel-miR-39 spike-in control. RNU-48 was used as an endogenous control for standardizing intracellular miRNA levels. The absolute copy numbers of miRNAs in Figures 7B and 12B-12D were calculated from standard curves created using miRNA mimes.

[0135] miRNA profiling. Reverse transcription of miRNAs isolated from body fluids and tumor tissue was performed using the MIRCURY LNA® RT kit (Qiagen) with cel-miR-39 as a spike-in control. qPCR was performed using the MIRCURY LNA® miRNA cancer focus PCR panel (Qiagen), which contains a primer set of 84 known cancer-related miRNAs. The reaction was performed using the StepOne Plus® real-time PCR system (Applied Biosystem) with MIRCURY SYBR® Green Master Mix (Qiagen). The cycle conditions were 2 minutes of reaction at 95°C, followed by 40 amplification cycles of 10 seconds at 95°C and 60 seconds at 56°C, and followed the melting curve. The Ct values ​​obtained from each panel were adjusted using an interplate calibrator. In all cases, only miRNAs detected in at least one of the three body fluid samples were included in subsequent analysis. The expression levels of each miRNA were normalized using the ΔCt method (ΔCt = Ct of each miRNA - Cel-miR-39 Ct). In all cases, the correlation between the ΔCt values ​​of each fluid-derived sample and the corresponding ΔCt values ​​of tumor tissue was evaluated by Spearman's test.

[0136] Statistical analysis. Statistical analysis was performed using GraphPad Prism 9.0 software (GraphPad Software). The normality of the between-group data distribution was evaluated using the Shapiro-Wilk test. The significance of data in in vitro and in vivo studies was evaluated using an unpaired two-sided Student t-test for two-group comparisons, and a one-way ANOVA or two-way ANOVA with Bonferroni correction for multiple comparisons. Unless otherwise specified, multiple comparisons were performed using unpaired samples. Unless otherwise specified, data are expressed as mean ± standard deviation. A p-value of less than 0.05 was considered statistically significant.

[0137] Example 2. CD147 and CD98 are tetraspanin + Define a subgroup of EVs that is distinct from other EVs. We reviewed two of the largest databases (Kalra et al. 2012; Keerthikumar et al. 2016) for proteins identified in EVs from diverse cell types and body fluids. The 100 most frequently identified EV proteins in the ExoCarta and Vesiclepedia databases included 77 common proteins, 10 of which were membrane proteins. These included three tetraspanins (CD63, tetraspanin-30, CD81, tetraspanin-28, CD9, tetraspanin-29) and seven less characterized proteins (CD147, basidine / emmprin, CD98, solute carrier family 3 member 2, CD71, transferrin receptor, CD29, integrin β1, CD49f, integrin α6, ATP1A1, Na + / K +This includes ATPase subunit α1, CLIC1, and chloride ion intracellular channel 1). Expression of three types of tetraspanins and five types of other common membrane proteins (CD147, CD98, CD71, CD29, CD49f) was confirmed by flow cytometry in human cell lines of diverse origins (three independent experiments per cell line). Specifically, these were 293T (fetal kidney), HeLa (cervical cancer), SKOV3 (OVCA), and 786-O (RCC) (data not shown; see Figure 1 of U.S. Provisional Application No. 63 / 487,144, and also Figure S1 of Ko et al. 2023). EVs were isolated from media prepared by these cell lines using a previously optimized method (Ko et al. 2019) that included ultrafiltration to remove soluble non-EV proteins and microparticles, followed by fractionation based on suspension density. The purified extracellular matrix (EVs) were visualized using a transmission electron microscope, confirming that their membrane structure was intact (Figure 1A), and their size distribution was determined by nanoparticle tracking analysis (Figure 1B). The expression rate of surface proteins in the EVs was evaluated by flow cytometry using the method applied in a previous study (Ko et al. 2019). The EV detection setup was optimized by acquiring microbeads of various diameters within the EV size range (Figures 2A-2B), and surface staining of proteins in the EVs was detected. Representative staining plots showing the percentage of EVs expressing specific surface markers are shown in Figure 3B of U.S. Provisional Application No. 63 / 487,144, and also refer to Figure S3B of Ko et al. 2023. 8% to 47% of the EVs contained either CD63, CD81, or CD9 (Figure 2C). CD147 and CD98 were detected in 19%–41% and 13%–33% of EVs, respectively, while CD71, CD29, and CD49f were detected in only 0.1%–16% of EVs (Figure 2C). Studies on the protein topology of EVs have revealed that several membrane proteins are presented in EVs in the opposite orientation to that of the cell membrane, i.e., "inside-out" (Cvjetkovic et al. 2016).This suggests that the low detection rates of CD71, CD29, and CD49f in EV may be due to masking by antibody epitopes. Therefore, these markers were excluded from subsequent analyses. To confirm the association of CD147, CD98, and the three tetraspanins with EV, these markers were evaluated by immunoblotting for all density gradient fractions. In fractions within the suspension density range of EV, the full-length forms, rather than truncated forms corresponding to the detached extracellular domains, were detected for all five proteins. (Figures 3A-3B; data from analyses in SKOV3 and 786-O cell lines are not shown; see Figure 4 in U.S. Provisional Application No. 63 / 487,144; also see Figure 1C in Ko et al. 2023).

[0138] The co-expression of CD147, CD98, and three types of tetraspanins in extracellular genes (EVs) was evaluated. Tetraspanins function as scaffolds for membrane organization by interacting with each other and with other membrane proteins, protruding only 4–5 nm from the membrane (Hemler 2005; Kitadokoro et al. 2001). Due to steric hindrance, reliable detection of these proteins is difficult with co-staining using multiple antibodies. To overcome this limitation, EVs expressing a specific surface marker (marker X) were removed. Subsequently, the proportion of EVs expressing another specific surface marker (marker Y) was measured in the remaining marker X-negative EV pool and the original overall EV population (Figure 4A). This approach was initially used to analyze EVs derived from 293T cells. Compared to the overall EV population, depleting EVs expressing a specific tetraspanin significantly reduced the proportion of EVs expressing either of the other two tetraspanins (Figure 4B). These findings suggest that tetraspanins... + The majority of EVs co-express at least two tetraspanins, but not all of them, which is consistent with several reports (Han et al. 2021; Mathieu et al. 2021; Tian et al. 2018). In contrast, CD147 + EV or CD98 +Depleting any of the EVs did not significantly reduce the proportion of EVs that were CD63+, CD81+, or CD9+ (Figure 4C). Conversely, CD63 + EV, CD81 + EV, or CD9 + Even if one of the EVs is depleted, CD147 + Or CD98 + The proportion of EVs that were negative did not decrease (Figure 4B). These results indicate that CD147 and CD98 are primarily expressed in tetraspanin-negative EVs. Furthermore, analysis of CD147 expression in CD98-negative EVs and CD98 expression in CD147-negative EVs revealed that the expression of these markers in EVs is almost mutually exclusive (Figure 4C). Similar results were obtained using EVs derived from HeLa cells, SKOV3 cells, and 786-O cells (data not shown; see Figures 5B-5C of US Provisional Application No. 63 / 487,144; also see Figure 5E of Ko et al. 2023).

[0139] Example 3. CD147 + and CD98 + EV biosynthesis is tetraspanin + Different from EVs Two broad types of extracellular vesicles (EVs) released from living cells have been reported, based on their intracellular origin. Exosomes originate from polyendoplasmic reticulum endosomes and range in diameter from 30 nm to 150 nm. On the other hand, microvesicles (ectosomes) are formed by outward budding of the cell membrane and range in diameter from 100 nm to 1 μm (Maas et al. 2017; Mathieu et al. 2019; Xu et al. 2018). CD147 + EV and CD98 + EV is tetraspanin +To further investigate possibilities other than EVs, the size of these EVs was evaluated. A constraint in determining the size of extracellular vesicles (EVs) expressing specific surface markers is that antibody binding to the marker alters the size of the EV, making it difficult to detach the antibody without compromising the integrity of the EV. To overcome this constraint, 293T cell lines expressing CD63, CD81, CD9, CD147, or CD98 as GFP fusion proteins were generated, and the size of GFP-positive EVs from each cell line was evaluated by flow cytometry and fluorescence nanoparticle tracking analysis. From these two independent analyses, CD147 + EVs and CD98-positive EVs are tetraspanic + They were shown to be larger than EVs (Figures 5A-5D). To confirm these findings, endogenous surface proteins in EVs were immunogold-labeled. Tetraspanins were mainly detected in small extracellular vesicles (EVs) (Figure 5E). This is consistent with reports that these proteins are contained in exosomes, but not exclusively in exosomes (Escola et al. 1998; Kowal et al. 2016; Mathieu et al. 2021). In contrast, CD147 and CD98 were mainly detected in large EVs (Figure 5E).

[0140] Confocal microscopy revealed that CD147 and CD98 are primarily localized to the cell membrane (Figure 6A). In contrast, CD63 and CD81 are primarily localized to the cytoplasm, while CD9 is present in both the cytoplasm and the membrane (Figure 6A). These differences in intracellular localization are attributed to CD147 + and CD98 + EV and tetraspanin +This suggests differences in EV biosynthesis. The most distinctive part of the exosome biosynthesis pathway is controlled by the ESCRT mechanism, which consists of four multi-subunit complexes (ESCRT-0, -I, -II, -III) and several accessory components (Henne et al. 2011). HGS (also known as HRS) and TSG101 are core components of ESCRT-0 and ESCRT-I, respectively (Henne et al. 2011), and knockdown of these components inhibits exosome secretion (Colombo et al. 2013). The expression levels of CD147, CD98, and the three tetraspanins did not change with knockdown of HGS or TSG101 (Figure 6B). In particular, knockdown of either HGS or TSG101 significantly inhibited secretion from tetraspanin-positive cells (EVs), but not CD147. + There was no effect on the secretion of EVs or CD98-positive cells (EVs) (Figure 6C-6D). These findings are related to CD147 + EV and CD98 + This suggests that the majority of EVs are not exosomes. High-magnification cell observation revealed that CD147 and CD98 are located in the sprouted and fragmented cell membrane region on the outside (Figure 6A). Taking these findings together, CD147 + EV and CD98 + EVs are probably microvesicles, tetraspanin + This supports the idea that it represents a subgroup of EVs that is distinct from other EVs.

[0141] Example 4. CD147 + EVs transport biologically active miRNAs to receptor cells. Among EV transporters, miRNAs have attracted considerable attention, but several excellent studies have raised objections regarding the content and importance of miRNAs in EVs (Albanese et al. 2021; Chevillet et al. 2014; Zhang et al. 2021). To investigate the possibility that miRNAs are enriched only in subpopulations of EVs, we developed an assay system to evaluate the export of miRNAs by EVs secreted from donor cells and the import of bioactive miRNAs into receptor cells by EVs (Figure 7A). To ensure that the bioactivity of miRNAs in receptor cells is due to EV-mediated miRNA transport, we selected the miR-302 cluster, which is not endogenously expressed in mature cells, as the test miRNA (Suh et al. 2004). We created a donor cell line in which miR-302 expression is induced by doxycycline (Figure 7B). Recipient cell lines expressing a dual reporter cassette containing the miR-302 target sequence (Gam et al. 2018) were also created (Figure 7A). The biological activity of miR-302, transported by extracellular vesicles (EVs) derived from donor cells and taken up by recipient cells, was evaluated by measuring mKate2 fluorescence (Figure 7A). The robustness of this system was confirmed by the inhibition of mKate2 fluorescence in recipient cells after stimulation with EVs derived from donor cells expressing miR-302 (Figure 7C; see also Figure 8C of U.S. Provisional Application No. 63 / 487,144 and Figure 4C of Ko et al. 2023 for data from 293T cells expressing mKate2 with the 3'miR-302c target sequence). Next, EVs expressing specific markers were removed from the donor cell-derived EVs, and recipient cells were stimulated using the remaining marker-negative EVs. Compared to unstimulated cells, the bioactivity of miR-302 did not decrease after CD147-negative EV stimulation, but decreased by 27–41% after CD98-negative EV stimulation (Figure 7D; see also Figure 8D in U.S. Provisional Application No. 63 / 487,144 and Figure 4D in Ko et al. 2023 for data from 293T cells expressing mKate2 with a 3'miR-302c target sequence).Tetraspanin-negative EV stimulation further reduced the biological activity of miR-302 (53-83%), showing an effect comparable to total (non-depleted) EV stimulation (Figure 7D). Tetraspanin by receptor cells. + CD147 + and CD98 + No significant difference was observed in EV uptake rates (Figure 7E). These results indicate that the majority of EV-related miR-302 is contained in tetraspanin-negative EVs. Consistent with our findings that CD147 and CD98 are mainly expressed in tetraspanin-negative EVs, the copy number of miR-302 was higher in CD98-positive EVs. + Significantly higher than EV, CD147 + The figures were even higher in EV (Figure 7F; data for miR-302c is not shown; see Figure 8F of U.S. Provisional Application No. 63 / 487,144; also see Figure 4F of Ko et al. 2023).

[0142] Example 5. CD147 + EVs are rich in miRNAs due to the interaction between CD147 and hnRNP A2 / B1. Subsequent research has shown that tetraspanin secreted from 293T cells, HeLa cells, and SKOV3 cells + EV, CD147 + EV, and CD98 + Extracellular viable cells (EVs) were isolated, and the total miRNA content was measured in an equal number of EVs from each subpopulation (Figure 8A). Tetraspanin+ EVs had the lowest miRNA content (Figure 8B). Tetraspanin + Compared to EV, CD98 derived from two cell lines + In EV cells, the total miRNA content was slightly significantly higher (2-4 times), and CD147 was derived from all three cell lines. + The EV was significantly higher (8-17 times), (Figure 8B). CD147 +The enrichment of miRNAs in EVs was confirmed by analyzing the small RNA content in each subpopulation of EVs using the Agilent 2100 Bioanalyzer™ (Figure 8C. See also Figure 9D in U.S. Provisional Application No. 63 / 487,144 and Figure S6 in Ko et al. 2023 for electrophoretic profiles of small RNAs isolated from EVs derived from 293T cells and marker-positive 293T cells). To rule out the possibility that miRNAs may be associated with components other than EVs, purified EVs in all experiments were treated with RNase before miRNA analysis. Since extracellular miRNAs not associated with EVs form complexes with high-density lipoproteins (Vickers et al. 2011), apolipoprotein A1 (ApoA1), a major component of high-density lipoproteins, was CD147 + EV and CD98 + It was confirmed that CD147 was not detected in EVs (Figure 8D). To confirm the findings in clinical specimens, EVs isolated from ascites fluid of OVCA patients and plasma of patients with both OVCA and RCC were analyzed, and similarly, CD147 was detected. + It was shown that miRNAs are significantly enriched in EVs (tetraspanin + (9 to 26 times the EV). (Figure 8E).

[0143] Several RNA-binding proteins control the selection of miRNAs into extracellular viable cells (EVs), and these have been detected in EVs (Lee et al. 2019; Santangelo et al. 2016; Temoche-Diaz et al. 2019; Villarroya-Beltri et al. 2013). One of these RNA-binding proteins, hnRNP A2 / B1, is found on CD147. + Detected in EV, but tetraspanin + EV and CD98 + It was not detected in EVs (Figure 9A). CD147 +To investigate the significance of hnRNPA2 / B1 in extracellular genes (EVs), we used 293T cells (hnRNP A2 / B1-KO) in which the HNRNPA2B1 gene was deleted by CRISPR / Cas9 gene editing. Knockout of hnRNP A2 / B1 did not alter the intracellular expression levels of CD147, CD98, and tetraspanin (Figure 9B), nor the expression of these surface markers in EVs (Figure 9C). In particular, immunoprecipitation assays revealed that CD147 interacts with hnRNP A2 / B1 (Figure 9D). Furthermore, CD147 derived from hnRNP A2 / B1-KO cells... + The total miRNA content in EVs is derived from CD147 derived from parental 293T cells. + The total miRNA content in extracellular viable cells (EVs) was significantly lower than that of EVs (Figures 9E-9F). These findings are related to CD147. + The selective enrichment of miRNAs in extracellular viable cells (EVs) is suggested to occur through the binding of hnRNP A2 / B1 to miRNAs and their interaction with CD147.

[0144] Example 6. CD147 is a candidate surface marker for cancer cell-derived extracellular matrix (EV). Almost all cell types release extracellular viable cells (EVs) (Xu et al. 2018), but there are no clearly defined surface markers that can distinguish EVs released into body fluids from cancer cells. CD147 is overexpressed in various solid tumors, but is also expressed in endothelial cells, fibroblasts, platelets, and leukocytes (Xin et al. 2016; Xiong et al. 2014). Several studies have shown that CD147 is overexpressed in colorectal cancer patients. + Elevated levels of extracellular viable cells (EVs) have been detected, but the origin cells of these EVs have not been identified (Tian et al. 2018; Yoshioka et al. 2014). To identify the origin cells of miRNA-rich EVs in body fluids, CD147 was collected longitudinally from plasma samples of human tumor xenograft mice. +Extracellular viable cells (EVs) were analyzed. For comparison, mouse plasma EVs expressing tetraspanin CD9 were analyzed. Cancer cell-derived EVs were distinguished from non-cancerous host cell-derived EVs using antibodies specific to human and mouse surface markers, respectively. Antibody species specificity was verified. The specificity of antibodies against human and mouse CD9, and human and mouse CD147, was confirmed by staining SKOV3 human OVCA cells and ID8 mouse OVCA cells (data not shown; see Figure 11A of US Provisional Application No. 63 / 487,144; also see Figure S9A of Ko et al. 2023). In HeLa xenograft mice, cancer cell-derived CD9+ and CD147 + The expression rate of EVs gradually increased with increasing tumor size (Figures 10A-10C). In particular, CD147 + A significant increase in EV expression rate is due to CD9 + It was detected much earlier than in EVs. That is, CD147 + EV tumors average 176 mm in size. 3 CD9 was expressed on day 28 (Figure 10C), whereas CD9 + EV tumors average 691 mm 3 CD147 derived from cancer cells was expressed on day 42 (Figure 10B). + In contrast to EVs, cancer cell-derived CD9 + EV expression rates did not significantly increase in the early stages of tumor development, but only significantly increased in more advanced stages (Figure 10B). Similar results were obtained in 786-O xenograft mice (Figures 10A-10C). Furthermore, comparative analysis of EVs based on cell origin showed that CD147 was expressed in both the HeLa and 786-O models. + It was revealed that EVs mainly originate from cancer cells (Figure 10D). In contrast, CD9 + It was found that the majority of extracellular genes (EVs) originated from non-cancerous host cells (Figure 10D). These findings are linked to CD147 + This indicates that EVs are primarily released from cancer cells, and from an early stage, and CD147 + This suggests that elevated EV levels may be a useful indicator of early-stage and / or low-volume malignancies.

[0145] Example 7. The prevalence of CD147+EV in cancer patients increases from the early stages of the disease. While there is strong evidence that extracellular matrix (EV) mediates cancer progression (Clement et al. 2020; Czystowska-Kuzmicz et al. 2019; Ko et al. 2019; Xu et al. 2018; Zhou et al. 2014), the heterogeneity of EV in cancer is not well understood. To further this understanding, we investigated tetraspanin in plasma samples from healthy adults and patients with benign gynecological diseases or ovarian cancer (OVCA). + EV, CD147 + EV, and CD98 + EVs were analyzed (Figures 11A-11C). The clinicopathological characteristics of the cases are shown in Table 3. Compared to healthy individuals, the total number of EVs was increased in patients with benign disease and early-stage OVCA, and even more so in patients with advanced-stage OVCA (Figure 11A). Tetraspanin + There was no significant difference in the prevalence of extracellular vein (EV) between healthy individuals and patients with benign disease or OVCA (Figure 11B). A similar trend was observed in the plasma of renal cell carcinoma (RCC) patients. The total number of EVs was significantly increased in patients with advanced-stage RCC (Figure 11D). Regardless of disease stage, there was no significant difference in tetraspanin prevalence between healthy individuals and RCC patients. + No significant difference was observed in the prevalence of extravasation (EV) (Figure 11E).

[0146] Tetraspanin + In contrast to the EV, the CD147 + EV and CD98 + Each EV represented only a small fraction (approximately 1.6%) of the total number of EVs in healthy individuals (Figure 11C). CD98 + The prevalence of EV was slightly (3 times) higher in early and advanced OVCA patients (Figure 11C), and 8 times higher in advanced RCC patients, but no significant increase was observed in early RCC patients (Figure 11F). In contrast, CD147 + The prevalence of extravasation glands (EVs) was approximately 20 times higher in patients with early and advanced OVCA, effectively differentiating OVCA patients, patients with benign diseases, and healthy individuals (Figure 11C). CD147+ In contrast to EV, there was no significant difference in the CA125 biomarker levels between patients with benign gynecological disease and patients with early OVCA (Figure 11G). These findings suggest that CD147 may be useful in detecting OVCA. + This suggests that measuring EV may be more effective than measuring CA125. Similarly, in patients with renal cell carcinoma (RCC), CD147 may be more effective in both early and advanced stages. + An increase in the EV expression rate was observed (Figure 11F).

[0147] Example 8. CD147 immunocapture of EV enhances the detection of cancer-derived circulating miRNAs. Fractionation based on suspension density is widely recognized as the gold standard for isolating EVs with high purity (Thery et al. 2018), and in this study, all EVs were isolated in combination with ultrafiltration. However, this separation method is labor-intensive and not practical in clinical laboratories. In clinical studies of EV-related miRNAs, other methods have been used to rapidly isolate EVs from body fluids but with lower purity, mainly using polymer-based reagents (e.g., EXOQUICK®) precipitation (Hannafon et al. 2016; Shin et al. 2021; Wang et al. 2022). EVs in body fluids have also been captured using antibodies against tetraspanin (Campos-Silva et al. 2019; Duijvesz et al. 2015; Logozzi et al. 2009). CD147 is a candidate surface marker for cancer cell-derived EVs, and the miRNA is CD147 +The finding of EV-rich miRNAs suggests that immune capture of EVs with CD147 may improve the detection rate of circulating cancer-derived miRNAs. To verify this possibility, plasma samples from tumor-bearing mice were divided into equal volumes, and miRNAs were isolated using either direct lysis of whole plasma, precipitation using EXOQUICK® reagent, or immune capture using CD147 antibody. Two xenograft models (HeLa, 786-O) were evaluated. In both groups, direct lysis and precipitation yielded 3-4 times more miRNAs than CD147 immune capture (Figure 12A). To evaluate cancer cell-specific miRNAs, miR-302 was analyzed in the HeLa model, as the tumor was established from miR-302-overexpressing HeLa cells, and miR-302 is not expressed in normal mature cells (Suh et al. 2004). In the 786-O model, miR-1233 was measured. This is because this miRNA is endogenously expressed in 786-O cells (Dias et al. 2017), and there is no mouse homologous gene. The copy numbers of miR-302a and miR-1233 in miRNA samples isolated by CD147 immunocapsulation from the plasma of mice with HeLa tumors and 786-O tumors were 3 to 5 times higher than those obtained by the other two methods (Figure 12B).

[0148] To evaluate whether isolation of circulating miRNAs by CD147 immunocapsulation improves the diagnostic performance of cancer-related miRNAs, circulating miRNAs were isolated from equal volumes (200 μL) of plasma samples from healthy individuals, early-stage renal cell carcinoma patients, and advanced-stage renal cell carcinoma patients using direct lysis and CD147 immunocapsulation. Subsequently, the copy number of miR-210 in each miRNA sample was evaluated. The copy number of miR-210 was evaluated because there are several independent reports that miR-210 is overexpressed in renal cell carcinoma and detected in the circulating blood of renal cell carcinoma patients (e.g., Iwamoto et al, 2013; Dias et al, 2017; Chen et al, 2018). The results showed that the copy number of miR-210 in miRNA samples isolated by direct lysis was significantly increased in patients with advanced-stage renal cell carcinoma compared to healthy individuals, but not significantly increased in patients with early-stage renal cell carcinoma (Figure 12C). On the other hand, when circulating miRNAs were isolated using CD147 immunocapsulation, a significant difference in miR-210 copy number was observed between healthy individuals and patients with early-stage renal cell carcinoma (Figure 12D). These findings suggest that isolating circulating miRNAs using CD147 immunocapsulation improves the sensitivity of detecting cancer cell-specific miRNAs.

[0149] Next, we investigated whether miRNAs isolated from the bodily fluids of cancer patients by CD147 immunocapsulation reflected the miRNA expression patterns of tumor tissue. First, miRNAs were isolated from the ascites fluid of OVCA patients using either direct lysis, precipitation with EXOQUICK® reagent, or CD147 immunocapsulation. For each case, the expression levels of 84 types of cancer-related miRNAs in the bodily fluid-derived miRNA samples were evaluated, and then the correlation with the expression levels of these miRNAs in the corresponding tumor tissue was assessed. The strongest correlation with the miRNA expression levels in tumors was obtained with bodily fluid-derived miRNA samples isolated by CD147 immunocapsulation (Figure 12E). Similar results were obtained using plasma-derived samples from OVCA or RCC patients and the corresponding tumor tissue (Figures 12F-12G). These findings suggest that CD147 immunocapsulation may be more effective than conventional methods for isolating circulating cancer-derived miRNAs for fluid biopsy.

[0150] The examples and embodiments described herein are for illustrative purposes only, and those skilled in the art will understand that various modifications or changes based thereon are suggested and that they fall within the spirit and scope of this application and the appended claims.

[0151] The inventions provided in this disclosure are described extensively and comprehensively herein. Each of the narrower species and subgenera groups included in the comprehensive disclosure also constitutes part of the invention. Furthermore, where a feature or aspect of the invention is described in terms of the Markush group, those skilled in the art will recognize that the invention is also described in terms of any individual member or subgroup of a member of the Markush group.

[0152] Reference materials JPEG2026510267000006.jpg75164 JPEG2026510267000007.jpg238164 JPEG2026510267000008.jpg248164 JPEG2026510267000009.jpg239164 JPEG2026510267000010.jpg247164 JPEG2026510267000011.jpg240164 JPEG2026510267000012.jpg240164 JPEG2026510267000013.jpg240164 JPEG2026510267000014.jpg135164

Claims

1. A method for isolating a group of extracellular vesicles rich in microRNA (miRNA) derived from cancer cells, (i) To provide biological samples derived from the subject; (ii) Contacting the biological sample with an antibody that specifically binds to CD147 for a certain period of time; (iii) CD147 containing CD147 on its surface ＋ Extracellular vesicles, CD147 ＋ Based on the binding of the antibody to CD147 on the surface of extracellular vesicles, the CD147 rich in cancer cell-derived miRNA is separated from other components of the biological sample. ＋ To provide an isolated population of extracellular vesicles. Methods that include...

2. A method for isolating a group of extracellular vesicles rich in microRNA (miRNA) derived from cancer cells, (i) To provide biological samples derived from the subject; (ii) If desired, obtain a sample containing extracellular vesicles from the biological sample; (iii) Contacting the biological sample from step (i) or the sample from step (ii) with an antibody that specifically binds to CD147 for a certain period of time; and (iv) CD147 containing CD147 on its surface ＋ Extracellular vesicles, CD147 ＋ Based on the binding of the antibody to CD147 on the surface of extracellular vesicles, the CD147 enriched with cancer cell-derived miRNA is separated from the other components of the biological sample in step (i) or the sample in step (ii). ＋ To provide an isolated population of extracellular vesicles. Methods that include...

3. The method according to claim 1 or 2, wherein the biological sample is obtained from a subject known to have cancer or suspected to have cancer.

4. The method according to claim 1 or 2, wherein the biological sample is a blood sample.

5. The method according to claim 4, wherein the blood sample is a plasma sample.

6. The method according to claim 1, wherein the biological sample is a group of extracellular vesicles isolated from a sample obtained from the subject.

7. The aforementioned CD147 ＋ An isolated population of extracellular vesicles was isolated from tetraspanin isolated from the same biological sample. ＋ The method according to claim 1 or 2, which is rich in miRNA compared to a population of extracellular vesicles.

8. said CD147 ＋ an isolated population of extracellular vesicles is at least 8-fold more miR-NA abundant than a population of extracellular vesicles, the method according to claim 7 ＋ having a miRNA content that is at least 8 times more abundant than the population of extracellular vesicles, the method according to claim 7

9. The method according to claim 1 or 2, wherein the subject has symptoms of one or more cancers.

10. The method according to claim 9, wherein the cancer is ovarian cancer.

11. The method according to claim 9, wherein the cancer is kidney cancer.

12. The method according to claim 1 or 2, wherein obtaining extracellular vesicles from the biological sample comprises culturing a plurality of cells derived from the biological sample in a culture medium for a certain period of time sufficient to cause the plurality of cells to release extracellular vesicles, thereby producing a conditioned medium containing the extracellular vesicles.

13. The method according to claim 12, wherein obtaining extracellular vesicles from the biological sample further comprises removing cells, cell debris, and fine particles from the conditioned medium and performing one or more centrifugation steps.

14. CD147 ＋ The method according to claim 1 or 2, wherein the isolation of an extracellular vesicle from other extracellular vesicles is performed by immunocapture.

15. The method according to claim 14, wherein an antibody that specifically binds to CD147 is bound to a solid support.

16. The method according to claim 15, wherein the solid support is a magnetic bead.

17. A method for detecting cancer-related miRNAs in a target, (i) CD147 rich in cancer cell-derived miRNA ＋ Isolating a population of extracellular vesicles from a biological sample derived from a subject known to or suspected to have cancer, according to the method described in any one of claims 1 to 15; (ii) CD147 ＋ To detect one or more cancer-associated miRNAs in an isolated population of extracellular vesicles. Methods that include...

18. The detection of one or more cancer-related miRNAs is the CD147 ＋ The method according to claim 17, comprising isolating RNA from an isolated population of extracellular vesicles.

19. The method according to claim 18, further comprising detecting one or more cancer-associated miRNAs, reverse transcription of the isolated RNA, and polymerase chain reaction (PCR) amplification of the one or more miRNA sequences.

20. The method according to any one of claims 17 to 19, further comprising determining the amount of one or more cancer-related miRNAs.

21. CD147 isolated from the biological sample derived from the aforementioned target ＋ The amount of one or more cancer-associated miRNAs in a population of extracellular vesicles is compared to CD147 isolated from biological samples derived from healthy individuals without cancer. ＋ The method according to any one of claims 17 to 20, further comprising comparing it with the amount of one or more cancer-related miRNAs in an extracellular vesicle.

22. The method according to any one of claims 17 to 21, wherein the detection of cancer-related miRNAs in the subject is used for diagnosing cancer, determining prognosis, and / or determining the risk of recurrence in the subject.

23. A method for treating a subject with cancer, (i) detecting one or more cancer-associated miRNAs in a subject according to the method described in any one of claims 17 to 22; (ii) administering one or more cancer treatments to the subject based on at least one of the types of cancer or stages of cancer identified in the subject. Methods that include...

24. A method for generating a report containing information regarding the detection results of cancer-related miRNA biomarkers, CD147 according to the method described in any one of claims 17 to 22 ＋ To detect one or more cancer-associated miRNA biomarkers in an isolated population of extracellular vesicles; and To generate the aforementioned report, where the report is useful for diagnosing cancer in the subject. Methods that include...

25. A method for identifying cancer-related miRNAs in a subject, (i) CD147 rich in cancer cell-derived miRNA ＋ Isolating a population of extracellular vesicles from a biological sample derived from at least one subject known to have cancer, according to the method described in any one of claims 1 to 15; (ii) CD147 from a biological sample derived from at least one subject known to have cancer ＋ Identifying one or more miRNAs in an isolated population of extracellular vesicles; (iii) CD147 from a biological sample derived from at least one subject known to have cancer ＋ One or more miRNAs in an isolated population of extracellular vesicles were compared to CD147 isolated from a control biological sample. ＋ Comparing a population of one or more miRNAs in a population of extracellular vesicles; and (iv) CD147 from a biological sample derived from at least one subject known to have cancer ＋ CD147 is present or abundant in isolated populations of extracellular vesicles, but not in isolated control biological samples. ＋ Identifying one or more cancer-associated miRNAs that are absent or deficient in the population of one or more miRNAs within a population of extracellular vesicles. Methods that include...

26. CD147 from biological samples derived from at least one subject known to have cancer ＋ Identifying one or more miRNAs in an isolated population of extracellular vesicles is the process described above for CD147 ＋ The method according to claim 25, comprising isolating RNA from an isolated population of extracellular vesicles.

27. The method according to claim 26, further comprising identifying one or more miRNAs, reverse transcription and sequencing of the isolated RNA.

28. CD147 from biological samples derived from at least one subject ＋ The method according to any one of claims 25 to 27, further comprising determining the quantity and / or identity of one or more miRNAs in an isolated population of extracellular vesicles.

29. A method for generating a report containing information regarding the results of the identification of cancer-related miRNA biomarkers, CD147 according to the method described in any one of claims 25 to 28 ＋ Identifying one or more cancer-associated miRNA biomarkers in isolated populations of extracellular vesicles; and To generate the aforementioned report, where the report is useful for diagnosing cancer in the subject. Methods that include...

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