Using extracellular vesicles to assess treatment efficacy of antibody-drug conjugate (ADC) therapies

Abstract

The present disclosure relates to methods of detecting and analyzing tumor resistance, e.g., over time, to antibody-drug-conjugates (ADCs) used to treat the cancer.

Description

[0001] USING EXTRACELLULAR VESICLES TO ASSESS TREATMENT EFFICACY OF ANTIBODY-DRUG CONJUGATE (ADC) THERAPIES

[0002] CLAIM OF PRIORITY

[0003] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 611,947, filed on December 19, 2023. The entire contents of the foregoing are hereby incorporated by reference.

[0004] FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0005] This invention was made with Government support under Grant No. R01GM138778 awarded by the National Institutes of Health. The Government has certain rights in the invention.

[0006] FIELD OF THE INVENTION

[0007] The disclosure is in the field of assessing the efficacy of cancer treatments using extracellular vesicles.

[0008] BACKGROUND OF THE INVENTION

[0009] Antibody -drug conjugates (ADCs) are targeted cancer therapy drugs that target specific antigens in cancer cells and release toxic drugs into the cancer cells. More recently approved ADCs show bystander effects in the tumor microenvironment. With high cell membrane permeability, the drugs can extend their cytotoxic effect not only for HER2 -present cells, but also for other neighboring cells regardless of their HER2 levels. However, despite the high efficacy, some patients show less efficacy, which could be associated with intrinsic or acquired resistance to the ADCs.

[0010] In one example, drug resistance is a current significant challenge in ovarian cancer treatment. For example, with the rapid growth of high-grade serous ovarian carcinoma (HGSOC), more than half of new' ovarian cancer patients are diagnosed in advanced stages, where the 5-year survival rate is disappointingly low, below 30%. Recurrence is also common, with the majority occurring within three years. The failure is mainly due to intrinsic or acquired treatment resistance. This is not uncommon for the targeted treatments with ADCs. Along with recent FDA approvals, including for the trastuzumab- deruxtecan (Enhertu®), interest in ADC development has arisen explosively, and over one hundred ADCs are undergoing clinical trials now. For example, for ovarian cancer, mirvetuximab soravtansine (Elahere®) targeting folate receptor a (FRa) as the ADC target antigen has been approved, and several other ADCs are under clinical trials for ovarian cancers targeting MUC16, Napi2b, and mesothelin as the ADC target antigens.

[0011] While clinical trial results and relatively rapid FDA approval show a rosy view of ADC development, growing resistance to ADCs in tumors is a significant hurdle to overcome for ADC-based therapy. Some patients show secondary resistance to ADCs, with tumors gaining resistance after primary clinical responses. For secondary resistance, there are a few key pathways to explain emerging resistance, including 1) loss or mutation of the target antigen, 2) changes in intracellular processing of ADCs, and 3) cellular resistance to cytotoxic payload.

[0012] A current unmet need is identifying patients who show signs of resistance to specific ADCs and to be able to pivot the treatment strategy to prolong overall survival. Furthermore, excessive administration of ineffective ADCs increases the side effects with negligible benefits to patients.

[0013] SUMMARY

[0014] To address this unmet need, we developed a liquid biopsy technology' that analyzes circulating extracellular vesicles (EVs). EVs carry biomolecules originating from their parental cells. In addition, molecular analysis of tumor-derived EVs (tEVs) reflect the molecular status of originating tumor cells. Considering tEVs are actively shed by tumor cells, these unique properties place EVs as prominent biomarkers for longitudinal treatment monitoring using the methods described herein. Molecular EV and / or tEV analysis through liquid biopsy provides unique opportunities to monitor temporal changes of tumors during therapy in a non-invasive manner. This also provides more frequent assessment and in-depth molecular profiling than currently available procedures based on imaging and biopsy.

[0015] In one aspect, the present disclosure provides methods of identifying treatment resistant cancers, either solid tumors or hematologic malignancies, in a subject. For example, the methods can be applied to patients being treated for a tumor, e.g., by ADCs. The ADCs can include, without limitation Inotuzumab ozogamicin, Gemtuzumab ozogamicin, Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin, Trastuzumab deruxtean. Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies and Ado-trastuzumab emtansine. Enfortumab vedotin, Fam-trastuzumab deruxtecan, Sacituzumab govitecan, Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors.

[0016] Accordingly, in one aspect the present disclosure provides methods of monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC), e.g., administered to a subject, to treat the tumor, the methods including (a) obtaining a sample from the subj ect at a first time point; (b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and (c) determining a presence or level of a tumor drug-resistance biomarker of the EVs isolated at the first time point, wherein a presence or level of the tumor drug-resistance biomarker indicates a resistance by the tumor to the ADC being administered to the patient.

[0017] In another aspect, the methods include monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC), by steps including (a) obtaining a sample containing cells from the tumor at a first time point; (b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and (c) determining a presence or level of a tumor drug-resistance biomarker of the EVs isolated at the first time point, wherein a presence or level of the tumor drugresistance biomarker indicates a resistance by the tumor to the ADC.

[0018] In another aspect, the methods include monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC), e.g., administered to a subject, to treat the tumor, by steps including (a) obtaining a sample containing cells from the tumor at a first time point; (b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and (c) determining a presence or level of ADC target antigens on the EVs isolated at the first time point, wherein a presence or level of the ADC target antigens indicate a likelihood of the efficacy of ADC for the originating tumor.

[0019] In any of these methods, step (b) can include isolating tumor-derived EVs (tEVs) from the sample based on detection of tumor biomarkers on the tEVs from the sample. In some embodiments, step (b) can include first isolating EVs from the sample, and then isolating tEVs from the EVs isolate from the sample, or the tEVs can be isolated directly from the sample.

[0020] In some implementations, step (c) includes comparing the presence or level of the tumor drug-resistance biomarker to a reference sample or reference level from a subject known to be healthy and / or cancer-free or to have cancer with a good response to the ADC, wherein a presence of the tumor drug-resistance biomarker or a presence of the ADC target antigens in the sample when there is no presence or low levels of the tumor drug-resistance biomarker or ADC target antigen in the reference, or when a level of the tumor drug-resistance biomarker that is higher in the sample than the reference level, indicates a resistance by the tumor to the ADC, e.g., the ADC being administered to the subject.

[0021] In some implementations, the methods can further include (d) obtaining a sample, e.g., from the subject, at a second time point, which is after the first time point; (e) isolating EVs or tEVs from the sample based on detection of EV biomarkers on the EVs or tumor biomarkers on the tEVs at the second time point; (f) determining a presence or level of a tumor drug-resistance biomarker or an ADC target antigen of the EVs or the tEVs isolated at the second time point; and (g) comparing a presence or level of the tumor drug-resistance biomarker or an ADC target antigen at the first time point to a presence or level of the tumor drug-resistance biomarker at the second time point, wherein a difference in a presence or level between the first time point and the second time point indicates that the tumor’s resistance to the ADC has changed over time.

[0022] In these methods, a presence of the tumor drug-resistance biomarker at the second time point, but not the first time point indicates that the tumor has developed a resistance to the ADC over time. In other embodiments, no presence of the ADC target antigen at the second time point, but a presence at the first time point indicates that the tumor lost the target antigen for the ADC and thus indicates a low efficacy for the ADC over time, because one of the main resistant mechanisms is the reduction or loss of ADC target antigens (e.g., HER2, MUC16, FRa) over time caused by the tumor.

[0023] In some embodiments, an increase in the level of the tumor drug-resistance biomarker at the second time point compared to the level at the first time point indicates that the tumor’s resistance to the ADC has increased over time. In other embodiments, a decrease in the level of the ADC target antigen at the second time point compared to the level at the first time point indicates that the tumor’s resistance to the ADC has increased over time.

[0024] In certain embodiments, the first time point is before treatment, and the second time point is during treatment or after treatment. In some embodiments, the first time point is after treatment has begun, and the second time point is during ongoing treatment.

[0025] In various implementations, the sample comprises a bodily fluid, such as blood or plasma, or the sample can include subject-derived organoids. In some implementations, the EVs or tEVs are captured on a nanosensor chip for processing. In various implementations of the methods described herein, the EVs or tEVs are labeled, e.g., with a reporter group, such as a fluorescent reporter group, that are bound to antibodies that bind specifically to one or more tumor drug-resistance biomarkers or ADC target antigens. Antibodies that bind specifically to tumor drug-resistance biomarkers or ADC target antigens tend not to bind, or to bind at a much lower level, to other proteins or other antigens that are not tumor drug-resistance biomarkers or ADV target antigens.

[0026] In the methods described herein, a change in one or more tumor drug-resistance biomarkers or ADC target antigens can be tracked over the course of a portion or all of the subject’s treatment.

[0027] In some embodiments, the ADC is selected from the group consisting of Inotuzumab ozogamicin. Gemtuzumab ozogamicin. Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin, Trastuzumab deruxtean, Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies, and Ado-trastuzumab emtansine, Enfortumab vedotin, Fam-trastuzumab deruxtecan. Sacituzumab govitecan. Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors.

[0028] In certain embodiments, an additional anti -cancer drug, e.g., a chemotherapeutic drug is added to the sample or is administered to the subject before a sample is obtained from the subject. In various implementations, the ADC target antigens include any one or more of HER2. MUC16 (4H11). and FOLR1, and / or the drug-resistance biomarkers include survivin and / or permeability glycoprotein 1 (PgP).

[0029] In another aspect, the methods described herein include monitoring a patient’s response to cancer drug administration, the method including (a) isolating tEVs from a patient blood sample, and (b) determining whether the tEVs exhibit one or more biomarkers that indicate resistance to the cancer drug being administered to the patient. In these methods, the patient’s blood sample is taken before treatment, during treatment, and after treatment and the determination whether the tEVs exhibit one or more biomarkers indicating cancer drug resistance is done for each sample taken to investigate whether the isolated tEVs exhibit differential levels in the drug-resistance tumor cells. The tEVs are isolated by capture on a nanosensor chip and are labeled with antibodies that bind specifically to target drug-resistance markers. The tEVs can be isolated from in vitro models and from human samples and the samples compared and changes in the markers can be tracked over the course of the patient’s treatment. In some embodiments, the cancer drug being monitored during treatment can be an ADC, for example, Inotuzumab ozogamicin, Gemtuzumab ozogamicin, Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin. Trastuzumab deruxtean, Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies and Ado-trastuzumab emtansine, Enfortumab vedotin, Famtrastuzumab deruxtecan, Sacituzumab govitecan, Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors.

[0030] The growing emphasis on personalized cancer therapy and precision medicine increases the need to establish reliable, highly specific clinical assays for real-time cancer treatment monitoring. The methods disclosed herein provide a unique opportunity to access molecular information of tumors through non-invasive, liquid biopsy assays more frequently during the course of the treatment, which is not currently possible. tEVs are suitable targets for treatment monitoring. TEVs carry biomolecules that reflect the molecular status of their originating tumors, and biomarkers in tEVs show how cells change upon drug treatment. Compared to rare circulating tumor cells (CTC), tEVs are much more abundant in circulation and address the heterogeneity of tumors from which CTC analysis suffers. Unlike soluble biomarkers (DNA, RNA, proteins), tEVs carry surface proteins representing their cellular origins. This allows us to differentially evaluate changes in the specific tumor drug-resistance biomarkers from tEVs and non- tEVs. Better understanding EV proteome changes to ADC response will revolutionize our understanding of resistance and capacity to detect it clinically. While focusing on ADCs, the success here will elevate EV assays for use as an omics-like tool and potential as a liquid biopsy for increased clinical success.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0032] Other features and advantages of the invention will be apparent from the following detailed description, and from the claims. DESCRIPTION OF THE DRAWINGS

[0033] FIG. 1 is a schematic representation illustrating the experimental scheme of FLEX-based ADC -resistance monitoring platform and the characterization of FLEX substrate.

[0034] FIGs. 2A-2E are a series of graphs that show the results of nanoparticle tracking analysis (NTA) of cell line-derived EVs for their size and concentration measurements.

[0035] FIG. 3 is a series of graphs showing the results of flow cytometry analysis to investigate target antigen expression levels in cells vs EVs.

[0036] FIGs. 4A-4E are a series of bar graphs that show the results of testing of ovarian cancer biomarkers for ovarian cancer EV capture (glass imaging) using EVs from the SkOV3 and OV90 ovarian cancer cell lines and the TIOSE6 benign cell line. The most effective dilutions in each experiment are highlighted with gray shading. Antibody titration was used for the glass substrate-EV detection method (imaged EVs on glass for titration before usinsg FLEX chip). After titration, ovarian cancer markers were detected in several ovarian cancer cell-derived EVs (FIG. 4A). showing the capture markers used for ovarian cancer-EVs were well expressed in ovarian cancer cell line EVs.

[0037] FIG. 5A is a bar graph showing a detected EV count from a plain gold (AU) substrate and from a FLEX substrate.

[0038] FIG. 5B is a graph showing the Limit of Detection (LOD) of bead-flow cytometry (which is the gold standard for EV detection) and of the FLEX platform, showing superior sensitivity of the FLEX platform. These data show the scheme of our EV -based detection for ADC resistance and the superior sensitivity of our platform for EV detection.

[0039] FIG. 6A is a bar graph showing the results of an investigation of ovarian cancerspecific biomarkers on cell-derived EVs. CD24, EpCAM, HE4, TNC, and VCAN were chosen for ovarian cancer markers based on these results.

[0040] FIG. 6B is a series of bar graphs showing the antibody concentrations for each of the different tumor biomarkers in different ovarian cancer cell line-derived tEVs and the titration of the capture antibodies. The concentration showing the highest signal-to-noise ratio was chosen for further studies.

[0041] FIG. 6C is a bar graph showing the evaluation of ovarian cancer capture antibodies using cell-derived EVs to provide a captured EV count.

[0042] FIG. 6D is a bar graph showing capture efficiency calculated by (captured EV count by cancer markers / captured EV count by CD63) x 100%. These data show that the capture strategy described herein using ovarian cancer markers are well-developed and specific to capture ovarian cancer markers, and therefore, this capture strategy will facilitate capturing ovarian cancer EVs in patient samples and detect ADC-related markers on ovarian cancer EVs to predict resistance.

[0043] FIG. 7A is a series of graphs showing expression levels of ADC target antigens, HER2 and FR1, in various ovarian cancer cell lines.

[0044] FIG. 7B is a bar graph showing the expression levels of ADC target antigens in ovarian cancer cell -derived EVs, detected by bulk EV-bead cytometry.

[0045] FIG. 7C is a graph showing the expression level of HER2 in ovarian cancer cell- derived EVs, detected by FLEX.

[0046] FIG. 7D is a dot plot of SK0V3-MUC 16 EVs and OV90 EVs, detected by FLEX. These data show that the target antigen expression on ovarian cancer cells and ovarian cancer EVs shows a high correlation, and our EV-detection method using FLEX also showed a very7high correlation between cell expression and EV expression. This data shows the capacity of the FLEX-based EV detection method for the prediction of ADC resistance for target antigen expression levels on cancer cells.

[0047] DETAILED DESCRIPTION

[0048] As described herein, the use of circulating biomarkers (e.g., circulating tumor cells, soluble proteins, and cell-free DNAs) from non-invasive "liquid biopsies" offers accessible and sequential probing of molecular information from primary and metastatic tumors, marking a new era in cancer management. EVs are membrane-enclosed vesicles actively released from cells, carry ing a diverse array of biomolecules like transmembrane proteins, intracellular proteins, and RNAs originating from their parent cells. Studies increasingly indicate that tumor cells secrete EVs at elevated rates compared to normal cells, and their presence has been identified in the blood of ovarian cancer patients (Jo et al., Advanced Science 10.27 (2023): 2301930; Yokoi et al., Science Advances 9.27 (2023): eade6958; and Zhang et al., BMC Cancer, 19 (2019): 1-13). Specifically, analyses of EVs and tumor-derived EVs (tEVs) offer minimally invasive repeated sampling and provide relatively unbiased insights into the entire tumor, less affected by sample scarcity or intratumoral heterogeneity'.

[0049] Despite their promising potential, the clinical application of EVs as tools for monitoring drug response is hindered by several technical challenges. Assays for detecting and quantifying EVs for specific markers often require large sample volumes and extensive processing, limiting sensitivity at the single EV level. The limited number of studies on EV-cargo associated with olaparib resistance complicates the identification of robust biomarker candidates. Moreover, the identification of cell-specific EVs and the interrogation of drug-resistance markers within specific EV subpopulations necessitate multiplexed analysis, ideally at a single EV resolution (Shao H. et al., 2018). The methods described herein have overcome these challenges in a manner that enables the successful integration of these tests into clinical practice.

[0050] General Methodology

[0051] The methods described herein include isolating EVs and / or tEVs from a sample, e.g., a whole blood sample, plasma sample, ascites, or urine, from a subject, e.g., at one, two, or more time points, e.g., before treatment, during treatment, and after treatment, for a hematologic malignancy or a solid tumor. To collect EVs and / or tEVs from cell cultures, one can obtain a medium that had been in contact with the cells for a sufficient time, e.g., 6, 12, 18, 24, 30, 36, 42, or 48 hours. The conditioned medium can be collected through a cell strainer, e.g., a 40 pm nylon strainer (e.g., from Thermo Fisher) and filtered, e.g., through a 0.2 pm membrane filter (e.g., from Millipore Sigma). The conditioned medium can be concentrated, e.g., with a Centricon® Plus-70 Centrifugal Filter (e.g., MWCO = 10 kDa, Millipore Sigma) and centrifuged, e.g., at 3,500 g for 30 minutes, at a low temperature, e.g., at 4°C.

[0052] Following concentration, EVs are isolated from the medium, e.g., by passing the medium through a size exclusion chromatography column (SEC). For example, an SEC column can be prepared with Sepharose® CL-4B (GE Healthcare) based on known protocols (see, e.g., Van Deun et al., Adv. Biosyst. 2020, 4, 1900310). Briefly, an 11 pm pore-sized nylon membrane (NY 1102500, Millipore Sigma) can be placed on the bottom of a syringe, e.g., a 10 ml syringe (BD Biosciences). The syringe can be stacked with 10 mL of Sepharose® and triple-washed with Phosphate Buffered Saline (PBS). Then, a bile sample is applied, and the 4th and 5th fractions (1 fraction = 1 mL) are collected to isolate the EVs. The collected sample can be concentrated using an Amicon Ultra-2 Centrifugal Filter (MWCO = 10 kDa, Millipore Sigma) and centrifuged at 3500 x g for 30 minutes at 4 °C. The isolated EVs can be resuspended in PBS.

[0053] Similar procedures can be applied to plasma samples, initially involving centrifugation to eliminate cell debris. EV isolation from plasma samples employs a modified SEC column, known as dual-mode chromatography (DMC)(see. e.g.. Van Deun 2020). This modification in the column is useful for clinical samples, aiding in eliminating plasma lipoprotein particles (LPPs) that might overlap in size with EVs, potentially causing artifacts and leading to an overestimation of EV counts.

[0054] Following EV isolation, the EVs are labeled, e.g., with a reporter group such as a fluorophore, such as TFP-AF555 using the published protocols (see, e.g., Ferguson et al., Sci. Adv. 2022, 8, eabm3453.). Briefly, EVs are mixed with a reporter group, e.g., 0.2 pl of TFP-AF555. followed by an incubation for a sufficient time to bind to and label the EVs, e.g., about one hour of incubation. The labeled EVs are filtered, e.g., with a 40 K MWCO column (Thermo Fisher) to remove the remaining dye and can then be diluted in PBS before use in the assays described herein. The TFP labeling is used to define the "EV signal" from the signal of the target of interest, e.g., specific surface protein biomarkers that are present on EVs, but not on other types of cells. This labeling is based on sulfo- NHS esters that bind to amine groups, which provides universal labeling of isolated EVs.

[0055] FIG. 1 shows the experimental scheme of fluorescence-amplified extracellular vesicle sensing (FLEX)-based ADC-resistance monitoring platform and the characterization of the FLEX substrate, which can be employed for single EV isolation and detection (Jeong et al., Advanced Science, 10.8 (2023): 2205148). As shown in FIG. 1, a tumor containing numerous cells can develop resistance to a specific ADC treatment as indicated by a decrease in certain tumor ADC resistance biomarkers, such as HER2, FOLR1, and other described herein. The tumor cells can also grow a payload resistance as shown by an increase in tumor drug-resistance biomarkers such as anti-apoptotic proteins, e.g., survivin and P-glycoprotein 1 (permeability glycoprotein, abbreviated as PgP). Survivin is a member of the inhibitor of apoptosis (IAP) family and functions to inhibit caspase activation, thereby leading to negative regulation of apoptosis or programmed cell death. This leads to an increase in apoptosis and a decrease in tumor growth. The survivin protein is expressed highly in most human tumors, but is completely absent in terminally differentiated cells. PgP is a glycoprotein that in humans is encoded by the ABCB1 gene and some cancer cells express large amounts of P-gP, further amplifying that effect and rendering these cancers multi drug resistant.

[0056] As further shown in FIG. 1, longitudinal monitoring can be done of ADC target antigens on tEVs and on payload resistance on tEVs. The FLEX chip, comprising periodic gold nanowell arrays, enhances immunofluorescence signals of EVs captured using turmor-specific biomarker antibodies, thereby increasing sensitivity. After chip surface preparation, EVs are loaded and allowed to attach to the surface of the chip. Fluorescence signals or reporter groups are amplified by the periodic nanowells on the FLEX chips. Single EV-based detection enables quantitative analysis of EVs, even with lower Limits of Detection ( LOD).

[0057] Subsequently, a blocking and fixation step is performed to reduce non-specific interactions with antibodies and preserve the structural and antigenic integrity of the target of interest, e.g., the surface tumor drug-resistance biomarkers. Specific primary and secondary antibodies can then be used to detect the EVs from a specific tumor type, e.g.. tumor-derived EVs (tEVs), and then to detect specific tumor resistance biomarkers with in all isolated EVs or only in the tEVs. The level of tumor drug resistance biomarkers that show a correlation with drug resistance or target antigen expression on tumor cells are measured and the changes in tumor drug-resistance biomarkers in all EV subpopulations and in tEV subpopulations are analyzed.

[0058] Images can be captured using a Zeiss upright automated epifluorescence microscope, e.g., with a magnification of 40x. Each fluorescence channel can be exposed for a sufficient time, e.g., 5 seconds. Image analysis can be conducted using ImageJ and custom-built Jupyter Notebook code. The analysis can include background intensity subtraction to increase the difference between real / noise signals. The ImageJ Comdet plugin can be employed to detect EV locations from the AF555 channel.

[0059] This use of longitudinal changes in EV and tEV tumor drug-resistance biomarkers can predict treatment efficacy and identify patients who develop resistance to a given therapy. The accuracy of the analysis is significantly improved by quantitative measurements of multiplexed marker analysis in single EVs. The methods described herein have been validated with in vitro samples for resistance to ADCs, and have been tested using serially collected human plasma samples of ovarian cancer patients.

[0060] Extracellular Vesicles (EVs)

[0061] Extracellular vesicles (EVs) are lipid-based microparticles, nanoparticle, or protein-rich aggregates present in a sample (e.g., a biological fluid) obtained from a subject. EVs also include membrane vesicles secreted from cell surfaces (ectosomes), internal stores (exosomes), cancer cells (oncosomes), or released as a result of apoptosis and cell death. In addition to lipid membranes, depending on their cell or tissue of origin, EVs can include additional components such as lipoproteins, proteins, nucleic acids, phospholipids, amphipathic lipids, gangliosides and other particles contained within the lipid membrane or encapsulated by the EVs. EVs can also be called nanovesicles. All cells likely release, secrete, or shed EVs, making them useful clinical diagnostic and therapeutic targets for a range of diseases. Non-limiting examples of normal or cancer cell types that can release EVs include liver cells (e.g., hepatocytes), lung cells, spleen cells, pancreas cells, colon cells, skin cells, bladder cells, eye cells, brain cells, esophagus cells, cells of the head, cells of the neck, cells of the ovary, cells of the testes, prostate cells, placenta cells, epithelial cells, endothelial cells, adipocyte cells, kidney cells, heart cells, muscle cells, blood cells (e.g., white blood cells, platelets), and combinations of the foregoing. Because EVs are involved in cell-cell communication, their characterization casts light upon their role in normal physiology and pathology7. EVs in biological fluids including saliva, urine, plasma, and serum can be interrogated as biomarkers of any number of cancers described herein. An EV enriched or isolated based on having particular tumor biomarkers is referred to herein as a tumor-derived EV (tEVs).

[0062] In some embodiments, an EV is between about 20 nm to about 200 nm in diameter. Individual EVs have -1 / 10,000 the surface area and - 1 / 1,000,000 the volume of a whole cell and are therefore difficult to detect using single cell analysis tools, including conventional flow cytometry7. As a result, most proteomic and genomic analysis is performed in bulk on thousands or millions of EVs. However, EVs in biofluids come from many different cell types, and from different locations from yvithin the cell (exosomes secreted from intracellular multi-vesicular bodies, ectosomes / microvesicles shed from the plasma membrane surface, membrane fragments released as a result of cell apoptosis, necrosis, etc.). Thus, in a bulk analysis, the signature from tumor EVs may be lost in the background of vesicles from other sources, and methods of enriching tEVs help capture a more robust tEV picture.

[0063] EVs represent new opportunities as circulating cancer biomarkers. These cell- derived membrane-bound vesicles contain protein and nucleic acid cargo, providing a representative “snapshot” of the content of the secreting cells. Large abundance and ubiquitous presence of tumor-derived EVs (tEVs) in bodily fluids (e.g., blood, urine) have shown the potential use of EVs as readily accessible biomarkers. Namely, tumor-derived EV (tEV) analyses can be minimally invasive for repeated sampling and afford relatively unbiased readouts of the entire tumor, less affected by the scarcity of the samples or intratumoral heterogeneity7. This suggests that the methods described herein have particular utility' for longitudinal disease monitoring and early detection of relapse. Previous studies shoyved that both the amount and molecular profiles of tEVs were shown to correlate with tumor burden. Therefore, EVs can function as a novel biomarker for liquid biopsy in personalized medicine. However, EVs are relatively new targets for analytical assays in clinics and possess unique physical and biological traits. They fall in size range much smaller than cells, but larger than proteins, and exist in a highly heterogeneous biological background. These properties impose technical difficulties, which often lead to variable findings. Furthermore, identifying cell-specific (e.g., tumor origins) EVs and interrogating drug-resistance markers within the subpopulation require multiplexed analysis, ideally in a single EV resolution.

[0064] Thus, the present disclosure provides methods of isolating and enriching tumor EV particles, for use in monitoring and / or evaluating whether tumor cells in a subject have become resistant to ADCs over time.

[0065] Antibody-Drug-Conjugate (ADC) Drug Resistance

[0066] ADC resistance can arise from altered protein recruitment and trafficking patterns. Analysis of changes in the extracellular vesicle proteome upon resistance identifies proteins that have altered distribution. We have found specific proteins in the EV proteome that play critical roles in ADC resistance to enable a better understanding of what drives resistance. Furthermore, EV analysis serves as a translatable, liquid biopsy tool to determine resistance in ovarian cancer patients.

[0067] ADCs, such as Inotuzumab ozogamicin, Gemtuzumab ozogamicin, Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin. Trastuzumab deruxtean, Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies and Ado-trastuzumab emtansine, Enfortumab vedotin, Famtrastuzumab deruxtecan, Sacituzumab govitecan, Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors, are now commonly used as first-line, second-line, and maintenance therapy in ovarian and other cancers, with many patients show ing complete remission. How ever, a significant portion of patients on ADC therapy eventually develop resistance.

[0068] EVs Present a Pathway to Characterize ADC Resistance

[0069] EVs are nano-sized, membrane-enclosed vesicles actively shed by cells. EVs carry a set of biomolecules (e.g., transmembrane and intracellular proteins, RNAs) from their originating cells, which can serve as cellular surrogates (see, e.g., Im et al., Nat Biotechnol. 2014; 32(5) 490-495. doi: 10.1038 / nbt.2886; Ramirez-Garrastacho et al., Br J Cancer. 2022; 126(3) 331-350. dor 10. 1038 / s41416-021-01610-8; Shao et al., Nat Med. 2012; 18(12) 1835-1840. doi: 10.1038 / nm.2994; Shao et al., Nat Commun. 2015; 6 6999. doi: 10. 1038 / ncomms7999). It is increasingly clear that EVs are secreted by tumor cells at higher rates than normal cells and can be identified in the blood of patients with cancer, e.g., ovarian cancer (Jo et al., Adv Sci (Weinh). 2023; e2301930. doi: 10.1002 / advs.202301930; Yokoi et al., Sci Adv. 2023; 9(27) eade6958. doi: 10.1126 / sciadv.ade6958; Zhang et al., Nat Biomed Eng. 2019; 3(6) 438-451. doi: 10. 1038 / s41551-019-0356-9). Namely, tumor-derived EV (tEV) analyses can be minimally invasive for repeated sampling and afford relatively unbiased readouts of the entire tumor, less affected by the scarcity of the samples or intratumoral heterogeneity. This suggests that tumor-derived EVs (tEVs) have particular utility for longitudinal disease monitoring and early detection of relapse (Lane et al.. Clin Transl Med. 2018; 7(1) 14. doi: 10.1186 / s40169-018-0192-7).

[0070] Our previous studies showed that both the amount and molecular profiles of tEVs were shown to correlate with tumor burden as well as treatment efficacy (see, e.g., Yang et al., Sci Transl Med. 2017; 9(391) eaal3226. doi: 10.1126 / scitranslmed.aal3226). As discussed herein, for ovarian cancer, we found that the EV proteome (proteins found in EVs) changes upon cellular development of ADC resistance, and these results suggest that EVs carry protein markers of ADC resistance. In particular, the tumor drugresistance biomarkers for ADCs include HER2, MUC16 (4H11), and FOLR1, which represent some of the ADC target antigens currently under investigation in clinical trials. Other tumor drug-resistance biomarkers include PgP and survivin.

[0071] Challenges with Quantitative, Multiplexed Single EV Analysis

[0072] Despite the promises, the major bottleneck of clinical applications of EV analysis is the development of reliable, sensitive, and quantitative EV analysis. Ideally, such analysis should be useful to analyze multiple biomarkers in single EVs. This is because i) almost all types of cells shed EVs as background; ii) tEV amounts can be minuscule in small sizes of primary tumors; iii) not all tEVs contain tumor biomarkers. Thus, molecular characterization of single EVs is technically challenging. Most EVs are small vesicles (<200 nm) with limited numbers of epitopes and surface areas for labeling (i.e., weak detectable signals), which often requires sophisticated multi-step signal amplification strategies, such as DNA barcodes or enzymatic signal amplification (digital ELISA). Flow cytometry often underestimates EV counts because many small vesicles could be missed due to their weak light scattering, or a swarm of vesicles could be counted as a single event. More importantly, many of these methods are suboptimal in detecting and quantifying very rare tumor-derived EVs in excessive background EVs and particles. Various single EV methods are summarized in Table 1 below.

[0073] Table 1. Single EV Detection Technologies

[0074] All of these single EV detection methods can be used in the methods disclosed herein, but the FLEX and nano-plasmonic exosome (nPLEX) methods discussed herein may be the most useful.

[0075] Methods of Monitoring ADC Resistance Using tEVs

[0076] The present methods include isolating particular EV populations (e.g., tumor- derived EVs (tEVs), e.g., ovarian cancer-derived tEVs) in a subject being treated for cancer and measuring the tEVs expression of tumor drug-resistance biomarkers over time and further administering ADC therapy informed by the relative levels of tumor drugresistance biomarker-positive (e.g., HER2, FOLR-1, MUC16 (4H11). PgP, and survivin) tEVs over time.

[0077] A subject is an individual (e.g., a mammal such as a human) having or suspected of having cancer, e.g., a patient diagnosed with cancer. In some embodiments, the subject can be receiving ADC therapy, and / or another type of cancer treatment (e.g., radiation, surgery). A sample is typically obtained from the subject, or the sample can be from a cell culture, e.g., containing cells from a subject. A sample can include, but is not limited to, cells, lysed cells, cellular extracts, nuclear extracts, extracellular fluid, media in which cells (e.g., cancer cells from the subject) are cultured, blood, plasma, serum, gastrointestinal secretions, homogenates of tissues or tumors, ascites, 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 implementations, the sample is obtained from a subject at multiple time points, e.g., at least two time points. In other implementations, a single sample can be compared to a reference, e.g., a similar sample from an individual known to be healthy and / or cancer-free or to have cancer with a good response to the ADC.

[0078] In some embodiments, the sample from the subject can be enriched for tEVs, e.g., based on the presence of EV tumor biomarkers, but this is not required. For example, for analyzing cultured cells, it may not be required that the tEVs are isolated from other EVs in the sample. However, for clinical samples, one may wish to include the step of first isolated EVs from the sample, and then isolating tEVs from the larger EV population.

[0079] An EV tumor biomarker profile can indicate the origin of a cancer or the type of cancer cells found in a sample from a subject. For example, MUC1. HER2. EGFR. and EpCAM are four biomarkers that can be used to identify breast cancer cells in a subject. Many EVs secreted by these breast cancer cells also contain these four tumor markers. Therefore, monitoring EVs that comprise one or more of MUC1, HER2, EGFR, and EpCAM, e.g., tEVs, in a subject can give information regarding a subject s breast cancer, including development of drug resistance.

[0080] Other EV tumor biomarkers include: EpCAM, miRNA-21, and CD24 for ovarian cancer); EpCAM, EGFR, MUC1, WNT2, and GPC1 for pancreatic ductal adenocarcinoma (PDAC)); EGFR and EGFRvIlI are tumor biomarkers of glioblastoma (GBM)); and EpCAM, EGFR, and MUC1 for cholangiocarcinoma); see, e.g., 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, A dv. Bio., 2000003 (2020); Jeong, et al., Plasmon-enhanced single extracellular vesicle analysis for cholangiocarcinoma diagnosis, Adv. Sci., 2205148 (2023). Hong, et al., CRISPR / Casl3a- Based MicroRNA Detection in Tumor-Derived Extracellular Vesicles, Adv. Sci.. 2023.

[0081] Any of these tumor markers can be used as EV tumor biomarkers in the methods described herein. Other EV tumor biomarkers including miRNA and other non-coding RNAs can be found in the art and readily appreciated by the skilled artisan. See, e.g., Huang, et al.. Non-coding RNA derived from extracellular vesicles in cancer immune escape: Biological functions and potential clinical applications, Cancer Lett., 2021. In some embodiments, the methods can include using antibodies or antigen binding portions thereof that bind to selected EV tumor biomarkers corresponding to a particular type of cancer to identify or enrich tEVs for further analysis. For example, the antibodies can be capture antibodies that are attached to a substrate (e.g., a plate, well, or beads). A sample from a subject, e.g., a sample comprising a population of EVs (optionally EVs obtained from a biofluid such as blood, serum, or plasma) can then be applied to the substrate, wherein the antibodies or antigen binding portions thereof that bind to the selected EV tumor biomarkers capture and enrich the EV population for tEVs having the specified EV tumor biomarkers.

[0082] Tumor drug-resistance biomarkers disclosed herein can then be evaluated in the tEVs. e.g., optionally using antibodies that specifically bind the tumor drug-resistance biomarkers, to determine a level of drug-resistance biomarker for that sample and subject. In some embodiments, the antibodies or antigen binding portions thereof that bind to the selected EV tumor drug-resistance biomarkers can be applied to a sample, wherein the sample has been previously enriched for tEVs. One method for 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 3D Au nanopillars (NPOP) substrate, wherein 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).

[0083] In some embodiments, the antibodies or antigen binding portions thereof that specifically bind to the selected EV tumor biomarkers can be applied as free antibodies to the EV sample, wherein the antibodies or antigen binding portions thereof that bind to the selected EV tumor biomarkers can be labeled (e.g., fluorescently labeled) or wherein the antibodies or antigen binding portions thereof that bind to the selected EV tumor biomarkers can be detected with a secondary antibody. In some embodiments the antibodies or antigen binding portions thereof that bind to the selected EV tumor markers can be applied before, after, concurrently with the antibodies or antigen binding portions thereof that bind to selected tumor drug-resistance biomarker(s).

[0084] In some embodiments, EVs from a sample can be enriched using a plasmon- enhanced EV capture method. In some embodiments, the plasmon-enhanced EV capture method includes EV capture using any substrate, e, g., plain substrate, nanostructures, beads, or other materials. In some embodiments, the plasmon-enhanced EV capture method includes EV capture using an NPOP substrate, wherein in some embodiments the NPOP substrate can be constructed and / or functionalized according to the methods described in the examples. EVs that have been enriched by isolation on an NPOP substrate can be probed for expression of EV tumor biomarker(s) and / or tumor drugresistance biomarker(s). Antibodies to EV tumor biomarkers can be applied to the EV- enriched sample, wherein the antibodies to EV tumor biomarker(s) can be labeled (e.g., fluorescently labeled) or wherein secondary antibodies can be used to detect the antibodies to EV tumor biomarker(s).

[0085] Antibodies to tumor drug-resistance biomarker(s) can be applied to the EV- enriched sample, wherein the antibodies to the tumor drug-resistance biomarker(s) can be labeled (e.g., fluorescently labeled) or wherein secondary antibodies can be used to detect the antibodies to the tumor drug-resistance biomarker(s). In some embodiments, the antibodies to EV tumor biomarker(s) and the antibodies to the tumor drug-resistance biomarker(s) can be applied to the sample and / or the EV-enriched sample at the same time. In some embodiments, the antibodies to the EV tumor biomarker(s) are applied to the sample and / or EV-enriched sample prior to when the antibodies to the tumor drugresistance biomark er(s) are applied to the sample and / or EV-enriched sample. In some embodiments, the antibodies to the EV tumor biomarker(s) are applied to the sample and / or EV-enriched sample after the antibodies to the tumor drug-resistance biomarker(s) are applied to the sample and / or EV-enriched sample.

[0086] In some embodiments, enriching EVs (e.g., tEVs) and probing the tEVs for levels of tumor drug-resistance biomarker(s) can be carried out at multiple time points (e.g., over time or longitudinally). For example, enriching EVs (e.g., tEVs) and probing the tEVs for levels of drug-resistance biomarkers can be carried out at one, two, three, four, five, or more time points. In some embodiments, the level (as determined by antibody detection) of tumor drug-resistance biomarker(s) at a first time point can be used to determine the relative level of the tumor drug-resistance biomarker(s) at a second time point by comparing the level of tumor drug-resistance biomarker(s) signal at the second time point to the tumor drug-resistance biomarker(s) signal at the first time point and noting an increase or decrease of the level of the tumor drug-resistance biomarker(s) signal. Similarly, the level of the tumor drug-resistance biomarker(s) signal at a third (or fourth, or fifth, etc.) time point can be compared to the level of drug-resistance biomarker(s) signal at the first time point, or the level of the drug-resistance biomarker(s) signal at the third (or fourth, or fifth, etc.) time point can be compared to the level of drug-resistance biomarker(s) signal at any previous time point to analyze whether there are any trends in drug-resistance biomarker(s) signal over time.

[0087] In some embodiments, a relative increase in levels of the tumor drug-resistance biomarker(s)-positive (e.g., HER2, FOLR-1, MUC16 (4H11), PgP, and survivin) tEVs over a previous level of tumor drug-resistance biomarker(s)-positive (e.g., HER2, FOLR- 1, MUC16 (4H11). PgP, and / or survivin-positive) tEVs indicates cancer cells in the subject are in the process of becoming, or have become, resistant to ADCs, for example the ADC used to treat the subject’s cancer. In some embodiments, a relative decrease or no significant change in levels of drug-resistance biomarker(s)-positive (e.g., HER2, FOLR-1, MUC16 (4H11), PgP, and / or survivin-positive) tEVs over the previous level of the tumor drug-resistance biomark er(s)-positive tEVs indicates cancer cells in the subject have not become resistant to the ADC (e.g., used to treat the subject’s cancer).

[0088] The relative levels of tumor drug-resistance biomarker(s) and biomarker(s)- positive tEVs thus can be used to determine whether the subject receives additional ADC treatments comprising the same drug used to treat the subject’s cancer between the previously evaluated time points or receives a different treatment using a different chemotherapeutic agent (or other treatment modality, such as immunotherapy, radiotherapy, or surgical resection).

[0089] In some embodiments, a relative increase in tumor drug-resistance biomarker(s) or biomarker(s)-positive (e.g., HER2, FOLR-1, MUC16 (4H11). PgP, and / or survivin) tEVs over a previous level of drug-resistance biomarker(s)-positive tEVs indicates whether the subject should continue to receive treatment with the same ADC. In some embodiments, a relative increase in drug-resistance biomarker(s)-positive tEVs over the previous level of drug-resistance biomarker(s)-positive tEVs indicates the subject should not receive further treatment with the same ADC. In some embodiments, a relative increase in drugresistance biomarker(s)-positive EVs over the previous level of drug-resistance biomarker(s)-positive tEVs indicates the subject should not receive treatment with any further ADC.

[0090] In some embodiments, the subject only receives further ADC treatments with the same drug if there is a decrease or no change in the tumor drug-resistance biomarker(s)- positive tEVs over the previous (or any previous) level of drug-resistance biomarker(s)- positive tEVs. In some embodiments, the subject receives further ADC treatments with the same drug only if there is not an increase in drug-resistance biomarker(s)-positive tEVs over the previous (or any previous) level of drug-resistance biomarker(s)-positive tEVs. In these conditions, administration of further treatments with the same drug is dependent on the relative level of drug-resistance biomarker(s)-positive tEVs over the previous (or any previous) level of drug-resistance biomarker(s)-positive tEVs.

[0091] As used herein, the terms “cancer." “tumor” or “tumor tissue” has the meaning as understood by one skilled in the art. A cancer, tumor, or tumor tissue can include tumor cells that are neoplastic cells with abnormal growth properties. Tumors, tumor tissue, and tumor cells can be benign or malignant. Cancer can include primary malignant cells or tumors (e.g., those whose cells have not migrated to sites in the subject’s body other than the site of the original malignancy or tumor) and secondary malignant cells or tumors (e.g., those arising from metastasis, the migration of malignant cells or tumor cells to secondary sites that are different from the site of the original tumor).

[0092] Examples of cancer include, but are not limited to, carcinoma, lymphoma, blastoma, sarcoma, and leukemia or lymphoid malignancies. Additional examples of such cancers are noted below and include: squamous cell cancer (e.g., epithelial squamous cell cancer), lung cancer including small-cell lung cancer, non-small cell lung cancer, adenocarcinoma of the lung and squamous carcinoma of the lung, cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer including gastrointestinal cancer, pancreatic cancer, glioblastoma, cervical cancer, ovarian cancer, liver cancer, cholangiocarcinoma, bladder cancer, hepatoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial cancer or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, prostate cancer, vulvar cancer, thyroid cancer, hepatic carcinoma, anal carcinoma, penile carcinoma, as well as head and neck cancer.

[0093] One of the benefits of the longitudinal monitoring of drug-resistance of the currently described methods is that drug-resistance in a subject can be detected prior to an observable increase in size of the subject’s cancer (e.g., tumor). With an early detection of drug-resistance, the longitudinal monitoring of drug-resistance of the currently described methods can terminate the toxic treatment early to reduce side effects or minimize unnecessary' treatment. The efficacy of the longitudinal monitoring of drug-resistance of the currently described methods for predicting drug-resistance can be at least 95%. Other benefits of the longitudinal monitoring of drug-resistance of the currently described methods include predicting drug treatment efficacy, minimizing the detection of residual diseases, and facilitating early detection of disease recurrence. Nanoplasmonic Technology for Single EV Analyses

[0094] Plasmonic EV sensing platforms, named nPLEX (nano-plasmonic exosome), that can rapidly detect and molecularly profile tumor-derived EVs in clinical samples have been described (see, e.g., US Patent No. 10,557,847B2). We further advanced the technology7for multiplexed single EV analysis (see, e.g., US Patent Application Publication No. US US-2023-0160809). Termed "FLEX (fluorescence-amplified extracellular vesicle sensing),” the technology harnesses plasmonic metallic nanostructures to amplify EVs’ fluorescence signals and significantly improve the detection sensitivity down to single EVs. The signal amplification occurs in multiple colors, enabling multiplexed, multichannel imaging and detection of single EVs. The results indicate that conventional fluorescence imaging using a plain substrate detected only 10-15% of total EVs due to weak fluorescence signals of small EVs; these weak signals get amplified by using nanoplasmonic chips. The assay is simple and compatible with conventional immunostaining and imaging but does not require any additional chemical reactions to achieve enough sensitivity for single EV detection. In particular, the plasmon enhancements allow us to use near-infrared fluorophores (e.g.. Cy7) that are barely used for EV imaging due to weak signals. This enabled us to develop multichannel single-EV imaging in a broader spectrum range.

[0095] The sensor chip comprises periodic gold nanowell arrays made on a Si wafer in a wafer scale, which addressed the main bottleneck of the technology with high-throughput chip production. Using the single EV analysis, we showed 1) a wide heterogeneity of EVs and their marker levels and 2) high sensitivity for rare target EV s not detected by conventional fluorescence detection due to weak signals. We also developed a new surface chemistry7that significantly reduces non-specific EV binding to plasmonic gold surfaces (see. e.g., Kim et al., ACS Appl Mater Interfaces, 2022 Jun 2: 10.1021 / acsami.2c07317. doi: 10. 1021 / acsami.2c07317; and PCT Application Publication No. WO 2023 / 220377), improving specific EV capture on the substrate surface (high specificity). Using the new platform, we demonstrated sensitive detection of tEVs and accurate quantification of temporal changes in tEVs, which could accurately identify7patients who are not responding to therapy.

[0096] EXAMPLES

[0097] As described in the following examples, ADC resistance models were created, and through whole proteomic and enrichment analysis, specific EV-cargo biomarkers p associated with olaparib response, e.g., resistance, were identified. The expression of these biomarkers at a single EV or tEV level was investigated using nPLEX.

[0098] Subsequently, these findings were validated using serial clinical samples from patients undergoing olaparib treatment with varying clinical outcomes.

[0099] The inventions described herein are further described in the following examples, which do not limit the scope of the invention described in the claims. The materials and methods described below have been used to generate the examples described herein.

[0100] Materials and Methods

[0101] Cell Lines

[0102] SKOV3, OV90, 0VCAR3. OVCA429, and Jurkat cell lines were purchased from American Type Culture Collection (ATCC). TIOSE6 cell line was obtained by transfecting NOSE cells with hTERT. SKOV3-MUC16 cell line was obtained from Dr. Yeku Oladepo at Mass General Hospital. SKOV3, SKOV3-MUC16, OV90, TIOSE6, OVCA429. and Jurkat cell lines were cultured using RPMI 1640 medium (Gibco) containing 10% (v / v) FBS (Gibco) and 1% (v / v) PS (Gibco). The OVCAR3 cell line was cultured using RPMI 1640 medium with 20% (v / v) FBS, 1% (v / v) PS, and 1.6 ml of human recombinant insulin (Gibco). All cell lines were tested using My co Strip® (Invivogen) and free from mycoplasma contamination.

[0103] EV Isolation

[0104] For EV isolation, cells were plated on a 150 mm cell culture dish and cell culture media was replaced with 50 ml of EV-depleted medium, supplemented with 2% of EV- depleted FBS (Gibco), and cultured for 48 hours. To isolate EVs, 10 ml of sepharose column was prepared a day before the isolation using CL-4B sepharose resin (Cytiva) for size exclusion chromatography (SEC). The EV-containing medium was passed through a 40 pm cell strainer (Thermo Fisher) and filtered through a 0.8 pm membrane filter unit (Millipore Sigma). Then, the media was concentrated with Centricon Plus-70 Centrifugal Filter, MWCO = 100 kDa (Millipore Sigma), and centrifuged at 3,500 g for 30 minutes at 4 °C. The concentrated medium was passed through with SEC column, and the 4th and 5th fractions were used for EV isolation, followed by concentration using Amicon Ultra-2 Centrifugal Filter (MWCO = 10 kDa, Millipore Sigma) and centrifuged at 3500 x g for 30 minutes at 4 °C. The isolated EVs were reconstituted in PBS, aliquoted, and stored in a -80 °C deep freezer. The concentration of EVs was determined using NTA analysis (Nanosight). Flow Cytometry Analysis of Cell Lines and EVs

[0105] For the cell surface flow cytometry, 1x106of ovarian cancer cell lines were trypsinized, washed with PBS, and stained with primary' antibodies in a cell staining buffer (BioLegend). Subsequently, cells were washed with cell staining buffer and stained with a secondary antibody. Stained cells were washed and kept in 1% PFA / PBS buffer until analysis. For the EV flow cytometry, 1 pl of EVs in PBS were attached to 0.5 pl of 4 pm aldehyde / sulfate beads (Thermofisher) by incubating for 30 minutes solely with beads and went through additional incubation for 2 hours in 1% BSA solution in PBS, total 200 pl. Then, the beads were washed with 1% BSA solution twice and blocked with 200 pl of 100 mM glycine buffer. Subsequently, beads were stained with primary antibodies for 1 hour and secondary’ antibodies for 1 hour, washed with 1% BSA buffer. For the primary and secondary antibodies used herein, see Table 2, below.

[0106] Table 2 - Antibody List

[0107] Samples were analyzed using an Attune® NxT flow cytometer (Thermofisher

[0108] Scientific) with FSC / SSC setting 80 / 260 for cells and 260 / 320 for beads. Flow cytometry' results were analyzed using FlowJo software.

[0109] EV Capture on FLEX chip To establish the ovarian cancer marker-specific capture setting of EVs, EVs from SKOV3, SKOV3-MUC16. OVCA429, OVCAR3. OV90, Jurkat and TIOSE cell lines were detected for CD24, EpCAM, HE4, TNC, and VCAN expression level by immunofluorescence imaging on the TPFE-printed glass substrate. 20 pl of EV solution was treated on a glass substrate, washed with PBS-T thrice, and blocked with 10% BSA / PBS. Then, the primary antibody was treated with various antibody concentrations, and the secondary antibody was treated. Then, the capture marker antibody concentration was titrated by finding the highest signal-to-noise ratio between the captured EV count of marker-positive EVs and marker-negative EVs on the FLEX chip. Titrated capture antibody concentration was used as ovarian cancer marker capture set in further ovarian cancer EV analysis.

[0110] For capturing EVs on the FLEX substrate, the FLEX chip w as cut into 5 mm x 5 mm size, washed with 100% isopropanol and DI water, and incubated in 1 mM Sodium citrate solution in DI water to induce the physisorption of capture antibodies on the gold surface of FLEX substrate. Then, chips were blown with nitrogen gas and incubated with capture antibody (CD24. EpCAM. HE4. TNC, VCAN) for 1 hour. Chips were washed thrice with PBS-T, blocked with 10% BSA / PBS, and fluorescently labeled EVs were treated on the FLEX surface.

[0111] EV Imaging on FLEX Chip

[0112] For visualization of EVs. lul of EVs were stained with fluorescence-tagged TFP. To prepare TFP, Azido-dPEG®i2-TFP ester (Quanta Biodesign, Cat# 10569, 100 mg, MW: 791.78) and AF555 or AF488 DBCO (Click Chemistry Tools, Cat# 1290-1, 1 mg, MW: 1105.32) were incubated for 2hr in RT. Fluorescent TFP-stained EVs w ere passed through the Zeba columns (7K molecular weight cutoff; Thermofisher Scientific) twice, following the manufacturer’s protocol. Then, fluorescently labeled EVs were specifically- captured on FLEX chips using an ovarian cancer marker capture set. Captured EVs were washed with PBS-T thrice, fixed with 4% paraformaldehyde (PFA) and blocked with 10% BSA solution in PBS. Then, the target detection antibody solution in 1% BSA / PBS was treated for 30 minutes, and when needed, a secondary antibody in 1% BSA solution was added for 15 minutes after washing out the primary antibody. FLEX chip samples were mounted using Prolong ProLong Gold Antifade mounting agent (Thermofisher Scientific), and images w ere obtained using an upright fluorescent microscope (Zeiss). Characterization of FLEX Chip

[0113] To test signal amplification of the FLEX chip, EVs were stained with AF488. AF555, and AF647 and treated to the plain gold substrate and FLEX substrate. These substrates were treated with sodium citrate solution, treated with EVs, and fixed with 4% PFA / PBS. Then, EV counts from each substrate were compared after imaging.

[0114] To test the sensitivity of the FLEX chip, OVCAR3 EVs were attached to an aldehyde / sulfate bead or treated to FLEX chips at various concentrations (from 104EVs to 108EVs in 20 pl). Then, the CD63 expression was detected using the flow Cytometry method or FLEX imaging method. Relative CD63 expression level was calculated by normalization with the highest expression level among several concentrations.

[0115] Image Analysis

[0116] Images were analyzed using ImageJ (Fiji) and custom Python code. For the image processing, background signals were subtracted by a rolling ball algorithm (radius = 20). Then, the ComDet plugin was used in ImageJ to detect EV locations and convert them to intensity profiles with x and y locations. Then, averaged fluorescence intensities were calculated from a 3 x 3 fixed pixel, and colocalization between fluorescence channels was calculated using the code.

[0117] Statistical Analysis

[0118] For the statistical analysis, Prism software (GraphPad) was used. Unless described otherwise, data are presented as the mean ± standard deviation (SD). Data were analyzed through one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test to calculate P-values for comparisons among more than two groups.

[0119] Example 1 - Application to Antibody-Drug Conjugate Treatment

[0120] The methodology described herein was applied to monitor changes in target antigen levels of tumors through molecular EV analysis. To exemplify, we targeted three ADC candidates: MUC16, folate receptor alpha (FOLR1), and HER2, which are currently undergoing clinical trials for ovarian cancer. We established multiple ovarian cancer cell lines that showed differential expression of those three ADC target antigens (Table 3 below). Table 3

[0121] We first isolated EVs from these cell lines and measured their size and concentrations to confirm the presence of isolated EVs.

[0122] FIGs. 2A-2E are a series of graphs that show the results of nanoparticle tracking analysis (NTA) of cell line-derived EVs for their size and concentration measurements. The numbers and size distributions of EVs were analyzed by nanoparticle tracking analysis (Nanosight® LM10, Malvern). Each sample was prepared by 1000-fold dilution with PBS and manually loaded in the chamber with a 1 mL syringe. The particles were detected using a 405 nm laser module, and their Brownian motions at room temperature were captured by a CCD camera with a camera level 10 in the NTA softw are. Each measurement w as recorded for 20 seconds with a 30-frame rate per second, and each sample was measured in quadruplicate. The particle analysis was conducted with a detection threshold of 2.

[0123] As shown in FIG. 3, we then profiled the levels of three ADC target antigen proteins (HER2, MUC16, and FOLR-1) in cells and their EVs using flow cytometry. FIG. 3 is a series of graphs showing the results of flow cytometry analysis to investigate target antigen expression levels in cells vs EVs. The results discussed below showed the specific expressions of those markers in cells, as expected in the above table, and similar profiles in their EVs. These results validated the use of EVs to interrogate expression levels of target proteins in their originating cancer cells. P

[0124] To further improve the accuracy of the analysis, we identified candidate ovarian cancer biomarkers (EpCAM. CD24, HE4, TNC. and VCAN) based on previously published literature. We first tested different antibody concentrations for each individual marker and identified optimal concentrations based on their specificity between ovarian cancer EVs and control EVs (TISOE6, ovarian benign cell line, and Jurkat, B-cells). In this analysis, we first captured EVs on glass substrates. After blocking with 10% BSa for 30 minutes, primary antibodies were applied for 30 minutes. After removing unbound, excessive antibodies, we applied AF647 secondary antibodies for 20 minutes. Finally, captured EVs were labeled for TFP-AF555. We then detected all captured EVs and marker-positive EVs, and calculated a marker-positive EV percentage. With the optimized EV concentrations, we applied the method to other cell line EVs.

[0125] As shown in FIGs. 4A-4E, the results of testing of ovarian cancer biomarkers for ovarian cancer EV capture (glass imaging) using EVs from the SkOV3 and OV90 ovarian cancer cell line and the TIOSE6 benign cell line. The most effective dilutions in each experiment are highlighted with gray shading. Antibody titration was used for the glass substrate-EV detection method (imaged EVs on glass for titration before using a FLEX chip). After titration, ovarian cancer markers were detected in several ovarian cancer cell- derived EVs (Figure 4A), showing the capture markers used for ovarian cancer-EVs were well expressed in ovarian cancer cell line EVs. Ovarian cancer markers were well- expressed in each marker positive cell-line derived EVs as their expression level increased according to increasing antibody concentration, while TIOSE6 benign cell lines EVs did not express markers, therefore showing minimal expression level in all antibody dilutions.

[0126] The results showed that CD24, EpCAM, HE4. and VCAN could be used to specifically identify ovarian cancer-denved EVs, and the most effective dilutions for each biomarker were used for further biomarker expression identification in FIG. 6A.

[0127] Example 2 - A FLEX-Based ADC-Resistance Monitoring Platform

[0128] FIG. 5A is a bar graph showing a detected EV count from a plain gold (AU) substrate and from a FLEX substrate. To test signal amplification of the FLEX chip, EVs were stained with three fluorescent dyes using TFP, AF488, AF555, and AF647 and treated to the plain gold substrate or FLEX substrate. EV counts from each substrate were compared after imaging, and the data showed that the FLEX substrate showed significant l ' l signal amplification in each fluorescence channels, and is therefore a perfect candidate for single-EV imaging, maximizing the number of EVs that can be detected.

[0129] FIG. 5B is a graph showing the Limit of Detection (LOD) of bead-flow cytometry (which is the gold standard for EV detection) and of the FLEX platform, showing superior sensitivity of the FLEX platform. These data show the scheme of our EV -based detection for ADC resistance and the superior sensitivity of our platform for EV detection. To test the sensitivity of the FLEX chip, 0VCAR3 EVs were attached to an aldehyde / sulfate bead or treated to FLEX chips at various concentrations, from 104EVs to 108EVs. Then, the CD63 expression was detected using the flow Cytometry method or FLEX imaging method. Relative CD63 expression level was calculated by normalization with the highest expression level among several concentrations, and CD63 expression level was detectable with only 103EVs on FLEX method, while more than 107EVs were required for bead-fl o\\ cytometry to detect CD63 expression on EVs, showing that FLEX method is -1000 fold higher sensitivity' than the “gold standard'’ method.

[0130] Example 3 - Capture Strategy for Ovarian Cancer EVs

[0131] FIG. 6A is a bar graph showing the results of an investigation of ovarian cancerspecific biomarkers on cell-derived EVs. CD24, EpCAM, HE4, TNC, and VCAN were chosen for ovarian cancer markers, and we detected the expression level of these markers on our ovarian cancer cell lines to develop an in vitro model for ADC resistance detection model. Each marker was detected based on the antibody dilution we titrated in FIG. 4A- 4E, and detected on several cell lines, and these data were used to generate FIG. 6B, to titrate the capture antibody concentration.

[0132] FIG. 6B is a series of bar graphs showing the antibody concentrations for each of the different tumor biomarkers (CD24, EpCAM, HE4, TNC and VCAN) in different ovarian cancer cell line-derived tEVs and the titration of the capture antibodies. The concentration showing the highest signal-to-noise ratio was chosen for further studies, to specifically capture ovarian cancer-derived EVs. The capture marker antibodyconcentration was titrated by finding the highest signal-to-noise ratio between the captured EV count of marker-positive EVs and marker-negative EVs on the FLEX chip with serially diluted capture antibody concentration, which w as conducted for each markers. Titrated capture antibody concentration was used as ovarian cancer marker capture set in further ovarian cancer EV analysis, for specific capture of ovarian cancer EVs from plasma. FIG. 6C is a bar graph showing the evaluation of ovarian cancer capture antibodies using cell-derived EVs to provide a captured EV count. After titration of capture antibody titration in FIG. 6B, we tested the ovarian cancer biomarker capture set, to see if these biomarker sets can effectively capture ovarian cancer EVs, better than benign TIOSE6 EVs. When the same EV counts were applied to each chip, we could see that 0VCAR3, SK0V3, SKOV3-MUC16, OV90, and OVCA429 EVs were more effectively captured, while only small numbers of TIOSE6 EVs were captured.

[0133] FIG. 6D is a bar graph showing capture efficiency calculated by (captured EV count by cancer markers / captured EV count by CD63) x 100%. We compared the number of EVs captured using ovarian cancer marker set (FIG. 6C) with the number of EVs captured with universal EV marker, CD63. These data show that the capture strategy described herein using ovarian cancer markers are well-developed and specific to capture ovarian cancer markers, and therefore, this capture strategy will facilitate capturing ovarian cancer EVs in patient samples and detect ADC-related markers on ovarian cancer EVs to predict resistance.

[0134] Example 4 - ADC Biomarker Detection on Single EV-Based FLEX Platform

[0135] We then tested various tumor ADC resistance biomarkers. FIG. 7A is a series of graphs showing expression levels of ADC target antigens. HER2 and FR1, in various ovarian cancer cell lines and the surrogate EVs. On the left of FIG. 7A, We first tested the target antigen, as ADC resistance biomarkers (HER2, FOLR1) expression of ovarian cancer cell lines, including SkOV3, SkOV3-MUC16, OV90, OVCA429, OVCAR3, and benign cell line TIOSE6. For detection of these biomarkers, ovarian cancer cell lines were analyzed by flow cytometry. Briefly, these ovarian cancer cell lines were trypsinized, washed with PBS, and stained with primary antibodies (HER2, FOLR1) in a cell staining buffer. Subsequently, cells were washed with cell staining buffer and stained with a secondary7antibody. These data showed that several ovarian cancer cell lines are positive for HER2 or FOLR1, showing that these HER2-, FOLR1- targeted ADCs can be used for ovarian cancers, and these cell lines are suitable in vitro model for ADC resistance detection.

[0136] HER2 positive or FR1 positive cell lines were used as positive control in subsequent in vitro experiments. On the right, EVs from these ovarian cancer cell lines were analyzed with bulk-EV flow cytometry. Bulk-EV flow cytometry is now the gold standard method for EV analysis, although bulk analysis lack of quantitative analysis and requires huge amount of EV samples. We utilized aldehyde / sulfate beads for this bulk-EV flow cytometry and stained with primary antibodies (HER2, FOLR1) in 1% BSA / PBS buffer. Subsequently, EV-bound beads were washed with 1% BSA / PBS buffer and stained with a secondary antibody. The results showed that most of the EVs showed similar marker expression trends as parental cells showing that EVs can reflect the molecular characteristics of parental cells, and w e could confirm that our biomarkers, HER2 and FOLR1, are detectable on EVs. therefore being suitable in vitro model for EV analysis.

[0137] FIG. 7B is a graph showing the expression level of HER2 in ovarian cancer cell- derived EVs, detected by FLEX. In FIG. 7B, we show ed that biomarker (HER2) expressed on these ovarian cancer cell line-derived EVs are detectable with our capturebased FLEX method. For visualization of EVs, EVs were stained with fluorescence- tagged TFP. Then, fluorescently labeled EVs were specifically captured on FLEX chips using an ovarian cancer marker capture set. Captured EVs w ere stained with target marker (HER2) antibody and the expression level was detected by imaging. This shows that our FLEX method is capable of detecting biomarkers on EVs as expected from gold standard method from FIG. 7 A.

[0138] In FIG. 7C, we calculated the correlation of HER2 expression level of ovarian cancer cell lines (based on the data on FIG. 7A) and our FLEX method data (FIG. 7B). The correlation showed P = 0.0023 and R2 = 0.9690, showing that our FLEX method perfectly predicted the marker expression level on the cell surface, and this implies that our FLEX platform can predict ADC resistance from liquid biopsy-based EV analysis.

[0139] FIG. 7D is a dot plot of SKOV3-MUC16 EVs and OV90 EVs, detected by FLEX in which representative data showed the significant signal from HER2-positive EV, SKOV3-MUC16, which is quite easily distinguishable from HER2-negative EV, OV90. This data show s the capacity of the FLEX-based EV detection method for the prediction of ADC resistance for target antigen expression levels on cancer cells.

[0140] OTHER EMBODIMENTS

[0141] Whilst the invention has been disclosed in particular embodiments, it will be understood by those skilled in the art that certain substitutions, alterations and / or omissions may be made to the embodiments without departing from the spirit of the invention. Accordingly, the foregoing description is meant to be exemplary’ only, and should not limit the scope of the invention. All references, scientific articles, patent publications, and any other documents cited herein are hereby incorporated by reference for the substance of their disclosure.

Claims

We claim:

1. A method of monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC) administered to a subject to treat the tumor, the method comprising(a) obtaining a sample from the subject at a first time point;(b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and(c) determining a presence or level of a tumor drug-resistance biomarker of the EVs isolated at the first time point, wherein a presence or level of the tumor drug-resistance biomarker indicates a resistance by the tumor to the ADC being administered to the patient.

2. A method of monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC), the method comprising(a) obtaining a sample containing cells from the tumor at a first time point;(b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and(c) determining a presence or level of a tumor drug-resistance biomarker of the EVs isolated at the first time point, wherein a presence or level of the tumor drug-resistance biomarker indicates a resistance by the tumor to the ADC.

3. A method of monitoring a tumor’s resistance to an antibody-drug-conjugate (ADC) administered to a subject to treat the tumor, the method comprising(a) obtaining a sample containing cells from the tumor at a first time point;(b) isolating and quantifying extracellular vesicles (EVs) from the sample based on detection of EV biomarkers on the EVs in the sample; and(c) determining a presence or level of ADC target antigens on the EVs isolated at the first time point, wherein a presence or level of the ADC target antigens indicate a likelihood of the efficacy of ADC for the originating tumor.

4. The method of any one of claims 1 to 3, wherein step (b) comprises isolating tumor- derived EVs (tEVs) from the sample based on detection of tumor biomarkers on the tEVs from the sample.

5. The method of claim 4, wherein step (b) comprises first isolating EVs from the sample, and then isolating tEVs from the EVs isolate from the sample.

6. The method of any one of claims 1 to 5, wherein step (c) comprises comparing the presence or level of the tumor drug-resistance biomarker to a reference sample or reference level from a subject known to be healthy and / or cancer-free or to have cancer with a good response to the ADC, wherein a presence of the tumor drug-resistance biomarker or a presence of the ADC target antigens in the sample when there is no presence or low levels of the tumor drug-resistance biomarker or ADC target antigen in the reference, or when a level of the tumor drug-resistance biomarker that is higher in the sample than the reference level, indicates a resistance by the tumor to the ADC, e.g., the ADC being administered to the subject.

7. The method of any one of claims 1 to 6, further comprising(d) obtaining a sample, e.g., from the subject, at a second time point, which is after the first time point;(e) isolating EVs or tEVs from the sample based on detection of EV biomarkers on the EVs or tumor biomarkers on the tEVs at the second time point;(f) determining a presence or level of a tumor drug -resistance biomarker or an ADC target antigen of the EVs or the tEVs isolated at the second time point; and(g) comparing a presence or level of the tumor drug-resistance biomarker or an ADC target antigen at the first time point to a presence or level of the tumor drug-resistance biomarker at the second time point, wherein a difference in a presence or level between the first time point and the second time point indicates that the tumor’s resistance to the ADC has changed over time.

8. The method of claim 7, wherein a presence of the tumor drug-resistance biomarker at the second time point, but not the first time point indicates that the tumor has developed a resistance to the ADC over time.

9. The method of claim 7, wherein no presence of the ADC target antigen at the second time point, but a presence at the first time point indicates that the tumor lost the target antigen for the ADC and thus indicates a low efficacy for the ADC over time.

10. The method of claim 7, wherein an increase in the level of the tumor drug-resistance biomarker at the second time point compared to the level at the first time point indicates that the tumor’s resistance to the ADC has increased over time.

11. The method of claim 7, wherein a decrease in the level of the ADC target antigen at the second time point compared to the level at the first time point indicates that the tumor’s resistance to the ADC has increased over time.

12. The method of claim 7, wherein the first time point is before treatment, and the second time point is during treatment or after treatment.

13. The method of claim 7, wherein the first time point is after treatment has begun, and the second time point is during ongoing treatment.

14. The method of any one of claims 1 to 13, wherein the sample comprises subject- derived organoids or a blood sample from the subject.

15. The method of any one of claims 1 to 14, wherein the EVs or tEVs are captured on a nanosensor chip for processing.

16. The method of any one of claims 1 to 15, wherein the EVs or tEVs are labeled with antibodies that bind specifically to one or more tumor drug-resistance biomarkers or ADC target antigens.

17. The method of any one of claims 7 to 16, wherein a change in one or more tumor drug-resistance biomarkers or ADC target antigens is tracked over the course of the subject’s treatment.

18. The method of any one of claims 1 to 17, wherein the ADC is selected from the group consisting of Inotuzumab ozogamicin, Gemtuzumab ozogamicin, Trastuzumab ematansine, Bentuximab vedotin, Polatuzumab vendotin, Belantamab mafodotin, Trastuzumab deruxtean, Moxetumomab pasudotox, and Loncastuximab tesirine for hematological malignancies, and Ado-trastuzumab emtansine, Enfortumab vedotin, Fam -trastuzumab deruxtecan, Sacituzumab govitecan, Cetuximab sarotalocan, Disitamab vedotin and Tisotumab vedotin for solid tumors.

19. The method of claim 18, wherein an additional anti-cancer drug, e.g., a chemotherapeutic drug is added to the sample or is administered to the subject before a sample is obtained from the subject.

20. The method of any one of claims 1 to 19, wherein the ADC target antigens comprise any one or more of HER2, MUC16 (4H11), and FOLR1.

21. The method of any one of claims 1 to 19, wherein the drug-resistance biomarkers comprise survivin and / or permeability glycoprotein.

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