Molecular analysis of extracellular vesicles (EVs) for predicting and monitoring drug resistance in cancer

Abstract

Provided herein is a method for predicting the response of a subject to a chemotherapy drug.Furthermore, provided is a method for monitoring the response of a subject to a chemotherapy drug over time, for example, a method for monitoring chemotherapy resistance.These methods include enriching a sample for tumor-derived extracellular vesicles (tEVs), and determining the relative level of the subject's drug resistance biomarker compared with the subject's drug resistance biomarker at a previous time point.

Description

[Technical Field]

[0001] Priority claims This application claims the benefit of U.S. Provisional Application No. 63 / 479,624, filed January 12, 2023, and U.S. Provisional Application No. 63 / 500,317, filed May 5, 2023, the entire contents of which are incorporated herein by reference.

[0002] Sequence Listing This application contains a Sequence Listing that has been submitted electronically as an XML file named 29539-0722WO1_SL_ST26.xml. The size of this XML file, created on January 8, 2024, is 16,740 bytes. The material in the XML file is incorporated herein by reference in its entirety.

[0003] Regarding the evaluation of molecular profiles of specific biomarkers in EVs as non-invasive biosignatures and prediction of pharmacological response and clinical outcome in patients suffering from cancer. [Background technology]

[0004] Cancer remains a global health challenge despite technological advances. Resistance to therapy and disease recurrence hinder the progress of treatment. New treatments are needed because many cancers develop resistance to current therapies. Summary of the Invention

[0005] Provided herein is a method for predicting chemotherapy resistance in a subject suffering from cancer, the method comprising the steps of: providing a sample from the subject; isolating, detecting, or enriching tumor-derived extracellular vesicles (tEVs) from the sample, preferably wherein the tEVs are labeled with an antibody or antigen-binding portion thereof that binds to a tumor marker and an antibody or antigen-binding portion thereof that binds to a chemotherapy resistance biomarker; determining the count of tEVs that are positive for a tumor marker and a chemotherapy resistance biomarker, or determining the intensity level of the tumor marker and the chemotherapy resistance biomarker expressed in the tEVs; and comparing the tEV count or marker intensity level determined in the previous step with a reference level that represents the response of the subject's cancer to chemotherapy, wherein a tEV count or marker intensity level determined in the previous step that differs from the reference level indicates whether the subject's cancer is resistant or sensitive to chemotherapy.

[0006] In some embodiments, the determining step of the above method further comprises using a plasmon-enhanced EV detection method. In some embodiments, the antibody or antigen-binding portion thereof that binds to the EV tumor marker further comprises a fluorescent dye. In some embodiments, the antibody or antigen-binding portion thereof that binds to the EV tumor marker comprises one or more antibodies or antigen-binding portions thereof that bind to EpCAM, EGFR, MUC1, and / or HER2. In some embodiments, the EVs are detected using a protein-reactive TFP dye that comprises a fluorescent dye. In some embodiments, the chemotherapy resistance biomarker comprises a protein or RNA. In some embodiments, the chemotherapy resistance biomarker is P-gp and survivin. In some embodiments, quantification of the EV tumor marker and chemotherapy resistance biomarker comprises expression, concentration, intensity, or co-localization. In some embodiments, quantification of expression, concentration, intensity, or co-localization is analyzed using multi-channel fluorescence imaging of a single EV. In some embodiments, the cancer comprises breast cancer, ovarian cancer, and non-small cell lung cancer. In some embodiments, the sample obtained from the subject suffering from cancer comprises tumor cells or plasma.

[0007] Also provided herein is a method for longitudinally monitoring drug resistance in a subject suffering from cancer, comprising the steps of: providing a sample from the subject, wherein the sample is obtained from the same subject at multiple time points during chemotherapy treatment; isolating tumor-derived EVs (tEVs) from the sample, wherein the tEVs are further dual-labeled with an EV tumor marker and a drug resistance biomarker; determining co-localization of the EV tumor marker and the drug resistance biomarker; and detecting a change in co-localization of the EV tumor marker and the drug resistance biomarker before and after chemotherapy treatment, thereby determining drug resistance in the subject based on the change in quantitative co-localization of the EV marker and the drug resistance biomarker before and after chemotherapy treatment.

[0008] Provided herein is a method for monitoring drug resistance over time in a subject suffering from cancer, comprising the steps of: isolating tumor extracellular vesicles (tEVs) in a first sample obtained from the subject at a first time point, wherein isolating the tEVs comprises applying the first sample to a functionalized substrate to capture the extracellular vesicles (EVs); labeling the EVs with an antibody or antigen-binding portion thereof that binds to a preselected EV tumor marker; labeling the EVs with an antibody or antigen-binding portion thereof that binds to a preselected drug resistance biomarker; determining the count of tEVs positive for a tumor marker and a chemotherapy resistance biomarker, or the level of a drug resistance biomarker, in the tEVs at the first time point; administering one or more doses of a chemotherapy drug; and isolating a second sample from the subject obtained at a second time point. isolating tEVs in the sample, where the isolating tEVs comprises applying the second sample to a functionalized substrate to capture EVs, labeling the EVs with an antibody or antigen-binding portion thereof that binds to a preselected EV tumor marker, and labeling the EVs with an antibody or antigen-binding portion thereof that binds to a preselected drug resistance biomarker; determining the count of tEVs positive for the tumor marker and chemotherapy resistance biomarker, or the level of the drug resistance biomarker, in the tEVs at the second time point; and administering one or more additional doses of a chemotherapy drug to the subject if the count of tEVs positive for the tumor marker and chemotherapy resistance biomarker, or the relative level of the drug resistance biomarker, has not increased from the first time point to the second time point.

[0009] In some embodiments, the method further comprises using a plasmon-enhanced EV detection method. In some embodiments, the tEVs are selected by a marker panel including EpCAM, EGFR, MUC1, and / or HER2. In some embodiments, the antibody or antigen-binding portion thereof that binds to the EV tumor marker further comprises a fluorescent dye. In some embodiments, the drug resistance biomarker comprises a protein or RNA. In some embodiments, the drug resistance biomarker is P-gp and survivin. In some embodiments, quantification of co-localization of the EV marker and the drug resistance biomarker is analyzed using multi-channel fluorescence imaging in a single EV. In some embodiments, the cancer comprises breast cancer, ovarian cancer, or non-small cell lung cancer. In some embodiments, the sample obtained from the subject suffering from cancer comprises plasma. In some embodiments, the method can identify drug resistance before the subject's tumor grows to an observable size. In some embodiments, the method further comprises recommending, prescribing, and / or administering a therapeutically effective amount of chemotherapy to the subject.

[0010] Also provided herein is a method for monitoring drug resistance over time in a subject afflicted with cancer, comprising the steps of: isolating tumor extracellular vesicles (tEVs) in a first sample obtained from the subject at a first time point, wherein isolating the tEVs comprises applying the sample to a surface comprising a capture antibody, or antigen-binding portion thereof, that binds to a preselected EV tumor marker, and labeling the captured tEVs with an antibody, or antigen-binding portion thereof, that binds to a preselected drug resistance biomarker; determining a count of tumor marker-positive and chemotherapy resistance biomarker-positive tEVs in the tEVs at the first time point, or a level of the drug resistance biomarker; administering one or more doses of a chemotherapy drug; and collecting a sample from the subject obtained at a second time point after the one or more doses of the chemotherapy drug. isolating tEVs in a second sample, where the isolating tEVs comprises applying the second sample to a surface comprising a capture antibody, or antigen-binding portion thereof, that binds to a preselected EV tumor marker and labeling the captured tEVs with an antibody, or antigen-binding portion thereof, that binds to a preselected drug resistance biomarker; determining the count of tEVs positive for the tumor marker and chemotherapy resistance biomarker, or the level of the drug resistance biomarker, in the tEVs at the second time point; and administering one or more additional doses of a chemotherapy agent to the subject if the count of tEVs positive for the tumor marker and chemotherapy resistance biomarker, or the relative level of the drug resistance biomarker, has not increased from the first time point to the second time point.

[0011] In some embodiments, the capture antibody or antigen-binding portion thereof that binds to an EV tumor marker comprises one or more antibodies or antigen-binding portions thereof that bind to EpCAM, EGFR, MUC1, and / or HER2. In some embodiments, the drug resistance biomarker comprises P-gp and / or survivin. In some embodiments, the cancer is breast cancer. In some embodiments, the chemotherapy is paclitaxel. In some embodiments, the first sample and / or the second sample comprises plasma. In some embodiments, the effectiveness of predicting chemotherapy resistance over time in subjects with cancer is at least 95%.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.Methods and materials for use in the present invention are described herein, but other suitable methods and materials known in the art can also be used.Materials, methods, and examples are merely illustrative and are not intended to be limiting.All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety.In the event of any discrepancy, the present specification, including definitions, shall prevail.

[0013] Other features and advantages of the invention will become apparent from the following detailed description and drawings, and from the claims. [Brief explanation of the drawings]

[0014] [Figure 1-1]Figures 1A-1G show the effects of paclitaxel in vitro via growth inhibition assays performed on various cell lines, including (Figure 1A) HCC1954, (Figure 1B) BT-474, (Figure 1C) MCF7, (Figure 1D) MDA-MB-231, (Figure 1E) HCC1937, and (Figure 1F) HCC1954-REPX cells. Symbols indicate the % cell viability compared to untreated control cells and are expressed as the mean (SEM) of three independent experiments repeated per drug concentration. In Figure 1G, the bar graph shows the estimated median lethal dose (LC50) of paclitaxel toxicity for the six cell lines tested, expressed as concentrations (µM). [Figure 1-2] Continued from Figure 1-1. [Figure 1-3] Continued from Figure 1-2. [Figure 2-1] Figures 2A-2G show nanoparticle tracking analysis (NTA) of EVs. Size redistribution was assessed for EVs secreted by the following cell lines: (Figure 2A) HS 371 T, (Figure 2B) HCC1954, (Figure 2C) BT-474, (Figure 2D) MCF7, (Figure 2E) MDA-MB-231, (Figure 2F) HCC1937, and (Figure 2G) HCC1954-REPX. The majority of the EV population falls within the size range of 85.7-179.1 nm, a size range commonly observed for small EVs. [Figure 2-2] Continued from Figure 2-1. [Figure 2-3] Continued from Figure 2-2. [Figure 2-4] Continued from Figure 2-3. [Figure 3-1]Figures 3A-3K show qPCR analysis of gene expression profiles. Molecular profiling of cell lines and their corresponding derived EVs was assessed for (Figure 3A) ABCB1; (Figure 3B, no EV data for ABCG2) ABCG2; (Figure 3C) TUBA1A; (Figure 3D) TUBB3; (Figure 3E) CIC-3; (Figure 3F) survivin; (Figure 3G) CCND1; (Figure 3H) PTGES3; (Figure 3I, no EV data for miR-421) miR-421; (Figure 3J, no EV data for miR-9) miR-9, and (Figure 3K) miR-21. RT-qPCR data are presented as the mean of the logarithm of 2-ΔΔCT values ​​for the relative expression of genes of interest (n = 11). Error bars represent the standard deviation between biological replicates. [Figure 3-2] Continued from Figure 3-1. [Figure 3-3] Continued from Figure 3-2. [Figure 3-4] Continued from Figure 3-3. [Figure 4] Figures 4A-4B show protein biomarkers in cell lines and cell line-derived EVs. Proteomic analysis was performed on the tested cell lines (Figure 4A) and their respective EVs (Figure 4B). As shown in Figure 4A, after fixation and permeabilization, cells were stained using primary antibodies for P-gp, survivin, cyclin D1, and TSG101 (used as an internal control) or isotype control. As shown in Figure 4B, cell line-derived EVs were captured on 5 µM beads and subsequently stained using the same antibodies used in Figure 4A. The heatmap shows the mean fluorescence intensity (MFI) of each sample normalized by the corresponding fluorescence signal of the isotype control antibody (MFITarget-MFIIisotype). [Figure 5-1]Figures 5A-5G show single-EV analysis of P-gp and survivin on an NPOP substrate. EVs were labeled in solution with a protein-reactive TFP dye (Alexa Fluor™ 555) and biomarker-specific fluorescent antibodies for P-gp and survivin or isotype controls. After TFP labeling, EVs were plated on a gold nanoplasmonic chip for plasmon-enhanced imaging by fluorescence microscopy. Figure 5A shows an overview of multichannel single-EV analysis. The NPOP substrate was functionalized with SH-PEG-COOH (1.0 kDa), and Alexa Fluor™ 555-labeled EVs were captured by EDC / NHS activation. Figures 5B-5D show P-gp-positive EVs identified by biomarker-specific antibodies measured in the Alexa Fluor™ 488 channel; P-gp (Figure 5B), IgG isotype control (Figure 5C), and the colocalization rate (%) of P-gp minus the IgG control (Figure 5D), demonstrating colocalization of biomarker-specific antibodies on the same EVs. Figures 5E-5G show survivin-positive EVs identified by biomarker-specific antibodies measured in the Alexa Fluor™ 647 channel; survivin (Figure 5E), IgG isotype control (Figure 5F), and the colocalization rate (%) of survivin minus the IgG control (Figure 5G), demonstrating colocalization of biomarker-specific antibodies on the same EVs. [Figure 5-2] Continued from Figure 5-1. [Figure 5-3] Continued from Figure 5-2. [Figure 6-1]Figures 6A-6C show multiplexed single tumor-derived EV (tEV) drug resistance marker detection in single tumor-derived EVs (tEVs) on an NPOP substrate. Figure 6A shows a representative image of selective capture of tEVs using QUAD markers (MUC1, HER2, EGFR, and EpCAM). In Figure 6B, the bar graph shows the number of positive tEVs identified in the Alexa Fluor™ 555-labeled QUAD marker antibody mix channel. In Figure 6C, the bar graph shows the number of positive tEVs identified in the colocalization of the Alexa Fluor™ 555-labeled QUAD marker antibody mix and the Alexa Fluor™ 647-labeled Pgp / Survivin antibody mix at two time points for each patient over a two-year period: pre-chemotherapy (T1) and pre-surgery (post-neoadjuvant chemotherapy, T2). [Figure 6-2] Continued from Figure 6-1. [Figure 6-3] Continued from Figure 6-2. [Figure 7-1] Figures 7A-7D show the analysis of changes in the number of tEVs and PgP / survivin-positive tEVs before and after paclitaxel treatment. EVs from two plasma samples obtained before and after paclitaxel neoadjuvant therapy were processed. Figure 7A shows the relative number of QUAD-positive EVs, and Figure 7B shows the percentage change in QUAD-positive EVs. Figure 7C shows the relative number of PgP / survivin-positive tEVs, and Figure 7D shows the percentage change in PgP / survivin-positive tEVs. Patients P2, P3, P5, P6, P7, P10, P11, P14, P16, P17, P18, and P20 had a pathological complete response to neoadjuvant therapy with paclitaxel, and patients P1, P4, P8, P9, P12, P13, P15, P19, P21, and P22 did not have a pathological complete response to neoadjuvant therapy with paclitaxel. [Figure 7-2] Continued from Figure 7-1. [Figure 8-1]Figures 8A-8F show an overview of upregulated genes in paclitaxel-resistant cancer cell lines (HCC1954 REPX, BT474 RETX) compared to wild-type cancer cell lines (HCC1954, BT474). Specifically, Figure 8A shows that MDR1 is upregulated in HCC1954 REPX and BT474 RETX cells compared to Hs371T, HCC1954, and BT474 cells. Figure 8B shows that survivin is upregulated in Hs371T, HCC1954, HCC1954 REPX, BT474, and BT474 REPX cells. Figure 8C shows that ABCG2 is upregulated in HCC1954 REPX and BT474 RETX cells compared to Hs371T, HCC1954, and BT474 cells. Figure 8D shows that miRNA9 is most highly upregulated in paclitaxel-resistant HCC1954 REPX. Figure 8E shows that miRNA421 is most highly upregulated in paclitaxel-resistant BT474 REPX. Figure 8F shows that miRNA21 is most highly upregulated in paclitaxel-resistant HCC1954 REPX and BT474 REPX cell lines. [Figure 8-2] Continued from Figure 8-1. [Figure 9] Figures 9A-9D show an overview of downregulated genes in paclitaxel-resistant cell lines (HCC1954 REPX, BT474 RETX) compared to wild-type (HCC1954, BT474). Specifically, Figure 9A shows downregulation of TUB1A1 across all cell lines tested. Figure 9B shows downregulation of TUBB across all cell lines tested. Figure 9C shows downregulation of PTGES3 across all cell lines tested. Figure 9D shows downregulation of cyclin D across all cell lines tested, with the greatest downregulation in Hs371T and HCC1954 REPX cell lines. [Figure 10-1]Figures 10A-10E show single-EV analysis using the NPOP substrate. The number of PgP- and survivin-positive EVs was significantly increased in paclitaxel-resistant cell lines. Figure 10A shows the number of EVs across the tested cell lines. Figure 10B shows the number of MDR1 EVs across the tested cell lines. Figure 10C shows the number of EVs for the IgG control. Figure 10D shows the colocalization of PgP-positive EVs with QUAD markers (MUC1, HER2, EGFR, and EpCAM) in the paclitaxel-resistant cell lines MDA-MB-231, HCC1937, and HCC1954 REPX. Figure 10E shows the colocalization of survivin-positive EVs with QUAD markers (MUC1, HER2, EGFR, and EpCAM) in the paclitaxel-resistant cell lines MDA-MB-231, HCC1937, and HCC1954 REPX. [Figure 10-2] Continued from Figure 10-1. DETAILED DESCRIPTION OF THE INVENTION

[0015] As shown herein, assessment of the molecular profile of specific biomarkers in EVs can be used as a non-invasive biosignature of pharmacological response and clinical outcome in patients suffering from cancer.

[0016] Provided herein are methods of predicting response to a chemotherapeutic drug (e.g., paclitaxel (px)) in a subject (e.g., a subject suffering from breast cancer) by determining the level of a drug resistance biomarker (e.g., p-glycoprotein (P-gP) and / or survivin) in a sample comprising tumor cells from the subject, optionally including a step of isolating EVs from the sample before determining the level. The P-gp and / or survivin protein or mRNA level of the sample can be compared to a reference level, and a level of the drug resistance biomarker (e.g., P-gp and / or survivin biomarker) below the reference level indicates that the tumor is sensitive to px. In some embodiments, the level is determined using a plasmon-enhanced single EV assay. 9In some embodiments, the method further comprises recommending, prescribing, and / or administering a therapeutically effective amount of px to the subject.

[0017] In some embodiments, the methods include assaying drug resistance biomarker (e.g., Pg-p / survivin) levels over time in a subject afflicted with cancer, wherein an increase in the drug resistance biomarker (e.g., P-gp and / or survivin biomarkers) indicates that the subject or tumor is developing resistance to a chemotherapeutic agent (e.g., px). In some embodiments, the methods further include treating with chemotherapy if the drug resistance biomarker (e.g., P-gp and / or survivin levels) increases above a threshold or increases relative to drug resistance biomarker levels from the subject at an earlier time point.

[0018] Methods for monitoring drug resistance using tEVs The methods of the present invention may include isolating a specific EV population (e.g., tumor-derived EVs) in a subject being treated for cancer, measuring tEV expression of a drug resistance biomarker over time, and administering further chemotherapy based on the relative levels of drug resistance biomarker-positive (e.g., P-gp and / or survivin-positive) tEVs over time.

[0019] The subject may be an individual (e.g., a mammal, such as a human) suffering from or suspected of suffering from cancer. In some embodiments, the subject may be undergoing chemotherapy and / or another type of cancer treatment (e.g., radiation therapy, surgery). A sample may be obtained from the subject. A sample may refer to any sample, including, but not limited to, cells, lysed cells, cell extracts, nuclear extracts, extracellular fluid, culture medium in which cells (e.g., cancer cells from a subject) were cultured, blood, plasma, serum, digestive secretions, tissue or tumor homogenates, synovial fluid, feces, saliva, sputum, cyst fluid, amniotic fluid, cerebrospinal fluid, peritoneal fluid, lung lavage fluid, semen, lymphatic fluid, tears, and prostatic fluid. In some embodiments, samples are obtained from the subject at multiple time points.

[0020] A sample from a subject can be enriched for EVs, for example, based on the presence of an EV tumor marker. In some embodiments, the method can include using an antibody or antigen-binding portion thereof that binds to a selected EV tumor marker corresponding to a particular type of cancer to identify or enrich tEVs for further analysis. For example, the antibody can be a capture antibody attached to a substrate (e.g., a plate, well, or beads). A sample from a subject, e.g., a sample containing a population of EVs (optionally EVs obtained from a biological fluid such as blood, serum, or plasma), can then be applied to the substrate, where the antibody or antigen-binding portion thereof that binds to the selected EV tumor marker captures and enriches the EV population for tEVs bearing the specified EV tumor marker. Drug resistance biomarkers can then be assessed in the tEVs, e.g., using an antibody that selectively binds to the resistance biomarker, and the level of the drug resistance biomarker in the sample and the subject can be determined.

[0021] In some embodiments, an antibody or antigen-binding portion thereof that binds to a selected EV tumor marker can be applied to a sample, where the sample has been previously enriched for EVs. One method of enriching a sample for EVs can include subjecting the sample to a plasmon-enhanced EV assay. For example, the sample can be applied to a 3D plasmonic nanostructure composed of spherical Au nanoparticles on a 3D Au nanopillar (NPOP) substrate, where EVs are captured by the NPOP substrate. See Park, et al. Self-assembly of nanoparticle-spiked pillar arrays for plasmonic biosensing, Adv. Funct. Mater., 1904257 (2019). In some embodiments, an antibody or antigen-binding portion thereof that binds to a selected EV tumor marker can be applied to an EV sample as a free antibody, where the antibody or antigen-binding portion thereof that binds to the selected EV tumor marker can be labeled (e.g., fluorescently labeled) or detected with a secondary antibody. In some embodiments, an antibody or antigen-binding portion thereof that binds to a selected EV tumor marker may be applied before, after, or simultaneously with an antibody or antigen-binding portion thereof that binds to a selected drug resistance biomarker.

[0022] In some embodiments, EVs from a sample can be enriched using plasmon-enhanced EV capture methods. In some embodiments, plasmon-enhanced EV capture methods include EV capture using any substrate, such as a planar substrate, nanostructures, beads, or other materials. In some embodiments, plasmon-enhanced EV capture methods include EV capture using an NPOP substrate, which may be constructed and / or functionalized according to the methods described in the Examples. EVs enriched by isolation on an NPOP substrate can be probed for the expression of EV tumor markers and / or drug resistance biomarkers. An antibody against an EV tumor marker can be applied to the EV-enriched sample, and the antibody against the EV tumor marker can be labeled (e.g., fluorescently labeled) or a secondary antibody can be used to detect the antibody against the EV tumor marker. An antibody against a drug resistance biomarker can be applied to the EV-enriched sample, and the antibody against the drug resistance biomarker can be labeled (e.g., fluorescently labeled) or a secondary antibody can be used to detect the antibody against the drug resistance biomarker. In some embodiments, an antibody against an EV tumor marker and an antibody against a drug resistance biomarker can be applied simultaneously to the sample and / or the EV-enriched sample. In some embodiments, the antibody against an EV tumor marker is applied to the sample and / or EV-enriched sample before the antibody against a drug resistance biomarker is applied to the sample and / or EV-enriched sample. In some embodiments, the antibody against an EV tumor marker is applied to the sample and / or EV-enriched sample after the antibody against a drug resistance biomarker is applied to the sample and / or EV-enriched sample.

[0023] A sample from a subject can be enriched for EVs using a capture antibody for a selected EV tumor marker, for example, as described herein, where the capture antibody for the selected EV tumor marker is embedded in or attached to a substrate. In some embodiments, to enrich a sample for EVs (e.g., tEVs), the sample is applied to a substrate containing the EV tumor marker capture antibody, creating an EV sample enriched in tumor-derived EVs bearing the selected tumor marker. After tEVs are captured by the capture antibody, a drug resistance biomarker can be detected using an antibody that can be applied to the tEV-enriched sample to determine, for example, the level or relative levels of drug resistance biomarker-positive tEVs. In some embodiments, the EV enrichment described herein can be combined to include other EV enrichment methods known in the art.

[0024] In some embodiments, enriching EVs (e.g., tEVs) and probing the tEVs for levels of a drug resistance biomarker can be performed at multiple time points (e.g., over time or longitudinally). For example, enriching EVs (e.g., tEVs) and probing the tEVs for levels of a drug resistance biomarker can be performed at one, two, three, four, five, or more time points. In some embodiments, the level of a drug resistance biomarker at a first time point (as determined by antibody detection) can be used to determine the relative level of the drug resistance biomarker at a second time point by comparing the amount of signal of the drug resistance biomarker at the second time point to the signal of the drug resistance biomarker at the first time point and noting an increase or decrease in the signal of the drug resistance biomarker. Similarly, the amount of signal of the drug resistance biomarker at the third (or fourth, fifth, etc.) time point can be compared to the amount of signal of the drug resistance biomarker at the first time point, or the amount of signal of the drug resistance biomarker at the third (or fourth, fifth, etc.) time point can be compared to the amount of signal of the drug resistance biomarker at any previous time point to analyze whether there is a trend in the signal of the drug resistance biomarker over time.

[0025] In some embodiments, a relative increase in the level of drug resistance biomarker-positive (e.g., P-gp and / or survivin-positive) tEVs relative to a previous level of drug resistance biomarker-positive (e.g., P-gp and / or survivin-positive) tEVs indicates that cancer cells in the subject are in the process of becoming resistant to or have become resistant to a chemotherapeutic agent, e.g., a chemotherapy used to treat the subject's cancer. In some embodiments, a relative decrease or no significant change in the level of drug resistance biomarker-positive (e.g., P-gp and / or survivin-positive) tEVs relative to a previous level of drug resistance biomarker-positive (e.g., P-gp and / or survivin-positive) tEVs indicates that cancer cells in the subject have not become resistant to a chemotherapeutic agent (e.g., a chemotherapy used to treat the subject's cancer).

[0026] Thus, the relative levels of drug resistance biomarkers and biomarker-positive tEVs can be used to determine whether the subject will receive additional chemotherapy treatments containing the same chemotherapy agent used to treat the subject's cancer between the previously assessed time points, or whether the subject will receive a different treatment using a different chemotherapy agent (or other treatment modality such as immunotherapy, radiation therapy, or surgical resection).

[0027] In some embodiments, a relative increase in drug resistance biomarker or biomarker-positive (e.g., P-gp and / or survivin-positive) tEVs relative to the level of a previous drug resistance biomarker-positive (e.g., P-gp and / or survivin-positive) tEV indicates whether the subject should continue treatment with the same chemotherapy drug. In some embodiments, a relative increase in drug resistance biomarker-positive tEVs relative to the level of a previous drug resistance biomarker-positive tEV indicates that the subject should not receive treatment with the same chemotherapy drug (e.g., further treatment with the same chemotherapy drug). In some embodiments, a relative increase in drug resistance biomarker-positive tEVs relative to the level of a previous drug resistance biomarker-positive EV indicates that the subject should not receive treatment with the same chemotherapy drug previously used in the subject (e.g., the subject should be treated with a chemotherapy drug not used or not previously used in the subject). In some embodiments, the subject receives further chemotherapy treatment with the same drug only if the drug resistance biomarker-positive tEVs are decreased or unchanged relative to the previous (or any previous) level of drug resistance biomarker-positive tEVs. In some embodiments, the subject receives further chemotherapy treatment with the same drug only if the drug resistance biomarker-positive tEVs are not increased relative to the previous (or any previous) level of drug resistance biomarker-positive tEVs. In such conditions, the administration of further treatment with the same chemotherapy drug depends on the relative level of drug resistance biomarker-positive tEVs relative to the previous (or any previous) level of drug resistance biomarker-positive tEVs.

[0028] The types of chemotherapeutic agents that may be administered to a subject include, but are not limited to, taxanes, including paclitaxel and docetaxel, microtubule-targeting agents such as maytansine and eribulin, and vinca alkaloids (e.g., vindesine, vinblastine, vinorelbine, vincristine); nitrosoureas (e.g., fotemustine, carmustine (BCNU), or lomustine (CCNU)); anthracyclines (e.g., doxorubicin, aldoxorubicin, epinephrine, erythroxine, riboflavin ... rubicin, and pegylated liposomal doxorubicin); alkylating agents (e.g., melphalan, dacarbazine (DTIC), temozolomide (TMZ), and oxazaphosphorines such as ifosfamide, cyclophosphamide, trofosfamide, evofosfamide, and palifosfamide); trabectedin; platinum-containing agents such as cisplatin or carboplatin; cytotoxic nucleoside analogs such as gemcitabine; methotrexate; etoposide;Pemetrexed, as well as other small molecule chemotherapeutic agents, such as crizotinib, ceritinib, alectinib, brigatinib, loratinib, capmatinib, tepotinib, gefitinib, erlontinib, lapatinib, icotinib, afatinib, osimertinib, neratinib, dacomitinib, almonertinib, tucatinib, medstarin, gilteritinib, and quizartinib , Pexidartinib, Sorafenib, Sunitinib, Pazopanib, Vandetanib, Axitinib, Cabozantinib, Regorafenib, Apatinib, Lenvatinib, Tivozanib, Fruquintinib, Nintedanib, Anlotinib, Erdafinib, Pemigatinib, Avaprinib, Ripretinib, Selpercatinib, Pralusetnib, Larotrectinib, Entreknib, I Matinib, dasatinib, nilotinib, bosutinib, radotinib, ponatinib, ibrutinib, acalabrutinib, zanubrutinib, ruxolitinib, fedratinib, vemurafenib, dabrafenib, encorafenib, trametinib, cobimetinib, binimetinib, selumetinib, palbociclib, ribociclib, abemaciclib, idelarib, copanlisib, dextromethorphan, dabrafenib ... These include buvelisib, alpelisib, temsirolimus, ebromus, sirolimus, tazemetostat, vorinostat, romidepsin, belinostat, tucidinostat, panobinostat, enasidenib, ivosidenib, venetoclax, vismodegib, sonidegib, glasdegib, bortezomib, carfilzomib, and ixazomib, e.g., Zhong et al., Signal Transduct Target Ther. 2021; 6: 201; Tian and Yao, Front Pharmacol. 2023;14: 1199292. In some embodiments, the drug resistance biomarker detects drug resistance in a subject to one or more of the chemotherapeutic agents listed above. In some embodiments, the subject is taking, or may have taken, one of the above-mentioned chemotherapeutic agents before, during, or after monitoring drug resistance using the tEV and drug resistance biomarkers described herein. In some embodiments, monitoring drug resistance in a subject includes monitoring drug resistance to one of the above-mentioned chemotherapeutic agents.

[0029] As used herein, the terms "cancer," "tumor," or "tumor tissue" have the meanings understood by those skilled in the art. Cancer, tumor, or tumor tissue can include tumor cells, which are neoplastic cells with abnormal growth characteristics. Tumors, tumor tissue, and tumor cells can be benign or malignant. Cancer includes primary malignant cells or tumors (e.g., cells that have not migrated to sites within a subject's body other than the site of the original malignant tumor or tumor) and secondary malignant cells or tumors (e.g., metastasis, resulting from the migration of malignant cells or tumor cells to a secondary site different from the site of the original tumor).

[0030] Examples of cancer include, but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, leukemia, or lymphoid malignancies, etc. Further examples of such cancers are described below and include squamous cell carcinoma (e.g., epithelial squamous cell carcinoma), lung cancer including small cell lung cancer, non-small cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma, peritoneal cancer, hepatocellular carcinoma, gastric cancer including gastrointestinal cancer, pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bile duct cancer, bladder cancer, hepatocellular carcinoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial or uterine cancer, salivary gland cancer, kidney or renal cancer, prostate cancer, vulvar cancer, thyroid cancer, liver cancer, anal cancer, penile cancer, and head and neck cancer.

[0031] One advantage of long-term monitoring of drug resistance using the currently described methods is that it can detect drug resistance in a subject before the subject's cancer (e.g., tumor) grows observably in size. By detecting drug resistance early, long-term monitoring of drug resistance using the currently described methods can allow for early termination of toxic treatments to reduce side effects or minimize unnecessary treatments. The efficacy of long-term monitoring of drug resistance using the currently described methods for predicting drug resistance can be at least 95%. Other advantages of long-term monitoring of drug resistance using the currently described methods include predicting the effectiveness of drug treatment, minimizing the detection of residual disease, and facilitating early detection of disease recurrence.

[0032] Extracellular vesicles (EVs) Extracellular vesicles (EVs) are lipid-based microparticles, nanoparticles, or protein-rich aggregates present in samples (e.g., biological fluids) obtained from a subject. Extracellular vesicles are also referred to in the art and herein as exosomes, microvesicles, or nanovesicles. Extracellular vesicles may include membrane vesicles secreted from the cell surface (ectosomes), membrane vesicles secreted from internal reservoirs (exosomes), membrane vesicles secreted from cancer cells (oncosomes), or membrane vesicles released as a result of apoptosis and cell death. In addition to the lipid membrane, depending on the cell or tissue of origin, EVs may contain additional components, such as lipoproteins, proteins, nucleic acids, phospholipids, amphipathic lipids, gangliosides, and other particles contained within the lipid membrane or encapsulated by the EV.

[0033] All cells can release, secrete, or excrete EVs, making them useful targets for clinical diagnosis and treatment of various diseases. Non-limiting examples of normal or cancerous cell types that can release EVs include liver cells (e.g., hepatocytes), lung cells, spleen cells, pancreatic cells, colon cells, skin cells, bladder cells, eye cells, brain cells, esophageal cells, head cells, cervical cells, ovarian cells, testicular cells, prostate cells, placental cells, epithelial cells, endothelial cells, adipocytes, kidney cells, cardiac cells, muscle cells, blood cells (e.g., leukocytes, platelets), and combinations thereof. Because EVs are involved in intercellular communication, their characterization elucidates their role in normal physiology and pathology. EVs in biological fluids, including saliva, urine, plasma, and serum, can be investigated as biomarkers for any of the various cancers described herein. EVs enriched or isolated for specific tumor markers are referred to as tumor-derived EVs (tEVs).

[0034] In some embodiments, extracellular vesicles have diameters of approximately 20 nm to approximately 200 nm. Individual EVs have a surface area approximately 1 / 10,000th that of a whole cell and a volume approximately 1 / 1,000,000th that of a whole cell, making them difficult to detect using single-cell analysis tools, including conventional flow cytometry. As a result, most proteomic and genomic analyses are performed on thousands to millions of EVs in bulk. However, EVs in biofluids originate from various cell types and originate from various intracellular locations, such as exosomes secreted from intracellular multivesicular bodies, ectosomes / microvesicles shed from the cell membrane surface, and membrane fragments released as a result of cell apoptosis and necrosis. Therefore, in bulk analyses, the signature of tumor EVs may be lost in the background of vesicles from other sources. Therefore, methods to enrich for tEVs can help capture a more robust tEV picture.

[0035] EVs have shown emerging potential as circulating cancer biomarkers. These cell-derived membrane-bound vesicles contain protein and nucleic acid cargo and provide a representative "snapshot" of secretory cell contents. The abundant and ubiquitous presence of tumor-derived EVs (tEVs) in bodily fluids (e.g., blood, urine) indicates their potential as readily accessible biomarkers. Specifically, tumor-derived EV (tEV) analysis is minimally invasive for repeated sampling, provides a relatively unbiased readout of the entire tumor, and is less affected by sample scarcity or intratumoral heterogeneity. This suggests that tEVs may be particularly useful for long-term disease monitoring and early detection of recurrence. Previous studies have shown that both the quantity and molecular profile of tEVs correlate with tumor burden and treatment response. Therefore, EVs may serve as novel biomarkers for liquid biopsies in personalized medicine. However, EVs are relatively new targets for clinical analytical assays and possess unique physical and biological properties. They are much smaller than cells but larger than proteins and exist in a highly heterogeneous biological background. These characteristics pose technical challenges and often lead to variable findings. Furthermore, identifying cell-specific (e.g., tumor-originating) EVs and investigating drug resistance markers within subpopulations ideally requires multiplexed analysis at single EV resolution.

[0036] Molecular characterization of single EVs is technically challenging. Most EVs are small vesicles (<200 nm) with limited epitope numbers and surface area for labeling (i.e., weak detectable signals). Therefore, sophisticated multistep signal amplification measures, such as DNA barcoding or enzymatic signal amplification (digital ELISA), are often required. Flow cytometry often underestimates EV counts because many small vesicles (<200 nm) may be missed due to weak light scattering, or swarms of vesicles may be counted as single events. More importantly, many of these methods are suboptimal for detecting and quantifying excessive background EVs and very rare tEVs within particles. Multiplexed analysis of EVs is particularly important for identifying the origin of individual EVs (e.g., tEVs) and molecularly profiling biomarkers associated with targeted therapies and mutations.

[0037] Thus, provided herein are methods for isolating and concentrating tumor EV particles to monitor and / or assess whether tumor cells in a subject have become resistant to chemotherapeutic drugs over time.

[0038] EV tumor marker Extracellular vesicles of tumor origin can carry tumor markers. EV tumor marker profiles can indicate the origin of cancer or the type of cancer cells identified in a subject. For example, MUC1, HER2, EGFR, and EpCAM are four markers that can be used to identify breast cancer cells in a subject. Many EVs secreted from these breast cancer cells also contain these four tumor markers. Therefore, monitoring EVs, such as tumor EVs (tEVs), containing one or more of MUC1, HER2, EGFR, and EpCAM in a subject can provide information about the subject's breast cancer, including the development of drug resistance. Therefore, the present inventors provide a method for determining whether a subject's cancer is becoming or has become resistant to chemotherapy by monitoring EVs containing one or more of MUC1, HER2, EGFR, and EpCAM in a subject. In some embodiments, monitoring tEVs expressing all four markers, MUC1, HER2, EGFR, and EpCAM, can be used to determine whether a subject's cancer is becoming or has become resistant to chemotherapy.

[0039] Other EV tumor markers include EpCAM, miRNA-21, and CD24 (e.g., as tumor markers for ovarian cancer); EpCAM, EGFR, MUC1, WNT2, and GPC1 (e.g., as tumor markers for pancreatic ductal adenocarcinoma (PDAC)); EGFR and EGFRvIII (e.g., as tumor markers for glioblastoma (GBM)); and EpCAM, EGFR, and MUC1 (e.g., as tumor markers for cholangiocarcinoma). See, for example, Im et al., Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor, Nat. Biotech. 2014; Yang, et al., Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy. Sci. Transl. Med. 9, eaal3226 (2017); Min et al., Plasmon-enhanced biosensing for multiplexed profiling of extracellular vesicles, Adv. Bio., See, e.g., Jeong, et al., Plasmon-enhanced single extracellular vesicle analysis for cholangiocarcinoma diagnosis, Adv. Sci., 2205148 (2023). Hong, et al., CRISPR / Cas13a-Based MicroRNA Detection in Tumor-Derived Extracellular Vesicles, Adv. Sci., 2023. Any of these tumor markers can be used as EV tumor markers in the methods described herein. Other EV tumor markers, including miRNAs and other non-coding RNAs, can be identified in the art and can be readily understood by those skilled in the art.See, for example, Huang, et al., Non-coding RNA derived from extracellular vesicles in cancer immune escape: Biological functions and potential clinical applications, Cancer Lett., 2021.

[0040] Drug resistance biomarkers Some cancer cells become drug resistant over time.When the cancer cells in the subject become drug resistant to the chemotherapeutic drug that is used to treat the cancer of the subject, then cancer will progress, and the subject may experience the worsening of cancer and / or the worsening of cancer symptoms.Therefore, it is important to understand whether the subject is becoming resistant to any chemotherapeutic drug that they are taking.

[0041] Drug resistance biomarkers exist, the detection of which indicates that the cells from which the sample was taken have become resistant to one or more drugs used to treat cancer. One example of a drug resistance biomarker is P-glycoprotein (P-gp). As shown herein, increased transcription and / or translation of P-gp has been observed in drug-resistant cancers. Therefore, P-gp is a general drug resistance biomarker because its overexpression is common in many drug-resistant cancers. Similarly, survivin is a member of the inhibitor of apoptosis (IAP) family. While survivin overexpression is common in most tumor cell types, it is typically absent in normal, non-malignant adult cells. Because survivin overexpression contributes to their resistance to apoptotic stimuli, survivin is a general drug resistance biomarker for many drug-resistant cancers.

[0042] Provided herein are methods comprising using P-gp and / or survivin as drug resistance biomarkers. The methods described herein comprise using P-gp and survivin as drug resistance biomarkers. The methods may comprise detecting and optionally quantifying the drug resistance biomarker using an antibody or antigen-binding portion thereof that binds to the drug resistance biomarker. In some embodiments, the antibody or antigen-binding portion thereof that binds to the drug resistance biomarker is detectably labeled, for example, fluorescently labeled. In some embodiments, the antibody or antigen-binding portion thereof that binds to the drug resistance biomarker is labeled with a secondary antibody. [Example]

[0043] The materials and methods described herein were used to generate the examples described herein.

[0044] material and method The following materials and methods were used in the examples below.

[0045] Drugs Paclitaxel (Selleckchem, USA) was dissolved in dimethyl sulfoxide (DMSO, AppliChem, Barcelona, ​​Spain) and stored at −80°C according to the manufacturer's instructions. Immediately before use, aliquots were diluted to the required concentration.

[0046] cell culture Human breast cancer cell lines, including HCC1954, BT474, MCF7, MDA-MB-231, and HCC1937, and normal breast cells, Hs371T, were purchased from the American Type Culture Collection (ATCC) and cultured at 37°C in 5% CO2. HCC1954, BT474, and HCC1937 cells were grown in RPM1-1640 (Hyclone), while MCF7, MDA-MB-231, and Hs371T cells were cultured in DMEM (Cyclone). All complete media contained 10% fetal bovine serum (FBS, ThermoFisher Scientific), 100 U / mL penicillin, and 100 μg / mL streptomycin (Millipore Sigma).

[0047] Establishment of paclitaxel-resistant subtypes from parental (control) cell lines To obtain paclitaxel-resistant subtypes, cells were continuously exposed to increasing concentrations of paclitaxel for 16–18 weeks. Briefly, HCC1954 cells were cultured at approximately 5 × 10 cells per T75 cell culture flask in 10 mL of complete growth medium. 5 The cells were seeded at a density of 1000 / mL. After 4–6 hours of incubation, relatively low concentrations of paclitaxel (ranging from 1 nM to 150 nM) were added to the medium. The cells were left in paclitaxel for 3 days or until stable cell regrowth was established. Periodic medium replenishment was performed during this period. The paclitaxel concentration was then increased 0.5- to 2-fold. This stepwise dose escalation was continued for 16–18 weeks until the paclitaxel concentration reached at least 10-fold the starting concentration. The HCC1954 paclitaxel-resistant cell line (HCC1954 REPX) was then maintained in the same medium as the parental cell line.

[0048] Drug sensitivity Drug susceptibility was determined by a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) colorimetric assay. Cells were first counted using a hemocytometer, seeded into 96-well plates, and cultured for 24 hours. Cells were then treated with paclitaxel for 72 hours: paclitaxel (1.00E-06-10 μM). Subsequently, MTT dye (Sigma-Aldrich; Merck KGaA) was added at a final concentration of 0.5 mg / ml for 3 hours, which was then replaced with medium, and formazan crystals were dissolved with DMSO. Optical density (OD; absorbance at 540 nm) was measured using a plate reader (Tecan). Data (mean ± SD of at least three independent experiments performed in triplicate) are presented as relative growth as a function of time after seeding.

[0049] EV isolation and characterization EVs were isolated as previously described [Min et al., 2020]. Briefly, cells were grown to 80–90% confluence in complete medium and washed twice with PBS to remove floating cells and FBS-derived vesicles. Cells were then incubated for 48 hours in conditioned medium supplemented with 1% exosome-depleted FBS, 100 U / mL penicillin, and 100 μg / mL streptomycin. This conditioned medium was filtered to remove cells and apoptotic bodies, and then concentrated using a centrifugal filter unit (Centricon Plus-70 centrifugal filter (MWCO = 10 kDa, Millipore Sigma)). The concentrate was then processed using size-exclusion chromatography (SEC) as previously described [Jeong et al., 2023].

[0050] A similar procedure was applied to plasma samples, first by centrifugation to remove cellular debris. A modified SEC column known as dual-mode chromatography (DMC) was used to isolate EVs from plasma samples. Plasma samples were centrifuged at 2,000 × g for 3 minutes to remove cellular debris, and the supernatant was collected. 500 μL of the supernatant diluted with 500 μL of PBS was used for separation by differential size-exclusion chromatography (EDMC) as previously reported [Woo et al., 2022].

[0051] Finally, the EV fraction was concentrated using an Amicon Ultra-2 centrifugal filter (MWCO = 10 kDa, Millipore Sigma) at 3,500 × g for 30 min at 4 °C and stored at −80 °C until use.

[0052] A NanoSight LM10 (Malvern) equipped with a 405 nm laser was used. Samples were diluted with fPBS to obtain the recommended particle concentration (25–100 particles / frame). Three 30-second videos were recorded for each test sample (camera level 14). The recorded videos were analyzed using NTA software (version 3.2) with a detection threshold of 3.

[0053] Quantitative real-time PCR Total RNA was extracted using TRIzol™ LS Reagent (Thermo Fisher Scientific) according to the manufacturer's instructions. RNA concentration was measured using a NanoDrop spectrophotometer (NanoDrop Technologies), and the dissolved RNA was stored at -20°C before use. cDNA synthesis was performed using the iSuperScript™ III First-Strand Synthesis SuperMix Kit (Thermo Fisher Scientific) according to the supplier's instructions. For real-time PCR, cDNA was amplified using the iSYBR™ Green PCR Master Mix Kit (Thermo Fisher Scientific).

[0054] Quantitative PCR reactions were performed in triplicate for each sample primer set, and the average of the three experiments was used as the relative quantification value. To accurately determine the starting copy number regardless of the exact quantity and quality of the input DNA, we also quantified an internal control gene (GAPDH) in each signal reaction. The primers were as follows:

[0055] TUBA1A / α-tubulin forward: 5′-CGGGCAGTGTTTGTAGACTTGG-3′ (SEQ ID NO: 1) and reverse: 5′-CTCCTTGCCAATGGTGTAGTGC-3′ (SEQ ID NO: 2);

[0056] TUBB3 / βIII-tubulin forward: 5′-GCGAGATGTACGAAGACGAC-3′ (SEQ ID NO: 3) and reverse: 5′-TTTAGACACTGCTGGCTTCG-3′ (SEQ ID NO: 4);

[0057] CIC-3 forward: 5'-CCTCTTTCCAAAGTATAGCAC-3' (SEQ ID NO: 5) and reverse: 5'-TTACTGGCATTCATGTCATTTC-3' (SEQ ID NO: 6);

[0058] ABCB1 / P-gp forward: 5′-TGCTCAGACAGGATGTGAGTTG-3′ (SEQ ID NO: 7) and reverse: 5′-AATTACAGCAAGCCTGGAACC-3′ (SEQ ID NO: 8);

[0059] ABCG2 / BCRP forward: 5′-TATAGCTCAGATCATTGTCACAGTC-3′ (SEQ ID NO: 9) and reverse: 5′-GTTGGTCGTCAGGAAGAAGAG-3′ (SEQ ID NO: 10);

[0060] survivin forward: 5'-ACCGCATCTCTACATTCAAG-3' (SEQ ID NO: 11) and reverse: 5'-CAAGTCTGGCTCGTTCTC-3' (SEQ ID NO: 12);

[0061] PTGES3 forward: 5'-CAAATGATTCCAAGCATAAAAGAAC-3' (SEQ ID NO: 13) and reverse: 5'-GGTAAATCTACATCCTCATCACCAC-3' (SEQ ID NO: 14);

[0062] CCND1 / cyclin D1 forward: 5'-GGATGCTGGAGGTCTGCGA-3' (SEQ ID NO: 15) and reverse: 5'-AGAGGCCACGAACATGCAAG-3' (SEQ ID NO: 16); and

[0063] GAPDH: forward: 5'-ACAGTCCAGCCGCATCTTC-3' (SEQ ID NO: 17) and reverse: 5'-GCCCAATACGACCAAATCC-3' (SEQ ID NO: 18).

[0064] To isolate small RNAs from cells and their derived EVs, the mirVana miRNA Isolation Kit (Thermo Fisher Scientific) and miRNeasy Serum / Plasma Kit (Qiagen) were used according to the manufacturer's isolation procedures. miRNA concentrations were quantified using the Qubit® microRNA Assay Kit (Thermo Fisher Scientific). RT-qPCR was performed using TaqMan MicroRNA Primers and Kits (Thermo Fisher Scientific) and the CFX Opus Dx Real-Time CR Detection System (Bio-Rad). The manufacturers have extensively tested and established the specificity of the U6 small RNA and miRNA primers. The reagent catalog numbers are as follows: hsa-miR-9 / ID000583: #4427975; hsa-miR-421 / ID002700: #4427975; hsa-miR-21 / ID000397: #4427975; and U6snRNA / ID001973: #7840156. Briefly, 10 ng of input RNA was used for each reverse transcription reaction, and reactions were set up according to the manufacturer's specifications. A master mix was prepared per reaction with 0.15 μl of 100 mM deoxynucleoside triphosphates (dNTPs), 1 μl of MultiScribe reverse transcriptase (50 U / μl), 1.5 μl of 10x reverse transcription buffer, 0.19 μl of RNase inhibitor (20 U / μl), and 4.1 μl of nuclease-free water. Seven microliters of the master mix was then combined with 3 μl of 5× reverse transcription TaqMan assay primer and 5 μl of RNA (10 ng total input). Thermal cycling conditions for reverse transcription were as follows: 16°C for 30 minutes, 42°C for 30 minutes, 85°C for 5 minutes, and a 4°C hold. Subsequent qPCR was performed using 1.33 μl of cDNA and TaqMan Fast Advanced Master Mix according to the manufacturer's specifications.Briefly, each qPCR reaction consisted of 10 μl of 2× TaqMan Fast Advanced Master Mix, 1 μl of 20× qPCR TaqMan assay primer, 7.67 μl of nuclease-free water, and 1.33 μl of cDNA. qPCR thermal cycling was performed as follows: 50°C for 2 min, 95°C for 20 s, and 40 cycles of 95°C for 1 s and 60°C for 20 s.

[0065] Relative gene expression levels between experimental groups were determined using the comparative Ct (2-ΔΔCt) method after normalization to a reference gene [Livak and Schmittgen, 2001].

[0066] EV capture on beads 50 μl of each fraction isolated by SEC (diluted or undiluted) or ultracentrifugation was incubated with 0.25 μl of aldehyde / sulfate-latex beads (φ = 4 μm; 5.5 × 106 particles / ml; Invitrogen) for 15 min at RT. EV samples were diluted with the same buffer (PBS) used for their elution. The bead concentration was chosen to be large enough to be easily detected by a flow cytometer with a sufficient number of events (≥ 5000) for adequate statistics, yet small enough to allow the use of small sample volumes.

[0067] Flow cytometry staining and analysis EVs and cells were stained using the same antibody and procedure. Cells were prepared for flow staining by fixing 500,000 cells per antibody condition with Fix and Permeabilization 4% paraformaldehyde in PBS and 1X Perm / Wash (ThermoFisher) and permeabilizing for 15 minutes at room temperature on a nutating mixer. Cells were washed in PBS / 1% BSA by centrifugation at 400 x g for 3 minutes (cells) or 1,000 x g for 1 minute (EVs on beads). Samples were resuspended in 100 μl of PBS / 1% BSA or in primary antibody diluted in the same buffer / volume and incubated for 30 minutes on a plate shaker set at medium speed (see Table 1 for a list of antibodies). Cells or EVs were then resuspended in 100 μl of the appropriate secondary antibody diluted 1:1,000 in PBS / 1% BSA and incubated for 30 minutes (protected from light) on a plate shaker. Samples were pelleted again, washed twice with 150 μl PBS / 1% BSA, and resuspended in 200 μl PBS / 1% BSA for flow analysis. Samples were analyzed using a BD LSR II flow cytometer (BD Biosciences). Pre-gating was performed to ensure single cells or beads were analyzed, and a total of 10,000 events were collected within the gated area. Samples were analyzed using FlowJo (v10) by measuring the median fluorescence intensity of the protein of interest and its corresponding isotype control. For comparison, the fold change in median fluorescence intensity was calculated by dividing the signal for the protein of interest by that of the isotype control.

[0068] [Table 1]

[0069] Fluorescent labeling of EVs EVs were labeled with the AF555 dye as previously described with minor modifications [Ferguson et al., 2022; Spitzberg et al., 2023]. Briefly, 3 μL of 300 ng of EVs in PBS was mixed with 2 μL of 100 mM sodium bicarbonate (Millipore Sigma) and 0.2 μL of TFP-AF555 dye [0.22 μM Azido-dPEG® 12 An equal mixture of 0.2 μM AFDye 555 DBCO (Click Chemistry Tools) and TFP ester (Quanta Biodesign) was mixed in the dark for 1 h at RT. The labeled EVs were diluted to the appropriate concentration in PBS before loading onto the substrate.

[0070] Functionalization and immunofluorescence staining We fabricated 3D plasmonic nanostructures composed of spherical Au nanoparticles on 3D Au nanopillar (NPOP) substrates. See Park, et al. Self-assembly of nanoparticle-spiked pillar arrays for plasmonic biosensing, Adv. Funct. Mater., 1904257 (2019). For analysis of cell line-derived EVs, all EVs were attached to the surface. The NPOP surface was functionalized with MUA, SH-PEG-COOH (0.4 kDa), and SH-PEG-COOH (1.0 kDa), respectively. Briefly, 10 mM 11-mercaptoundecanoic acid (Millipore Sigma) and 1-octanethiol (Millipore Sigma) were mixed in absolute ethanol and applied to the NPOP substrate for 2 hours, followed by sequential washing with absolute ethanol and water. Furthermore, for SH-PEG-COOH (0.4 kDa and 1.0 kDa) treatment, 0.25 mM of each SH-PEG-COOH (Nanocs) prepared in water was applied to the NPOP substrate for 4 hours and then washed with water. Subsequently, the NPOP substrate was treated with a mixture of 50 mM 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC, ThermoFisher Scientific) and 125 mM sulfo-N-hydroxysulfosuccinimide (NHS, ThermoFisher Scientific) in 0.1 M MES (pH 6.0) for 7 minutes. After activation, TFP-AF555-labeled EVs were loaded onto the NPOP substrate and incubated for 30 minutes. EVs were then fixed and permeabilized using 4% paraformaldehyde (Electron Microscopy Sciences) in PBS and 1X Perm / Wash buffer (BD Bioscience) for 10 minutes, followed by blocking with BSA (ThermoFisher Scientific) in PBS for 30 minutes. Subsequent fluorescent labeling of EVs was achieved by sequential incubation with primary and secondary antibodies, as shown in Table 2.

[0071] For plasma-derived EVs, the NPOP substrate was precoated with QUAD markers (MUC1, HER2, EGFR, and EpCAM) to selectively capture tEVs. The chip was incubated in 100 mM sodium citrate solution at room temperature for 1 hour. After this, the chip was washed with distilled water and dried with nitrogen gas. The capture antibody (QUAD) was then mixed in 1% goat serum / PSB 1x buffer and added to the slide for 1 hour of incubation at room temperature. After 3–4 washes with PBS, the QUAD antibody was blocked with 10% goat serum for 20 minutes at room temperature. TFP-AF555-labeled EVs were then loaded onto the NPOP substrate and incubated for 1 hour. EVs were fixed and permeabilized for 10 minutes using PBS containing 4% paraformaldehyde (Electron Microscopy Sciences) and 1x Perm / Wash buffer (BD Biosciences), followed by 3–4 washes with PBS. Finally, EVs were fluorescently labeled by sequential incubation with primary and secondary antibodies as shown in Table 2.

[0072] After immunolabeling, substrates were washed with PBS and images were captured using a Zeiss upright automated epifluorescence microscope equipped with a 40× (NA = 0.95) objective and an sCMOS camera (Hamamatsu Photonics K.K.).

[0073] [Table 2]

[0074] Image Processing Images were analyzed using ImageJ and custom-built Jupyter Notebook code. Background intensity was subtracted using the rolling ball method (radius = 20), and EV locations were detected from the AF555 channel using the ImageJ Comdet plugin. A 3x3 average pixel intensity was obtained from the AF647 and DL755 channels. For IgG control experiments, an intensity threshold was defined for the AF647 and DL755 channels, which was defined as the mean + 3 × standard deviation.

[0075] [Example 1] Growth inhibitory effect of paclitaxel on various cancer cell lines MTT assays were performed on each cell line to determine the inhibitor concentration 50 (IC50) of paclitaxel in the six included breast cancer cell lines. The IC50 values ​​for paclitaxel in the tested cell models were 1.4E-02 μM ± 0.027 sd for HCC1954 (Figure 1A), 0.1 μM ± 0.063 sd for BT474 (Figure 1B), 0.2 μM ± 0.001 sd for MCF7 (Figure 1C), 0.6 μM ± 0.011 sd for MD-MB-231 (Figure 1D), and 9.9 μM ± 0.097 sd for HCC1937 (Figure 1E).

[0076] To generate an in vitro resistant cell model (HCC1954 REPX), the HCC1954 cell line was treated with increasing concentrations of paclitaxel and cell viability was reassessed after 18 weeks of treatment. A significant increase in paclitaxel IC50 was observed in the HCC1954 REPX cell subtype (1.8 μM ± 0.072 sd, Figure 1F) compared to the parent HCC1954 cell line (1.4E-02 μM ± 0.027 sd, Figure 1G), indicating a 128.57-fold increase in paclitaxel resistance.

[0077] [Example 2] Size characterization of EVs EV fractions were isolated from seven breast cancer cell lines, HCC1954 (Figure 2B), BT474 (Figure 2C), MCF7 (Figure 2D), MDA-MB-231 (Figure 2E), HCC1937 (Figure 2F), and HCC1954 REPX (Figure 2G), as well as the normal cell line Hs 371 T (Figure 2A), and further characterized for size distribution and concentration analysis by NTA. The particle populations showed a high degree of homogeneity with respect to size, falling within the expected range for EVs (range 85.7-179.1 nm).

[0078] [Example 3] Comparative gene expression profiles analyzed by qPCR Eleven markers potentially associated with paclitaxel resistance were tested: TUBA1A / α-tubulin, TUBB3 / βIII-tubulin, CIC-3, ABCB1 / P-gp, ABCG2 / BCRP, survivin, PTGES3, CCND1 / cyclin D1, miR-9, miR-421, and miR-21. These selected markers were assessed in both cells and their corresponding EVs.

[0079] The expression levels of the ABC transporter ABCB1 / P-gp (Figure 3A and Figure 10B) were significantly higher in the less sensitive cell lines MDA-MB-231, HCC1937, and HCC1954 REX compared with the more sensitive cell lines HCC1954, BT474, and MCF7. Furthermore, ABCB1 / P-gp expression was significantly reduced in the normal cell subtype Hs371T. A similar expression trend was also observed in EVs isolated from the tested cell types (Figure 3A), demonstrating a direct correlation between increased ABCB1 / P-gp expression and enhanced paclitaxel resistance. In the case of ABCG2 / BCRP (Figure 3B), a positive correlation was observed between increased ABCG2 / BCRP expression and elevated IC50 values ​​at the cellular level, but ABCG2 / BCRP was not detected in EVs. These findings affirm extensive studies highlighting the interplay between ABCB1 / P-gp and paclitaxel resistance and highlight the potential of ABCB1 / P-gp in predicting response to paclitaxel.

[0080] Our results showed decreased expression of TUBA1A / α-tubulin in MDA-MB-231, HCC1937, and HCC1954 REX cell lines compared with HCC1954, BT474, and MCF7 cell lines (Figure 3C and Figure 9A). However, TUBA1A / α-tubulin expression in all EVs appeared similar, with a decrease observed only in EVs derived from the normal cell subtype Hs371T. Furthermore, as shown in Figure 3D and Figure 9B, a slight correlation was observed between TUBB3 / βIII-tubulin expression in cells and paclitaxel sensitivity, with a trend toward decreased TUBB3 / βIII-tubulin expression observed in MDA-MB-231 and HCC1937 cell lines. Interestingly, TUBB3 / βIII-tubulin expression levels were similar in EVs isolated from the parental HCC1954 cell line and HCC 1954 REPX. In the case of the microtubule modulator CIC-3, our results showed that CIC-3 expression was reduced in MDA-MB-231, HCC1937, and HCC 1954 REX cell lines compared with HCC 1954, BT474, and MCF7 cell lines (Figure 3E). Unfortunately, CIC-3 levels in EVs were significantly lower, except for EVs derived from HCC1954 REPX (Figure 3E). Collectively, these results suggest possible differential modulation of various tubulin isoforms and the regulation of polymeric and free forms. Consequently, these three biomarkers, TUBA1A / α-tubulin, TUBB3 / βIII-tubulin, or CIC-3, may not be completely reliable indicators given their complexity and potential interactions with additional factors that may affect their accuracy.

[0081] Survivin expression (Figure 3F and Figure 8B) showed a significant increase in MDA-MB-231 and HCC1937 cell lines, paclitaxel-resistant HCC1954 PX, and their corresponding EVs, in contrast to the more sensitive normal cell lines and their corresponding EVs.

[0082] Genes controlling cell cycle and proliferation, such as CCND1 / cyclin D1 and PTGES3, have been reported to be associated with the response to paclitaxel through interactions with G2 / M-related events [Adekeye et al., 2022, 34920330; Bao et al., 2020, 32796817]. Decreased CCND1 / cyclin D1 mRNA levels (Figure 3G and Figure 9D) were observed in MDA-MB-231 and HCC1937 cell lines compared with HCC 1954, BT474, and MCF7 cell lines, except for HCC 1954 REPX cells. A similar trend was observed in the corresponding EVs (Figure 3G). PTGES3 (Figure 3H) showed completely opposite expression in cell lines and EVs. PTGES2 expression was lower in the less sensitive cell lines, MDA-MB-231, HCC1937, and HCC1954 REPX, compared with the more sensitive cell lines, HCC1954, BT474, and MCF7. However, PTGES2 expression was increased in EVs derived from HCC1954, BT474, and MCF7 cell lines compared with EVs derived from MDA-MB-231, HCC1937, and HCC1954 REPX cell models (Figure 3H).

[0083] In the case of miRNA molecules, miR-421 (Figure 3I and Figure 8E) and miR-9 (Figure 3J and Figure 8D) showed higher expression in the MDA-MB-231, HCC1937, and HCC1954 REX cell lines, but these miRNAs were not detected in the corresponding EVs using similar techniques. Only miR-21 (Figure 3K and Figure 8F) could be detected and analyzed at both the cellular and EV levels. Increased expression was observed in the less sensitive and resistant models MDA-MB-231, HCC1937, and HCC1954 REX compared to the sensitive and normal breast cancer models HCC1954, BT474, MCF7, and Hs371 T.

[0084] [Example 4] Protein expression of P-gp, survivin, and cyclin D1 in cell lines and EVs According to the qPCR results, the protein expression of P-gp, survivin, and cyclin D1 was evaluated in the breast cancer cell models HCC1954, BT474, MCF7, MDA-MB231, HCC1937, and HCC1954, as well as in a normal cell line (Hs 371 T; Figure 4A) along with their respective EVs (Figure 4B). TSG-101 served as an internal control.

[0085] The data revealed a correlation between P-gp and survivin protein levels and paclitaxel sensitivity (Figures 4A-4B). Protein concentrations of both biomarkers were elevated in less sensitive cells (MDA-MB-231 and HCC1937) compared with levels observed in sensitive cell lines (HCC1954, BT474, and MCF7) and normal cell line (Hs371T). Notably, expression of these two proteins was lower in HCC1954 REPX compared with the less sensitive cell models, but remained higher than expression in the parental cell subtype (HCC1954). The protein profiles of P-gp and survivin in EVs mirrored their expression in cells.

[0086] Cyclin D1 protein expression was decreased in less sensitive cell lines (Figures 3G and 9D), but protein levels in EVs showed no clear correlation with paclitaxel sensitivity.

[0087] [Example 5] Single-EV analysis of P-gp and survivin on NPOP substrates using dual labeling of EVs and biomarkers Further investigations were performed on the expression of P-gp and survivin at the individual EV level. The molecular profiling capabilities of NPOP substrates for P-gp and survivin were evaluated in EVs derived from six breast cancer cells (HCC1954, BT474, MCF7, MDA-MB-231, HCC1937, and HCC1954 REPX) and one normal cell line (Hs372T). First, cell line-derived EVs (Figure 5A) were labeled with Alexa Fluor™ 555 (AF555) and conjugated to functionalized substrates using an SH-PEG-COOH linker. Subsequent immunofluorescence staining was performed using optimized concentrations of antibodies against P-gp (Figure 5B-5D) or survivin (Figure 5E-G), followed by labeling with Alexa Fluor™ 488 or Alexa Fluor™ 647 dyes. The counts of Alexa Fluor™ 488-labeled EVs (biomarker channel - P-gp) or Alexa Fluor™ 647-labeled EVs (biomarker channel - survivin) co-localized with the Alexa Fluor™ 555 signal (EV channel) were analyzed and converted to percentage co-localization values. Isotype labels were used as controls.

[0088] Both P-gp and survivin showed high colocalization with EV markers in EV samples from MDA-MB-231, HCC1937, and HCC1954 REX cell lines compared with HCC1954, BT474, and MCF7 cell lines (Figures 4B-4G and Figures 10D-10E). Isotype controls showed only a few instances of colocalization in EVs from all cell lines. These findings reaffirm that the presence of EVs positive for P-gp and survivin strongly correlates with the acquisition of paclitaxel resistance.

[0089] [Example 6] Long-term monitoring of paclitaxel response in breast cancer patients: a proof-of-principle study In breast cancer, QUAD markers (MUC1, HER2, EGFR, and EpCAM) are useful for identifying and isolating tumor-derived extracellular vesicles (tEVs), providing a potential method for detecting and studying circulating cancer-associated EVs in the body. Employing a multiplexed approach, combined with the discriminatory power of the QUAD marker mix, we selectively distinguished tEVs from normal host EVs and quantified the colocalization of P-gp and survivin to predict paclitaxel response (Figure 6A).

[0090] In the pilot study, 44 patient samples (n=22) were analyzed. Two plasma samples were collected per patient before chemotherapy (T1) and after neoadjuvant chemotherapy (T2). Clinical details of these patients are listed in Table 4.

[0091] Within this cohort, an increase in colocalized counts visualized by the Alexa Fluor™ 5554-labeled QUAD marker antibody mix and the Alexa Fluor™ 647-labeled P-gp / survivin antibody mix was observed at T2 compared to T1 in 11 patients classified as poor responders (Figures 6B-6C). Conversely, the remaining 11 patients were classified as good responders, as they showed a decrease in colocalized EVs at T2 compared to T1. Of these 22 patients, 21 of 22 (>95% success rate, Figure 6C) were successfully identified as paclitaxel responders or paclitaxel non-responders based on the colocalization of the QUAD marker and P-gp / survivin biomarkers.

[0092] [Table 3-1]

[0093] [Table 3-2]

[0094] cN: pre-treatment clinical stage N; cT: pre-treatment clinical stage T; Pgr: progesterone receptor; Er: estrogen receptor; c-erbB2: human epidermal growth factor receptor 2.

[0095] References

[0096] [Table 4]

[0097] Other embodiments While the present invention has been described in conjunction with its detailed description, it should be understood that the foregoing description is intended to illustrate, but not to limit, the scope of the invention, which is defined by the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

1. 1. A method for predicting chemotherapy resistance in a subject suffering from cancer, comprising: (a) providing a sample from said subject; (b) isolating, detecting, or enriching tumor-derived extracellular vesicles (tEVs) from the sample, preferably wherein the tEVs are labeled with an antibody or antigen-binding portion thereof that binds to a tumor marker and an antibody or antigen-binding portion thereof that binds to a chemotherapy resistance biomarker; (c) determining the counts of tEVs positive for tumor markers and chemotherapy resistance biomarkers, or determining the intensity levels of tumor markers and chemotherapy resistance biomarkers expressed in the tEVs; (d) comparing the tEV counts or the marker intensity level determined in step (c) with a reference level representative of the response of the subject's cancer to chemotherapy, wherein the tEV counts or the marker intensity level determined in step (c) that differs from the reference level indicates whether the subject's cancer is resistant or sensitive to the chemotherapy. and

2. 10. The method of claim 1, further comprising using plasmon-enhanced EV detection in step (c).

3. 3. The method of claim 1 or 2, wherein the antibody or antigen-binding portion thereof that binds to the EV tumor marker further comprises a fluorescent dye.

4. 4. The method of any one of claims 1 to 3, wherein the antibody or antigen-binding portion thereof that binds to the EV tumor marker comprises one or more antibodies or antigen-binding portions thereof that bind to EpCAM, EGFR, MUC1, and / or HER2.

5. 5. The method of claim 1, wherein the EVs are detected using a protein-reactive TFP dye, the TFP dye comprising a fluorescent dye.

6. 10. The method of claim 1, wherein the chemotherapy resistance biomarker comprises a protein or RNA.

7. The method of claim 6, wherein the chemotherapy resistance biomarkers are P-gp and survivin.

8. The method of any one of claims 1 to 7, wherein the quantification of the EV tumor marker and the chemotherapy resistance biomarker comprises expression, concentration, intensity, or co-localization.

9. The method of claim 8, wherein quantification of expression, concentration, intensity, or co-localization is analyzed using multi-channel fluorescence imaging in single EVs.

10. 10. The method of claim 1, wherein the cancer comprises breast cancer, ovarian cancer, and non-small cell lung cancer.

11. The method of claim 1 or 2, wherein the sample comprises tumor cells or plasma.

12. 1. A method for longitudinal monitoring of drug resistance in a subject suffering from cancer, comprising: (a) providing samples from said subject, said samples being obtained from the same subject at multiple time points during chemotherapy treatment; (b) isolating tumor-derived EVs (tEVs) from the sample, wherein the tEVs are further dual-labeled with an EV tumor marker and a drug resistance biomarker; (c) determining the co-localization of the EV tumor marker and the drug resistance biomarker; (d) detecting a change in co-localization of the EV tumor marker and the drug resistance biomarker before and after the chemotherapy treatment, thereby determining drug resistance in the subject based on the change in quantitative co-localization of the EV marker and the drug resistance biomarker before and after the chemotherapy treatment. and

13. 1. A method for monitoring drug resistance over time in a subject suffering from cancer, comprising: (a) isolating tumor extracellular vesicles (tEVs) in a first sample obtained from a subject at a first time point, wherein isolating the tEVs comprises: (i) applying the first sample to a functionalized substrate to capture extracellular vesicles (EVs); (ii) labeling the EVs with an antibody or antigen-binding portion thereof that binds to a preselected EV tumor marker; (iii) labeling the EVs with an antibody or antigen-binding portion thereof that binds to a preselected drug resistance biomarker. and (b) determining the counts of tEVs positive for tumor markers and chemotherapy resistance biomarkers, or the level of drug resistance biomarkers, in said tEVs at the first time point; (c) administering one or more doses of a chemotherapeutic agent; (d) isolating tEVs in a second sample from the subject obtained at a second time point, wherein isolating the tEVs comprises: (i) applying the second sample to a functionalized substrate to capture EVs; (ii) labeling the EVs with an antibody or antigen-binding portion thereof that binds to the preselected EV tumor marker of step (a); (iii) labeling the EVs with an antibody or antigen-binding portion thereof that binds to the preselected drug resistance biomarker of step (a). and (e) determining the counts of the tEVs positive for tumor markers and chemotherapy resistance biomarkers, or the levels of the drug resistance biomarkers, in the tEVs at the second time point; (f) administering one or more additional doses of the chemotherapeutic agent to the subject if the counts of the tEVs positive for tumor markers and chemotherapy resistance biomarkers, or the relative levels of the drug resistance biomarkers, have not increased from the first time point to the second time point. and

14. The method of claim 12 or 13, further comprising using plasmon-enhanced EV detection.

15. The method of any one of claims 12 to 14, wherein the tEVs are selected by a marker panel comprising EpCAM, EGFR, MUC1, and / or HER2.

16. The method of any one of claims 12 to 15, wherein the antibody or antigen-binding portion thereof that binds to an EV tumor marker further comprises a fluorescent dye.

17. The method of any one of claims 12 to 16, wherein the drug resistance biomarker comprises a protein or RNA.

18. The method of any one of claims 12 to 17, wherein the drug resistance biomarkers are P-gp and survivin.

19. The method of any one of claims 12 to 18, wherein the quantification of the co-localization of an EV marker and a drug resistance biomarker is analyzed using multi-channel fluorescence imaging in a single EV.

20. The method of any one of claims 12 to 19, wherein the cancer comprises breast cancer, ovarian cancer, or non-small cell lung cancer.

21. The method of any one of claims 12 to 20, wherein the sample comprises plasma.

22. The method of any one of claims 12 to 21, wherein drug resistance can be identified before the subject's tumor grows to an observable size.

23. 23. The method of any one of claims 12 to 22, further comprising the step of recommending, prescribing and / or administering a therapeutically effective amount of chemotherapy to the subject.

24. 1. A method for monitoring drug resistance over time in a subject suffering from cancer, comprising: (a) isolating tumor extracellular vesicles (tEVs) in a first sample obtained from a subject at a first time point, wherein isolating tEVs comprises applying the sample to a surface comprising a capture antibody, or antigen-binding portion thereof, that binds to a preselected EV tumor marker, and labeling the captured tEVs with an antibody, or antigen-binding portion thereof, that binds to a preselected drug resistance biomarker; (b) determining the counts of tEVs positive for tumor markers and chemotherapy resistance biomarkers, or the level of drug resistance biomarkers, in said tEVs at the first time point; (c) administering one or more doses of a chemotherapeutic agent; (d) isolating tEVs in a second sample from the subject obtained at a second time point after one or more administrations of the chemotherapeutic agent, wherein isolating the tEVs comprises applying the second sample to a surface comprising a capture antibody, or antigen-binding portion thereof, that binds to a preselected EV tumor marker from step (a), and labeling the captured tEVs with an antibody, or antigen-binding portion thereof, that binds to a preselected drug resistance biomarker of step (a); (e) determining the counts of tEVs positive for tumor markers and chemotherapy resistance biomarkers, or the level of drug resistance biomarkers, in the tEVs at the second time point; (f) administering one or more additional doses of a chemotherapy drug to the subject if the counts of the tEVs positive for tumor markers and chemotherapy resistance biomarkers, or the relative levels of the drug resistance biomarkers, have not increased from the first time point to the second time point. and

25. 25. The method of claim 24, wherein the capture antibody or antigen-binding portion thereof that binds to the EV tumor marker comprises one or more antibodies or antigen-binding portions thereof that bind to EpCAM, EGFR, MUC1, and / or HER2.

26. The method of claim 24 or 25, wherein the drug resistance biomarkers comprise P-gp and / or survivin.

27. The method of any one of claims 24 to 26, wherein the cancer is breast cancer.

28. The method of any one of claims 1 to 27, wherein the chemotherapy is paclitaxel.

29. The method of any one of claims 24 to 28, wherein the first sample and / or the second sample comprises plasma.

30. 30. The method of any one of claims 1 to 29, wherein the method is at least 95% effective in predicting chemotherapy resistance over time in subjects suffering from cancer.

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