Method for detecting the presence of a pathology in a subject, said method based on the analysis of extracellular vesicles

Abstract

A method for the diagnosis of diseases, in particular hematological malignancies or solid tumors, in a subject comprises the steps of isolating (21,61) an extracellular vesicle pellet (3a,3b) obtained from a sample (2) of a biological fluid previously taken from the subject; measuring (31,41,51-52), from said pellet, parameters representative of the six biomarkers (I) size, (II) total amount of the extracellular vesicles, (III) amount of the extra- cellular vesicles expressing on their own surface (III) a first antigen and (IV) a first and a second antigen, (V) mean fluorescence intensity for each of said antigens and (VI) nucleic acid content of the extracellular vesicles; performing a first comparison of the biomarker-representative parameters with respective cut-off values; assigning a partial score to each parameter or to the six biomarkers on the basis of the above first comparison step; calculating a final score, i.e. a subject score, as the sum of the partial scores; performing a second comparison of the final score with at least one predetermined diagnostic threshold value, preferably with two diagnostic upper and lower values; estimating the probability diagnosis of said pathology on the basis of the above second comparison step. The combination of the six biomarkers, in the context of personalized medicine, overcomes the prior art method limitations due to the high clinical heterogeneity between subjects, in terms of sensitivity, with reference to a given pathology, for one rather than the other biomarkers and their relative parameters selected from the six.

Description

TITLEMETHOD FOR DETECTING THE PRESENCE OF A PATHOLOGY IN A SUBJECT, SAID METHOD BASED ON THE ANALYSIS OF EXTRACELLULAR VESICLESDESCRIPTIONScope of the invention

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

[0002] In particular, the invention relates to the diagnosis of blood tumors of the lymphoid and myeloid type, e.g. multiple myeloma, chronic lymphocitic leukaemia, acute myeloid leukaemia, lymphomas in general, etc.; the diagnosis of solid tumors, such as tumors of the nervous system, of the head or the neck, gastro-intestinal tumors, pancreatic tumors, liver tumors, testicular tumors, kidney tumors, lung tumors, breast tumors, uterine cervical tumors, bladder tumors, skin tumors, sarcomas, etc.; the diagnosis of autoimmune diseases such as systemic lupus erythematosus, diabetes, rheumatoid arthritis; the diagnosis of cardiovascular diseases such as atherosclerosis, myocardial ischemia, cardiac fibrosis; the diagnosis of inflammatory diseases such as diabetes, pancreatitis, fibrosis, as well as the diagnosis of degenerative diseases, e.g. Alzheimer’s disease.Prior art

[0003] The diagnosis / prognosis of several diseases, and in particular of most neoplasms, is traditionally performed by analyzing samples from bone marrow biopsy and bone marrow aspirate, in the case of hematological neoplasms, in samples from tumor biopsy, in the case of solid neoplasms, in combination with imaging techniques.

[0004] These methods have major limitations, such as high invasiveness, inapplicability in the case of inaccessible tumors, unrepeatability on the same subject, and inability to assess the molecular and spatial heterogeneity of thetumor. For this reason, techniques based on the search for new tumor biomarkers in the blood are finding increasing application. In particular, the so- called extracellular vesicles (EVs), as well as circulating free proteins, circulating free DNA / small RNA and lipids, can be found in the blood along with normal blood cells.

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

[0006] EVs can be regarded as a kind of circulating cell biopsy. Indeed, their surface contains biomarkers, including proteins and carbohydrates, inherited from the surface of the cell from which they originate. These biomarkers enable the classification and targeting of cell- and tissue-specific EVs. More specifically, the cargo carried by EVs includes, but is not limited to, proteins, metabolites, DNA and RNA. This content depends not only on the cell of origin, but also on the pathophysiological state of the donor, on cellular conditions such as oxidative or metabolic stress, and on the response to therapies. EVs also reflect molecular / protein changes, including spatial and molecular heterogeneity of the tumor, induced by the disease and / or by the therapy to which the patient is subjected.

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

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

[0009] Different features or parameters of EVs, such as their concentration, proteins, lipids, surface antigens and their genomic content (microRNA, mRNA and DNA) were analyzed / quantified by different methods. This allowed thesefeatures or parameters to be defined as potential biomarkers to diagnose tumors and identify their molecular profile.

[0010] In particular, Laurenzana I., Trino S., Lamorte D., [ ], De Luca L., Caivano A., 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, developed a procedure in which the steps are performed of: obtaining a serum sample from a subject’s peripheral blood; isolating a pellet of EVs from serum by centrifugation, e.g. in a bench centrifuge; suspending the EV pellet in a saline solution; taking a first part of EV pellet; characterizing on the first part of EV pellet by Nanoparticle Tracking Analysis (NTA), thus obtaining: a size distribution curve of EVs, and a first value of EVs concentration in the first part of EV pellet; taking a second part of EV pellet; characterizing the second part of EV pellet by flow cytometry, obtaining: a concentration value of EVs in the second part of EV pellet; a concentration value, in the second part of EV pellet, of EVs specific for a first antigen; a concentration value, in the second part of EV pellet, of EVs specific for a second antigen; a concentration value, in the second part of EV pellet, of EVs specific, at the same time, for the first antigen and for the second antigen; a positive amount of fluorescence for the first antigen and for the second antigen; taking a third part of said EV pellet; manually extracting nucleic acids from the third part of EV pellet by means of a kit.

[0011] However, the above-mentioned prior art is affected by several technical problems.

[0012] Firstly, a small number of biomarkers associated with EVs are taken into account. This may not allow changes to be appreciated, because a singleparameter may not be informative as it is not affected by the disease. Indeed, current characterization methods of EVs are based on the analysis of one or two features of EVs, such as their size, amount and the presence of proteins or RNA. Changes in the cells from which the EVs originate result in a modification of several parameters of the EVs. In a context of personalized medicine, the analysis of a limited number of parameters may not be sufficient to discriminate a healthy subject from a diseased subject, to define a prognosis, to ensure therapeutic monitoring and to enable the assessment of minimal residual disease (MRD) in the case of neoplasms that change with time and possible treatments.

[0013] Secondly, the implementation of the conventional isolation methods in clinical practice is limited by volume requirements, complicated sample processing, low reproducibility, and low purity of EVs due to contamination with serum proteins and EV aggregates. Moreover, specific tools for isolation and characterization of EVs, including ultracentrifuge or microfluidic systems, are not available in clinical laboratories. In addition, isolating and analyzing a specific population of EVs results in the loss of representativeness and complexity of all EVs. of the invention

[0014] It is therefore an object of the present invention to provide a method, based on the analysis of extracellular vesicles (EVs), for identifying a pathology in a subject, wherein said method can solve the above-mentioned problems of the prior art, wherein said pathology is a solid tumor or blood cancer, or an autoimmune disease, or a cardiovascular disease, or an inflammatory disease, or a degenerative disease.

[0015] According to various aspects of the invention, this object is achieved by methods, as defined in claims 1 and 32, for detecting the presence or the absence of said pathology in a subject, on the basis of EVs obtained from a sample of a bodily fluid previously taken from said subject. Advantageous embodiments of the invention are defined in the dependent claims.

[0016] According to one aspect of the invention, the method comprises the steps of: defining at least a first and a second reference antigen; isolating a pellet of EVs obtained from a sample of a biological fluid previously withdrawn from said subject;measuring a plurality of parameters representative of six tumor biomarkers from the EV pellet, said six biomarkers comprising:I. a size of the EVs;II. a total amount of the EVs;III. an amount of the EVs expressing the first antigen on their own surface, i.e. of the EVs that are positive for the first antigen;IV. an amount of the EVs expressing two antigens simultaneously on their own surface, i.e. the first antigen and the second antigen;V. a mean fluorescence intensity for each of the first antigen and the second antigen;VI. a nucleic acid content of EVs; inputting respective predetermined cut-off values for the biomarker-representative parameters; performing a first comparison of these parameters with the respective cutoff values; assigning a partial score to each of the biomarker-representative parameters or to said six biomarkers on the basis of the first comparison step; calculating a final score for the subject, in particular, as a summation of said partial scores, in relation to that pathology; performing a second comparison of the final score with at least one predetermined diagnostic threshold value; establishing a diagnosis of probability of the disease in the subject on the basis of the second comparison.

[0017] Such pathology can be: a hematological malignancy, i.e. a blood cancer of the lymphoid and myeloid type, in particular Chronic Lymphocitic Leukaemia (CLL); Multiple Myeloma (MM); Acute Myeloid Leukaemia (AML); lymphoma in general, etc; a solid tumor, in particular Non-Small Cell Lung Cancer; a tumor of the nervous system; a tumor of the head or the neck; a gastro-intestinal tumor; a pancreatic tumor; a liver tumor; a testicular tumor; a kidney tumor; a lung tumor; a breast tumor; a uterine cervical tumor; a bladder tumor; a skin tumor; a sarcoma, etc; an autoimmune disease, in particular systemic lupus erythematosus; diabetes; rheumatoid arthritis;cardiovascular disease, in particular atherosclerosis and myocardial ischaemia, cardiac fibrosis; an inflammatory disease in particular diabetes; pancreatitis; fibrosis; a degenerative disease, in particular Alzheimer’s disease.

[0018] In addition to the general advantages of using EVs, in which a pure circulating element representative of an entire body-system is non-invasively isolated, the method defined above allows the first of the above-mentioned problems to be solved, through the identification of the six biomarkers l-VI associated with EVs and the use of an instrumentation that is normally available in clinical laboratories, and subsequent combination of the biomarkers to define the probability of a disease.

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

[0020] The potentiality of the above-mentioned biomarkers to detect a wide range of pathologies in a subject is known from the literature, in particular from Laurenzana I. et al, cit.. However, it has been observed that the patients affected by a given pathology can be positive to only a group of those biomarkers, and that the combinations of the biomarkers to which they are positive can be different from a diseased patient to another one. For instance, if only three out of those six biomarkers were informative, there would be twenty possible combinations to be taken into account. Now, by the method according to the invention, all the biomarkers are taken into account, i.e. are analyzed, and a final score is calculated as a sum of the contributions, i.e. the partial scores, of all six biomarkers. Moreover, the partial scores and the threshold values have been established in such an inventive way that, as it has been shown by the experience, even if the diseased subjects are positive to biomarkers according to different combinations, they all receive a score, according to the method of the invention, classifying them as diseased patients.

[0021] On the contrary, an analysis limited to only three, or even four or five biomarkers selected a priori would not be efficient in assessing the presenceof the disease because the only three / four / five biomarkers selected to be analyzed, in certain subjects, might not be informative of the disease.

[0022] In fact, due to the high clinical heterogeneity between subjects, in a conventional approach it can happen that, by limiting the analysis to a small number of predetermined biomarkers, a first subject turns out to be ill while a second subject does not, and this is not because the second subject is not actually ill, but because the three (for example) biomarkers that, in said subject, are not sensitive to the disease have been analyzed.

[0023] To the Inventors’ knowledge, no such combination of as many as six biomarkers has so far been described in the scientific literature.

[0024] Moreover, thanks to the combined analysis of the six biomarkers associated with EVs, the method of the invention is able to discriminate, evaluate and quantify any changes in the disease that occur both at the time of the diagnosis and at disease evolution, which would not be possible to detect by analyzing only one biomarker of EVs. This means that even, if one of the biomarkers of EVs may not be informative, because it does not vary as an effect of the disease, the other biomarkers compensate for this defect.

[0025] Moreover, as far as the Inventors are aware, no one has described a procedure for analyzing EVs, in particular EVs taken from serum, in the context of diagnosing diseases of various kinds and in particular haematological malignancies such as Chronic Lymphocitic Leukaemia (CLL), Multiple Myeloma (MM), Acute Myeloid Leukaemia (AML), and solid tumors like Non-Small Cell Lung Cancer (NSCLC), and others.

[0026] Other advantages and improvements that can be achieved through the method, without considering the specific EV isolation procedure, besides improving the reliability of the diagnosis, are: the possibility of using the method to identify specific therapeutic targets and to follow the evolution of the disease, as well as to predict the response to a therapy and for MRD, in particular, in the case of tumors; the possibility of analyzing the spatial and molecular heterogeneity of the disease.

[0027] In conclusion, still in the context of personalized medicine, the method according to the invention can be integrated in a clinical practice allowing for non-invasive cancer diagnostics.

[0028] The biological fluid can be selected from the group comprised of: blood, serum or plasma; urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid; seminal fluid; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascitic fluid; vitreous fluid. In particular, the biological fluid is peripheral blood. In this case, the EV pellet may be extracted from a plasma sample or from a serum sample obtained from the peripheral blood sample.

[0029] Advantageously, the EV pellet is extracted from the serum sample, and the step of isolating an EV pellet includes a step of suspending the EV pellet in a saline solution, thus obtaining a wet EV pellet. The use of serum as a source of EVs is advantageous, as serum is actually and routinely handled in clinical practice.

[0030] More in detail, in one embodiment, a subject’s peripheral blood is collected in serological tubes with separator gels, from which the serum sample is obtained, and then EVs are isolated, typically according to the procedure described in doi: 10.2147 / IJN.S303391 .

[0031] Preferably, the method also includes a step of: taking a first part of the wet EV pellet; and the steps of measuring representative parameters of the size I and of the total amount II of EVs are performed jointly byNanoparticle Tracking Analysis on the first part of the wet EV pellet, obtaining: a size distribution curve I of EVs, and a first concentration value II of EVs in the wet EV pellet.

[0032] In particular, the step of isolating an EV pellet may include steps of: taking a second part of the wet EV pellet; and the steps of measuring parameters representative of: the total amount (II) of the EVs, the amount (III) of the EVs expressing the first antigen; the amount (IV) of EVs expressing both the first antigen and the second antigen; the positive fluorescence (V) for the first antigen and the second antigen are carried out jointly by a step of: flow cytometry of the second part of the wet EV pellet, obtaining:a concentration value (II) of the total EVs in the second part of the wet EV pellet; a concentration value (III) of EVs specific for a first antigen in the second part of the wet EV pellet; a concentration value of EVs specific for a second antigen in the second part of the wet EV pellet; a concentration value (IV), of EVs simultaneously specific for both the first antigen and the second antigen in the second part of the wet EV pellet; an amount of positive fluorescence (V) for the first antigen and for the second antigen.

[0033] Advantageously, the step of measuring parameters representative of a nucleic acid content is a step of determining a specific micro-RNA content (VI), and comprises the steps of: manually extracting nucleic acids from the wet EV pellet; sequencing nucleic acids extracted from the wet EV pellet.

[0034] Preferably, the step of measuring parameters representative of a nucleic acid content (VI) comprises the steps of: automatically extracting nucleic acids (RNA) from the EV pellet by means of an automatic extractor; performing a fluorometric quantization of micro-RNA; performing a retrotranscription of a portion of the RNA into cDNA; determining the micro-RNA using a droplet digital PCR technique.

[0035] Advantageously, the measurement of parameters representative of a nucleic acid content (VI) is carried out by performing both: a manual extraction of nucleic acids from wet EV pellet, followed by sequencing of the nucleic acids thus extracted, and an automatic extraction by means of an automatic extractor from dry EV pellet obtained from a serum centrifuged and non-suspended in a saline solution, followed by fluorometric quantization, c-DNA retro-transcription and application of droplet digital PCR technique, wherein the manual extraction and the automatic extraction are performed on respective first EV pellet and second EV pellet isolated from a first and second part, respectively, of the serum sample.

[0036] Preferably, the step of performing a second comparison of the final score is performed with respect to two diagnostic threshold values comprising: an upper diagnostic threshold value, and a lower diagnostic threshold value, and the diagnosis is: high probability of the pathology, if the final score is higher than or equal to the upper diagnostic threshold value; moderate probability of the pathology, if the final score is between the upper and lower diagnostic threshold values; low probability of pathology presence, if the final score is lower than or equal to the lower diagnostic threshold value.

[0037] In an exemplary embodiment, in the step of defining a plurality of partial scores, a partial score is assigned to each of the six biomarkers.

[0038] In particular, the partial score for each biomarker is a first number, if at least one of the biomarker-representative parameters has a value: higher than an own cut-off value of said cut-off values, if the cut-off value has a > sign; lower than an own cut-off value, if the cut-off value has a < sign; a second number lower than the first number, if each parameter of the biomarker-representative parameters has a value: lower than or equal to an own cut-off value, if the cut-off value has a > sign; higher than or equal to an own cut-off value, if the cut-off value has a < sign.

[0039] In a first modification of this embodiment, the first number and second number are the same number for all the six biomarkers, in particular the first number and the second number are equal to 1 and 0, respectively, and the upper and lower diagnostic threshold values are equal to 4 and 2, respectively.

[0040] In other modifications of this embodiment, the first number and the second number are assigned independently for each of the six biomarkers. In particular, the first number is a same first lower number for the following biomarkers: the size (I) of the EVs, andthe amount (II) of the EVs; a same first intermediate number for the following biomarkers: the amount (III) of the EVs expressing the first antigen, and the mean fluorescence intensity (V) for each of the first antigen and the second antigen, and a same first upper number for the following biomarkers: the amount (IV) of the EVs expressing both the first antigen and the second antigen, and the nucleic acid (VI) content of the EVs.

[0041] For example, the first lower, intermediate and upper numbers are equal to 1 , 2 and 3, respectively, the second number is equal to 0 and the upper and lower diagnostic threshold values are equal to 6 and 3, respectively.

[0042] In another exemplary embodiment, the step of defining a plurality of partial scores includes a step of defining a plurality of sub-partial scores, wherein a sub-partial score is assigned to each of the parameters representing the six biomarkers, and the sub-partial score for each parameter of the parameters is a third number, if the parameter has a value: higher than an own cut-off value of said cut-off values, if the cut-off value has a > sign lower than an own cut-off value of said cut-off values, if the cut-off value has a < sign a fourth number lower than the first number, if the parameter has a value: lower than or equal to an own cut-off value, if the cut-off value has a > sign higher than or equal to an own cut-off value, if the cut-off value has a < sign, and the partial score, for each biomarker, is calculated by summing the subpartial scores assigned to the parameters representative of that biomarker.

[0043] In a first modification of the present embodiment, the third number and the fourth number are the same number for all the parameters, in particular they are equal to 1 and 0, and the upper and lower diagnostic threshold values are equal to 10 and 5, respectively.

[0044] In other modifications of this embodiment, the third number and the fourth number are assigned independently for each of the parameters. In particular, the third number is a same third lower number for the parameters representative of the following biomarkers: the size (I) of said EVs, and the amount (II) of said EVs a same third intermediate number for the parameters representative of the following biomarkers: the amount (III) of EVs expressing the first antigen, and the mean fluorescence intensity (V) for each of the first antigen and the second antigen, and a same third upper number for the parameters representative of the following biomarkers: the amount (IV) of EVs expressing both the first antigen and the second antigen, and the nucleic acid (VI) content of EVs.

[0045] For example, the third lower, intermediate and upper numbers are equal to 1 , 2 and 3, respectively, the fourth number is equal to 0 and the upper and lower diagnostic threshold values are equal to 14 and 7, respectively.

[0046] In a further exemplary embodiment, a step is provided of defining a plurality of groups of biomarkers, each group of biomarkers including at least one of the six biomarkers, the partial score assigned to each biomarker is a sub-partial score; in the step of defining a plurality of partial scores, a plurality of sub-partial scores is assigned to each biomarker, and the partial score is calculated, for each group of biomarkers, by summing the sub-partial scores assigned to the biomarkers of that group of biomarkers, wherein each of the sub-partial scores is equal: a fifth number, if at least one of the parameters representative of at least one biomarker of the group has a value: higher than an own cut-off value of said cut-off values, if the cut-off value has a > signlower than an own cut-off value of said cut-off values, if the cut-off value has a < sign a sixth number lower than the fifth number, if each parameter of the parameters representative of the biomarker of the group has a value: lower than or equal to an own cut-off value, if the cut-off value has a > sign higher than or equal to an own cut-off value, if the cut-off value has a < sign.

[0047] In a first modification of this embodiment, the fifth number and the sixth number are the same number for all six biomarkers, in particular they are equal to 1 and 0, and the upper and lower diagnostic threshold values are equal to 3 and 1 , respectively.

[0048] In other modifications of the present embodiment, the fifth number and the sixth number are independently associated for the six biomarkers. In particular, the first number is a same fifth lower number for the following biomarkers: the size (I) of the EVs, and the amount of the EVs (II); a same fifth intermediate number for the following biomarkers: the amount (III) of EVs expressing the first antigen, and the amount (IV) of EVs expressing both the first antigen and the second antigen, and the mean fluorescence intensity (V) for each of the first antigen and the second antigen, and a fifth upper number for the following biomarker: the nucleic acid (VI) content of EVs.

[0049] For example, the fifth lower, intermediate and upper numbers are equal to 1 , 2 and 5, respectively, the sixth number is equal to 0 and the upper and lower diagnostic threshold values are equal to 8 and 3, respectively.

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

[0051] The ROC curve also makes it possible to identify the optimal cut-off value, also referred to as the “best cut-off”, i.e. the test value that maximizes the difference between the proportion of true positives, i.e. the proportion of individuals who have an impaired test value among all those actually affected by the disease, and the proportion of false positives, i.e. the proportion of individuals who, despite having an impaired test value, are not affected by the disease of interest. In this way, a cut-off value can be obtained for each of the above-mentioned EV parameters. That is, the “best” parameters were combined to define the specific signature according to the tumor analyzed .

[0052] Preferably, the isolation steps of the first pellet and the second pellet of EVs from the first and second part of the serum sample, respectively, are performed independently of each other by centrifugation in a bench centrifuge.

[0053] According to another aspect of the invention, a method for detecting the presence or the absence of a disease in a subject comprises a step of measuring a content (VI) of nucleic acids in a serum sample or, more in general, in a sample of a biological fluid such as urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid; seminal fluid; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascitic fluid; vitreous fluid, the step of measuring a content of nucleic acids comprising in turn the steps of: isolating a pellet of EVs from the serum sample; extracting nucleic acids (RNA) from the EV pellet by means of an automatic extractor; performing a fluorometric quantization of micro-RNA; performing a retrotranscription of a portion of RNA into cDNA; micro-RNA determination using a digital PCR technique, in particular in particular a droplet digital PCR technique.

[0054] The solution to the second problem, among those mentioned above, is obtained by applying the EV purification method described above (doi: 10.2147 / IJN.S303391 ) in which, by providing a centrifugation step in a bench centrifuge, available in every clinical laboratory, it allows the isolation of a heterogeneous and pure population of EVs.

[0055] Moreover, the two problems mentioned above are solved synergistically by the method the combines the isolation of the EVs and the characterization of the six biomarkers associated with the EVs.Briefof the

[0056] The invention will be illustrated below with a description of some embodiments, by way of example and not limitation, with reference to the accompanying drawings, in whichFig. 1 A shows a plot of difference data between healthy subjects and Chronic Lymphocitic Leukaemia (CLL) patients with respect to the size of extracellular vesicles (EVs), indicated by the parameter D10; Fig. 1 B shows the ROC curve associated with the data in Fig. 1 A.Fig. 2A shows a plot of difference data between healthy subjects and CLL- diseased patients with respect to the size of EVs, indicated by the parameter D90; Fig. 2B shows the ROC curve associated with the data in Fig. 2A.Fig. 3A shows a plot of difference data between healthy subjects and CLL- diseased patients with respect to the size of EVs, indicated by the mean diameter parameter; Fig. 3B shows the ROC curve associated with the data in Fig. 3A.Fig. 4A shows a plot of difference data between healthy subjects and CLL- diseased patients with respect to the size of the EVs, indicated by the mode parameter; Fig. 4B shows the ROC curve associated with the data in Fig. 4A.Fig. 5A shows a plot of difference data between healthy subjects and Multiple Myeloma (MM) diseased patients with respect to the size of the EVs, indicated by the parameter D10; Fig. 5B shows the ROC curve associated with the data in Fig. 5A.Fig. 6A shows a plot of difference data between healthy subjects and MM- diseased patients with respect to the size of the EVs, indicated by the parameter D90; Fig. 6B shows the ROC curve associated with the data in Fig. 6A.Fig. 7A shows a plot of difference data between healthy subjects and MM- diseased patients with respect to the size of the EVs, indicated by the meandiameter parameter; Fig. 7B shows the ROC curve associated with the data in Fig. 7A.Fig. 8A shows a plot of difference data between healthy subjects and MM- diseased patients with respect to the size of the EVs, indicated by the mode parameter; Fig. 8B shows the ROC curve associated with the data in Fig. 8A.Fig. 9A shows a plot of data differences between healthy subjects and Acute Myeloid Leukaemia (AML) diseased patients with respect to the size of the EVs, indicated by the parameter D10; Fig. 9B shows the ROC curve associated with the data in Fig. 9A.Fig. 10A shows a plot of difference data between healthy subjects and AML-diseased patients with respect to the size of the EVs, indicated by the parameter D90; Fig. 10B shows the ROC curve associated with the data in Fig. 10A.Fig. 1 1 A shows a plot of difference data between healthy subjects and AML-diseased patients with respect to the size of the EVs, indicated by the mean diameter parameter; Fig. 1 1 B shows the ROC curve associated with the data in Fig. 1 1A.Fig. 12A shows a plot of difference data between healthy subjects and AML-diseased patients with respect to the size of the EVs, indicated by the mode parameter; Fig. 12B shows the ROC curve associated with the data in Fig. 12A.Fig. 13A shows a plot of data differences between healthy subjects and Non-Small Cell Lung Cancer (NSCLC) diseased patients with respect to the size of the EVs, indicated by the parameter D10; Fig. 13B shows the ROC curve associated with the data in Fig. 13A.Fig. 14A shows a plot of difference data between healthy subjects and NSCLC-diseased patients with respect to the size of the EVs, indicated by the parameter D90; Fig. 14B shows the ROC curve associated with the data in Fig. 14A.Fig. 15A shows a plot of difference data between healthy subjects and NSCLC-diseased patients with respect to the size of the EVs, indicated bythe mean diameter parameter; Fig. 15B shows the ROC curve associated with the data in Fig. 15A.Fig. 16A shows a plot of difference data between healthy subjects and NSCLC-diseased patients with respect to the size of the EVs, indicated by the mode parameter; Fig. 16B shows the ROC curve associated with the data in Fig. 16A.Fig. 17A shows a plot of difference data between healthy subjects and CLL-diseased patients with respect to the amount of EVs in the serum, obtained by Nanoparticle Tracking Analysis (NTA); Fig. 17B shows the ROC curve associated with the data in Fig. 17A.Fig. 18A shows a plot of difference data between healthy subjects and CLL-diseased patients with respect to the amount of EVs in the serum, obtained by flow cytometry; Fig. 18B shows the ROC curve associated with the data in Fig. 18A.Fig. 19A shows a plot of difference data between healthy subjects and MM-diseased patients with respect to the amount of EVs in the serum, obtained by NTA; Fig. 19B shows the ROC curve associated with the data in Fig. 19A.Fig. 20A shows a plot of difference data between healthy subjects and MM-diseased patients with respect to the amount of EVs in the serum, obtained by flow cytometry; Fig. 20B shows the ROC curve associated with the data in Fig. 20A.Fig. 21 A shows a plot of difference data between healthy subjects and AML-diseased patients with respect to the amount of EVs in the serum, obtained by NTA; Fig. 21 B shows the ROC curve associated with the data in Fig. 21 A.Fig. 22A shows a plot of difference data between healthy subjects and AML patients with respect to the amount of EVs in the serum, obtained by flow cytometry; Fig. 22B shows the ROC curve associated with the data in Fig. 22A.Fig. 23A shows a plot of difference data between healthy subjects and NSCLC-diseased patients with respect to the amount of EVs in the serum,obtained by NTA; Fig. 23B shows the ROC curve associated with the data in Fig. 23A.Fig. 24A shows a plot of difference data between healthy and NSCLC patients with respect to the amount of EVs in the serum, obtained by flow cytometry; Fig. 24B shows the ROC curve associated with the data in Fig. 24A.Fig. 25A shows a plot of difference data between healthy subjects and CLL-diseased patients with respect to the amount of EVs positive for a specific surface antigen, CD19; Fig. 25B shows the ROC curve associated with the data in Fig. 25A.Fig. 26A shows a plot of difference data between healthy subjects and CLL-patients with respect to the amount of EVs positive for a specific surface antigen, CD20; Fig. 26B shows the ROC curve associated with the data in Fig. 26A.Fig. 27A shows a plot of difference data between healthy subjects and CLL-patients with respect to the amount of EVs positive for a specific surface antigen, CD200; Fig. 27B shows the ROC curve associated with the data in Fig. 27A.Fig. 28A shows a plot of difference data between healthy subjects and MM-patients with respect to the amount of EVs positive for a specific surface antigen, CD38; Fig. 28B shows the ROC curve associated with the data in Fig. 28A.Fig. 29A shows a plot of difference data between healthy subjects and MM-patients with respect to the amount of EVs positive for a specific surface antigen, CD138; Fig. 29B shows the ROC curve associated with the data in Fig. 29A.Fig. 30A shows a plot of difference data between healthy subjects and AML patients with respect to the amount of EVs positive for a specific surface antigen, CD34; Fig. 30B shows the ROC curve associated with the data in Fig. 30A.Fig. 31 A shows a plot of difference data between healthy subjects and AML patients with respect to the amount of EVs positive for a specific surface antigen, CD1 17; Fig. 31 B shows the ROC curve associated with the data in Fig. 31 A.Fig. 32A shows a plot of difference data between healthy subjects and AML patients with respect to the amount of EVs positive for a specific surface antigen, CXCR4; Fig. 32B shows the ROC curve associated with the data in Fig. 32A.Fig. 33A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the amount of EVs positive for a specific surface antigen, EpCAM; Fig. 33B shows the ROC curve associated with the data in Fig. 33A.Fig. 34A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the amount of EVs positive for a specific surface antigen, CD137; Fig. 34B shows the ROC curve associated with the data in Fig. 34A.Fig. 35A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the amount of EVs positive for a specific surface antigen, PD-L1 +; Fig. 35B shows the ROC curve associated with the data in Fig. 35A.Fig. 36A shows a plot of difference data between healthy subjects and CLL-patients with respect to the amount of EVs positive for two antigens, CD19 and CD20; Fig. 36B shows the ROC curve associated with the data in Fig. 36A.Fig. 37A shows a plot of difference data between healthy subjects and MM-patients with respect to the amount of EVs simultaneously positive for two antigens, CD38 and CD138; Fig. 37B shows the ROC curve associated with the data in Fig. 37A.Fig. 38A shows a plot of difference data between healthy subjects and AML patients with respect to the amount of EVs positive for two antigens, CD34 and CD1 17; Fig. 38B shows the ROC curve associated with the data in Fig. 38A.Fig. 39A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the amount of EVs positive for two antigens, CD137 and EpCAM; Fig. 39B shows the ROC curve associated with the data in Fig. 39A.Fig. 40A shows a plot of difference data between healthy subjects and CLL-patients with respect to the Mean Fluorescence Intensity (MFI) of EVs positive for a specific antigen, CD19; Fig. 30B shows the ROC curve associated with the data in Fig. 30A.Fig. 41 A shows a plot of difference data between healthy subjects and CLL-patients with respect to MFI of EVs positive for a specific antigen, CD20; Fig. 31 B shows the ROC curve associated with the data in Fig. 31 A. Fig. 42A shows a plot of difference data between healthy subjects and CLL-patients with respect to the MFI of EVs positive for a specific antigen, CD200; Fig. 32B shows the ROC curve associated with the data in Fig. 32A.Fig. 43A shows a plot of difference data between healthy subjects and MM-patients with respect to the MFI of EVs positive for a specific antigen, CD38; Fig. 33B shows the ROC curve associated with the data in Fig. 33A. Fig. 44A shows a plot of difference data between healthy subjects and MM-patients with respect to the MFI of EVs positive for a specific antigen, CD138; Fig. 34B shows the ROC curve associated with the data in Fig. 34A.Fig. 45A shows a plot of difference data between healthy subjects and AML patients with respect to the MFI of EVs positive for a specific antigen, CD34; Fig. 45B shows the ROC curve associated with the data in Fig. 45A. Fig. 46A shows a plot of difference data between healthy subjects and AML patients with respect to the MFI of EVs positive for a specific antigen, CD1 17; Fig. 46B shows the ROC curve associated with the data in Fig. 46A.Fig. 47A shows a plot of difference data between healthy subjects and AML patients with respect to the MFI of EVs positive for a specific antigen, CXCR4; Fig. 47B shows the ROC curve associated with the data in Fig. 47A.Fig. 48A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the difference of MFI of EVs positive for a specific antigen, PD-L1 BV421 ; Fig. 48B shows the ROC curve associated with the data in Fig. 48A.Fig. 49A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the difference MFI of EVs positive for a specific antigen, EpCAM BV421 ; Fig. 49B shows the ROC curve associated with the data in Fig. 49A.Fig. 50A shows a plot of difference data between healthy subjects and CLL-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-484, obtained by droplet digital PCR (ddPCR) and expressed as copies / pl; Fig. 50B shows the ROC curve associated with the data in Fig. 50A.Fig. 51 A shows a plot of difference data between healthy subjects and CLL-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-93-5p, obtained by ddPCR and expressed as copies / pl; Fig. 51 B shows the ROC curve associated with the data in Fig. 51 A.Fig. 52A shows a plot of difference data between healthy subjects and MM-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-122-5p, obtained by sequencing and expressed as reads; Fig. 52B shows the ROC curve associated with the data in Fig. 52A.Fig. 53A shows a plot of difference data between healthy subjects and MM-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-125b-5p, obtained by sequencing and expressed as reads; Fig. 53B shows the ROC curve associated with the data in Fig. 53A.Fig. 54A shows a plot of difference data between healthy subjects and MM-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-203a, obtained by sequencing and expressed as reads; Fig. 54B shows the ROC curve associated with the data in Fig. 54A.Fig. 55A shows a plot of difference data between healthy subjects and MM-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-320d, obtained by sequencing and expressed as reads; Fig. 55B shows the ROC curve associated with the data in Fig. 55A.Fig. 56A shows a plot of difference data between healthy subjects and MM-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-320c, obtained by sequencing and expressed as reads; Fig. 56B shows the ROC curve associated with the data in Fig. 56A.Fig. 57A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-150-5p, obtained by sequencing and expressed as reads; Fig. 57B shows the ROC curve associated with the data in Fig. 57A.Fig. 58A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-155-5p, obtained by sequencing and expressed as reads; Fig. 58B shows the ROC curve associated with the data in Fig. 58A.Fig. 59A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-10a-5p, obtained by sequencing and expressed as reads; Fig. 59B shows the ROC curve associated with the data in Fig. 59A.Fig. 60A shows a plot of difference data between healthy and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR- 146b-5p, obtained by sequencing and expressed as reads; Fig. 60B shows the ROC curve associated with the data in Fig. 60A.Fig. 61 A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-122-5p, obtained by sequencing and expressed as reads; Fig. 61 B shows the ROC curve associated with the data in Fig. 61 A.Fig. 62A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-203a-3p, obtained by sequencing and expressed as reads; Fig. 62B shows the ROC curve associated with the data in Fig. 62A.Fig. 63A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-10a-5p, obtained by sequencing and expressed as reads; Fig. 63B shows the ROC curve associated with the data in Fig. 63A.Fig. 64A shows a plot of difference data between healthy subjects and AML-patients with respect to the nucleic acid content of EVs, in particular hsa-miR-181 a-5p, obtained by sequencing and expressed as reads; Fig. 64B shows the ROC curve associated with the data in Fig. 64A;Fig. 65A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the nucleic acid content of EVs, in particular hsa-miR-125-5p, obtained by droplet digital PCR (ddPCR) and expressed as copies / pl; Fig. 65B shows the ROC curve associated with the data in Fig. 65A;Fig. 66A shows a plot of difference data between healthy subjects and NSCLC patients with respect to the nucleic acid content of EVs, in particular hsa-miR-451 a, obtained by ddPCR and expressed as copies / pl; Fig. 66B shows the ROC curve associated with the data in Fig. 66A;Fig. 67 is a flow chart of a preferred embodiment of the method, according to one aspect of the invention, for diagnosing a disease in a subject on the basis of EVs obtained from a blood serum sample previously taken from said subject;Fig. 68 is a flow chart of a method, according to another aspect of the invention, for diagnosing a disease in a subject on the basis of an analysis of the nucleic acid content in EVs obtained from a blood serum sample previously taken from said subject.Examples

[0057] The procedure was tested on the serum patients suffering from cancer, more precisely:Chronic Lymphatic Leukaemia (CLL), no. 88 patientsMultiple Myeloma (MM), no. 20 patientsAcute Myeloid Leukaemia (AML), no. 40 patientsNon-Small Cells Lung Cancer (NSCLC) no. 21 patients as well as 25 healthy subjects.

[0058] Extracellular vesicles were isolated from the serum of the above- mentioned subjects.

[0059] In the comparison between CLL-patients and healthy subjects, the six biomarkers associated with EVs:I) size,II) amount of EVs,III) amount of positive EVs per single surface antigen,IV) amount of positive EVs per double surface antigen,V) mean fluorescence intensity of surface antigens, andVI) nucleic acid content were analyzed, and cut-off values were defined to discriminate between healthy subjects and patients.The following surface antigens were analyzed in the EVs: CD200, CD19 and CD20 individually and the combination of CD19 and CD20 and hsa-miR-484 and hsa-miR-93-5p were quantified.

[0060] In the comparison between MM patients and healthy subjects, the six biomarkers l-VI as above associated with EVs were analyzed, and cut-off values were defined to discriminate between healthy subjects and patients. The following surface antigens were analyzed in the EVs: CD38 and CD138 individually and in combination. The microRNAs identified are: hsa-miR-203a, hsa- miR-320c, hsa-miR-320d, hsa-miR-122-5p, hsa-miR-125-5p, and others.

[0061] In the comparison between AML patients and healthy subjects, the six biomarkers l-VI as above associated with EVs were analyzed, and cut-off values were defined to discriminate between healthy subjects and patients. The following surface antigens on EVs were analyzed: CD34, CD117 and CXCR4 individually and the combination of CD34 and CD1 17. The microRNAs identified are: hsa-miR-150-5p, hsa-miR-181 b-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, hsa-miR-181 a-5p and others.

[0062] In the comparison between NSCLC patients and healthy subjects, the six biomarkers l-VI as above associated with EVs were analyzed, and cutoff values were defined to discriminate between healthy subjects and patients. In particular, the following surface antigens were analyzed on EVs: EpCAM, CD137 and PD-L1 individually and the combination of EpCAM and CD137. The microRNAs identified are: hsa-miR-125-5p, hsa-miR-451 a and others.

[0063] Figs. 1 A-16B show data of the difference between healthy subjects and cancer patients plotted against the size of the EVs in different haematological malignancies, such as Chronic Lymphocitic Leukaemia (CLL, Figs. 1 A-4B),Multiple Myeloma (MM, Figs. 5A-8B), Acute Myeloid Leukaemia (AML, Figs. 9A- 12B) and in solid tumors, such as Non-Small Cell Lung Cancer (NSCLC, Figs. 13A-16B). The size of the EVs is indicated by several parameters obtained from the NTA analysis, such as D10 (Figs. 1 A-B, 5A-B, 9A-B and 13A-B), which indicates the point in the size distribution where 10% of the EVs are contained, D90 (Figs. 2A-B, 6A-B, 10A-B and 14A-B), which indicates the point where 90% of the EVs are contained, the mean diameter (Figs. 3A-B, 7A-B, 1 1 A-B and 15A- B) and the mode (Figs. 4A-B, 8A-B, 12A-B and 16A-B), which indicates the most represented size. There are two graphs for each of these parameters. The first graph (Figs. 1 -16 / A) shows the difference between healthy subjects and patients. Each symbol, circle or square, represents a subject. The larger horizontal bar indicates the mean value and the smaller horizontal bars indicate the standard deviation. The second graph (Figs. 1 -16 / B) shows the ROC curve with the respective area under the curve (AUC) and the respective statistical power p, indicated by a corresponding number of asterisks: *p<0.05, **p<0.01 , ***p<0.001 , **** p<0.0001 . The AUC is a measure of how accurate a test is: the closer its value is to 1 , the more accurate the test is.

[0064] Figs. 1 -16 / A show that patients (88 CLL, 14 MM, 20 AML, 21 NSCLC) have EVs with all parameters D10, D90, mean and mode higher than healthy subjects (max n=25). This difference between patients and healthy subjects is significant.

[0065] The analysis of the ROC curves (Figs. 1 -16 / B) shows that the tests in the different neoplasms are accurate. In particular: for CLL, the best test is the one based on D10 (AUC=0.71 , p=0.0018, Fig. 1 B); for MM, the best test is the one based on D10 (AUC=0.74, p=0.02, Fig. 5B); for AML, the best tests are the one based on D10 (AUC=0.81 , p=0.0003, Fig. 9B) and the one based on D90 (AUC=0.81 , p=0.0009, Fig. 10B); for NSCLC, the best tests are the one based on D90 (AUC=0.70, p=0.02, Fig. 14B) and the one based on the mean (AUC=0.69, p=0.03, Fig. 15B).

[0066] Figs. 17A-24B show data on the difference between healthy subjects and diseased patients plotted against the amount of EVs in the serum in CLL (Figs. 17A-18B), MM (Figs. 19A-20B), AML (Figs. 21A-22B) and NSCLC(Figs. 23A-24B). The amount of EVs was obtained by two different methods such as NTA (Figs. 17A / B, 19A / B, 21 A / B, 23A-B) and flow cytometry (Figs. 18A / B, 20A / B, 22A / B, 24A-B). For each amount EVs / ml of serum, obtained with the specific technique, the differences between healthy subjects and diseased patients (Figs. 17-24 / A) and the corresponding ROC curves (Figs. 17-24 / B) are shown, indicating the respective AUC and statistical power values. Again, each symbol represents a subject. The horizontal bar in the graphs indicates the mean with the standard deviation.

[0067] Figs. 17-24 / A show that patients (88 CLL, 14 MM, 20 AML, 21 NSCLC) have an amount of serum EVs higher than healthy subjects (max n=25), which is, generically, statistically significant (p<0.05).

[0068] The analysis of the ROC curves (Figs. 17-24 / B) shows that all tests are accurate with an AUC between 0.66 and 0.99.

[0069] In particular, it was obtained that: for CLL the best test is the one in which the amount of EVs in the serum is evaluated by the NTA (AUC=0.76, p<0.0001 ); for MM the best test is the one in which the amount of EVs in the serum is evaluated by the NTA (AUC=0.82, p=0.007); for AML, the best test is the one in which the amount of EVs in the serum is evaluated by the NTA (AUC=0.99, p<0.0001 ); for NSCLC, the best test is the one in which the amount of EVs in the serum is evaluated by the NTA (AUC=0.67, p=0.05).

[0070] Figs. 25A-35B show data of the difference between healthy subjects and diseased patients plotted against the amount of EVs that are positive for a specific surface antigen (CD) in CLL (Figs. 25A-27B), MM (Figs. 28A-29B), AML (Figs. 30A-32B) and NSCLC (Figs. 33A-35B). The amount of positive EVs per single antigen was obtained by flow cytometry. For each amount of EVs positive for a specific antigen (CDX+ EVs / ml serum), the differences between healthy subjects and diseased patients (Figs. 25-35 / A) and the corresponding ROC curves (Figs. 25-35 / B) are shown, indicating the respective AUC and statistical power values. Again, each symbol represents a subject, and the horizontal bar in the graphs indicates the mean and the standard deviation.

[0071] The surface antigens that were considered are neoplasia-dependent and, therefore, they may differ between the diseases studied. In particular,EVs positive for CD19 (CD19+ EVs / ml), for CD20 (CD20+ EVs / ml), for CD200 (CD200+ EVs / ml) in the case of CLL;EVs positive for CD38 (CD38+ EVs / ml), for CD138 (CD138+ EVs / ml) in the case of MM;EVs positive for CD34 (CD34+ EVs / ml), for CD1 17 (CD1 17+ EVs / ml), for CXCR4 (CXCR4+ EVs / ml) in the case of AML;EVs positive for EpCAM (EpCAM+ EVs / ml), for CD137 (CD137+ EVs / ml), for PD-L1 (PD-L1 + EVs / ml) in the case of NSCLC were quantified.

[0072] Figs. 25A-35B show that:CLL patients (n=88) had a statistically significant higher amount of EVs positive for CD19 or CD20 or CD200 than healthy subjects (n=19);CLL patients (n=14) had a statistically significant higher amount of EVs positive for CD38 or CD138 than healthy subjects (n=20);CLL patients (n=10) had more EVs positive for CD34 or CD1 17 or CXCR4 than healthy subjects (n=6);CLL patients (n=10) had more EVs positive for EpCAM or CD137 or PD- L1 than healthy subjects (n=6).

[0073] The analysis of the ROC curves (Figs. 25-35 / B) shows that most tests are accurate with an AUC between 0.560 and 0.880. In particular, within the different CDs analyzed, it was obtained that: for CLL, the best test is the one in which the amount of EVs positive for CD20 (AUC=0.88, p < 0.0001 ) was evaluated; for MM, the best test is the one in which the amount of EVs positive for CD38 (AUC=0.85, p=0.0006) was evaluated; for AML, the best test is the one in which the amount of EVs positive for CD34 (AUC=0.88, p=0.01 ) was evaluated; for NSCLC, the best test is the one in which the amount of EVs positive for EpCAM (AUC=0.86, p=0.04) was evaluated.

[0074] Figs. 36A-39B show data of the difference between healthy subjects and diseased patients plotted against the amount of EVs that are positive for two surface antigens in CLL (Figs. 36A and 36B), MM (Figs. 37A and 37B), AML (Figs. 38A and 38B) and NSCLC (Figs. 39A and 39B). The amount of EVs simultaneously positive for two CDs was obtained by flow cytometry. For eachamount of EVs simultaneously positive for two surface antigens (CDX+ CDY+ EVs / ml serum), the difference between healthy subjects and diseased patients (Figs. 36-39 / A) and the corresponding ROC curves (Figs. 36-39 / B) are shown, indicating the respective AUC and statistical power values. Again, each symbol represents a subject, and the horizontal bar in the graphs indicates the mean with the standard deviation.

[0075] In particular,EVs simultaneously positive for CD19 and CD20 (CD19+ CD20+ EVs / ml) in the case of CLL;EVs simultaneously positive for CD38 and CD138 (CD38+ CD138+ EVs / ml) in the case of MM;EVs simultaneously positive for CD34 and CD1 17 (CD34+ CD1 17+ EVs / ml) in the case of AML;EVs simultaneously positive for CD137 and EpCAM (CD137+EpCAM+ EVs / ml) in the case of NSCLC were quantified.

[0076] Figs. 36A-39B show that:CLL patients (n=88) had a statistically significant higher number of EVs simultaneously positive for CD19 and CD20 than healthy subjects (n=19); CLL patients (n=15) had more EVs simultaneously positive for CD38 and CD138 than healthy subjects (n=20), which was statistically significant;CLL patients (n=10) had more EVs simultaneously positive for CD34 and CD1 17 than healthy subjects (n=6);CLL patients (n=6) had more EVs simultaneously positive for CD137 and EpCAM than healthy subjects (n=5).

[0077] The analysis of the ROC curves (Figs. 36-39 / B) shows that all tests are accurate with an AUC between 0.71 and 0.86.

[0078] Figs. 40A / 49B show data of the difference between healthy subjects and diseased patients EVs plotted against the Mean Fluorescence Intensity (MFI) or difference of MFI (MFI diff) of the EVs positive for a specific surface antigen (CD) in CLL (Figs. 40A-42B), MM (Figs. 43A-44B), AML (Figs. 45A-47B) and NSCLC (Figs. 48A-49B). The MFI of EVs positive for a specific CD was obtained by flow cytometry. For the MFI of CD-specific positive EVs, the difference between healthy subjects and diseased patients EVs is shown (Figs. 40-49 / A) and the corresponding ROC curves (Figs. 40-49 / B) with the respective AUC and statistical power values. Again, each symbol represents a subject, and the horizontal bar in the graphs indicates the mean with standard deviation.

[0079] In particular, the MFI ofEVs positive for CD19 (MFI CD19) or for CD20 (MFI CD20) or for CD200 (MFI CD200) in the CLL;EVs positive for CD38 (MFI CD38) or CD138 (MFI CD138) in MM;EVs positive for CD34 (MFI CD34) or CD1 17 (MFI CD1 17) or CXCR4 (MFI CXCR4) in AML;EVs positive for PD-L1 (PD-L1 BV421 MFI diff ) or EpCAM (EpCAM BV421 MFI diff ) in NSCLC were analyzed.

[0080] Figs. 40A / 49B show that:CLL patients (n=88) had a statistically significant higher MFI of EVs positive for CD20 or CD200 than the corresponding EVs of healthy individuals (n=19) and a lower MFI of EVs positive for CD19 than the corresponding EVs of healthy individuals (n=19).CLL patients (n=15) had a statistically significant lower MFI of EVs positive for CD38 or CD138 than the corresponding EVs of healthy individuals (n=20);CLL patients (n=10) had a higher MFI of EVs positive for CD34 or CXCR4 than the corresponding EVs of healthy individuals (n=6), while they had a lower MFI of EVs positive for CD1 17 than the corresponding EVs of healthy individuals (n=6).

[0081] CLL patients (n=21 ) had a higher MFI diff of EVs positive for EpCAM or PDL1 than the corresponding EVs of healthy individuals (n=5), The analysis of the ROC curves (Figs. 40-49 / B) shows that all tests are accurate with an AUC between 0.50 and 0.79. In particular, within the MFI or MFI diff of the EVs positive for the different CDs analyzed, it was obtained that: for CLL, the best test is the MFI of CD20 positive EVs (AUC=0.79, p<0.0001 ); for MM, the best test is the MFI of CD38 positive EVs (AUC=0.78, p=0.005); for AML, the best test is the MFI of CD34 positive EVs (AUC=0.76, p=0.08).for NSCLC, the best only test is the MFI diff of PDL1 positive EVs (AUC=0.71 , p=0.14).

[0082] Figs. 50A-66B show the difference in nucleic acid content, in particular microRNA (miR), of EVs between healthy subjects and patients with CLL (Figs. 50A-51 B), MM (Figs. 52A-56B), AML (Figs. 57A-64B) and NSCLC (Figs. 65A-66B). The amount of mi Rs of the EVs was calculated with two parameters, reads (Figs. 52A-64B) or copies / pl (Figs. 50A-51 B and Figs. 65A-66B), which were obtained by sequencing or by ddPCR, respectively, of the RNA extracted from the EVs (see figure 67).

[0083] For CLL, hsa-miR-484 (Fig. 50A) and hsa-miR-93-5p (Fig. 51 A) were quantified by ddPCR, obtaining the miR copy number per pl reaction (miR copies / pl). These two miRs are relevant because they are associated with neoplasia according to scientific literature

[0084] For NSCLC, hsa-miR-125-5p (Fig. 65A) and hsa-miR-451 a (Fig. 66A) were quantified by ddPCR, obtaining the miR copy number per pl reaction (miR copies / pl). These two miRs are relevant because they are associated with tumor according to scientific literature.

[0085] For MM and AML, sequencing of the small RNA (seqRNA) extracted from the EVs of healthy subjects and patients with MM and AML was performed. From the seqRNA, a list of miRs differentially expressed, in terms of reads, between healthy subjects and diseased patients was obtained, from which the following miRs were selected and reported: in the MM: hsa-miR-122-5p (Fig. 52A); hsa-miR-125b-5p (Fig. 53A); hsa-miR-203a-3p (Fig. 54A); hsa-miR-320d (Fig. 55A); hsa-miR-320c (Fig. 56A); in the AML: hsa-miR-150-5p (Fig. 57A); hsa-miR-155-5p (Fig. 58A); hsa-miR-10a-5p (Fig. 59A); hsa-miR-146b-5p (Fig. 60A); hsa-miR-122-5p (Fig. 61 A);hsa-miR-203a-3p (Fig. 62A); hsa-miR-222-5p (Fig. 63A); hsa-miR-181 a-5p (Fig. 64A).

[0086] For each parameter, reads or copy number / pl, the difference between healthy subjects and diseased patients is shown (Figs. 50-66 / A) and the corresponding ROC curves (Figs. 50-66 / B) with the respective AUC and statistical power values. Again, each symbol represents a subject. The horizontal bar in the graphs indicates the mean with standard deviation. The analyzed miR is indicated below the x-axis.

[0087] Figs. 50A-66A show that the amount of: miR-484 in EVs is lower in patients with CLL (n=10) than in healthy subjects (n=16, p=0.009); miR-93-5p in EVs is lower in patients with CLL (n=10) than in healthy subjects (n=16, p=0.0394); miR-122-5p, miR-125b-5p, miR-203a-3p, miR-320d, miR-320c in EVs are higher in patients with MM (n=5) than in healthy subjects (n=7, p=0.03, p=0.06 and p=0.04). miR-150-5p, miR-155-5p, miR-10a-5p, miR-146b-5p, miR-122-5p, miR- 203a-3p, miR-222-5p, miR-181 a-5p in EVs are higher in patients with AML (n=20) than in healthy subjects (n=8, p<0.05) ; miR-125b-5p in EVs is higher in patients with NSCLC (n=7) than in healthy subjects (n=5); miR-451 a in EVs is higher in patients with NSCLC (n=6) than in healthy subjects (n=-5, p<0.01 ).

[0088] The analysis of the ROC curves (Figs. 50-66 / B) shows that all tests are accurate with an AUC between 0.65 and 1 .00.

[0089] In particular, for the different mi Rs analyzed, it was obtained that: for CLL, the best test is the one in which the copy number / pl of miR-484 is evaluated (AUC=0.8, p=0.01 1 ); for MM, the best test is the one in which the reads of miR-125b-5p and miR203a-3p are evaluated (AUC=0.90, p=0.01 ); for AML, the best test is the one in which miR-146b-5p reads are evaluated (AUC=0.93, p=0.0004);for NSCLC, the best test is the one in which the copy number / pl of miR- 451 a is evaluated (AUC=1 ,00, p=0.006).

[0090] Tables 1 -4 show the following information for CLL, MM, AML and NSCLC neoplasms, respectively: Group of biomarkers, Biomarker, Parameters, Cut-off, Sensitivity, Specificity and Statistical power. The threshold-values (cutoffs) of each parameter, with the corresponding values for sensitivity, specificity and statistical power, which allow us to discriminate between healthy subjects and diseased patients CLL, MM, AML or NSCLC, were obtained by ROC analysis with the Graphpad Prism software.

[0091] The ROC curve analysis returns a list of possible cut-offs each associated with a combination of Sensitivity (proportion of true positives or diseased = proportion of correctly classified diseased) and Specificity (proportion of correctly classified healthy). In order to choose the best cut-off, i.e. the one that combines and maximizes Sensitivity and Specificity, the Youden index was calculated via the link mdapp.ca / youden-index-calculator-479 / by entering the value of Sensitivity and Specificity. The closer the value of this index is to 1 , the better the combination of Sensitivity and Specificity of the test.Table 1 - Chronic Lymphatic Leukaemia (CLL)Table 2 - Multiple Myeloma (MM)Table 3 - Acute Myeloid Leukaemia (AML)Table 4 - Non Small Cell Lung Cancer (NSCLC)Example 1Scoring each biomarker by assigning the same weight to each one

[0092] On a subject’s serum, after obtaining the values of all the parame- ters for the single biomarkers, a score is assigned to each biomarker.

[0093] The score for each biomarker is equal to:1 , if at least one of the parameters defining the biomarker has: a value higher than its reference cut-off, if this cut-off has a > sign a value lower than its reference cut-off, if this cut-off has a < sign - 0, if all the parameters defining the biomarker have: a value lower than or equal to its reference cut-off, if this cut-off has a > sign a value higher than or equal to its reference cut-off, if this cut-off has a < sign

[0094] A 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.

[0095] Based on the value of the final score obtained this way, the probability that the subject is ill is defined. In particular:the probability is high, if the final score is higher than or equal to 4; the probability is moderate, if the final score has a value of 3; the probability is low, if the final score is lower than or equal to 2.Example 2Scoring each biomarker by assigning a different weight to each one

[0096] On a subject’s serum, after obtaining the values of all the parameters for the individual biomarkers, a score is assigned to each biomarker.

[0097] The score for each biomarker is equal to:1 for the biomarkers “EVs size” and “EVs amount”, 2 for the biomarkers “Amount of the EVs positive for a single CD” and “EVs fluorescence”, or 3 for the biomarkers “Amount of the EVs positive for double CD” and “Nucleic acid content”, if at least one of the parameters defining the biomarker has a value higher than its reference cut-off, if this cut-off has a > sign a value lower than its reference cut-off, if this cut-off has a < sign0, if all the parameters defining the biomarker have: a value lower than or equal to its reference cut-off, if this cut-off has a > sign a value higher than or equal to its reference cut-off, if this cut-off has a < sign

[0098] A 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.

[0099] Based on the value of the final score obtained this way, the probability that the subject is ill will be defined. In particular: the probability is high, if the final score is higher than or equal to 6; the probability is moderate, if the final score has a value between 4 and 5; the probability is low, if the final score is lower than or equal to 3.Example 3:Scoring each parameter by assigning equal weight to each one

[0100] On a subject’s serum, after obtaining the values of all the parameters for the individual biomarkers, a score is assigned to each parameter.

[0101] The sub-partial score for each parameter is equal to:1 , if its value is:higher than its reference cut-off, if this cut-off has a > sign lower than its reference cut-off, if this cut-off has a < sign 0, if its value is: lower than or equal to its reference cut-off, if this cut-off has a > sign higher than or equal to its reference cut-off, if this cut-off has a < sign

[0102] A final score is calculated as the sum of the sub-partial scores obtained for each parameter (or from the sum of the partial score of each biomarker, which is the same). Specifically, the score can have a value between 0 and 21 .

[0103] Based on the value of the final score obtained this way, the probability that the subject is ill will be defined. In particular: the probability is high, if the final score is higher than or equal to 10; the probability is moderate, if the final score has a value between 6 and 9; the probability is low, if the final score is lower than or equal to 5.Example 4Scoring each parameter by assigning it a different weight

[0104] On a subject’s serum, after obtaining the values of all the parameters for the individual biomarkers, a sub-partial score is assigned to each parameter.

[0105] The sub-partial score for each parameter is equal to:1 for the parameters of the biomarkers “EVs size” and “EVs amount”, 2 for the parameters of the biomarkers “Amount of the EVs positive for a single CD” and “EVs fluorescence”, 3 for the parameters of the biomarkers “Amount of the EVs positive for double CD” and “Nucleic acid content”, if its value is higher than its reference cut-off, if this cut-off has a > sign lower than its reference cut-off, if this cut-off has a < sign0, if its value is: lower than or equal to its reference cut-off, if this cut-off has a > sign higher than or equal to its reference cut-off, if this cut-off has a < sign

[0106] A final score is calculated as the sum of the sub-partial scores obtained for each parameter (or from the sum of the partial score of each biomarker, which is the same). Specifically, the score may have a value between 0 and 45.

[0107] Based on the value of the final score obtained this way, the probability that the subject is ill will be defined. In particular:the probability is high, if the final score is higher than or equal to 14; the probability is moderate, if the final score has a value between / equal to 8 and 13; the probability is low, if the final score is lower than or equal to 7.Example 5Sum of the three groups of biomarkers assigning the same value to each group

[0108] On a subject’s serum, after obtaining the values of all the parameters for the individual biomarkers, a partial score is assigned to each group of biomarkers. The partial score of each group of biomarkers is: for the group "Size / Amount of EVs”, an integer value between 0 and 2 (0,1 or 2), as the group includes two biomarkers I and II. In fact, each biomarker has a sub-partial score value of:1 , if at least one of the parameters defining the biomarker has: a value higher than its reference cut-off, if this cut-off has a> sign a value below its reference cut-off, if this cut-off has a < sign0, if all the parameters defining the biomarker have: a value lower than or equal to its reference cut-off, if this cut-off has a > sign a value higher than or equal to its reference cut-off, if this cutoff has a < sign for the group "Surface of EVs”, an integer value between 0 and 3 (0, 1 , 2 or 3), as the group includes the three biomarkers III, IV and V. The same applies as above for the sub-partial score of the biomarkers; for the group "EVs content”, a value between 0 and 1 , as the group includes only the biomarker VI. The same applies as above for the sub-partial score of the biomarkers.

[0109] A final score is calculated as the sum of the partial scores obtained for each group of biomarkers. Specifically, the score can have a value between 0 and 6.

[0110] Based on the value of the final score obtained this way, the probability that the subject is ill will be defined. In particular: the probability is high, if the final score is higher than or equal to 3the probability is moderate, if the final score has a value of 2 the probability is low, if the final score is lower than or equal to 1 .

[0111] On a subject’s serum, after obtaining the values of all the parameters for the individual biomarkers, a partial score is assigned to each group of biomarkers. The partial score is: for the group "Size / Amount of EVs”, a value of 0 or 1 or 2 because the class includes two biomarkers I, II. Each biomarker has a sub-partial score value of:1 , if at least one of the parameters defining the biomarker has: a value higher than its reference cut-off, if this cut-off has a> sign a value lower than its reference cut-off, if this cut-off has a < sign 0, if all the parameters defining the biomarker have: a value lower than or equal to its reference cut-off, if this cut-off has a > sign a value higher than or equal to its reference cut-off, if this cutoff has a < sign for the group “Surface of EVs”, a value of 0 or 2 or 4 or 6 because the class includes the three biomarkers III, IV and V. Each biomarker has a subpartial score value of:2, if at least one of the parameters defining the biomarker has: a value higher than its reference cut-off, if this cut-off has a> sign a value lower than its reference cut-off, if this cut-off has a < sign 0, if all the parameters defining the biomarker have: a value lower than or equal to its reference cut-off, if this cut-off has a > sign a value higher than or equal to its reference cut-off, if this cutoff has a < sign for the group "EVs content”, a value between 0 and 3 because the class includes only the biomarker VI. Each biomarker has a sub-partial score value of:3, if at least one of the parameters defining the biomarker has: a value higher than its reference cut-off, if this cut-off has a> sign a value lower than its reference cut-off, if this cut-off has a < sign 0, if all the parameters defining the biomarker have: a value lower than or equal to its reference cut-off, if this cut-off has a > sign a value higher than or equal to its reference cut-off, if this cutoff has a < sign

[0112] A final score is calculated as the sum of the partial scores obtained for each group of biomarkers. Specifically, the final score may have a value between 0 and 1 1

[0113] Based on the value of the final score obtained this way, the probability that the subject is ill will be defined. In particular: the probability is high, if the score is higher than or equal to 8 the probability is moderate, if the score has a value between 4 and 7 the probability is low, if the score is lower than or equal to 3

[0114] Fig. 67 is a flow chart of a preferred variant of the method for diagnosing a pathology in a subject on the basis of EVs 3a, 3b obtained from a serum sample 2 from blood 1 previously taken from said subject. Said variant comprises the two aspects of the invention, described above, i.e. it is based on a total score obtained by combining the partial scores assigned to a plurality of parameters representative of the six comprising: a size (I) of said EVs; a total amount (II) of said EVs; an amount (III) of said EVs expressing said first antigen on its own surface, i.e. positive for said first antigen; an amount (IV) of said EVs expressing both said first antigen and said second antigen;MFI (V) for each of said first antigen and said second antigen; a nucleic acid (VI) content of these EVs, and at the same time measure representative parameters of nucleic acid content by determining micro-RNAs using a ddPCR technique 65 applied to a 3b pelletobtained by automatic extraction from a 2b serum sample, after fluorometric quantification 63 and retrotranscription 64.

[0115] In fact, this variant of the method involves steps of: obtaining a serum sample 2 from a subject’s peripheral blood 1 ; isolating a first 3a pellet of EVs from a first 2a part of the serum sample 2; suspending the first pellet of EVs in a saline solution, thus obtaining a wet EV pellet 3a; taking a first part of wet EV pellet;31 NTA analysis on the first wet part of EV pellet, thus obtaining: a size distribution curve of EVs, and a first concentration value of EVs in the wet EV pellet; taking a second part of wet EV pellet; flow cytometry 41 of the second part of the wet EV pellet, obtaining a concentration value, in the second part of the wet EV pellet, of total EVs; a concentration value, in the second part of the wet EV pellet, of EVs specific for a first antigen; a concentration value, in the wet EV pellet, of EVs specific for a second antigen; a concentration value, in the second part of the wet EV pellet, of EVs specific for both the first antigen and the second antigen; a positive amount of fluorescence for the first antigen and for the second antigen; first determination of a specific micro-RNA content, comprising the steps of: manual extraction 51 of nucleic acids from a third wet part of EV pellet (3a),52 sequencing nucleic acids extracted from wet EV pellet 3a second determination of a specific micro-RNA content, comprising the steps of:isolation 61 of a second pellet 3b of EVs from a second part 2b of the serum sample 2 by centrifugation;62 extraction of nucleic acids (RNA) from the second pellet 3b using an automatic extractor; fluorometric quantization 63 of micro-RNA in the second pellet 3b; -performing a retrotranscription 64 of a portion of RNA into cDNA;65 determination of micro-RNAs using a ddPCR technique.

[0116] In the isolation step 61 of EVs, for each case, a serum sample, stored at -80°C was thawed at room T or alternatively at +4°C, maintaining these conditions overnight. The thawed serum was vortexed and an amount of 200 pl was transferred into a 1 .5 ml tube and subjected to an initial centrifugation step at 200g and 4°C for 5 min using a bench centrifuge, in this case a Thermo Scientific MicroCL 21 R centrifuge. Then, a first supernatant was collected without removing the pellet (precipitate), transferred to a new 1.5 ml tube and subjected to a second centrifugation step at 14300g and 4°C for one hour, again using a bench centrifuge. Then, a second supernatant was aspirated using a P1000 pipette. The EV pellet was resuspended in 200 pl of a saline solution (PBS), filtered 0.22 pm and vortexed. The resulting EVs suspension was subjected to a third centrifugation step at 14300g and 4°C for 1 hour. After removing the supernatant without dragging the EV pellet, it was left to dry, ready for RNA extraction.

[0117] In the subsequent step of automatic RNA extraction from the EVs, the “Maxwell RSC miRNA Plasma and Serum” kit (Promega, no. AS1680) was used, after some modifications, in combination with the Maxwell RSC automatic extractor (Promega).

[0118] This kit is designed for the purification of total RNA and miRNA from plasma, serum or EVs previously isolated and resuspended in a solution. In particular, the kit gives optimal results when using 50 to 200 pl of EVs resuspended in TBS, TE or water. EVs resuspended in PBS perform best when less than 75 pl are used for purification.

[0119] The combination with the automatic extractor allows rapid RNA purification and involves minimal sample handling by the operator, maximizing simplicity and data reproducibility. The Maxwell RSC instrument uses pre-dispensed cartridges with the necessary reagents for nucleic acid purification and can simultaneously extract up to 16 samples in approximately 70 min. To prevent RNA degradation, it is necessary to work in an RNase-free environment. This requires careful cleaning of the work surface and pipettes used.

[0120] In the present invention, the kit has been modified to allow it to be used on a dry pellet of EVs and not on a volume of EVs.

[0121] In detail, the EV pellet was resuspended in 230 pl of lysis buffer (Buffer C) provided by the kit and vortexed for 20 sec, after which 200 pl of H2O Nuclease-Free, 80 pl of proteinase K provided by the kit was added, then vortexed again for 20 sec. Subsequently, the suspension was incubated at 55°C for 1 1 min, vortexing every 3 min for 20 sec.

[0122] In the original protocol associated with the kit, incubation at 37°C for 15 min is required.

[0123] The lysed sample thus obtained was then treated with the above- mentioned extractor, following its operations with the help of the Maxpre software, specifically, the “Maxwell RSC miRNA Plasma and Serum” program.

[0124] In more detail, the cartridge supplied with the kit was inserted into the extractor tray by placing well 1 facing away from the elution tubes, then 10 pl of DNase was added to well 4, after any reconstruction of the same, available in lyophilized form, when first used, according to a procedure available in the protocol.

[0125] After inserting the tip provided by the kit into position 8 of the cartridge, all the lysed sample was transferred to position 1 of the cartridge. A 0.5 ml elution tube provided by the kit was placed on the bridge of the tray to add 80 pl of H2O Nuclease-Free.

[0126] In the original protocol, the use of H2O in the amount of 50 pl is provided for.

[0127] Through the aforementioned program, the automatic extraction procedure was then started and the tubes with the RNA samples were retrieved.

[0128] The RNA obtained as above was subjected to the fluorometric quantitation step 63 using the “Qubit microRNA Assay” kit (Life Technologies, ref. no. Q32880) in combination with the QLIIBIT 4.0 Fluorometer (ThermoFisher Scientific). The above kit shows a detection range of 0.5 to 150 ng, and includesa concentrated assay reagent, dilution buffer and 2 pre-diluted miRNA standards.

[0129] For this type of analysis, it is essential to work in an RNase-free environment. Moreover, Qubit only provides optimal performance when all solutions are at room temperature; small temperature variations can affect the accuracy of the assay. Therefore, appropriate precautions known to the branch technician must be observed.

[0130] The fluorometric quantization step 63 in turn comprises: a working solution preparation step common to standards and samples, which consists of diluting the reagent in the buffer 1 :200 after calculating the quantities bearing in mind the volume of the supplied test tubes; a subsequent calibration curve preparation step, in which standards 1 and 2 are combined with the working solution (specifically, 10 pl / 190 pl) in respective first and second tubes, then vortexed for 2-3 sec and incubated at room T and in the dark for 2 min, and the standards are then read out using specific software, after inserting the first and then the second tube into the Qubit reading chamber, to obtain a calibration curve with the concentration values; a sample reading step, for which respective tubes are prepared by mixing the RNA with the working solution (specifically, 20 pl / 180 pl), then incubating the tubes at room T and in the dark for 2 min, and reading the samples assisted by specific software, after inserting each tube into the Qubit reading chamber, to obtain the concentration of the original sample in ng / pl and the concentration of the sample in the Qubit tube in ng / ml.

[0131] The extracted RNA was subjected to cDNA retro-transcription step 64 using the “TaqMan Advanced miRNA cDNA Synthesis Kit” (AppliedBiosystems, no. A28007). The retro-transcription step in turn comprises a poly-A-tail addition step at 3”, lasting about 60 min, in which the samples and cDNA synthesis reagents are thawed on ice, vortexed and spinned, and then proceed to calculate the volume of RNA to be retrotranscribed corresponding to e.g. to 100 pg and the calculation of the mix volume based on the number of samples to be retrotranscribed including the 10% surplus for each component of the mix and taking into account the finalreaction volume (mix + H2O + RNA), specifically 5 pl (in this regard, an example of a mix may include, in terms of volumes for a reaction 0.5 pl of 10X Poly(A) buffer, 0.5 pl of ATP, 0.3 pl of Poly(A) enzyme for a total reaction volume of 1 .3 pl) and to the preparation of the reaction mix in a 1 .5 ml tube, transferring for each sample into a 0.2 pl 1 .3 pl of mix, adding 100 pg of RNA and making up to volume (5 pl) with RNase-free H2O, followed by a first reaction performed in the thermal cycler following a predetermined temperature profile, specifically 45” at 37°C and 10” at 65°C, then letting it cool down to 4°C; an adaptor addition step at 5”, lasting about 60 min, in which a new reaction mix is prepared by calculating the volume based on the number of samples in a 1.5 ml tube, (e.g. according to the following quantities, expressed in pl: 5X DNA Ligase Buffer 3, PEG 8000 at 50% 4.5, 25X Ligation Adaptor 0.6, RNA Ligase 1 .5, RNase-free H2O 0.4, for a total volume of 10 pl), then a predetermined amount of mix is transferred into each tube containing the product from the first reaction (e.g. 10 pl for a total of 15 pl), mixing and pinning to eliminate bubbles, after which the tubes are subjected to a second reaction performed in the thermal cycler following a predetermined temperature profile, specifically 60” at 16°C, then allowed to cool to 4°C. an actual retrotranscription step in cDNA of all miRNAs using universal primers, for a duration of about 20”, in which a further reaction mix is prepared by calculating the volume based on the number of samples in a 1 .5 ml tube, (e.g. according to the following quantities, expressed in pl: Buffer 5X RT 6, dNTP mix (25 mM) 1 .2, 20X Universal RT primer 1 .5, 10X RT Enzyme Mix 3, H2O RNase-free 0.4, for a total volume of 15 pl), then a predetermined amount of mix is transferred into each tube containing the product from the second reaction (e.g. 15 pl for a total of 30 pl), mixing and then pinning to eliminate bubbles, after which the tubes are subjected to a third reaction performed in the thermal cycler following a predetermined temperature profile, specifically 15” at 42°C and 5” at 85°C, then allowed to cool down to 4°C, if necessary bringing the product to -20°C for storage; this can then be followed by a cDNA amplification step (lasting about 32 min), in which a further reaction mix is prepared by calculating the volume based on the number of samples in a 1.5 ml tube, (e.g. according to thequantities, expressed in pl: 2X miR-Amp Master Mix 25, 20X miR-Amp Primer Mix 2.5, H2O RNAase-free 17.54, for a total volume of 45 pl), then a predetermined amount of mix is transferred into a new tube together with the product from the third reaction (e.g. 45 pl for a total of 5 pl), mixing and pinning then to eliminate bubbles, after which the tubes are subjected to a fourth reaction performed in the thermal cycler following a predetermined temperature profile, specifically 5” at 42°C (1 cycle), 3" at 95°C + 30" at 60°C (14 cycles), 10” at 99°C, then allowed to cool down to 4°C, if necessary bringing the product to -20°C for storage.

[0132] This product was finally subjected to step 65 micro RNA quantitation by droplet digital PCR (ddPCR), using the QX200 droplet digital PCR system (BioRad Laboratories) after preparing the reactions with the 2x ddPCR Supermix for probes (BioRad) and the TaqMan miRNA probes (Applied Biosystem).

[0133] For each miRNA to be analyzed, the mix was prepared for the total number of RNA samples to be analyzed (typically 1 1 pl of 2x ddPCR Supermix and 1 pl of the specific miRNA probe) multiplied by the number of samples, adding one or two blanks.

[0134] The reaction was prepared in a 96-well plate by aliquoting 12 pl of the mix into each well and adding 10 pl of RNA for each sample or 10 pl of water for blanks to give a total volume of 22 pl in each well, then spiking 10 times and transferring 20 pl into the middle row of wells of the cartridge assembled in its holder, then aliquoting 70 pl of "Droplet generator OIL for probes" into the bottom row of wells of the cartridge, which was secured in the "Droplet Generator" and the end of the process was signaled. The droplets (in this case about 40 pl each) were transferred to a sealed 96-well plate, and inserted into the plate sealer, first one way, then the other, and then into the thermal cycler, a program was set up comprising enzyme activation steps, 1 cycle of 10” at 95°C denaturation, 40 cycles of 15" at 95°C; annealing / extension, 40 cycles of 1 ” at 58°C; inactivation of the enzymes, 1 cycle of 10" at 98°C, then bringing the temperature to 4°C, a temperature that was maintained for 30” before transferring the plate to the Reader, thus allowing it to be read with the aid of the QuantaSoft software, after setting the threshold on the basis of the blank. The total copies / pl were then determined taking into account the dilution factor used.

[0135] The nonparametric Mann-Whitney Student t test was used to identify significant differences between healthy subjects and diseased patients for all sixparameters. Differences with a p value <0.05 were considered statistically significant. As already described, each parameter for the healthy subjects and diseased patients' group was analyzed by means of a ROC curve with the aid of Graph Pad Prism 6 software. This analysis returns a) a graph with a curve relating the sensitivity and specificity of the diagnostic test as the cut-off value (cutoff value) and the value of the area under the curve (AUC), which is indicative of diagnostic accuracy, and b) a table with several numerical values (cut-offs) associated with a combination of the sensitivity and specificity percentage.

[0136] As is well known, tests of this type are more accurate the closer its ROC curve is to the upper left-hand corner of the graph. The point closest to this angle represents the cut-off value that maximizes both the sensitivity and specificity of the test. If the AUC is 0.5, the test is not informative (the curve would correspond to the bisector, and the test would fail to discriminate diseased from healthy), but, if the AUC has a value between 0.5 and 1 , then the test is progressively more accurate.

[0137] From the table, the value that maximises both the sensitivity and specificity of the test was selected as the cut-off. The objective of the test is to correctly classify a subject (diseased or healthy). Misclassified patients are called “false positives” and “false negatives”.

[0138] The sensitivity of the diagnostic test is the proportion of diseased people who are correctly classified (sensitivity = true positives / tot diseased = VP / (VP+FN)). Conversely, the specificity of the test is the proportion of healthy people who are correctly classified (specificity = true negative / tot healthy = VN / (VN+FP)).

[0139] Fig. 68 is a flow chart of a method, according to another aspect of the invention, for diagnosing a pathology in a subject on the basis of an analysis of the nucleic acid content in EVs obtained from a blood serum sample previously taken from said subject, wherein the same steps as shown in Fig. 67 are provided for the determination of said content as an additional procedure comprising manual extraction from pellets of EVs in suspension and sequencing.

[0140] The same method applies to samples of other biological fluids such as urine; saliva; cerebrospinal fluid; bronchoalveolar lavage fluid; amniotic fluid;seminal fluid; breast milk; sweat; tears; synovial fluid; pleural fluid; pericardial fluid; peritoneal fluid; ascitic fluid; vitreous fluid.

[0141] The foregoing description of embodiments of the invention, and examples thereof, is capable of showing the invention from a conceptual point of view in such a way that others, using the known technique, will be able to modify and / or adapt in various applications such embodiments without further research and without departing from the inventive concept, and, therefore, it is understood that such adaptations and modifications will be considered equivalent to the embodiments described. The means and materials for realizing the various functions may be of various kinds without departing from the scope of the invention. It is understood that the expressions or terminology used are purely descriptive and, therefore, not limiting.

Claims

CLAIMS1. A method for detecting the presence or the absence of a pathology in a subject, said method comprising the steps of: defining at least a first reference antigen and a second reference antigen; isolating (21 ,61 ) an extracellular vesicle pellet (3a, 3b) obtained from a sample (2) of a biological fluid, said sample previously withdrawn from said subject; measuring (31 ,41 ,51 -52) a plurality of parameters representative of six biomarkers ( I- VI) from said extracellular vesicle pellet (3a, 3b), said biomarkers comprising: a size (I) of said extracellular vesicles; a total amount (II) of said extracellular vesicles; an amount (III) of said extracellular vesicles expressing said first antigen on their own surface, i.e. positive for said first antigen; an amount (IV) of said extracellular vesicles expressing both said first and said second antigens; a mean fluorescence intensity (V) for each of said first antigen and said second antigen; a nucleic acid content (VI) of said extracellular vesicles, inputting respective predetermined cut-off values for said biomarkerrepresentative parameters; performing a first comparison of said biomarker-representative parameters with said respective cut-off values; assigning a partial score to each of said biomarker-representative parameters or to said six biomarkers on the basis of said first comparison step; calculating a final score for said subject; performing a second comparison of said final score with at least one predetermined diagnostic threshold value; establishing a diagnosis of probability of said pathology in said subject on the basis of said second comparison.

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

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

4. The method according to claim 3, wherein said extracellular vesicle pellet (3a, 3b) is extracted from a plasma sample or from a serum sample (2) obtained from said peripheral blood sample (1 ).

5. The method according to claim 4, wherein said extracellular vesicle pellet (3a) is extracted from said serum sample (2a), and said step of isolating (21 ) an extracellular vesicle pellet (3a) comprises a step of: suspending (22) said extracellular vesicle pellet in a saline solution, thus obtaining a wet extracellular vesicle pellet (3a).

6. The method according to claim 5, further comprising a step of: taking (30) a first part of said wet extracellular vesicle pellet (3a); and said steps of measuring representative parameters of said size (I) and of said total amount (II) of said extracellular vesicles are performed jointly byNanoparticle Tracking Analysis (31 ) on said first part of said wet extracellular vesicle pellet (3a), thus obtaining: a size distribution curve of said extracellular vesicles, and a first concentration value of said extracellular vesicles in said wet extracellular vesicle pellet.

7. The method according to claim 5, wherein said step of isolating an extracellular vesicle pellet comprises the step of: taking (40) a second part of said wet extracellular vesicle pellet (3a); and said steps of measuring parameters representative of: said total amount (II) of said extracellular vesicles, said amount (III) of said extracellular vesicles expressing said first antigen;said amount (IV) of said extracellular vesicles expressing both said first antigen and said second antigen; said positive fluorescence (V) for said first antigen and for said second antigen are carried out jointly by a step of: flow cytometry (41 ) of said second part of said wet extracellular vesicle pellet, obtaining: a concentration value (II) of the total extracellular vesicles in said second part of said wet extracellular vesicle pellet; a concentration value (III) of extracellular vesicles specific for a first antigen in said second part of said wet extracellular vesicle pellet; a concentration value of extracellular vesicles specific for a second antigen in said second part of wet extracellular vesicle pellet; a concentration value (IV) of extracellular vesicles specific for both said first antigen and said second antigen in said second part of wet extracellular vesicle pellet; an amount of positive fluorescence (V) for said first antigen and for said second antigen.

8. The method according to claim 5, wherein said step of measuring parameters representative of a content (VI) of nucleic acids is a step of determining a content (VI) of specific micro-RNA, and comprises the steps of: manually extracting (51 ) nucleic acids from said wet extracellular vesicle pellet; sequencing (52) said nucleic acids extracted from said wet extracellular vesicle pellet.

9. The method according to claim 4, wherein said step of measuring parameters representative of a nucleic acid content (VI) comprises the steps of: automatically extracting (62) nucleic acids (RNA) from said extracellular vesicle pellet by means of an automatic extractor; performing a fluorometric quantization (63) of micro-RNA; performing a retrotranscription (64,65) of a portion of said RNA into cDNA;determining (65) said micro-RNA (VI) using a digital PCR technique, in particular a droplet digital technique.

10. The method according to claims 8 and 9, wherein said step (51 ) of manually extracting said nucleic acids and said step (62) of automatically extracting nucleic acids (RNA) by means of an automatic extractor are performed on respective first and second EV pellets (3a, 3b) isolated from a first part and a second part (2a, 2b), respectively, of said serum sample (2).

11. The method according to claim 1 , wherein said final score is calculated as a summation of said partial score12. The method according to claim 1 , wherein said step of performing a second comparison of said final score is carried out with respect to two diagnostic threshold values comprising: an upper diagnostic threshold value, and a lower diagnostic threshold value, and said diagnosis is: high probability of said pathology, if said final score is higher than or equal to said upper diagnostic threshold value; moderate probability of said pathology, if said final score is between said upper and lower diagnostic threshold values; low probability of said pathology, if said final score is lower than or equal to said lower diagnostic threshold value.

13. The method according to claim 1 wherein, in said step of defining a plurality of partial scores, a partial score is assigned to each of said six biomarkers (l-VI).

14. The method according to claim 13, wherein said partial score, for each biomarker (l-VI) of said biomarkers, is a first number, if at least one of said biomarker-representative parameters has a value: higher than an own cut-off value of said cut-off values, if said cut-off value has a > sign; lower than an own cut-off value of said cut-off values, if said cutoff value has a < sign;a second number lower than said first number, if each parameter of of said biomarker-representative parameters has a value: lower than or equal to an own cut-off value, if said cut-off value has a > sign; higher than or equal to an own cut-off value, if said cut-off value has a < sign.

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

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

17. The method according to claim 16, wherein said first number a same first lower number for the following biomarkers: said size (I) of said extracellular vesicles, and said amount (II) of said extracellular vesicles; a same first intermediate number for the following biomarkers: said amount (III) of said extracellular vesicles expressing said first antigen, and said mean fluorescence intensity (V) for each of said first antigen and said second antigen; and a same first upper number for the following biomarkers: said amount (IV) of said extracellular vesicles expressing both said first antigen and said second antigen, and said nucleic acid (VI) content of said extracellular vesicles.

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

19. The method according to claim 13, whereinsaid step of defining a plurality of partial scores includes a step of defining a plurality of sub-partial scores, wherein a sub-partial score of said subpartial scores is assigned to each parameter representative of said six biomarkers, and said sub-partial score, for each of said parameters, is: a third number, if said parameter has a value: higher than an own cut-off value of said cut-off values, if said cut-off value has a > sign; lower than an own cut-off value of said cut-off values, if said cutoff value has a < sign; a fourth number lower than said third number, if said parameter has a value: lower than or equal to an own cut-off value, if said cut-off value has a > sign; higher than or equal to an own cut-off value, if said cut-off value has a < sign, and said partial score, for each of said biomarkers, is calculated by summing said sub-partial scores assigned to said parameters representative of said biomarker.

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

21. The method according to claim 19, wherein said third number and said fourth number are assigned independently for each of said parameters.

22. The method according to claim 21 , wherein said third number is: a same third lower number for said parameters representative of the following biomarkers: said size (I) of said extracellular vesicles, and said amount (II) of said extracellular vesicles; a same third intermediate number for said parameters representative of the following biomarkers:said amount (III) of said extracellular vesicles expressing said first antigen, and said mean fluorescence intensity (V) for each of said first antigen and said second antigen, and a same third upper number for said parameters representative of the following biomarkers: said amount (IV) of said extracellular vesicles expressing both said first antigen and said second antigen, and said nucleic acid (VI) content of these extracellular vesicles.

23. The method according to claim 22, wherein said third lower, intermediate and upper numbers are equal to 1 , 2 and 3, respectively, said fourth number is equal to 0 and said upper and lower diagnostic threshold values are equal to 14 and 7, respectively.

24. The method according to claim 1 , wherein: a step is provided of defining a plurality of groups of biomarkers, each group of biomarkers thereof including at least one of said six biomarkers; said partial score assigned to each of said biomarkers is a sub-partial score; in said step of defining a plurality of partial scores, a plurality of subpartial scores is assigned to each of said biomarkers, and said partial score is calculated, for each group of biomarkers, by summing said sub-partial scores assigned to said biomarkers of said group of biomarkers, wherein each of said sub-partial scores is equal to: a fifth number, if at least one of said parameters representative of at least one biomarker of said group has a value: higher than an own cut-off value of said cut-off values, if said cut-off value has a > sign; lower than an own cut-off value of said cut-off values, if said cutoff value has a < sign; a sixth number lower than said fifth number, if each parameter of said parameters representative of said biomarker of said group has a value:lower than or equal to an own cut-off value, if said cut-off value has a > sign; higher than or equal to an own cut-off value, if said cut-off value has a < sign.

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

26. The method according to claim 24, wherein said fifth number and said sixth number are independently associated for said six biomarkers.

27. The method according to claim 26, wherein said fifth number is: a same fifth lower number for the following biomarkers: said size (I) of said extracellular vesicles, and said amount (II) of said extracellular vesicles; a same fifth intermediate number for the following biomarkers: said amount (III) of said extracellular vesicles expressing said first antigen; said amount (IV) of said extracellular vesicles expressing both said first antigen and said second antigen; and said mean fluorescence intensity (V) for each of said first antigen and said second antigen; and a fifth upper number for the following biomarker: said nucleic acid (VI) content of said extracellular vesicles.

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

29. The method according to any of the previous claims, wherein the data obtained for each of said biomarkers are analyzed according to a statistical ROC (Receiver Operating Characteristics) curve test,in particular, said respective cut-off values are obtained through said ROC curve as a value of said test that maximizes the difference between a proportion of true positives and a proportion of false positives.

30. The method according to claim 1 , wherein said pathology is a hematological malignancy.

31. The method according to claim 1 , wherein said hematological malignancy is selected from the group comprised of: Chronic Lymphocitic Leukaemia; Multiple Myeloma; Acute Myeloid Leukaemia.

32. The method according to claim 1 , wherein said pathology is a solid tumor.

33. The method according to claim 1 , wherein said solid tumor is Non-Small Cell Lung Cancer.

34. The method according to claim 10, wherein said steps of isolating (21 ,61 ) said first pellet (3a) and said second pellet (3b) of extracellular vesicles from said first part (2a) and said second part (2b) of said serum sample, respectively, are performed, independently from one another, by centrifugation in a bench centrifuge.

35. A method for detecting the presence or the absence of a disease in a subject, comprising a step of measuring a nucleic acid content (VI) in a serum sample, comprising steps of: isolating a pellet (3b) of extracellular vesicles from said serum sample (2b); extracting (62) nucleic acids (RNA) from said pellet (3b) of extracellular vesicles by means of an automatic extractor; performing a fluorometric quantization (63) of micro-RNA; performing a retrotranscription (64) of a portion of said RNA into cDNA; determining (65) said micro-RNA (VI) using a droplet digital PCR technique.

36. A method for detecting the presence or the absence of a disease in a subject, comprising a step of measuring a nucleic acid content (VI) in a sample of a biological fluid, comprising steps of:isolating a pellet (3b) of extracellular vesicles from said sample of said biological fluid (2b); extracting (62) nucleic acids (RNA) from said pellet (3b) of extracellular vesicles by means of an automatic extractor; - performing a fluorometric quantization (63) of micro-RNA; performing a retrotranscription (64) of a portion of said RNA into cDNA; determining (65) said micro-RNA (VI) using a digital PCR technique, in particular a droplet digital PCR technique.

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