Extracellular vesicle analysis-based methods for detecting presence of lesion in subject

Abstract

A method for diagnosing a disease in a subject, in particular a hematological malignancy or a solid tumor, comprising the steps of: isolating (21, 61) an extracellular vesicle precipitate (3a, 3b) obtained from a sample (2) of a biological fluid previously taken from the subject; measuring (31, 41, 51-52), from said precipitation, parameters representative of six biomarkers: (I) the size of the extracellular vesicles; (II) the total amount of extracellular vesicles; (III) an amount of extracellular vesicles expressing the first antigen on their own surface and (IV) an amount of extracellular vesicles expressing the first antigen and the second antigen; (V) an average fluorescence intensity for each of the antigens; and (VI) the nucleic acid content of the extracellular vesicles; first comparing a parameter representative of the biomarker with a corresponding critical value; based on the first comparison step, assigning a partial score to each parameter or to the six biomarkers; calculating a final score, namely the score of the subject, as the sum of the partial scores; a second comparison of the final score with at least one predetermined diagnostic threshold, preferably with two diagnostic thresholds, an upper diagnostic threshold and a lower diagnostic threshold; based on the second comparison step, a probabilistic diagnosis of the lesion is estimated. Under the background of personalized medical treatment, the combination of the six biomarkers overcomes the limitation of the prior art method caused by high clinical heterogeneity between subjects, which is specifically embodied in sensitivity to a given lesion; in other words, a certain biomarker, not other biomarkers, selected from the six biomarkers and related parameters of the biomarkers are different.

Description

manual Scope of the Invention

[0001] This invention relates to a method for diagnosing various lesions in a subject. The method is based on the analysis of extracellular vesicles (EVs) extracted from a sample of biological fluid previously taken from the subject.

[0002] Specifically, this invention relates to the diagnosis of hematologic malignancies of lymphoid and myeloid types, such as multiple myeloma, chronic lymphocytic leukemia, acute myeloid leukemia, and general lymphoma; the diagnosis of solid tumors, such as tumors of the nervous system, head or neck, gastrointestinal tumors, pancreatic tumors, liver tumors, testicular tumors, kidney tumors, lung tumors, breast tumors, cervical tumors, bladder tumors, skin tumors, and sarcomas; the diagnosis of autoimmune diseases, such as systemic lupus erythematosus, diabetes, and rheumatoid arthritis; the diagnosis of cardiovascular diseases, such as atherosclerosis, myocardial ischemia, and cardiac fibrosis; the diagnosis of inflammatory diseases, such as diabetes, pancreatitis, and fibrosis; and the diagnosis of degenerative diseases, such as Alzheimer's disease. Existing technology

[0003] The diagnosis / prognosis of several diseases, especially most tumors, has traditionally been made by analyzing samples from bone marrow biopsies and bone marrow punctures (in the case of hematologic malignancies), samples from tumor biopsies (in the case of solid tumors), and combining them with imaging techniques.

[0004] These methods have significant limitations, such as high invasiveness, inapplicability in cases of inaccessible tumors, non-reproducibility on the same subjects, and inability to assess the molecular and spatial heterogeneity of tumors. For this reason, technologies based on searching for novel tumor biomarkers in the blood are gaining increasing traction. In particular, so-called extracellular vesicles (EVs), as well as circulating free proteins, circulating free DNA / small RNAs, and lipids, can be found in the blood alongside normal blood cells.

[0005] EVs are lipid bilayer particles secreted by all cells and released not only in the blood but also in other bodily fluids such as urine, semen, cerebrospinal fluid, and breast milk. EVs range in size from 30 nm to 10 μm and can be classified (among others) as exosomes (30 nm to 150 nm), microvesicles (100 nm to 1000 nm), etc.

[0006] EVs can be considered a type of circulating cell biopsy. In fact, their surfaces contain biomarkers inherited from the surface of their originating cells, including proteins and carbohydrates. These biomarkers enable the classification and targeting of cell- and tissue-specific EVs. More specifically, the contents carried by EVs include, but are not limited to, proteins, metabolites, DNA, and RNA. These contents depend not only on the originating cell but also on the donor's pathophysiological state, cellular conditions (such as oxidative or metabolic stress), and the response to treatment. EVs also reflect molecular / protein changes induced by disease and / or treatments received by the patient, including the spatial and molecular heterogeneity of the tumor.

[0007] Therefore, EVs can serve as a source of biomarkers for specific symptoms of the body or disease. EV-mediated signaling has been shown to play important roles in many physiological and pathological conditions, such as tumors, as well as neurodegenerative diseases, cardiovascular diseases, autoimmune diseases, and metabolic disorders.

[0008] Several protocols are available for isolating EVs. Most of these protocols involve differential ultracentrifugation and microfluidic methods, as well as the use of antibody- or polymer-based kits. All of these methods isolate specific EV populations, such as exosomes or microvesicles.

[0009] Different characteristics or parameters of EVs, such as their concentration, protein, lipids, surface antigens, and genomic contents (microRNA, mRNA, and DNA), have been analyzed / quantified using various methods. This allows these characteristics or parameters to be defined as potential biomarkers for diagnosing tumors and identifying their molecular profiles.

[0010] Specifically, Laurenzana I., Trino S., Lamorte D., [...], De Luca L., Caivano A. developed a protocol in Analysis of Amount, Size, Protein Phenotype and Molecular Content of Circulating Extracellular Vesicles Identifies New Biomarkers in Multiple Myeloma, International Journal of Nanomedicine 2021:16 3141-3160, which involves performing the following steps: - Obtain serum samples from the peripheral blood of the subjects; - The EV precipitate is separated from the serum by centrifugation, for example in a benchtop centrifuge; - Suspend the EV precipitate in a saline solution; -Take the first portion of the EV precipitate; - The first portion of the EV precipitate was characterized by nanoparticle tracking analysis (NTA) to obtain: -EV size distribution curve, and -The first value of EV concentration in the first part of the EV precipitate; - Take the second part of the EV precipitate; - The second part of the EV precipitation was characterized by flow cytometry, yielding: - The concentration value of EV in the second part of the EV precipitation; - The concentration value of EVs that are specific to the first antigen in the second part of the EV precipitation; - The concentration value of EVs that are specific to the second antigen in the second part of the EV precipitation; - The concentration value of EVs that are specific to both the first and second antigens in the second part of the EV precipitation; - The amount of positive fluorescence against the first antigen and against the second antigen; - Take the third portion of the EV precipitate; - Manually extract nucleic acids from the third part of the EV precipitate using a kit.

[0011] However, the existing technologies mentioned above are affected by several technical problems.

[0012] First, the number of biomarkers considered for EV is limited. This can lead to undetectable changes because a single parameter may not be informative, as it is not affected by the disease. In fact, current EV characterization methods are based on the analysis of one or two characteristics of the EV, such as its size, quantity, and the presence of protein or RNA. Changes in the EV's originating cell lead to alterations in several parameters of the EV. In the context of personalized medicine, the analysis of a limited number of parameters may be insufficient to distinguish between healthy and diseased subjects, insufficient to define prognosis, insufficient to ensure treatment monitoring, and insufficient to assess minimal residual disease (MRD) in cases of tumor changes over time and potentially treatable tumor conditions.

[0013] Secondly, the implementation of conventional isolation methods in clinical practice is limited by volume requirements, complex sample handling, low reproducibility, and low EV purity due to contamination by serum proteins and EV aggregates. Furthermore, clinical laboratories lack the specific tools available for EV isolation and characterization, including ultracentrifuges or microfluidic systems. Additionally, isolating and analyzing specific EV populations leads to a loss of representativeness and complexity across all EV populations. Summary of the Invention

[0014] Therefore, one object of the present invention is to provide a method for identifying lesions in a subject based on extracellular vesicle (EV) analysis, wherein the method can solve the problems of the prior art mentioned above, wherein the lesions are solid tumors or blood cancers, or autoimmune diseases, or cardiovascular diseases, or inflammatory diseases, or degenerative diseases.

[0015] According to various aspects of the invention, this objective is achieved by a method, as defined in claims 1 and 32, for detecting the presence or absence of said lesion in the subject based on an EV obtained from a sample of bodily fluids previously taken from the subject. Advantageous embodiments of the invention are defined in the dependent claims.

[0016] According to one aspect of the present invention, the method includes the following steps: - Define at least a first reference antigen and a second reference antigen; - Separate the EV precipitate obtained from a sample of biofluid previously taken from the subject; - Multiple parameters representing six tumor biomarkers were measured from EV precipitation, the six biomarkers including: The dimensions of the I.EV; II. Total EV volume; III. The amount of EVs expressing the first antigen on their own surface, i.e., the amount of EVs that are positive for the first antigen; IV. The amount of EV of two antigens simultaneously expressed on its own surface, namely the first antigen and the second antigen; V. Average fluorescence intensity against each of the first and second antigens; VI.EV nucleic acid content; - Enter the corresponding predetermined threshold value for the parameter representing the biomarker; - Compare these parameters with the corresponding critical values; -Based on the first comparison step, assign partial scores to each of the parameters representing biomarkers or to the six biomarkers; - For each subject, in particular, the final score associated with the lesion is calculated as the sum of the partial scores; - Perform a second comparison between the final score and at least one predetermined diagnostic threshold; -Based on the second comparison, establish a probabilistic diagnosis of diseases in the subjects.

[0017] Such lesions can be: - Hematologic malignancies, namely lymphoid and myeloid blood cancers, especially chronic lymphocytic leukemia (CLL); multiple myeloma (MM); acute myeloid leukemia (AML); general lymphoma, etc. Solid tumors, especially non-small cell lung cancer; nervous system tumors; head or neck tumors; gastrointestinal tumors; pancreatic tumors; liver tumors; testicular tumors; kidney tumors; lung tumors; breast tumors; cervical tumors; bladder tumors; skin tumors; sarcomas, etc. - Autoimmune diseases, especially systemic lupus erythematosus; diabetes; rheumatoid arthritis; - Cardiovascular diseases, especially atherosclerosis, myocardial ischemia, and cardiac fibrosis; - Inflammatory diseases, especially diabetes; pancreatitis; fibrosis; - Degenerative diseases, especially Alzheimer's disease.

[0018] In addition to the general advantages of using EVs (where pure circulating components representing the entire body system are non-invasively separated), the method defined above allows for addressing the first problem mentioned above by identifying six EV-related biomarkers I-VI and using instruments commonly available in clinical laboratories, and subsequently combining the biomarkers to define disease probability.

[0019] The method according to the invention not only expands the number of biomarkers I-VI analyzed, but also scores each biomarker, combines biomarkers, and calculates a final score. This allows for more possible combinations of the six biomarkers, thereby enabling more precise and subjective diagnoses, as required in the context of personalized medicine.

[0020] The potential of the aforementioned biomarkers to detect a wide range of lesions in subjects is known from the literature, particularly from Laurenzana I. et al., cited in this article. However, it has been observed that patients affected by a given lesion may be positive for only one set of those biomarkers, and the combination of biomarkers in which they are positive may differ between different patients with the disease. For example, if only three of those six biomarkers are informative, then twenty possible combinations would need to be considered. Now, by the method according to the invention, all biomarkers are considered, i.e., analyzed, and the final score is calculated as the sum of the contributions (i.e., partial scores) of all six biomarkers. Furthermore, the partial scores and thresholds have been established in such an inventive manner that, as experience has shown, even if a patient is positive for a biomarker based on different combinations, they all obtain a score classifying them as a patient with the disease according to the method according to the invention.

[0021] Conversely, limiting the analysis to only three, or even four or five biomarkers selected a priori, will not be effective in assessing the presence of disease, because in some subjects, the three / four / five biomarkers selected for analysis may not provide information about the disease.

[0022] In fact, due to the high clinical heterogeneity among subjects, it is possible in conventional methods for the first subject to be found to be diseased while the second subject is not, by limiting the analysis to a few predetermined biomarkers. This is not because the second subject is actually not diseased, but because the three (e.g.) biomarkers that are not sensitive to the disease in the subject have been analyzed.

[0023] To the inventor's knowledge, no combination of up to six biomarkers has ever been described in scientific literature to date.

[0024] Furthermore, due to the combined analysis of six biomarkers associated with EV, the method of this invention is able to distinguish, assess, and quantify any changes in the disease that occur at diagnosis and during disease progression, which would be undetectable by analyzing only one biomarker of EV. This means that even if one of the biomarkers of EV may not be informative because it does not change with the effects of the disease, other biomarkers will compensate for this deficiency.

[0025] Furthermore, to the inventor's knowledge, no procedure has been described for analyzing EVs, particularly serum-derived EVs, in the context of diagnosing various diseases, especially hematologic malignancies such as chronic lymphocytic leukemia (CLL), multiple myeloma (MM), acute myeloid leukemia (AML), and solid tumors such as non-small cell lung cancer (NSCLC).

[0026] Other advantages and improvements achievable through this method, without considering specific EV separation procedures, include, in addition to improved diagnostic reliability: - The potential to use this method to identify specific therapeutic targets and track disease evolution, as well as to predict responses to treatment and its potential applications in MRD, particularly in the case of tumors; - The possibility of analyzing the spatial and molecular heterogeneity of diseases.

[0027] In summary, still within the context of personalized medicine, the method according to the present invention can be integrated into clinical practice to achieve non-invasive cancer diagnosis.

[0028] The biofluid can be selected from the group consisting of: blood, serum, or plasma; urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid; semen; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascites; vitreous fluid. In particular, the biofluid is peripheral blood. In this case, EV precipitate can be extracted from plasma or serum samples obtained from peripheral blood samples.

[0029] Advantageously, EV precipitate is extracted from serum samples, and the step of separating the EV precipitate includes suspending the EV precipitate in an aqueous saline solution to obtain a wet EV precipitate. Using serum as the source of EV is advantageous because serum is practically and routinely handled in clinical practice.

[0030] More specifically, in one embodiment, peripheral blood of the subject is collected in a serum tube with a separating gel, from which a serum sample is obtained, and then EVs are typically separated according to the process described in doi: 10.2147 / IJN.S303391.

[0031] Preferably, the method further includes the following steps: - Take the first part of the EV precipitate; Furthermore, the steps for measuring representative parameters of EV size I and total mass II are performed jointly as follows. - Nanoparticle tracking analysis was performed on the first part of the wet EV precipitate to obtain: -EV size distribution curve I, and -First concentration value II of EV in wet EV precipitate.

[0032] Specifically, the step of separating EV precipitates may include the following steps: - Take the second part of the wetted EV precipitate; And the steps for measuring parameters representing the following items: - Total EV (II). - The amount of EV expressing the first antigen (III). - The amount of EV (IV) expressing both the first and second antigens. - Positive fluorescence (V) against the first and second antigens. The following steps will be performed together: - The second fraction of the wet EV precipitate was subjected to flow cytometry to obtain: - The concentration value of total EV in the second part of the wet EV precipitate (II); - The concentration value of EVs specific to the first antigen in the second part of the wet EV precipitate (III). - The concentration value of EVs specific to the second antigen in the second part of the wet EV precipitate; - The concentration value (IV) of EVs that are specific to both the first and second antigens in the second part of the wet EV precipitate. - Positive fluorescence intensity (V) against the first antigen and against the second antigen.

[0033] Advantageously, the step of measuring the parameter representing nucleic acid content is the step of determining the content of a specific microRNA (VI), and includes the following steps: - Manual extraction of nucleic acids from wet EV precipitate; - Sequencing of nucleic acids extracted from wet EV precipitate.

[0034] Preferably, the step of measuring the parameter representing nucleic acid content (VI) includes the following steps: - Nucleic acid (RNA) is automatically extracted from EV precipitate using an automated extractor; - Perform fluorescence quantification of microRNA; - Reverse transcribe a portion of the RNA into cDNA; - MicroRNA was determined using droplet digital PCR technology.

[0035] Advantageously, the parameter representing nucleic acid content (VI) is measured by performing the following two actions: - Nucleic acid was manually extracted from wet EV precipitate, and then the extracted nucleic acid was sequenced. -Dried EV precipitate obtained from centrifuged serum that was not suspended in saline solution was automatically extracted using an automated extractor, followed by quantitative real-time analysis, cDNA reverse transcription, and droplet digital PCR. Manual extraction and automated extraction were performed on the corresponding first EV precipitate and second EV precipitate separated from the first and second parts of the serum sample, respectively.

[0036] Preferably, the step of performing the second comparison of the final score is performed relative to two diagnostic thresholds, which include: - Upper diagnostic threshold, and -Lower diagnostic threshold, And the diagnosis was: - A high probability of lesion if the final score is higher than or equal to the upper diagnostic threshold; - The moderate probability of the lesion if the final score is between the upper and lower diagnostic thresholds; - The probability of lesion presence is low if the final score is below or equal to the lower diagnostic threshold.

[0037] In one exemplary implementation, in the step of defining multiple partial scores, a partial score is assigned to each of the six biomarkers.

[0038] Specifically, the partial score for each biomarker is - The first value, if it represents the value of at least one of the parameters of the biomarker: - A threshold value higher than the threshold value of the biomarker itself, if the threshold value is marked with a > sign; - Below the threshold value of the biomarker itself, if the threshold value is marked with a < symbol; - The second value, which is lower than the first value, if it represents the value of each parameter in the biomarker parameters: - Less than or equal to the biomarker’s own threshold value, if the threshold value is marked with a > sign; - A threshold value that is higher than or equal to the biomarker itself, if that threshold value is marked with a < symbol.

[0039] In the first modification of this implementation scheme, for all six biomarkers, the first value and the second value are the same value, specifically the first value and the second value are equal to 1 and 0, respectively, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 4 and 2, respectively.

[0040] In other modifications to this implementation scheme, a first value and a second value are independently assigned to each of the six biomarkers. Specifically, the first value... - For the following biomarkers, the same first lower value applies: -EV dimensions (I), and -EV amount (II); - For the following biomarkers, the same first intermediate value is used: - The amount of EV expressing the first antigen (III), and -The average fluorescence intensity (V) against each of the first and second antigens, and - For the following biomarkers, the same first highest value applies: - The amount of EV (IV) expressing both the first and second antigens, and -EV nucleic acid (VI) content.

[0041] For example, the first lower value, the first middle value, and the first higher value are equal to 1, 2, and 3, respectively, the second value is equal to 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 6 and 3, respectively.

[0042] In another exemplary embodiment, the step of defining multiple component scores includes the step of defining multiple sub-component scores, wherein a sub-component score is assigned to each of the parameters representing the six biomarkers, and the sub-component score of each of these parameters is - The third value, if the value of this parameter is: - A threshold value higher than the biomarker's own threshold value, if the threshold value is marked with a > sign. - Below the threshold value of the biomarker itself, if the threshold value is marked with a < symbol. - The fourth value, which is lower than the first value, if the value of this parameter is: - Less than or equal to the biomarker's own threshold value, if that threshold value is marked with a > sign. - A threshold value higher than or equal to the biomarker's own value, if that threshold value is marked with a < sign. Furthermore, for each biomarker, the partial score is calculated by summing the sub-partial scores assigned to the parameters representing that biomarker.

[0043] In the first modification of this implementation scheme, the third and fourth values ​​are the same for all parameters, specifically they are equal to 1 and 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 10 and 5, respectively.

[0044] In other modifications to this implementation scheme, a third and fourth value are assigned independently for each parameter. Specifically, the third value... - For the parameter representing the following biomarkers, the same third lowest value is used: - The dimensions (I) of the EV, and - The amount of EV (II) - For the parameters representing the following biomarkers, the same third intermediate value is used: - The amount of EV expressing the first antigen (III), and -The average fluorescence intensity (V) against each of the first and second antigens, and - For the parameters representing the following biomarkers, the same third-highest value is used: - The amount of EV (IV) expressing both the first and second antigens, and -EV nucleic acid (VI) content.

[0045] For example, the third lower value, the third middle value, and the third higher value are equal to 1, 2, and 3, respectively, the fourth value is equal to 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 14 and 7, respectively.

[0046] In another exemplary implementation, - Provides steps for defining multiple biomarker groups, each including at least one of six biomarkers. - The fractional score assigned to each biomarker is a sub-fractional score; - In the step of defining multiple component scores, multiple sub-component scores are assigned to each biomarker, and - For each biomarker group, the partial score is calculated by summing the sub-partial scores assigned to the biomarkers in that biomarker group. The fraction for each sub-part is equal to: - The fifth value, if it represents the value of at least one parameter among the parameters of at least one biomarker in the group: - A threshold value higher than the biomarker's own threshold value, if the threshold value is marked with a > sign. - Below the threshold value of the biomarker itself, if the threshold value is marked with a < symbol. - The sixth value, which is lower than the fifth value, represents the value of each parameter in the group of biomarkers: - Less than or equal to the biomarker's own threshold value, if that threshold value is marked with a > sign. - A threshold value that is higher than or equal to the biomarker itself, if that threshold value is marked with a < symbol.

[0047] In the first modification of this implementation scheme, the fifth and sixth values ​​are the same for all six biomarkers, specifically they are equal to 1 and 0, and the upper diagnostic threshold and lower diagnostic threshold are equal to 3 and 1, respectively.

[0048] In other modifications to this implementation scheme, the fifth and sixth values ​​are independently associated with the six biomarkers. Specifically, the first value... - For the following biomarkers, the same fifth lower value applies: -EV dimensions (I), and -EV amount (II); - For the following biomarkers, the same fifth intermediate value is used: - The amount of EV expressing the first antigen (III), and - The amount of EV (IV) expressing both the first and second antigens, and -The average fluorescence intensity (V) against each of the first and second antigens, and - For the following biomarkers, it is the fifth highest value: -EV nucleic acid (VI) content.

[0049] For example, the fifth lower value, the fifth middle value, and the fifth higher value are equal to 1, 2, and 5 respectively, the sixth value is equal to 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 8 and 3 respectively.

[0050] Advantageously, data obtained for each individual parameter are analyzed by applying statistical ROC (Receiver Operating Characteristic) curve testing. The ROC curve is a well-known statistical technique that measures the accuracy of a diagnostic test across the entire range of possible values. The ROC curve measures the consistency between the test of interest and the presence / absence of a specific disease. Therefore, it represents a preferred method for validating diagnostic tests.

[0051] ROC curves also enable the identification of optimal cutoff values, also known as "optimal cut-off values," which are test values ​​that maximize the difference between the proportion of true positives (i.e., the proportion of individuals with abnormal test values ​​among all individuals actually affected by the disease) and the proportion of false positives (i.e., the proportion of individuals with abnormal test values ​​but not affected by the disease of interest). In this way, cutoff values ​​can be obtained for each of the EV parameters mentioned above. That is, "optimal" parameters are combined to define a specific characteristic spectrum based on the tumor being analyzed.

[0052] Preferably, the steps of separating the first EV precipitate and the second EV precipitate from the first and second portions of the serum sample are performed independently of each other by centrifugation in a benchtop centrifuge.

[0053] According to another aspect of the invention, a method for detecting the presence or absence of a disease in a subject includes the step of measuring the nucleic acid content (VI) in a serum sample, or more generally, in a sample of the following biological fluids, such as urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid; semen; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascites; vitreous fluid, wherein the step of measuring the nucleic acid content sequentially includes the following steps: - Separate EV precipitate from serum samples; - Nucleic acid (RNA) was extracted from EV precipitate using an automated extractor; - Perform fluorescence quantification of microRNA; - Reverse transcribe a portion of the RNA into cDNA; - Use digital PCR technology, especially droplet digital PCR technology, to determine microRNA.

[0054] Of the problems mentioned above, the solution to the second problem was obtained by applying the EV purification method described above (doi: 10.2147 / IJN.S303391), in which a centrifugation step in a benchtop centrifuge available in every clinical laboratory is provided, which allows for the separation of heterogeneous and pure EV populations.

[0055] Furthermore, by combining the methods of EV isolation and the characterization of six EV-related biomarkers, the two problems mentioned above were addressed synergistically. Attached Figure Description

[0056] The invention will now be illustrated by way of example, with reference to the accompanying drawings, by describing some exemplary and non-limiting embodiments, in which:

[0057] - Figure 1A A graph showing the difference in extracellular vesicle (EV) size between healthy subjects and patients with chronic lymphocytic leukemia (CLL) is presented, with the EV size indicated by parameter D10. Figure 1B It shows the relationship with Figure 1A ROC curves related to the data.

[0058] - Figure 2A A graph showing the difference in EV size between healthy subjects and patients with CLL is presented, with EV size indicated by parameter D90; Figure 2B It shows the relationship with Figure 2A ROC curves related to the data.

[0059] - Figure 3A A graph showing the difference in EV size between healthy subjects and patients with CLL is presented, with EV size indicated by the mean diameter parameter. Figure 3B It shows the relationship with Figure 3A ROC curves related to the data.

[0060] - Figure 4A A graph showing the difference in EV size between healthy subjects and patients with CLL is presented, with EV size indicated by the mode parameter; Figure 4B It shows the relationship with Figure 4A ROC curves related to the data.

[0061] - Figure 5A A graph showing the difference in EV size between healthy subjects and patients with multiple myeloma (MM) is presented, with EV size indicated by parameter D10; Figure 5B It shows the relationship with Figure 5A ROC curves related to the data.

[0062] - Figure 6A A graph showing the difference in EV size between healthy subjects and patients with MM is presented, with EV size indicated by parameter D90; Figure 6B It shows the relationship with Figure 6A ROC curves related to the data.

[0063] - Figure 7A A graph showing the difference in EV size between healthy subjects and patients with MM is presented, with EV size indicated by the mean diameter parameter; Figure 7B It shows the relationship with Figure 7A ROC curves related to the data.

[0064] - Figure 8A A graph showing the difference in EV size between healthy subjects and patients with MM is presented, with EV size indicated by the mode parameter; Figure 8B It shows the relationship with Figure 8A ROC curves related to the data.

[0065] - Figure 9AA graph showing the data differences in EV size between healthy subjects and patients with acute myeloid leukemia (AML) is shown, with EV size indicated by parameter D10; Figure 9B It shows the relationship with Figure 9A ROC curves related to the data.

[0066] - Figure 10A A graph showing the difference in EV size between healthy subjects and patients with AML is presented, with EV size indicated by parameter D90; Figure 10B It shows the relationship with Figure 10A ROC curves related to the data.

[0067] - Figure 11A A graph showing the difference in EV size between healthy subjects and patients with AML is presented, with EV size indicated by the mean diameter parameter. Figure 11B It shows the relationship with Figure 11A ROC curves related to the data.

[0068] - Figure 12A A plot of data showing the difference in EV size between healthy subjects and patients with AML is shown, with EV size indicated by the mode parameter; Figure 12B It shows the relationship with Figure 12A ROC curves related to the data.

[0069] - Figure 13A A graph showing the data differences in EV size between healthy subjects and patients with non-small cell lung cancer (NSCLC) is shown, with EV size indicated by parameter D10; Figure 13B It shows the relationship with Figure 13A ROC curves related to the data.

[0070] - Figure 14A A graph showing the difference in EV size between healthy subjects and NSCLC patients is presented, with EV size indicated by parameter D90. Figure 14B It shows the relationship with Figure 14A ROC curves related to the data.

[0071] - Figure 15A A graph showing the difference in EV size between healthy subjects and NSCLC patients is presented, with EV size indicated by the mean diameter parameter. Figure 15B It shows the relationship with Figure 15A ROC curves related to the data.

[0072] - Figure 16A A plot of data on the difference in EV size between healthy subjects and patients with NSCLC is shown, with EV size indicated by the mode parameter; Figure 16B It shows the relationship with Figure 16A ROC curves related to the data.

[0073] - Figure 17A A graph showing the difference in serum EV levels between healthy subjects and patients with CLL, obtained through nanoparticle tracking analysis (NTA); Figure 17B It shows the relationship with Figure 17A ROC curves related to the data.

[0074] - Figure 18A A graph showing the difference in serum EV levels between healthy subjects and patients with CLL, obtained by flow cytometry; Figure 18B It shows the relationship with Figure 18A ROC curves related to the data.

[0075] - Figure 19A A graph showing the difference in serum EV levels between healthy subjects and patients with MM, obtained via NTA, is presented. Figure 19B It shows the relationship with Figure 19A ROC curves related to the data.

[0076] - Figure 20A A graph showing the difference in serum EV levels between healthy subjects and patients with MM disease, obtained by flow cytometry; Figure 20B It shows the relationship with Figure 20A ROC curves related to the data.

[0077] - Figure 21A A graph showing the difference in serum EV levels between healthy subjects and AML patients, obtained via NTA, is presented. Figure 21B It shows the relationship with Figure 21A ROC curves related to the data.

[0078] - Figure 22A A graph showing the difference in serum EV levels between healthy subjects and AML patients, obtained by flow cytometry; Figure 22B It shows the relationship with Figure 22A ROC curves related to the data.

[0079] - Figure 23A A graph showing the difference in serum EV levels between healthy subjects and NSCLC patients, obtained via NTA, is presented. Figure 23B It shows the relationship with Figure 23A ROC curves related to the data.

[0080] - Figure 24A A graph showing the difference in serum EV levels between healthy subjects and NSCLC patients, obtained by flow cytometry; Figure 24BIt shows the relationship with Figure 24A ROC curves related to the data.

[0081] - Figure 25A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen, CD19, between healthy subjects and patients with CLL; Figure 25B It shows the relationship with Figure 25A ROC curves related to the data.

[0082] - Figure 26A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD20 between healthy subjects and CLL patients; Figure 26B It shows the relationship with Figure 26A ROC curves related to the data.

[0083] - Figure 27A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD200 between healthy subjects and CLL patients; Figure 27B It shows the relationship with Figure 27A ROC curves related to the data.

[0084] - Figure 28A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD38 between healthy subjects and MM patients; Figure 28B It shows the relationship with Figure 28A ROC curves related to the data.

[0085] - Figure 29A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD138 between healthy subjects and MM patients; Figure 29B It shows the relationship with Figure 29A ROC curves related to the data.

[0086] - Figure 30A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD34 between healthy subjects and AML patients; Figure 30B It shows the relationship with Figure 30A ROC curves related to the data.

[0087] - Figure 31A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD117 between healthy subjects and AML patients; Figure 31B It shows the relationship with Figure 31A ROC curves related to the data.

[0088] - Figure 32AA graph showing the difference in the amount of EVs that are positive for the specific surface antigen CXCR4 between healthy subjects and AML patients; Figure 32B It shows the relationship with Figure 32A ROC curves related to the data.

[0089] - Figure 33A A graph showing the difference in the amount of EVs that are positive for the specific surface antigen EpCAM between healthy subjects and NSCLC patients; Figure 33B It shows the relationship with Figure 33A ROC curves related to the data.

[0090] - Figure 34A A graph showing the difference in the amount of EVs that are positive for a specific surface antigen CD137 between healthy subjects and NSCLC patients; Figure 34B It shows the relationship with Figure 34A ROC curves related to the data.

[0091] - Figure 35A This study demonstrates the correlation between healthy subjects and NSCLC patients regarding the specific surface antigen PD-L1. + A graph showing the difference in the amount of positive EVs; Figure 35B It shows the relationship with Figure 35A ROC curves related to the data.

[0092] - Figure 36A The graph shows the difference in the amount of EVs that are positive for the two antigens CD19 and CD20 between healthy subjects and CLL patients; Figure 36B It shows the relationship with Figure 36A ROC curves related to the data.

[0093] - Figure 37A The graph shows the difference in the amount of EVs that are simultaneously positive for two antigens, CD38 and CD138, between healthy subjects and MM patients. Figure 37B It shows the relationship with Figure 37A ROC curves related to the data.

[0094] - Figure 38A The graph shows the difference in the amount of EVs that are positive for the two antigens CD34 and CD117 between healthy subjects and AML patients. Figure 38B It shows the relationship with Figure 38A ROC curves related to the data.

[0095] - Figure 39A The figure shows the difference in the amount of EVs that are positive for the two antigens CD137 and EpCAM between healthy subjects and NSCLC patients; Figure 39B It shows the relationship with Figure 39A ROC curves related to the data.

[0096] - Figure 40A The graph shows the difference in mean fluorescence intensity (MFI) between healthy subjects and CLL patients regarding EVs that are positive for a specific antigen, CD19. Figure 30B It shows the relationship with Figure 30A ROC curves related to the data.

[0097] - Figure 41A A graph showing the difference in MFI between healthy subjects and CLL patients with EVs that are positive for a specific antigen, CD20; Figure 31B It shows the relationship with Figure 31A ROC curves related to the data.

[0098] - Figure 42A A graph showing the difference in MFI between healthy subjects and CLL patients regarding EVs that are positive for a specific antigen, CD200; Figure 32B It shows the relationship with Figure 32A ROC curves related to the data.

[0099] - Figure 43A A graph showing the difference in MFI between healthy subjects and MM patients with EVs that are positive for a specific antigen CD38 is presented. Figure 33B It shows the relationship with Figure 33A ROC curves related to the data.

[0100] - Figure 44A A graph showing the difference in MFI between healthy subjects and MM patients with EVs that are positive for a specific antigen CD138 is presented. Figure 34B It shows the relationship with Figure 34A ROC curves related to the data.

[0101] - Figure 45A A graph showing the difference in MFI between healthy subjects and AML patients with EVs that are positive for a specific antigen, CD34; Figure 45B It shows the relationship with Figure 45A ROC curves related to the data.

[0102] - Figure 46A A graph showing the difference in MFI between healthy subjects and AML patients regarding EVs that are positive for a specific antigen, CD117; Figure 46B It shows the relationship with Figure 46A ROC curves related to the data.

[0103] - Figure 47AA graph showing the difference in MFI between healthy subjects and AML patients with EVs that are positive for the specific antigen CXCR4 is presented. Figure 47B It shows the relationship with Figure 47A ROC curves related to the data.

[0104] - Figure 48A A graph showing the difference in MFI values ​​between healthy subjects and NSCLC patients with EVs that are positive for the specific antigen PD-L1 BV421 is presented. Figure 48B It shows the relationship with Figure 48A ROC curves related to the data.

[0105] - Figure 49A A graph showing the difference in MFI values ​​between healthy subjects and NSCLC patients regarding EVs that are positive for the specific antigen EpCAM BV421 is presented. Figure 49B It shows the relationship with Figure 49A ROC curves related to the data.

[0106] - Figure 50A The graph shows the difference in nucleic acid content of EVs, particularly hsa-miR-484, between healthy subjects and CLL patients. This nucleic acid content was obtained by droplet digital PCR (ddPCR) and is expressed in copies / µL. Figure 50B It shows the relationship with Figure 50A ROC curves related to the data.

[0107] - Figure 51A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-93-5p, between healthy subjects and CLL patients. This nucleic acid content was obtained by ddPCR and is expressed in copies / µL. Figure 51B It shows the relationship with Figure 51A ROC curves related to the data.

[0108] - Figure 52A The graph shows the differences in EV nucleic acid content, particularly hsa-miR-122-5p, between healthy subjects and MM patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 52B It shows the relationship with Figure 52A ROC curves related to the data.

[0109] - Figure 53A The graph shows the differences in EV nucleic acid content, particularly hsa-miR-125b-5p, between healthy subjects and MM patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 53B It shows the relationship with Figure 53A ROC curves related to the data.

[0110] - Figure 54A The graph shows the differences in EV nucleic acid content, particularly hsa-miR-203a, between healthy subjects and MM patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 54B It shows the relationship with Figure 54A ROC curves related to the data.

[0111] - Figure 55A The graph shows the differences in EV nucleic acid content, particularly hsa-miR-320d, between healthy subjects and MM patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 55B It shows the relationship with Figure 55A ROC curves related to the data.

[0112] - Figure 56A The graph shows the differences in EV nucleic acid content, particularly hsa-miR-320c, between healthy subjects and MM patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 56B It shows the relationship with Figure 56A ROC curves related to the data.

[0113] - Figure 57A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-150-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 57B It shows the relationship with Figure 57A ROC curves related to the data.

[0114] - Figure 58A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-155-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 58B It shows the relationship with Figure 58A ROC curves related to the data.

[0115] - Figure 59A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-10a-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 59B It shows the relationship with Figure 59A ROC curves related to the data.

[0116] - Figure 60AThe graph shows the difference in nucleic acid content of EVs, particularly hsa-miR-146b-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 60B It shows the relationship with Figure 60A ROC curves related to the data.

[0117] - Figure 61A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-122-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 61B It shows the relationship with Figure 61A ROC curves related to the data.

[0118] - Figure 62A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-203a-3p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 62B It shows the relationship with Figure 62A ROC curves related to the data.

[0119] - Figure 63A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-10a-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 63B It shows the relationship with Figure 63A ROC curves related to the data.

[0120] - Figure 64A The graph shows the difference in EV nucleic acid content, particularly hsa-miR-181a-5p, between healthy subjects and AML patients. This nucleic acid content was obtained through sequencing and is expressed as read count. Figure 64B It shows the relationship with Figure 64A ROC curves related to the data;

[0121] - Figure 65A The graph shows the difference in nucleic acid content of EVs, particularly hsa-miR-125-5p, between healthy subjects and NSCLC patients. This nucleic acid content was obtained by droplet digital PCR (ddPCR) and is expressed in copies / µL. Figure 65B It shows the relationship with Figure 65A ROC curves related to the data;

[0122] - Figure 66AThe graph shows the difference in nucleic acid content of EVs, particularly hsa-miR-451a, between healthy subjects and NSCLC patients. This nucleic acid content was obtained by ddPCR and is expressed in copies / µL. Figure 66B It shows the relationship with Figure 66A ROC curves related to the data;

[0123] - Figure 67 This is a flowchart of a preferred embodiment of a method for diagnosing a disease in a subject based on EVs obtained from a serum sample previously taken from the subject, according to one aspect of the present invention;

[0124] - Figure 68 This is a flowchart of a method for diagnosing a disease in a subject based on analysis of the nucleic acid content in EVs obtained from a serum sample previously taken from the subject, according to another aspect of the present invention. Example

[0125] This process was tested in the serum of patients with cancer, more precisely: - Chronic lymphocytic leukemia (CLL), 88 patients - Multiple myeloma (MM), 20 patients - Acute myeloid leukemia (AML), 40 patients - Non-small cell lung cancer (NSCLC), 21 patients And 25 healthy subjects.

[0126] Extracellular vesicles were isolated from the serum of the subjects mentioned above.

[0127] In a comparison between CLL patients and healthy subjects, six biomarkers associated with EV were analyzed: I) Size, II) EV quantity, III) The amount of positive EVs for a single surface antigen. IV) The amount of positive EVs against the dual surface antigens, V) The average fluorescence intensity of the surface antigen, and VI) Nucleic acid content, Furthermore, a threshold value was defined to distinguish between healthy subjects and patients.

[0128] The following surface antigens were analyzed in EVs: CD200, CD19, and CD20 individually, and combinations of CD19 and CD20, as well as hsa-miR-484 and hsa-miR-93-5p, were quantified.

[0129] In comparisons between MM patients and healthy subjects, the six biomarkers I-VI associated with EV were analyzed, and cutoff values ​​were defined to distinguish between healthy subjects and patients. The following surface antigens were analyzed in EV: CD38 and CD138, alone and in combination. MicroRNAs identified included: hsa-miR-203a, hsa-miR-320c, hsa-miR-320d, hsa-miR-122-5p, and hsa-miR-125-5p.

[0130] In comparisons between AML patients and healthy subjects, the six biomarkers I-VI associated with EVs were analyzed, and cutoff values ​​were defined to distinguish between healthy subjects and patients. The following surface antigens on EVs were analyzed: CD34, CD117, and CXCR4 alone, as well as combinations of CD34 and CD117. MicroRNAs identified included: hsa-miR-150-5p, hsa-miR-181b-5p, hsa-miR-155-5p, hsa-miR-10a-5p, hsa-miR-125b-5p, hsa-miR-146b-5p, hsa-miR-122-5p, hsa-miR-203a-3p, hsa-miR-222-3p, and hsa-miR-181a-5p.

[0131] In comparisons between NSCLC patients and healthy subjects, the six biomarkers I-VI associated with EVs were analyzed, and cutoff values ​​were defined to distinguish between healthy subjects and patients. Specifically, the following surface antigens were analyzed on EVs: EpCAM, CD137, and PD-L1 alone, as well as combinations of EpCAM and CD137. MicroRNAs identified included hsa-miR-125-5p and hsa-miR-451a.

[0132] Figures 1A to 16B Data showing differences between healthy subjects and cancer patients were presented for various hematologic malignancies such as chronic lymphocytic leukemia (CLL). Figures 1A to 4B Multiple myeloma (MM) Figures 5A to 8B Acute myeloid leukemia (AML) Figures 9A to 12B ) and solid tumors such as non-small cell lung cancer (NSCLC, Figures 13A to 16B Plotting EV dimensions in the NTA analysis. EV dimensions are indicated by several parameters obtained from the NTA analysis, such as D10 (…). Figures 1A to 1B , Figures 5A to 5B , Figures 9A to 9B and Figures 13A to 13B ), which indicates that the size distribution contains 10% of the EV; D90 ( Figures 2A to 2B , Figures 6A to 6B , Figures 10A to 10Band Figures 14A to 14B ), which indicates the point containing 90% of the EV; average diameter ( Figures 3A to 3B , Figures 7A to 7B , Figures 11A to 11B and Figures 15A to 15B ); and mode ( Figures 4A to 4B , Figures 8A to 8B , Figures 12A to 12B and Figures 16A to 16B The graphs (Figures 1 through 16 / A) show the differences between healthy subjects and patients. Each symbol (circle or square) represents a subject. Larger horizontal bars indicate the mean, and smaller horizontal bars indicate the standard deviation. The second graph (Figures 1 through 16 / B) shows the ROC curves, with their respective areas under the curve (AUC) and statistical power p, indicated by the corresponding number of asterisks: *p≤0.05, **p≤0.01, ***p≤0.001, ****p≤0.0001. AUC is a measure of test accuracy: the closer its value is to 1, the more accurate the test.

[0133] Figures 1 through 16 / A show that all parameters of EV (D10, D90, mean, and mode) were higher in patients (88 CLL, 14 MM, 20 AML, and 21 NSCLC) than in healthy subjects (up to n=25). This difference between patients and healthy subjects was significant.

[0134] Analysis of the ROC curves (Figures 1 to 16 / B) shows that the test is accurate in different tumors. Specifically: - For CLL, the best test is based on D10 (AUC=0.71, p=0.0018). Figure 1B ); - For MM, the best test is based on the D10 test (AUC=0.74, p=0.02, Figure 5B ); - For AML, the best test is the D10-based test (AUC=0.81, p=0.0003). Figure 9B ) and D90-based tests (AUC=0.81, p=0.0009, Figure 10B ); - For NSCLC, the best test is based on D90 (AUC=0.70, p=0.02). Figure 14B ) and mean-based tests (AUC=0.69, p=0.03, Figure 15B ).

[0135] Figures 17A to 24B Data on differences between healthy subjects and patients with the disease were presented, specifically for CLL ( Figures 17A to 18B ), MM ( Figures 19A to 20B ), AML ( Figures 21A to 22B ) and NSCLC ( Figures 23A to 24B A graph was plotted of EV levels in serum. EV levels were obtained using two different methods, such as NTA ( Figure 17A / 17B、 Figure 19A / 19B、 Figure 21A / 21B、 Figures 23A to 23B ) and flow cytometry ( Figure 18A / 18B、 Figure 20A / 20B、 Figure 22A / 22B、 Figures 24A to 24B For each quantity (EV / ml serum) obtained using a specific technique, the differences between healthy subjects and diseased patients are shown (Figures 17 to 24 / A) and the corresponding ROC curves (Figures 17 to 24 / B), indicating their respective AUC and statistical power values. Again, each symbol represents a single subject. The horizontal bars in the figures indicate the mean versus standard deviation.

[0136] Figures 17 to 24 / A show that the serum EV levels in patients (88 CLL, 14 MM, 20 AML, and 21 NSCLC) were higher than those in healthy subjects (up to n=25), which was generally statistically significant (p<0.05).

[0137] Analysis of the ROC curves (Figures 17 to 24 / B) shows that all tests were accurate, with AUCs ranging from 0.66 to 0.99.

[0138] Specifically, the following were obtained: - For CLL, the best test is the test that assesses the amount of EV in serum via NTA (AUC=0.76, p<0.0001). - For MM, the best test is the test to assess the amount of EV in serum via NTA (AUC=0.82, p=0.007). - For AML, the best test is the test that assesses the amount of EV in serum via NTA (AUC=0.99, p<0.0001). - For NSCLC, the best test is the test that assesses the amount of EV in serum via NTA (AUC=0.67, p=0.05).

[0139] Figures 25A to 35B Data showing differences between healthy subjects and patients with the disease were presented for CLL ( Figures 25A to 27B ), MM ( Figures 28A to 29B ), AML ( Figures 30A to 32B ) and NSCLC ( Figures 33A to 35BThe amount of EVs positive for a specific surface antigen (CD) was plotted. The amount of positive EVs for a single antigen was obtained by flow cytometry. For each amount of EVs positive for a specific antigen (CDX), the plotted values ​​were plotted. + The figure (EV / ml serum) shows the difference between healthy subjects and patients with the disease (Figures 25 to 35 / A) and the corresponding ROC curves (Figures 25 to 35 / B), indicating their respective AUC and statistical power values. Again, each symbol represents a subject, and the horizontal bars in the figures indicate the mean and standard deviation.

[0140] The surface antigens considered are tumor-dependent; therefore, they may differ between the diseases studied. Specifically, the following items were quantified: - In the case of CLL, EVs that are positive for CD19 (CD19) + EV / ml), targeting CD20-positive EVs (CD20 + EV / ml), targeting CD200-positive EVs (CD200) + EV / ml); - In the case of MM, EVs that are positive for CD38 (CD38) + EV / ml), targeting CD138-positive EVs (CD138) + EV / ml); - In the case of AML, EVs that are positive for CD34 (CD34) + EV / ml), targeting CD117-positive EVs (CD117 + EV / ml), targeting CXCR4-positive EVs (CXCR4 + EV / ml); - In the case of NSCLC, EVs that test positive for EpCAM (EpCAM) + EV / ml), targeting CD137-positive EVs (CD137 + EV / ml), targeting PD-L1-positive EVs (PD-L1) + EV / ml).

[0141] Figures 25A to 35B It is shown that: - The number of EVs that were positive for CD19, CD20, or CD200 in CLL patients (n=88) was statistically significantly higher than in healthy subjects (n=19). - The number of CD38 or CD138-positive EVs in CLL patients (n=14) was statistically significantly higher than in healthy subjects (n=20). - CLL patients (n=10) had more EVs that were positive for CD34, CD117 or CXCR4 than healthy subjects (n=6). - CLL patients (n=10) had more EVs that were positive for EpCAM, CD137, or PD-L1 than healthy subjects (n=6).

[0142] Analysis of the ROC curves (Figures 25 to 35 / B) shows that most tests are accurate, with AUCs ranging from 0.560 to 0.880. Specifically, for the different CDs analyzed, the following results were obtained: - For CLL, the best test is one that assesses the amount of CD20-positive EV (AUC=0.88, p<0.0001). - For MM, the best test is a test to assess the amount of CD38-positive EV (AUC=0.85, p=0.0006). - For AML, the best test is one that assesses the amount of CD34-positive EVs (AUC=0.88, p=0.01). - For NSCLC, the best test is one that assesses the amount of EV that is positive for EpCAM (AUC=0.86, p=0.04).

[0143] Figures 36A to 39B Data showing differences between healthy subjects and patients with the disease were presented for CLL ( Figure 36A and Figure 36B ), MM ( Figure 37A and Figure 37B ), AML ( Figure 38A and Figure 38B ) and NSCLC ( Figure 39A and Figure 39B The amount of EVs positive for both surface antigens was plotted. The amount of EVs positive for both CD4 antigens was obtained by flow cytometry. For each amount of EVs positive for both surface antigens (CD4X), the amount was plotted. + CDY + The figure (EV / ml serum) shows the difference between healthy subjects and patients with the disease (Figures 36 to 39 / A) and the corresponding ROC curves (Figures 36 to 39 / B), indicating their respective AUC and statistical power values. Again, each symbol represents a subject, and the horizontal bars in the figures indicate the mean and standard deviation.

[0144] Specifically, the following items were quantified: - In the case of CLL, EVs that are positive for both CD19 and CD20 (CD19) + CD20 + EV / ml); - In the case of MM, EVs that are simultaneously positive for both CD38 and CD138 (CD38 + CD138 + EV / ml); - In the case of AML, EVs that are simultaneously positive for both CD34 and CD117 (CD34) + CD117 + EV / ml); - In the case of NSCLC, EVs that are simultaneously positive for CD137 and EpCAM (CD137 + EpCAM + EV / ml).

[0145] Figures 36A to 39B It is shown that: - The number of EVs that were simultaneously positive for both CD19 and CD20 was statistically significantly higher in CLL patients (n=88) than in healthy subjects (n=19). - CLL patients (n=15) had more EVs that were positive for both CD38 and CD138 than healthy subjects (n=20), which was statistically significant; - CLL patients (n=10) had more EVs that were simultaneously positive for CD34 and CD117 than healthy subjects (n=6). - CLL patients (n=6) had more EVs that were positive for both CD137 and EpCAM than healthy subjects (n=5).

[0146] Analysis of the ROC curves (Figures 36 to 39 / B) shows that all tests were accurate, with AUCs ranging from 0.71 to 0.86.

[0147] Figure 40A / Figure 49B Data showing the difference in EV between healthy subjects and patients with the disease were presented for CLL ( Figures 40A to 42B ), MM ( Figures 43A to 44B ), AML ( Figures 45A to 47B ) and NSCLC ( Figures 48A to 49B The mean fluorescence intensity (MFI) or MFI difference (MFI diff) of EVs positive for a specific surface antigen (CD) is plotted in the figure. The MFI for CD-specific positive EVs was obtained by flow cytometry. For the MFI of CD-specific positive EVs, the differences between EVs from healthy subjects and those from diseased patients are shown (Figures 40 to 49 / A), along with the corresponding ROC curves (Figures 40 to 49 / B) and their respective AUC and statistical power values. Again, each symbol represents a subject, and the horizontal bars in the figure indicate the mean and standard deviation.

[0148] Specifically, the MFI of the following items was analyzed: - In CLL, EVs that are positive for CD19 (MFI CD19), CD20 (MFI CD20), or CD200 (MFI CD200); - In MM, EVs that are positive for CD38 (MFI CD38) or CD138 (MFI CD138); - In AML, EVs that are positive for CD34 (MFI CD34), CD117 (MFI CD117), or CXCR4 (MFI CXCR4); - In NSCLC, EVs that are positive for PD-L1 (PD-L1 BV421 MFI difference) or EpCAM (EpCAM BV421 MFI difference).

[0149] Figure 40A / Figure 49B It is shown that: - The MFI for CD20 or CD200 positive EVs in CLL patients (n=88) was statistically significantly higher than that in the corresponding EVs in healthy individuals (n=19), and the MFI for CD19 positive EVs was lower than that in the corresponding EVs in healthy individuals (n=19). - The MFI of CLL patients (n=15) against CD38 or CD138 positive EVs was statistically significantly lower than that of healthy individuals (n=20) with the corresponding EVs; - CLL patients (n=10) had a higher MFI against CD34 or CXCR4-positive EVs than the corresponding EVs in healthy individuals (n=6), while their MFI against CD117-positive EVs was lower than the corresponding EVs in healthy individuals (n=6).

[0150] The MFI difference for EpCAM or PDL1-positive EVs in CLL patients (n=21) was higher than that in the corresponding EVs in healthy individuals (n=5). Analysis of the ROC curves (Figures 40 to 49 / B) showed that all tests were accurate, with AUCs between 0.50 and 0.79. Specifically, in the MFI or MFI difference for the different CD-positive EVs analyzed, the following was obtained: - For CLL, the best test is MFI of CD20 positive EV (AUC=0.79, p<0.0001). - For MM, the best test is MFI of CD38 positive EV (AUC=0.78, p=0.005). - For AML, the best test is MFI for CD34-positive EV (AUC=0.76, p=0.08). - For NSCLC, the only optimal test is the MFI difference of PDL1-positive EVs (AUC=0.71, p=0.14).

[0151] Figures 50A to 66B It shows healthy subjects and CLL patients ( Figures 50A to 51B MM patients () Figures 52A to 56B AML patients Figures 57A to 64B ) and NSCLC patients ( Figures 65A to 66B The differences in nucleic acid content, particularly microRNA (miR), between EVs were analyzed. The miR content of EVs was calculated using two parameters: the number of reads (…). Figures 52A to 64B ) or copy / microliter ( Figures 50A to 51B and Figures 65A to 66B These parameters were obtained by sequencing RNA extracted from EVs or by ddPCR (see [link to documentation]). Figure 67 ).

[0152] For CLL, hsa-miR-484 was quantified by ddPCR ( Figure 50A ) and hsa-miR-93-5p ( Figure 51A The number of miR copies per microliter of reaction (miR copies / µL) was obtained. These two miRs are related because, according to scientific literature, they are associated with tumor formation.

[0153] For NSCLC, hsa-miR-125-5p was quantified by ddPCR. Figure 65A ) and hsa-miR-451a ( Figure 66A The number of miR copies per microliter of reaction (miR copies / µL) was obtained. These two miRs are related because, according to scientific literature, they are associated with tumors.

[0154] For MM and AML, small RNAs (seqRNAs) extracted from healthy subjects and EVs of MM and AML patients were sequenced. From the seqRNAs, a list of miRs differentially expressed (by read count) between healthy subjects and patients was obtained. The following miRs were selected and reported: -In MM: -hsa-miR-122-5p ( Figure 52A ) -hsa-miR-125b-5p ( Figure 53A ) -hsa-miR-203a-3p ( Figure 54A ) -hsa-miR-320d ( Figure 55A ) -hsa-miR-320c ( Figure 56A ) - In AML: -hsa-miR-150-5p ( Figure 57A ) -hsa-miR-155-5p ( Figure 58A ); -hsa-miR-10a-5p ( Figure 59A ) -hsa-miR-146b-5p ( Figure 60A ) -hsa-miR-122-5p ( Figure 61A ) -hsa-miR-203a-3p ( Figure 62A ) -hsa-miR-222-5p ( Figure 63A ) -hsa-miR-181a-5p ( Figure 64A ).

[0155] For each parameter, read count or copy number per microliter, the differences between healthy subjects and patients with the disease are shown (Figures 50 to 66 / A), along with the corresponding ROC curves (Figures 50 to 66 / B) and their respective AUC and statistical power values. Again, each symbol represents one subject. The horizontal bars in the figures indicate the mean versus standard deviation. The analyzed miR is indicated below the x-axis.

[0156] Figures 50A to 66A The quantities of the following items are shown: miR-484 in EVs was lower in CLL patients (n=10) than in healthy subjects (n=16, p=0.009). miR-93-5p in EVs was lower than in healthy subjects (n=16, p=0.0394) in CLL patients (n=10). miR-122-5p, miR-125b-5p, miR-203a-3p, miR-320d, and miR-320c in EVs were higher than in healthy subjects (n=7, p=0.03, p=0.06, and p=0.04). The levels of miR-150-5p, miR-155-5p, miR-10a-5p, miR-146b-5p, miR-122-5p, miR-203a-3p, miR-222-5p, and miR-181a-5p in EVs were higher than in healthy subjects (n=8, p<0.05). miR-125b-5p in -EVs was higher in NSCLC patients (n=7) than in healthy subjects (n=5); miR-451a in -EV was higher in NSCLC patients (n=6) than in healthy subjects (n=-5, p<0.01).

[0157] Analysis of the ROC curves (Figures 50 to 66 / B) shows that all tests were accurate, with AUCs ranging from 0.65 to 1.00.

[0158] Specifically, for the different miRs analyzed, we obtain: - For CLL, the best test is the test to evaluate the copy number / µL of miR-484 (AUC=0.8, p=0.011). - For MM, the best test is the test that evaluates the number of reads for miR-125b-5p and miR203a-3p (AUC=0.90, p=0.01). - For AML, the best test is the test that evaluates the number of reads of miR-146b-5p (AUC=0.93, p=0.0004). - For NSCLC, the best test is the miR-451a copy number / µL test (AUC=1.00, p=0.006).

[0159] Tables 1 through 4 present the following information for CLL, MM, AML, and NSCLC tumors: biomarker set, biomarker, parameter, cutoff value, sensitivity, specificity, and statistical power. The threshold values ​​(cutoff values) for each parameter, along with the corresponding sensitivity, specificity, and statistical power values, which distinguish healthy subjects from patients with CLL, MM, AML, or NSCLC, were obtained through ROC analysis using Graphpad Prism software.

[0160] ROC curve analysis returns a list of possible cutoff values, each associated with a combination of sensitivity (the proportion of true positives or sick subjects = the proportion of correctly classified sick subjects) and specificity (the proportion of correctly classified healthy subjects). To select the optimal cutoff value—that is, the cutoff value that combines and maximizes sensitivity and specificity—the Youden index is calculated via the link mdapp.ca / youden-index-calculator-479 / by inputting the values ​​of sensitivity and specificity. The closer the index value is to 1, the better the combination of sensitivity and specificity of the test.

[0161] Table 1 - Chronic Lymphoblastic Leukemia (CLL)

[0162]

[0163] Table 2 - Multiple Myeloma (MM)

[0164]

[0165] Table 3 - Acute Myeloid Leukemia (AML)

[0166]

[0167] Table 4 - Non-small cell lung cancer (NSCLC)

[0168] Example 1 Each biomarker was scored by assigning it an equal weight.

[0169] In the serum of the subjects, after obtaining the values ​​of all parameters of a single biomarker, a score is assigned to each biomarker.

[0170] The score for each biomarker is equal to: -1, if at least one of the parameters defining the biomarker has: - Values ​​higher than their reference threshold, if the threshold is marked with a > sign. - Values ​​below their reference threshold, if the threshold is marked with a < symbol. -0, if all parameters defining the biomarker have: - Values ​​that are lower than or equal to their reference threshold, if the threshold is marked with a > sign. - Values ​​that are higher than or equal to their reference critical value, if the critical value is marked with a < symbol.

[0171] The final score is calculated as the sum of the partial scores obtained for each biomarker. Specifically, the final score can have a value between 0 and 6.

[0172] Based on the final score obtained in this way, the probability of the subject developing the disease is defined. Specifically: - The probability is high if the final score is 4 or higher; - If the final score is 3, the probability is moderate; - If the final score is less than or equal to 2, the probability is low. Example 2 Each biomarker was scored by assigning different weights to each biomarker.

[0173] In the serum of the subjects, after obtaining the values ​​of all parameters for each biomarker, a score is assigned to each biomarker.

[0174] The score for each biomarker is equal to: - For biomarkers "EV size" and "EV amount" being 1, for biomarkers "amount of EV positive for a single CD" and "EV fluorescence" being 2, or for biomarkers "amount of EV positive for dual CD" and "nucleic acid content" being 3, if at least one of the parameters defining the biomarker has - Values ​​higher than their reference threshold, if the threshold is marked with a > sign. - Values ​​below their reference threshold, if the threshold is marked with a < symbol. -0, if all parameters defining the biomarker have: - Values ​​that are lower than or equal to their reference threshold, if the threshold is marked with a > sign. - Values ​​that are higher than or equal to their reference critical value, if the critical value is marked with a < symbol.

[0175] The final score is calculated as the sum of the partial scores obtained for each biomarker. Specifically, the final score can have a value between 0 and 12.

[0176] The probability of a subject having the disease will be defined based on the final score obtained in this way. Specifically: - The probability is high if the final score is 6 or higher; - If the final score is between 4 and 5, the probability is moderate; - If the final score is less than or equal to 3, the probability is low. Example 3 : Each parameter is scored by assigning equal weight to each parameter.

[0177] In the serum of the subjects, after obtaining the values ​​of all parameters for each biomarker, a score is assigned to each parameter.

[0178] The sub-score for each parameter is equal to: -1, if its value is: - Higher than its reference threshold, if the threshold is marked with a > sign. - If the value is below its reference threshold, and that threshold is marked with a < symbol. -0, if its value is: - Less than or equal to its reference threshold, if the threshold is marked with a > sign. - Higher than or equal to its reference critical value, if the critical value is marked with a < symbol.

[0179] The final score is calculated as the sum of the sub-scores obtained for each parameter (or the sum of the sub-scores for each biomarker, which are the same). Specifically, this score can have values ​​between 0 and 21.

[0180] The probability of a subject having the disease will be defined based on the final score obtained in this way. Specifically: - The probability is high if the final score is 10 or higher; - If the final score is between 6 and 9, the probability is moderate; - If the final score is less than or equal to 5, the probability is low. Example 4 Each parameter is scored by assigning different weights to each parameter.

[0181] In the serum of the subjects, after obtaining the values ​​of all parameters for each biomarker, a sub-score is assigned to each parameter.

[0182] The sub-score for each parameter is equal to: - The parameter for biomarkers "EV size" and "EV amount" is 1; the parameter for biomarkers "Amount of EV positive for a single CD" and "EV fluorescence" is 2; and the parameter for biomarkers "Amount of EV positive for dual CD" and "Nucleic acid content" is 3. If its value... - Higher than its reference threshold, if the threshold is marked with a > sign. - If the value is below its reference threshold, and that threshold is marked with a < symbol. -0, if its value is: - Less than or equal to its reference threshold, if the threshold is marked with a > sign. - Higher than or equal to its reference critical value, if the critical value is marked with a < symbol.

[0183] The final score is calculated as the sum of the sub-scores obtained for each parameter (or the sum of the sub-scores for each biomarker, which are the same). Specifically, this score can have values ​​between 0 and 45.

[0184] The probability of a subject having the disease will be defined based on the final score obtained in this way. Specifically: - The probability is high if the final score is 14 or higher; - If the final score is between 8 and 13 or equal to 8 and 13, the probability is moderate; - If the final score is less than or equal to 7, the probability is low. Example 5 Sum the values ​​from the three biomarker groups and assign the same value to each group.

[0185] After obtaining values ​​for all parameters of each biomarker in the subjects' serum, a partial score was assigned to each biomarker group. The partial score for each biomarker group was as follows: - For the "EV Size / Quantity" group, an integer value between 0 and 2 (0, 1, or 2) is used because this group includes two biomarkers, I and II. In practice, each biomarker has the following sub-score values: -1, if at least one of the parameters defining the biomarker has: - Values ​​higher than their reference threshold, if the threshold is marked with a > sign. - Values ​​below their reference threshold, if the threshold is marked with a < symbol. -0, if all parameters defining the biomarker have: - Values ​​that are lower than or equal to their reference threshold, if the threshold is marked with a > sign. - Values ​​that are higher than or equal to their reference critical value, if the critical value is marked with a < symbol. - For the "EV Surface" group, an integer value between 0 and 3 (0, 1, 2, or 3) is used because this group includes three biomarkers, III, IV, and V. The same applies to the sub-scores of biomarkers; - For the "EV content" group, values ​​between 0 and 1 are used because this group only includes biomarker VI. The same applies to the sub-scores of biomarkers.

[0186] The final score is calculated as the sum of the partial scores obtained for each biomarker group. Specifically, this score can have values ​​between 0 and 6.

[0187] The probability of a subject having the disease will be defined based on the final score obtained in this way. Specifically: - If the final score is 3 or higher, the probability is high. - If the final score is 2, the probability is moderate. - If the final score is less than or equal to 1, the probability is low. Example 6 : The biomarker groups were summed by assigning different values ​​to each group of biomarkers.

[0188] In the subjects' serum, after obtaining values ​​for all parameters of each biomarker, a partial score was assigned to each biomarker group. The partial scores were: - For the "EV size / quantity" group, the value is 0, 1, or 2 because this category includes two biomarkers, I and II. Each biomarker has the following sub-score values: -1, if at least one of the parameters defining the biomarker has: - Values ​​higher than their reference threshold, if the threshold is marked with a > sign. - Values ​​below their reference threshold, if the threshold is marked with a < symbol. -0, if all parameters defining the biomarker have: - Values ​​that are lower than or equal to their reference threshold, if the threshold is marked with a > sign. - Values ​​that are higher than or equal to their reference critical value, if the critical value is marked with a < symbol. - For the "EV Surface" group, the value is 0, 2, 4, or 6 because this category includes three biomarkers: III, IV, and V. Each biomarker has the following sub-score values: -2, if at least one of the parameters defining the biomarker has: - Values ​​higher than their reference threshold, if the threshold is marked with a > sign. - Values ​​below their reference threshold, if the threshold is marked with a < symbol. -0, if all parameters defining the biomarker have: - Values ​​that are lower than or equal to their reference threshold, if the threshold is marked with a > sign. - Values ​​that are higher than or equal to their reference critical value, if the critical value is marked with a < symbol. - For the "EV content" group, values ​​between 0 and 3 are used because this category only includes biomarker VI. Each biomarker has the following sub-score values: -3, if at least one of the parameters defining the biomarker has: - Values ​​higher than their reference threshold, if the threshold is marked with a > sign. - Values ​​below their reference threshold, if the threshold is marked with a < symbol. -0, if all parameters defining the biomarker have: - Values ​​that are lower than or equal to their reference threshold, if the threshold is marked with a > sign. - Values ​​that are higher than or equal to their reference critical value, if the critical value is marked with a < symbol.

[0189] The final score is calculated as the sum of the partial scores obtained for each biomarker group. Specifically, the final score can have a value between 0 and 11.

[0190] The probability of a subject having the disease will be defined based on the final score obtained in this way. Specifically: - If the score is 8 or higher, the probability is high. - If the score is between 4 and 7, the probability is moderate. - If the score is less than or equal to 3, the probability is low.

[0191] Figure 67This is a flowchart of a preferred variant of a method for diagnosing lesions in a subject based on EV 3a, 3b obtained from a serum sample 2 previously taken from the subject's blood 1, according to one aspect of the invention. The variant includes the two aspects of the invention described above, namely, it is based on a total score obtained by combining partial scores assigned to multiple parameters representing six biomarkers, including: - The dimensions (I) of the EV; - The total amount of the EV (II); - The amount of the EV that expresses the first antigen (i.e., is positive for the first antigen) on its own surface (III). - The amount (IV) of the EV expressing both the first antigen and the second antigen; -MFI(V) for each of the first antigen and the second antigen; -The nucleic acid (VI) content of these EVs, Furthermore, following quantitative fluorescence 63 and reverse transcription 64, microRNA was measured using ddPCR technology 65 applied to 3b precipitates automatically extracted from 2b serum samples to determine representative parameters of nucleic acid content.

[0192] In fact, this variation of the method involves the following steps: - Serum sample 2 was obtained from the peripheral blood 1 of the subject; - Separate the first EV precipitate 3a from the first portion 2a of serum sample 2; - The first EV precipitate was suspended in a brine solution to obtain wet EV precipitate 3a; - Take the first part of the EV precipitate; -31 NTA analysis was performed on the first wet fraction of the EV precipitate to obtain: -EV size distribution curve, and - The first concentration value of EV in wet EV precipitate; - Take the second part of the wetted EV precipitate; -41 The second part of the wet EV precipitate was subjected to flow cytometry to obtain - The concentration of total EV in the second part of the wet EV precipitate; - The concentration value of EVs specific to the first antigen in the second part of the wet EV precipitate; - The concentration of EVs specific to the second antigen in the wet EV precipitate; - The concentration values ​​of EVs that are specific to both the first and second antigens in the second part of the wet EV precipitate. - The amount of positive fluorescence against the first antigen and against the second antigen; - The first determination of the content of specific microRNAs includes the following steps: -51 Nucleic acid was manually extracted from the third wet fraction of the EV precipitate (3a). -52 pairs of nucleic acids extracted from wet EV precipitate 3a were sequenced. - A second determination of the content of specific microRNAs, including the following steps: -61 The second EV precipitate 3b was separated from the second part 2b of serum sample 2 by centrifugation; -62 Nucleic acid (RNA) was extracted from the second precipitate 3b using an automated extractor; -63 Fluorescent quantification of microRNA in second precipitate 3b; -64 reverse transcribes a portion of RNA into cDNA; -65 MicroRNAs were determined using ddPCR technology.

[0193] In step 61 of EV separation, for each case, the serum sample stored at -80°C is thawed at room temperature or alternatively at +4°C overnight. The thawed serum is vortexed, and 200 µl is transferred to a 1.5 ml tube and centrifuged initially at 200 g for 5 minutes at 4°C using a benchtop centrifuge (in this case, a Thermo Scientific MicroCL 21R centrifuge). The first supernatant is then collected without removing the precipitate (precipitate), transferred to a new 1.5 ml tube, and centrifuged again at 14300 g for 1 hour at 4°C using a benchtop centrifuge. The second supernatant is then aspirated using a P1000 pipette. The EV precipitate is resuspended in 200 µl of saline solution (PBS), filtered through a 0.22 µm filter, and vortexed. The resulting EV suspension is centrifuged a third time at 14300 g for 1 hour at 4°C. After removing the supernatant without carrying away the EV precipitate, the suspension is dried and ready for RNA extraction.

[0194] In the subsequent step of automatically extracting RNA from EVs, a modified Maxwell RSC miRNAPlasma and Serum kit (Promega, AS1680) was used in conjunction with the Maxwell RSC automated extractor (Promega).

[0195] This kit is designed for purifying total RNA and miRNA from previously isolated and resuspended plasma, serum, or EVs in solution. In particular, the kit yields optimal results when using 50 µl to 200 µl of EVs resuspended in TBS, TE, or water. EVs resuspended in PBS perform best when the volume used for purification is less than 75 µl.

[0196] The combination with an automated extractor allows for rapid RNA purification with minimal operator sample handling, maximizing simplicity and data reproducibility. The Maxwell RSC instrument uses pre-filled cartridges containing the necessary reagents for nucleic acid purification and can extract up to 16 samples simultaneously in approximately 70 minutes. To prevent RNA degradation, operation must be performed in an RNase-free environment. This requires careful cleaning of the work surface and the pipettes used.

[0197] In this invention, the kit has been modified to allow its use on dry EV precipitates, rather than on a given volume of EV.

[0198] Specifically, the EV precipitate was resuspended in 230 µl of lysis buffer (buffer C) provided in the kit and vortexed for 20 seconds. Then, 200 µl of nuclease-free H2O and 80 µl of proteinase K provided in the kit were added, and the mixture was vortexed again for 20 seconds. Subsequently, the suspension was incubated at 55 °C for 11 minutes, vortexing for 20 seconds every 3 minutes.

[0199] The original protocol associated with the kit requires incubation at 37°C for 15 minutes.

[0200] The lysed samples obtained in this way are then processed using the extractor mentioned above, following its operation with the help of Maxpre software, specifically using the "Maxwell RSC miRNA Plasma and Serum" program.

[0201] More specifically, insert the cartridge included with the kit into the extractor tray, with well 1 facing away from the elution tube, and then add 10 µl of DNase to well 4. For first-time use, add any DNase that is reconstituted in lyophilized form, following the procedure available in the protocol.

[0202] After inserting the provided pipette tip into position 8 of the cartridge, transfer all lysed samples to position 1 of the cartridge. Place the 0.5 ml elution tubes provided with the kit on the tray rack and add 80 µl of nuclease-free H2O.

[0203] The original plan specified the use of 50 µl of H2O.

[0204] The aforementioned procedure is then followed by an automated extraction process, and the tube containing the RNA sample is retrieved.

[0205] The RNA obtained above was used for quantitative fluorescence measurement step 63 using the "Qubit microRNA Assay" kit (Life Technologies, reference number Q32880) in conjunction with a QUIBIT 4.0 fluorometer (ThermoFisher Scientific). The kit shows a detection range of 0.5 ng to 150 ng and includes concentrated assay reagents, dilution buffer, and two pre-diluted miRNA standards.

[0206] For this type of analysis, the procedure must be performed in an RNase-free environment. Furthermore, Qubit provides optimal performance only when all solutions are at room temperature; small temperature variations can affect the accuracy of the assay. Therefore, appropriate precautions known to those skilled in the art must be followed.

[0207] The fluorescence quantification step 63 includes the following steps in sequence: - A working solution preparation step for both standards and samples, which includes diluting the reagents in buffer at a ratio of 1:200 after calculating the number of tubes provided, taking into account the volume of the test tubes provided; -The subsequent calibration curve preparation step involves mixing standards 1 and 2 with the working solution (specifically, 10 µl / 190 µl) in the corresponding first and second tubes, then vortexing for 2 to 3 seconds and incubating in the dark at room temperature for 2 minutes. After inserting the first tube, and then the second tube, into the Qubit reading chamber, the standards are read using specific software to obtain a calibration curve with concentration values. - Sample reading steps are performed by preparing appropriate tubes by mixing RNA with the working solution (specifically, 20 µl / 180 µl), incubating at room temperature in the dark for 2 minutes, and then reading the samples with the aid of specific software after inserting each tube into the Qubit reading chamber to obtain the concentration of the original sample (in ng / µl) and the concentration of the sample in the Qubit tube (in ng / ml).

[0208] The extracted RNA was reverse transcribed into cDNA using the "TaqMan Advanced miRNA cDNA Synthesis Kit" (AppliedBiosystems, No. A28007), step 64. The reverse transcription steps included, in sequence, the following steps: - The step of adding a poly-A tail at the 3'' end lasts approximately 60 minutes, during which the sample and cDNA synthesis reagent are thawed on ice, vortexed, and centrifuged, followed by the following: calculating the volume of RNA to be reverse transcribed, for example, corresponding to 100 pg; calculating the mixture volume based on the number of samples to be reverse transcribed, including 10% of each component of the mixture and considering the final reaction volume (mixture + H2O + RNA), specifically 5 µl (in this regard, an example of the mixture could include, for the reaction volume, 0.5 µl of 10X Poly(A) buffer, 0.5 µl of ATP, and 0.3 µl of Poly(A) enzyme, for a total reaction volume of 1.3 µl); and preparing the reaction mixture in 1.5 ml tubes, for each sample, transferring 1.3 µl of the mixture to a 0.2 µl tube, adding 100 pg of RNA and using RNase-free... The volume of H2O was adjusted to 5µl; then the first reaction was carried out in a thermal cycler according to the predetermined temperature profile, specifically at 37°C for 45 seconds, at 65°C for 10 seconds, and then cooled to 4°C. - The step of adding the adaptor to the 5'' end lasts approximately 60 minutes, during which a new reaction mixture is prepared by calculating the volume based on the number of samples in a 1.5 ml tube (e.g., in µl of the following quantities: 3 µl of 5X DNA ligase buffer, 4.5 µl of 50% PEG 8000, 0.6 µl of 25X ligator, 1.5 µl of RNA ligase, 0.4 µl of RNase-free H2O, for a total volume of 10 µl). The predetermined amount of mixture is then transferred to each tube containing the product of the first reaction (e.g., 10 µl, for a total volume of 15 µl), mixed, and centrifuged to remove air bubbles. The tubes are then placed in a thermal cycler for a second reaction according to a predetermined temperature profile, specifically at 16 °C for 60 seconds, and then allowed to cool to 4 °C. - The step of actually reverse transcribing all miRNAs into cDNA using universal primers lasts approximately 20 seconds, during which another reaction mixture is prepared by calculating the volume based on the number of samples in a 1.5 ml tube (e.g., in µl of the following quantities: 6 µl of 5X RT buffer, 1.2 µl of dNTP mixture (25 mM), 1.5 µl of 20X universal RT primers, 3 µl of 10X RT enzyme mixture, 0.4 µl of RNase-free H2O, total volume 15 µl). The predetermined amount of mixture is then transferred to each tube containing the product of the second reaction (e.g., 15 µl, total volume 30 µl), mixed, and then centrifuged to eliminate air bubbles. The tubes are then placed in a thermal cycler for a third reaction according to a predetermined temperature profile, specifically 15 seconds at 42°C, 5 seconds at 85°C, and then cooled to 4°C. If necessary, the product is stored at -20°C. -Then the cDNA amplification step (lasting approximately 32 minutes) can be performed, in which another reaction mixture is prepared by calculating the volume based on the amount of sample in a 1.5 ml tube (e.g., based on the following quantities in µl: 25 µl of 2X miR-Amp premix, 2.5 µl of 20X miR-Amp primer mixture, 17.54 µl of RNase-free H2O, total volume 45 µl). The predetermined amount of mixture, along with the product from the third reaction, is then transferred to a new tube (e.g., 45 µl, total volume 5 µl), mixed, and centrifuged to remove air bubbles. The tube is then placed in a thermal cycler for a fourth reaction according to a predetermined temperature profile: 42 °C for 5 seconds (1 cycle), 95 °C for 3 seconds + 60 °C for 30 seconds (14 cycles), 99 °C for 10 seconds, and then cooled to 4 °C. If necessary, the product is stored at -20 °C.

[0209] The product was then subjected to step 65, which involved microRNA quantification via droplet digital PCR (ddPCR) using a QX200 droplet digital PCR system (BioRad Laboratories) after the reaction system was prepared with 2x ddPCR premix (BioRad) and TaqMan miRNA probes (Applied Biosystem).

[0210] For each miRNA to be analyzed, a mixture is prepared by multiplying the total number of RNA samples to be analyzed (typically 11 µl of 2x ddPCR premix and 1 µl of the specific miRNA probe) by the number of samples, and one or two blank controls are added.

[0211] The reaction system was prepared in a 96-well plate by aliquoting 12 µl of the mixture into each well and adding 10 µl of RNA to each sample or 10 µl of water to the blank control, so that the total volume of each well was 22 µl. The mixture was then pipetted 10 times, and 20 µl was transferred to the middle row of wells of a cartridge assembled on its support. Then, 70 µl of "Dropletgenerator OIL for probes" was aliquoted into the bottom row of wells of the cartridge. The cartridge was then fixed in the "Droplet Generator". A signal indicated the end of the process. Transfer microdroplets (approximately 40 µl each in this case) into sealed 96-well plates, insert them into the plate sealer, first in one orientation, then the other, and then place them in a thermal cycler. Set the program, including an enzyme activation step, one cycle of denaturation at 95 °C for 10 seconds, 40 cycles of denaturation at 95 °C for 15 seconds; annealing / extension, 40 cycles of denaturation at 58 °C for 1 second; enzyme inactivation, one cycle of denaturation at 98 °C for 10 seconds, then lower the temperature to 4 °C and hold it for 30 seconds. Transfer the plate to a reader, and read it using QuantaSoft software after setting a threshold according to the blank control. Then determine the total copy number per µL, taking into account the dilution factor used.

[0212] The nonparametric Mann-Whitney Student t-test was used to identify significant differences in all six parameters between healthy subjects and patients with the disease. Differences with a p-value ≤ 0.05 were considered statistically significant. As previously described, each parameter was analyzed by ROC curves for both the healthy and patient groups using GraphPad Prism 6 software. The analysis returned: a) a graph showing the relationship between the sensitivity and specificity of the diagnostic test and the cutoff value, along with the area under the curve (AUC), which indicates diagnostic accuracy; and b) a table containing several numerical values ​​(cutoff values) associated with the combination of sensitivity and specificity percentages.

[0213] As is well known, the closer the ROC curve of such tests is to the top left corner of the graph, the more accurate the test is. The point closest to that corner represents the critical value that maximizes both the sensitivity and specificity of the test. If the AUC is 0.5, the test has no informational value (the curve will correspond to the bisector, and the test will be unable to distinguish between diseased and healthy individuals), but if the AUC value is between 0.5 and 1, the test gradually becomes more accurate.

[0214] From the table, select the value that maximizes both the sensitivity and specificity of the test as the cutoff value. The goal of the test is to correctly classify subjects (disease-related or healthy). Misclassified patients are called "false positives" and "false negatives".

[0215] The sensitivity of a diagnostic test is the proportion of correctly classified patients (sensitivity = true positive / total number of patients = VP / (VP + FN)). Conversely, the specificity of a test is the proportion of correctly classified healthy individuals (specificity = true negative / total number of healthy individuals = VN / (VN + FP)).

[0216] Figure 68 This is a flowchart of a method for diagnosing in vivo pathologies in a subject based on the analysis of nucleic acid content in EVs obtained from a serum sample previously taken from the subject, according to another aspect of the present invention, wherein the method is provided in accordance with... Figure 67 The same steps shown are used to determine the content, as an additional process including manual extraction and sequencing from the suspended EV precipitate.

[0217] The same method can be applied to samples of other biological fluids such as urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid; semen; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascites; and vitreous fluid.

[0218] The foregoing description of embodiments and examples of the present invention demonstrates the invention conceptually, in a manner that allows others to modify and / or adapt such embodiments in various applications using known techniques without further study and without departing from the spirit of the invention. Therefore, it should be understood that such adaptations and modifications are considered equivalent to the described embodiments. Tools and materials used to achieve the various functions can be of various types without departing from the scope of the invention. It should be understood that the expressions or terms used are purely descriptive and therefore not restrictive.

Claims

1. A method for detecting the presence or absence of lesions in a subject, the method comprising the following steps: - Define at least a first reference antigen and a second reference antigen; - Separate (21, 61) extracellular vesicle precipitates (3a, 3b) obtained from the biological fluid sample (2), the sample having been previously taken from the subject; - Measured (31, 41, 51-52) parameters representing six biomarkers (I-VI) from the extracellular vesicle deposits (3a, 3b), said biomarkers including: - The size of the extracellular vesicles (I); - The total amount of the extracellular vesicles (II); - The amount of the extracellular vesicles that are positive for the first antigen expressed on their own surface (III). - The amount of the extracellular vesicles expressing both the first antigen and the second antigen (IV); -The average fluorescence intensity (V) against each of the first antigen and the second antigen; - The nucleic acid content (VI) of the extracellular vesicles. - Input the corresponding predetermined threshold value for the parameter representing the biomarker; - Perform a first comparison between the parameter representing the biomarker and the corresponding critical value; -Based on the first comparison step, assign partial scores to each of the parameters representing the biomarkers or to the six biomarkers; - Calculate the final score of the subject; - Perform a second comparison between the final score and at least one predetermined diagnostic threshold; - Based on the second comparison, a probabilistic diagnosis of the lesion in the subject's body is established.

2. The method according to claim 1, wherein the biological fluid is selected from the group consisting of: blood; urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid; semen; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascites; vitreous fluid.

3. The method according to claim 1, wherein the biological fluid is peripheral blood (1).

4. The method according to claim 3, wherein the extracellular vesicle precipitates (3a, 3b) are extracted from plasma samples or serum samples (2) obtained from the peripheral blood sample (1).

5. The method according to claim 4, wherein the extracellular vesicle precipitate (3a) is extracted from the serum sample (2a), and the step of separating (21) the extracellular vesicle precipitate (3a) comprises the following steps: - The extracellular vesicle precipitate (22) is suspended in a saline solution to obtain a wet extracellular vesicle precipitate (3a).

6. The method according to claim 5, further comprising the following step: - Take the first portion of the wet extracellular vesicle precipitate (3a) described in (30); The steps of measuring representative parameters of the size (I) and total number (II) of the extracellular vesicles are performed jointly by the following: - Nanoparticle tracking analysis (31) was performed on the first portion of the wet extracellular vesicle precipitate (3a) to obtain: -The size distribution curve of the extracellular vesicles, and - The first concentration value of the extracellular vesicles in the wet extracellular vesicle precipitate.

7. The method of claim 5, wherein the step of separating the extracellular vesicle precipitate comprises the following steps: - Take the second portion of the wet extracellular vesicle precipitate (3a) described in (40); And the steps of measuring parameters representing the following: -The total amount of the extracellular vesicles (II); - The amount (III) of the extracellular vesicles expressing the first antigen; - The amount (IV) of the extracellular vesicles expressing both the first antigen and the second antigen; -The positive fluorescence (V) against the first antigen and against the second antigen The following steps will be performed together: - The second portion of the wet extracellular vesicle deposit was subjected to flow cytometry (41) to obtain: - The concentration value of total extracellular vesicles in the second part of the wet extracellular vesicle precipitate (II); - The concentration value (III) of extracellular vesicles specific to the first antigen in the second portion of the wet extracellular vesicle precipitate. - The concentration value of extracellular vesicles specific to the second antigen in the second portion of the wet extracellular vesicle precipitate; - The concentration value (IV) of extracellular vesicles in the second portion of the wet extracellular vesicle precipitate that are specific to both the first antigen and the second antigen. - The amount of positive fluorescence (V) against the first antigen and against the second antigen.

8. The method of claim 5, wherein the step of measuring the parameter representing nucleic acid content (VI) is a step of determining the content of a specific microRNA (VI), and includes the following steps: - Manually extract (51) nucleic acids from the wet extracellular vesicle precipitate; - Sequencing of the nucleic acid extracted from the wet extracellular vesicle precipitate (52).

9. The method of claim 4, wherein the step of measuring the parameter representing nucleic acid content (VI) comprises the following steps: - Nucleic acid (RNA) was automatically extracted from the extracellular vesicle precipitate using an automated extractor; - Perform fluorescence quantification of microRNA (63); - A portion of the RNA was reverse transcribed (64, 65) into cDNA; - Using digital PCR technology, especially droplet digital technology, the microRNA (VI) described in (65) was determined.

10. The method according to claims 8 and 9, wherein the step (51) of manually extracting the nucleic acid and the step (62) of automatically extracting the nucleic acid (RNA) by an automated extractor are performed on the corresponding first EV precipitate (3a) and second EV precipitate (3b) separated from the first part (2a) and the second part (2b) of the serum sample (2), respectively.

11. The method of claim 1, wherein the final score is calculated as the sum of the partial scores.

12. The method of claim 1, wherein the step of performing the second comparison of the final score is performed relative to two diagnostic thresholds, the two diagnostic thresholds comprising: - Upper diagnostic threshold, and -Lower diagnostic threshold, And the diagnosis is: - The high probability of the lesion if the final score is higher than or equal to the upper diagnostic threshold; - The moderate probability of the lesion if the final score is between the upper diagnostic threshold and the lower diagnostic threshold; - The low probability of the lesion if the final score is lower than or equal to the lower diagnostic threshold.

13. The method according to claim 1, wherein, In the step of defining multiple partial scores, a partial score is assigned to each of the six biomarkers (I-VI).

14. The method of claim 13, wherein for each of the biomarkers (I-VI), the fractional score is - A first value, if the value of at least one of the parameters representing the biomarker is: - A threshold value higher than the threshold value of the biomarker itself, if the threshold value is marked with a > sign; - The threshold value of the biomarker itself is below the threshold value if the threshold value is marked with a < symbol; - A second value lower than the first value, if the value of each parameter among the parameters representing the biomarker is: - Less than or equal to a threshold value of the biomarker itself, if the threshold value is marked with a > sign; - A threshold value higher than or equal to the biomarker itself, if the threshold value is marked with a < symbol.

15. The method according to claims 12 and 14, wherein for all six biomarkers (I-VI), the first value and the second value are the same value, in particular the first value and the second value are equal to 1 and 0, respectively, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 4 and 2, respectively.

16. The method of claim 14, wherein for each of the six biomarkers (I-VI), the first value and the second value are independently assigned.

17. The method of claim 16, wherein the first numerical value - For the following biomarkers, the same first lower value applies: - The size (I) of the extracellular vesicles, and - The amount of the extracellular vesicles (II); - For the following biomarkers, the same first intermediate value is used: - The amount (III) of the extracellular vesicles expressing the first antigen, and - The average fluorescence intensity (V) for each of the first antigen and the second antigen; and - For the following biomarkers, the same first highest value applies: - The amount (IV) of the extracellular vesicles expressing both the first antigen and the second antigen, and - The nucleic acid (VI) content of the extracellular vesicles.

18. The method according to claims 12 and 17, wherein the first lower value, the first intermediate value, and the first higher value are equal to 1, 2, and 3, respectively, the second value is equal to 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 6 and 3, respectively.

19. The method of claim 13, wherein The step of defining multiple component scores includes the step of defining multiple sub-component scores, wherein a sub-component score is assigned to each parameter representing the six biomarkers, and For each of the parameters, the sub-part score is: - The third value, if the value of the parameter is: - A threshold value higher than the threshold value of the biomarker itself, if the threshold value is marked with a > sign; - The threshold value of the biomarker itself is below the threshold value if the threshold value is marked with a < symbol; - A fourth value lower than the third value, if the value of the parameter is: - Less than or equal to a threshold value of the biomarker itself, if the threshold value is marked with a > sign; - A threshold value higher than or equal to the biomarker itself, if the threshold value is marked with a < sign. Furthermore, for each of the biomarkers, the partial score is calculated by summing the sub-partial scores assigned to the parameters representing the biomarker.

20. The method of claim 19, wherein for all said parameters, the third value and the fourth value are the same value, in particular the third value and the fourth value are equal to 1 and 0 respectively, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 10 and 5 respectively.

21. The method of claim 19, wherein for each of the parameters, the third value and the fourth value are assigned independently.

22. The method of claim 21, wherein the third value is: - For the parameter representing the following biomarkers, the same third lower value is used: - The size (I) of the extracellular vesicles, and - The amount of the extracellular vesicles (II); - For the parameters representing the following biomarkers, the same third intermediate value is used: - The amount (III) of the extracellular vesicles expressing the first antigen, and - The average fluorescence intensity (V) for each of the first antigen and the second antigen, and - For the parameter representing the following biomarkers, it is the same third highest value: - The amount (IV) of the extracellular vesicles expressing both the first antigen and the second antigen, and - The nucleic acid (VI) content of the extracellular vesicles.

23. The method of claim 22, wherein the third lower value, the third intermediate value, and the third higher value are equal to 1, 2, and 3, respectively, the fourth value is equal to 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 14 and 7, respectively.

24. The method according to claim 1, wherein: - Steps are provided for defining multiple groups of biomarkers, each group of which includes at least one of the six biomarkers; - The partial score assigned to each of the biomarkers is a sub-partial score; - In the step of defining multiple component scores, multiple sub-component scores are assigned to each of the biomarkers, and - For each biomarker group, the partial score is calculated by summing the sub-partial scores assigned to the biomarkers in the biomarker group. Each of the sub-part fractions mentioned above is equal to: - The fifth value, if it represents the value of at least one parameter among the parameters of at least one biomarker in the group: - A threshold value higher than the threshold value of the biomarker itself, if the threshold value is marked with a > sign; - The threshold value of the biomarker itself is below the threshold value if the threshold value is marked with a < symbol; - A sixth value lower than the fifth value, if representing the value of each parameter among the parameters of the biomarker in the group: - Less than or equal to a threshold value of the biomarker itself, if the threshold value is marked with a > sign; - A threshold value higher than or equal to the biomarker itself, if the threshold value is marked with a < symbol.

25. The method of claim 24, wherein for all six biomarkers, the fifth value and the sixth value are the same value, in particular the fifth value and the sixth value are equal to 1 and 0, respectively, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 3 and 1, respectively.

26. The method of claim 24, wherein the fifth value and the sixth value are independently associated for the six biomarkers.

27. The method of claim 26, wherein the fifth numerical value is: - For the following biomarkers, the same fifth lower value applies: - The size (I) of the extracellular vesicles, and - The amount of the extracellular vesicles (II); - For the following biomarkers, the same fifth intermediate value is used: - The amount (III) of the extracellular vesicles expressing the first antigen; - The amount (IV) of the extracellular vesicles expressing both the first antigen and the second antigen; and - The average fluorescence intensity (V) for each of the first antigen and the second antigen; and - For the following biomarkers, it is the fifth highest value: - The nucleic acid (VI) content of the extracellular vesicles.

28. The method according to claims 12 and 27, wherein the fifth lower value, the fifth intermediate value, and the fifth higher value are equal to 1, 2, and 5, respectively, the sixth value is equal to 0, and the upper diagnostic threshold and the lower diagnostic threshold are equal to 8 and 3, respectively.

29. The method according to any one of the preceding claims, wherein data obtained for each of the biomarkers are analyzed based on a statistical ROC (Reception Operating Characteristic) curve test. Specifically, the corresponding critical value is obtained through the ROC curve and serves as the test value that maximizes the difference between the true positive rate and the false positive rate.

30. The method of claim 1, wherein the lesion is a hematologic malignancy.

31. The method of claim 1, wherein the hematologic malignancy is selected from the group consisting of: chronic lymphocytic leukemia; multiple myeloma; acute myeloid leukemia.

32. The method according to claim 1, wherein the lesion is a solid tumor.

33. The method according to claim 1, wherein the solid tumor is non-small cell lung cancer.

34. The method according to claim 10, wherein the steps (21, 61) of separating the first extracellular vesicle precipitate (3a) and the second extracellular vesicle precipitate (3b) from the first portion (2a) and the second portion (2b) of the serum sample, respectively, are performed independently of each other by centrifugation in a benchtop centrifuge.

35. A method for detecting the presence or absence of a disease in a subject, comprising the step of measuring the nucleic acid content (VI) in a serum sample, the method comprising the following steps: -Extracellular vesicle precipitate (3b) was separated from the serum sample (2b); - Nucleic acid (RNA) was extracted (62) from the extracellular vesicle precipitate (3b) using an automated extractor; - Perform fluorescence quantification of microRNA (63); - A portion of the RNA was reverse transcribed (64) into cDNA; - The microRNA (VI) described in (65) was determined using droplet digital PCR technology.

36. A method for detecting the presence or absence of a disease in a subject, comprising the step of measuring the nucleic acid content (VI) in a sample of a biological fluid, the method comprising the following steps: - Separate extracellular vesicle precipitates (3b) from the sample (2b) of the biological fluid; - Nucleic acid (RNA) was extracted (62) from the extracellular vesicle precipitate (3b) using an automated extractor; - Perform fluorescence quantification of microRNA (63); - A portion of the RNA was reverse transcribed (64) into cDNA; - The microRNA (VI) described in (65) was determined using digital PCR technology, particularly droplet digital PCR technology.

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