Biomarkers associated with extracellular particles

Abstract

The present invention relates to a method of screening for or surveillance of a disease or cancer in a biological sample obtained from an individual, comprising detecting the presence of one or more disease or cancer biomarkers on or in extracellular vesicles particles (EPs) in an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category. The method of screening for or surveillance of a disease or cancer comprises obtaining an enriched fraction of EPs from a particular tissue, and detecting the presence of one or more disease or cancer biomarkers on or in EPs in the enriched fraction of EPs. Also provided are a method of identifying a disease or cancer biomarker present at reduced or elevated level on or in EPs in an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category, a system for analysing a biological sample obtained from an individual comprising an biomarker detection subsystem and a processor couplable to the biomarker detection subsystem, a use of one or more cancer biomarkers in the detection of cancer in a biological sample obtained from an individual, and an antibody and a primer, and uses thereof, for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual.

Description

[0001] Biomarkers Associated with Extracellular Particles

[0002] Incorporation by Reference

[0003] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

[0004] Cross Reference

[0005] This application claims the benefit of Great Britain Application No. 2415105.2, filed October 14, 2024, Great Britain Application No. 2416864.3, filed November 15, 2024, and Great Britain Application No. 2509310.5, filed June 12, 2025, each of which is hereby incorporated by reference in its entirety.

[0006] Background

[0007] The present invention relates to a method of screening for or surveillance of a disease or cancer in a biological sample obtained from an individual. The present invention also relates to a system for analysing a biological sample obtained from an individual. The present invention also relates to the use of one or more cancer biomarkers in the detection of cancer in a biological sample obtained from an individual. The present also relates to an antibody and a primer, and uses thereof, for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual. The present invention further relates to a non-volatile data carrier carrying process control code to implement the aforesaid methods. The present invention also relates to a method of identifying a disease or cancer biomarker for a disease or cancer of a tissue of a defined category.

[0008] Screening for and the surveillance of cancer or another disease play an important role in clinical management of the disease because it increases the chances of successful treatment and long-term survival. Patients may be enrolled in screening programmes for 2 Docket No. 59888-703.601 early cancer diagnosis, prognosis or to monitor treatment response. However, detection rates remain low because the tests lack sufficient sensitivity and poor adherence to such programmes is common because they require appointments separate from routine blood tests.

[0009] For example, patients with cirrhosis are at high risk for developing hepatocellular carcinoma (HCC). Patients with cirrhosis are recommended to undergo HCC surveillance by ultrasound, with or without alpha-fetoprotein (AFP) every 6 months (Singal et al., 2023). The performance of ultrasound with or without AFP for HCC surveillance is sub-optimal. Cirrhotic patients also adhere poorly to ultrasound surveillance testing. The expanding population at risk of developing HCC is placing an increasing economic burden on healthcare systems due to the sub-standard surveillance offered by ultrasound and AFP, which often miss tumors smaller than 1 cm and cannot reliably indicate which patients require further evaluation with MRI or CT.

[0010] One approach that has been proposed to address these challenges is a liquid biopsy test for cancer. A liquid biopsy test possesses the advantage of being minimally-invasive and convenient. In addition, it is feasible to obtain several samples from patients across key time points for screening purposes. A blood test with improved sensitivity and specificity could alleviate this economic burden and likely enhance patient adherence to surveillance programs. However, identifying HCC-specific blood biomarkers remains challenging due to the complexity of liver disease and the difficulty in distinguishing liver- derived biomarkers from confounding factors in the blood.

[0011] Extracellular particles (hereafter referred to as “EPs”) are heterogeneous lipid-bilayer- encapsulated particles, such as extracellular vesicles (EVs) including exosomes, microvesicles, ectosomes, oncosomes, and apoptotic vesicles, and non-vesicular extracellular particles (NVEPs) including lipoprotein particles (LPPs), ribonucleoprotein particles (RNPs), protein aggregates, exomeres and supermeres, which are naturally secreted or released by cells in tissue and which circulate in extracellular spaces in biofluids such as blood. EPs play an important role in regulating both cellular homeostasis and intercellular communication. In particular, they play a role in (1) removing constituents from cells that may be erroneously produced or are in excess (for example due to abnormal functions linked to an existing disease), and (2) carrying bioactive molecules to local or distant recipient cells (for example to prevent or spread a disease). 3 Docket No. 59888-703.601

[0012] EPs are therefore enriched in biomarkers (such as proteins (surface and cytosolic), sugars, nucleic acid molecules (DNA, micro-RNA and other RNA), lipids and metabolites) which reflect the molecular composition of their cellular sources. Their content reflects real-time biology rather than cell death, making tissue-specific EVs a uniquely informative and complementary modality for biomarker discovery and disease monitoring.

[0013] However, realizing the full potential of EP-based biomarkers is hindered by the multicellular heterogeneity of circulating EPs. A liquid biopsy contains a complex array of molecular components. It is estimated that 99.8% of total EPs in blood are originated from cells naturally residing in the blood tissue, including but not limited to platelets, erythrocytes and lymphocytes, 0.16% from adipose tissue and only 0.03% originate from vital tissues, including but not limited to muscle, brain, skin, colon, lung, liver, heart or pancreas (Li et al., 2020). As a matter of example, it is estimated that only 0.0362% of EPs in blood are from a liver origin.

[0014] Most studies rely on bulk EP isolation methods, such as ultracentrifugation or sizeexclusion chromatography (SEC), which are useful for recovering EPs from soluble proteins, nucleic acids and a great abundance of lipoproteins but do not separate EPs from other EPs and between EP sub-types.

[0015] Therefore, improved approaches to identifying and detecting cancer biomarkers are desired.

[0016] Summary of the Invention

[0017] The disclosure arises from the recognition that EPs originating from a specific tissue act as dynamic circulating snapshots of tissue activity that can be accessed through a liquid biopsy. As such, unlike cell-free (cfDNA) and circulating tumor DNA (ctDNA), which are primarily released during apoptosis or necrosis, EVs offer dynamic molecular snapshots of the living cellular milieu. 4 Docket No. 59888-703.601

[0018] It is therefore beneficial to isolate EPs from a particular category of tissue from complex biofluids containing EP mixtures originated from several or all tissues in an organism prior to identifying biomarkers in order to screen for cancer. This improves the specificity and sensitivity of the cancer diagnosis. It may even result in the identification of cancer biomarkers which do not act as biomarkers on a systemic level (i.e. systemic levels of the biomarker do not do not correlate with the presence or absence of cancer) but which do act as biomarkers in relation to EPs from tissue of a particular category. International Patent Application No. PCT / GB2024 / 051281 , the contents of which are hereby incorporated by reference, reports a method of enriching a fraction of EPs from a particular category of tissue from a complex biofluid using biomarkers which have been identified and validated as being present at elevated levels in EPs from that tissue by comparison to at least one other tissue, ex vivo. The enriched fraction of EPs from a particular category of tissue then have utility for the diagnosis of cancer in that tissue.

[0019] Harnessing the full depth and breadth of “omics” in EPs promises to enhance liquid biopsy performance and range of applications - potentially improving early disease detection, minimal residual disease (MRD) monitoring, discovery of therapeutic targets and filling diagnostic gaps especially in conditions characterized by low cellular turnover and cf / ctDNA shedding.

[0020] In one aspect, disclosed herein is a method of determining whether a subject has or is at risk for a disease. The method can comprise (a) assaying extracellular particles from a target tissue among a plurality of tissues obtained from the subject to detect one or more disease biomarkers; and (b) using at least the one or more disease biomarkers detected in (a) to determine that said subject has or is at risk of having said disease, wherein the determining has an accuracy, a sensitivity, or a specificity of at least 60%. The disease can comprise a cancer or liver disease. The cancer can comprise hepatocellular carcinoma (HCC). The liver disease can comprise cirrhosis, Hepatitis B, or MASH. The method can further comprise computer-generating a report indicative of said subject having or being at risk for said disease. Computer-generating the report can comprise processing the detected one or more disease biomarkers using one or more machine learning algorithms. The assaying can further comprise classifying using a plurality of disease biomarkers, a disease state of the subject. The disease state can be selected from: a healthy state, a cancer state, a cirrhosis state, a MASH state, or a Hepatitis B state. The determination can have an accuracy, a sensitivity, or a specificity 5 Docket No. 59888-703.601 of at least 60%. The determination can have an accuracy, a sensitivity, or a specificity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.

[0021] For example, presented herein are data from a blood test using a multiomics biomarker signature, derived directly from hepatocyte extracellular vesicles (H-EVs), for the early detection of HCC. Thus, some embodiments prioritize organ specificity before targeting disease biomarkers. Moreover, some embodiments are not biased for chronological age nor sex. In addition, embodiments of the invention overcome the weaknesses of the prior art testing modalities (e.g. ultrasound) by providing, for example, an earlier diagnosis with improved outcome, increased patient adherence, objective and uniform result interpretation, and / or faster turnaround time. Furthermore, some embodiments may be used to replace such prior art testing modalities (e.g. ultrasound). Some embodiments have a minimum specificity of 85 to 90 % and / or a minimum sensitivity of 60 to 70 % in disease detection, preferably, early-stage HCC detection.

[0022] According to a first aspect, there is provided a method of screening for or surveillance of cancer in a biological sample obtained from an individual, wherein the method comprises: a) obtaining, from the biological sample, an enriched fraction of extracellular particles (EPs) which are secreted or released from cells of a tissue of a defined category, and b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0023] (i) the presence of one or more cancer biomarkers,

[0024] (ii) the absence of one or more cancer biomarkers, or

[0025] (iii) the presence of one or more cancer biomarkers and the absence of one or more cancer biomarkers.

[0026] Preferably, the or each cancer biomarker is a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite, preferably wherein the RNA molecule or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule.

[0027] Conveniently, the or each cancer biomarker is selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, 6 Docket No. 59888-703.601

[0028] GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine (PS), hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir- 106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17- 5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir- 486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186- 5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa- mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir- 21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir-5193, hsa- mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa- mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir- 152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a- 5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir- 23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa- mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir- 92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, and a combination of two or more thereof.

[0029] Advantageously, the or each cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g- 5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two or more thereof.

[0030] Conveniently, the or each cancer biomarker is selected from the group consisting of GPC-3, CD13, GLLIT1 , hsa-miR-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir- 23a-3p, hsa-mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof.

[0031] Advantageously, the cancer biomarkers consist of GPC-3, CD13, GLLIT1 , hsa-miR- 203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let- 7g-5p, or a fragment thereof. 7 Docket No. 59888-703.601

[0032] Conveniently, the or each cancer biomarker is selected from the group consisting of hsa- miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, hsa-miR-221-3, a fragment thereof, and a combination of two or more thereof.

[0033] Advantageously, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa- miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0034] The cancer biomarker can behsa-miR-124-3p or a fragment thereof.

[0035] In some embodiments, the cancer biomarker is GPC-3, GLLIT1 , CD13, or any combination thereof.

[0036] According to a second aspect, there is provided a method of screening for or surveillance of a disease in a biological sample obtained from an individual, wherein the method comprises: a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category, and b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0037] (i) the presence of one or more disease biomarkers,

[0038] (ii) the absence of one or more disease biomarkers, or

[0039] (iii) the presence of one or more disease biomarkers and the absence of one or more disease biomarkers.

[0040] Conveniently, the disease is liver disease, such that the disease biomarker is a liver disease biomarker.

[0041] Preferably, the disease is cancer, such that the disease biomarker is a cancer biomarker, preferably the cancer is HCC.

[0042] The cancer can be characterized by shedding low amounts of ctDNA. In some embodiments, the cancer is characterized by shedding low amounts of ctDNA at an early stage (i.e. , stage I). An early stage can comprise a lack of metastases.

[0043] Advantageously, the detection step comprises two or more disease biomarkers, and wherein the two or more disease biomarkers comprise two or more different types of 8 Docket No. 59888-703.601 disease biomarker being selected from the group consisting of a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, and a metabolite, preferably wherein the RNA molecule or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule.

[0044] Preferably, the two or more different types of disease biomarker comprises a protein or fragment of a protein and an RNA molecule or a fragment of an RNA molecule.

[0045] Conveniently, the two or more different types of cancer biomarker are detected using two different techniques.

[0046] According to a third aspect, there is provided a use of one or more cancer biomarkers in the detection of cancer in a biological sample obtained from an individual, wherein the or each cancer biomarker is TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , a polypeptide of CD13 (also known as ANPEP) or a fragment thereof, GLLIT1 , JAM3, a polypeptide of PIGR or a fragment thereof, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine (PS), hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa- mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir- 25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir- 103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir- 449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c- 3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir- 5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa- mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir- 126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa-mir-148a- 3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir- 23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa- mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361- 9 Docket No. 59888-703.601

[0047] 5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir- 4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa- mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, and a combination of two or more thereof.

[0048] Preferably, the use comprises a further cancer biomarker being GBA or a fragment thereof.

[0049] Conveniently, the or each cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g- 5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two of more thereof.

[0050] Preferably, the or each cancer biomarker is selected from the group consisting of GPC- 3, CD13, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof.

[0051] Conveniently, the cancer biomarkers consist of GPC-3, CD13, GLLIT1 , hsa-miR-203a- 3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g- 5p, or a fragment thereof.

[0052] Conveniently, the or each cancer biomarker is selected from the group consisting of hsa- miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, hsa-miR-221-3, a fragment thereof, and a combination of two or more thereof.

[0053] Advantageously, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa- miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0054] Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0055] Advantageously, the or each cancer biomarker is present on or in EPs which are secreted or released from cells from a tissue of a defined category.

[0056] Preferably, the use comprises: 10 Docket No. 59888-703.601 a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category, and b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0057] (i) the presence of one or more cancer biomarkers,

[0058] (ii) the absence of one or more cancer biomarkers, or

[0059] (iii) the presence of one or more cancer biomarkers and the absence of one or more cancer biomarkers.

[0060] Conveniently, step (a) comprises capturing EPs which are secreted or released from cells of the tissue of the defined category in the biological sample, wherein the captured EPs display one or more tissue biomarkers.

[0061] According to a fourth, there is provided a method of screening for or surveillance of cancer in a biological sample obtained from an individual, wherein the method comprises: a) obtaining and capturing, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category and display one or more tissue biomarkers. b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0062] (i) the presence of one or more cancer biomarkers,

[0063] (ii) the absence of one or more cancer biomarkers, or

[0064] (iii) the presence of one or more cancer biomarkers and the absence of one or more cancer biomarkers.

[0065] Advantageously, the or each tissue biomarker is a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite.

[0066] Preferably, or each tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2, SLCO1B1, SLC38A3, TMEM56, UNC93A, SLC22A9, SLC2A2 and FXYD1.

[0067] Conveniently, the tissue biomarkers consist of ASGR1 , ASGR2, TFR2, and SLCO1B1.

[0068] Advantageously, the EPs are captured using one or more tissue binding agents being capable of binding specifically to the or each respective tissue biomarker. 11 Docket No. 59888-703.601

[0069] Preferably, the or each tissue binding agent comprises an antibody or antigen binding fragment thereof, an aptamer, a lectin, a lipid-binding protein or domain, or a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide.

[0070] Conveniently, the or each tissue binding agent is provided attached to a substrate, preferably wherein the substrate comprises a magnetic bead or nanoparticle, a gold bead or nanoparticle, a polystyrene bead, an affinity chromatography column, a microplate, a microfluidics channel or a biochip with a surface made of gold, silicon oxide, glass, graphene or polystyrene.

[0071] Preferably, the EPs are captured using two or more tissue binding agents being capable of binding specifically to two or more respective tissue biomarkers, each tissue binding agent being provided in a proportion of 10-50% of the total, wherein the total mixture of tissue binding agents is equal to 100% or wherein each tissue binding agent is provided in equal proportions.

[0072] Conveniently, the or each tissue biomarker is ASGR1 and the or each tissue binding agent is an anti-ASGR1 antibody or antigen binding fragment thereof, the or each tissue biomarker is ASGR2 and the or each tissue binding agent is an anti-ASGR2 antibody or antigen binding fragment thereof, the or each tissue biomarker is TFR2 and the or each tissue binding agent is an anti-TFR2 antibody or antigen binding fragment thereof, the or each tissue biomarker is SLCO1 B1 and the or each tissue binding agent is an anti- SLCO1 B1 antibody or antigen binding fragment thereof, the or each tissue biomarker is SLC38A3 and the or each tissue binding agent is an anti-SLC38A3 antibody or antigen binding fragment thereof, the or each tissue biomarker is TMEM56 and the or each tissue binding agent is an anti-TMEM56 antibody or antigen binding fragment thereof, the or each tissue biomarker is LINC93A and the or each tissue binding agent is an anti- LINC93A antibody or antigen binding fragment thereof, the or each tissue biomarker is SLC22A9 and the or each tissue binding agent is an anti-SLC22A9 antibody or antigen binding fragment thereof, the or each biomarker is SLC2A2 and the or each tissue binding agent is an anti-SLC2A2 antibody or antigen binding fragment thereof, or the or each tissue biomarker is FXYD1 and the or each tissue binding agent is an anti-FXYD1 antibody or antigen binding fragment thereof, or a combination thereof, preferably wherein the or each tissue biomarker is ASGR1 and the or each tissue binding agent is an anti-ASGR1 antibody or antigen binding fragment thereof, the or each tissue 12 Docket No. 59888-703.601 biomarker is ASGR2 and the or each tissue binding agent is an anti-ASGR2 antibody or antigen binding fragment thereof, the or each tissue biomarker is TFR2 and the or each tissue binding agent is an anti-TFR2 antibody or antigen binding fragment thereof, or the or each tissue biomarker is SLCO1 B1 and the or each tissue binding agent is an anti- SLCO1 B1 antibody or antigen binding fragment thereof, or a combination thereof.

[0073] Advantageously, the EPs are captured using four binding agents consisting of the anti- ASGR1 antibody or antigen binding fragment thereof, the anti-ASGR2 antibody or antigen binding fragment thereof, the anti-SLCO1 B1 antibody or antigen binding fragment thereof and the anti-TFR2 antibody or antigen binding fragment thereof, and wherein the anti-ASGR1 antibody or antigen binding fragment thereof is provided in a proportion of 30-50% of the total, and each of the other antibodies or antigen binding fragments thereof is provided in a proportion of 10-30% of the total, wherein the total mixture of the binding agents is equal to 100%.

[0074] Preferably, step (a) further comprises isolating the EPs displaying the or each tissue biomarker which are captured using the or each tissue biomarker.

[0075] Conveniently, the isolated EPs are intact EPs.

[0076] Advantageously, the step (a) further comprises the step of releasing the contents of the captured or isolated EPs.

[0077] Conveniently, the contents are released by permeabilising the membrane of captured or isolated EPs.

[0078] Preferably, the or each cancer biomarker is detected using one or more cancer binding agents being capable of binding specifically to the or each respective cancer biomarker.

[0079] Conveniently, the or each cancer binding agent comprises an antibody or antigen binding fragment thereof, an aptamer, a lectin, a lipid-binding protein or domain, or a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide.

[0080] Advantageously, the RNA oligonucleotide or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule. 13 Docket No. 59888-703.601

[0081] Preferably, when the or each cancer biomarker is a protein or a fragment of a protein, the cancer binding agent comprises an antibody or antigen binding fragment thereof.

[0082] Alternatively, when the or each cancer biomarker is an RNA molecule or a fragment thereof, the cancer binding agent comprises a DNA or RNA oligonucleotide primer or a fragment thereof.

[0083] Preferably, the detection step comprises transcribing the RNA molecule or a fragment thereof into a complementary DNA (cDNA) sequence, and wherein the binding agent is a DNA oligonucleotide primer for the amplification of the cDNA sequence.

[0084] Conveniently, the first or third aspect further comprises: c) quantifying the level of the or each cancer biomarker in order to determine a detected level of the or each cancer biomarker or the combination of the cancer biomarkers.

[0085] Advantageously, the first or third aspect further comprises: d) comparing the detected level of the one or more cancer biomarkers, or the combination thereof, to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level, and e) determining that the individual has cancer of the tissue of the defined category when the detected level of the or each cancer biomarkers, or the combination thereof, is reduced or elevated in comparison to a threshold level, wherein the threshold level is a detected level of the or each cancer biomarker, or the combination thereof, on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0086] Preferably, the detected level includes the level of one or more serological proteins detected in the biological sample, wherein the one or more serological proteins is selected from the group consisting of AFP, AFP-L3, and DCP, or a combination of two or more thereof.

[0087] Conveniently, the detected level includes the level of one or more serological proteins detected in the biological sample, wherein the one or more serological proteins is 14 Docket No. 59888-703.601 selected from the group consisting of AFP and DCP, or a combination thereof, preferably, wherein the serological proteins consist of AFP and DCP.

[0088] In some embodiments, clinical or demographic information strengthens the detection. The clinical or demographic information can strengthen the detection by at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, or more. Clinical or demographic information can comprise, age, sex, gender, race, ethnicity, diet, food security, average alcohol consumption, average tobacco consumption, personal medical history, familial medical history, geographical location, income, or any combination thereof. Personal or familial medical history can comprise laboratory results (e.g., liver function tests, complete blood count, lipid panel, and / or basic metabolic panel), body weight, blood pressure, resting heart rate, current or past medications, current or past diagnoses, current or past surgical procedures, or any combination thereof.

[0089] According to a fifth aspect, there is provided a system for analysing a biological sample obtained from an individual, comprising: a biomarker detection subsystem for detecting a level of one or more disease or cancer biomarkers on or in EPs in the enriched fraction of EPs; and a processor, couplable to the biomarker detection subsystem, configured to: a) compare the detected level of the one or more disease or cancer biomarkers to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level; and b) determine that the individual has disease or cancer of a tissue of a defined category when the detected level of the one or more disease or cancer biomarkers detected in the biological sample from the individual is reduced or elevated in comparison to the threshold level, wherein the threshold level is a detected level of the or each disease or cancer biomarker on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0090] Preferably, the system further comprises: an EP enrichment subsystem configured to obtain, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category. 15 Docket No. 59888-703.601

[0091] Conveniently, the or each cancer biomarker is a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite, preferably wherein the RNA oligonucleotide or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule.

[0092] Advantageously, the or each cancer biomarker is selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine (PS), hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir- 106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17- 5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir- 486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186- 5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa- mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir- 21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir-5193, hsa- mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa- mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir- 152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a- 5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir- 23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa- mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir- 92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, and a combination of two or more thereof.

[0093] Preferably, the or each cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g- 16 Docket No. 59888-703.601

[0094] 5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two or more thereof.

[0095] Advantageously, the or each cancer biomarker is selected from the group consisting of GPC-3, CD13, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a- 3p, hsa-mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof.

[0096] Preferably, the cancer biomarkers consist of GPC-3, CD13, GLLIT1 , hsa-miR-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g-5p, or a fragment thereof.

[0097] Conveniently, the or each cancer biomarker is selected from the group consisting of hsa- miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, hsa-miR-221-3, a fragment thereof, and a combination of two or more thereof.

[0098] Advantageously, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa- miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0099] Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0100] Conveniently, the determining step comprises two or more cancer biomarkers and the detected level of the two or more cancer biomarkers is calculated by applying a weighting to the detected level of each cancer biomarker, preferably wherein the detected level of each cancer biomarker being a proportion of 10-50% of the total level, wherein the total detected level of the cancer biomarkers is equal to 100% or wherein the detected level of each cancer biomarker is in equal proportions.

[0101] Preferably, the detected level includes the level of one or more serological proteins detected in the biological sample, wherein the one or more serological proteins is selected from the group consisting of AFP, AFP-L3, DCP, and a combination of two or more thereof.

[0102] Conveniently, the detected level includes the level of one or more serological proteins detected in the biological sample, wherein the one or more serological proteins is 17 Docket No. 59888-703.601 selected from the group consisting of AFP, DCP, and a combination thereof, preferably, wherein the serological proteins consist of AFP and DCP.

[0103] Advantageously, the detection step comprises two or more cancer biomarkers, and wherein the two or more cancer biomarkers comprise two or more types of cancer biomarker being selected from the group consisting of a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, and a metabolite.

[0104] Preferably, the RNA molecule or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule.

[0105] Advantageously, the two or more different types of cancer biomarker comprises a protein or fragment of a protein and an RNA molecule or a fragment of an RNA molecule.

[0106] Preferably, the two or more different types of cancer biomarker are detected using two different techniques.

[0107] Conveniently, the detection step comprises two or more cancer biomarkers and wherein the two or more cancer biomarkers comprise one or more proteins or a fragment thereof, and one or more RNA molecules or a fragment thereof.

[0108] Advantageously, the tissue of the defined category is liver tissue, such that the cancer is liver cancer and the tissue is liver tissue, preferably wherein the cells from which the EPs are secreted are hepatocytes, Kupffer cells, stellate cells or liver resident dendritic cells.

[0109] Preferably, the biological sample comprises a mixture of EPs, preferably wherein the mixture of EPs comprises EPs from tissue of the defined category and EPs from at least one different category of tissue.

[0110] Conveniently, the biological sample is a biofluid, preferably blood, urine, saliva, lymph, bile, cerebrospinal fluid, phlegm, mucus, tears, Bronchoalveolar Lavage (BAL) fluid, earwax, sweat, faeces, breast milk, interstitial fluids, vaginal fluids, semen, gastric juice, blister fluid or cyst fluid.

[0111] Advantageously, the cancer is hepatocellular carcinoma (HCC). 18 Docket No. 59888-703.601

[0112] Preferably, the EPs are extracellular vesicles (EVs) or non-vesicular extracellular particles (NVEPs), preferably wherein the EPs are exosomes, ectosomes, microvesicles or apoptotic vesicles (apoVs), preferably wherein the NVEPs are lipoprotein particles (LPPs), ribonucleoprotein particles (RNPs), protein aggregates, exomeres or supermeres.

[0113] According to a sixth aspect, there is provided a non-volatile data carrier carrying processor control code to implement the first, second and fourth aspects.

[0114] According to a seventh aspect, there is provided a method of identifying a cancer biomarker for cancer of a tissue of a defined category, comprising: a) obtaining, from a biological sample obtained from an individual having cancer of the tissue of the defined category, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category, b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0115] (i) the presence of the candidate cancer biomarker, or

[0116] (ii) the absence of the candidate cancer biomarker, and c) selecting the candidate cancer biomarker as a cancer biomarker when the detected level of the candidate cancer biomarker is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the candidate cancer biomarker detected on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0117] According to an eighth aspect, there is provided method of identifying a disease biomarker for a disease of a tissue of a defined category, comprising: a) obtaining, from a biological sample obtained from an individual having disease of the tissue of the defined category, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0118] (i) the presence of the candidate disease biomarker, or

[0119] (ii) the absence of the candidate disease biomarker, and 19 Docket No. 59888-703.601 c) selecting the candidate disease biomarker as a disease biomarker when the detected level of the candidate disease biomarker is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the candidate disease biomarker detected on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0120] Conveniently, the disease is liver disease, such that the candidate disease biomarker is a candidate liver disease biomarker.

[0121] Preferably, the disease is cancer, such that the candidate disease biomarker is a candidate cancer biomarker, preferably the cancer is HCC.

[0122] Advantageously, the cancer is characterized by shedding low amounts of ctDNA.

[0123] Conveniently, step (a) comprises capturing EPs which are secreted or released from cells of the tissue of the defined category in the biological sample, wherein the captured EPs display one or more tissue biomarkers.

[0124] Preferably, step (a) further comprises isolating the EPs displaying the or each tissue biomarker which are captured using the or each tissue biomarker.

[0125] Conveniently, the isolated EPs are intact EPs.

[0126] Advantageously, step (a) further comprises the step of releasing the contents of the captured or isolated EPs.

[0127] Conveniently, the contents are released by permeabilising the membrane of captured or isolated EPs.

[0128] Advantageously, the or each tissue biomarker is a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite. 20 Docket No. 59888-703.601

[0129] Preferably, or each tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2, SLCO1 B1 , SLC38A3, TMEM56, UNC93A, SLC22A9, SLC2A2 and FXYD1.

[0130] Conveniently, the tissue biomarkers consist of ASGR1 , ASGR2, TFR2, and SLCO1 B1.

[0131] Advantageously, the EPs are captured using one or more tissue binding agents being capable of binding specifically to the or each respective tissue biomarker.

[0132] Preferably, the or each tissue binding agent comprises an antibody or antigen binding fragment thereof, an aptamer, a lectin, a lipid-binding protein or domain, or a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide.

[0133] Conveniently, the or each tissue binding agent is provided attached to a substrate, preferably wherein the substrate comprises a magnetic bead or nanoparticle, a gold bead or nanoparticle, a polystyrene bead, an affinity chromatography column, a microplate, a microfluidics channel or a biochip with a surface made of gold, silicon oxide, glass, graphene or polystyrene.

[0134] Preferably, the EPs are captured using two or more tissue binding agents being capable of binding specifically to two or more respective tissue biomarkers, each tissue binding agent being provided in a proportion of 10-50% of the total, wherein the total mixture of tissue binding agents is equal to 100% or wherein each tissue binding agent is provided in equal proportions.

[0135] Conveniently, the or each tissue biomarker is ASGR1 and the or each tissue binding agent is an anti-ASGR1 antibody or antigen binding fragment thereof, the or each tissue biomarker is ASGR2 and the or each tissue binding agent is an anti-ASGR2 antibody or antigen binding fragment thereof, the or each tissue biomarker is TFR2 and the or each tissue binding agent is an anti-TFR2 antibody or antigen binding fragment thereof, the or each tissue biomarker is SLCO1 B1 and the or each tissue binding agent is an anti- SLCO1 B1 antibody or antigen binding fragment thereof, the or each tissue biomarker is SLC38A3 and the or each tissue binding agent is an anti-SLC38A3 antibody or antigen binding fragment thereof, the or each tissue biomarker is TMEM56 and the or each tissue binding agent is an anti-TMEM56 antibody or antigen binding fragment thereof, the or each tissue biomarker is LINC93A and the or each tissue binding agent is an anti- 21 Docket No. 59888-703.601

[0136] LINC93A antibody or antigen binding fragment thereof, the or each tissue biomarker is SLC22A9 and the or each tissue binding agent is an anti-SLC22A9 antibody or antigen binding fragment thereof, the or each biomarker is SLC2A2 and the or each tissue binding agent is an anti-SLC2A2 antibody or antigen binding fragment thereof, or the or each tissue biomarker is FXYD1 and the or each tissue binding agent is an anti-FXYD1 antibody or antigen binding fragment thereof, or a combination thereof, preferably wherein the or each tissue biomarker is ASGR1 and the or each tissue binding agent is an anti-ASGR1 antibody or antigen binding fragment thereof, the or each tissue biomarker is ASGR2 and the or each tissue binding agent is an anti-ASGR2 antibody or antigen binding fragment thereof, the or each tissue biomarker is TFR2 and the or each tissue binding agent is an anti-TFR2 antibody or antigen binding fragment thereof, or the or each tissue biomarker is SLCO1 B1 and the or each tissue binding agent is an anti- SLCO1 B1 antibody or antigen binding fragment thereof, or a combination thereof.

[0137] Advantageously, the EPs are captured using four binding agents consisting of the anti- ASGR1 antibody or antigen binding fragment thereof, the anti-ASGR2 antibody or antigen binding fragment thereof, the anti-SLCO1 B1 antibody or antigen binding fragment thereof and the anti-TFR2 antibody or antigen binding fragment thereof, and wherein the anti-ASGR1 antibody or antigen binding fragment thereof is provided in a proportion of 30-50% of the total, and each of the other antibodies or antigen binding fragments thereof is provided in a proportion of 10-30% of the total, wherein the total mixture of the binding agents is equal to 100%.

[0138] Preferably, step (a) further comprises isolating the EPs displaying the or each tissue biomarker which are captured using the or each tissue biomarker.

[0139] Conveniently, the isolated EPs are intact EPs.

[0140] Advantageously, step (a) further comprises the step of releasing the contents of the captured or isolated EPs.

[0141] Preferably, the or each candidate cancer biomarker is detected using one or more cancer binding agents being capable of binding specifically to the or each respective candidate cancer biomarker. 22 Docket No. 59888-703.601

[0142] Conveniently, the or each cancer binding agent comprises an antibody or antigen binding fragment thereof, an aptamer, a lectin, a lipid-binding protein or domain, or a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide.

[0143] Advantageously, the RNA oligonucleotide or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule.

[0144] Preferably, when the or each candidate cancer biomarker is a protein or a fragment of a protein, the cancer binding agent comprises an antibody or antigen binding fragment thereof.

[0145] Alternatively, when the or each candidate cancer biomarker is an RNA molecule or a fragment thereof, the cancer binding agent comprises an RNA oligonucleotide or a fragment thereof.

[0146] Advantageously, the tissue of the defined category is liver tissue, such that the cancer is liver cancer.

[0147] Preferably, the cancer is HCC.

[0148] According to an ninth aspect, there is provided an antibody for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, wherein the antibody specifically binds to a cancer biomarker, and wherein the cancer biomarker is selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1 A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, and Phosphatidylserine (PS), or a fragment thereof.

[0149] According to a tenth aspect, there is provided a use of an antibody for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, wherein the antibody specifically binds to a cancer biomarker, and wherein the cancer biomarker is selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, 23 Docket No. 59888-703.601

[0150] RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC- 3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, and Phosphatidylserine (PS), or a fragment thereof.

[0151] Preferably, the cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, and Phosphatidylserine (PS), or a fragment thereof.

[0152] Advantageously, the cancer biomarker is selected from the group consisting of GPC-3, CD13, and GLUT 1 , or a fragment thereof.

[0153] According to an eleventh aspect, there is provided a primer for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, wherein the primer is for the amplification of a cancer biomarker, and wherein the cancer biomarker is selected from the group consisting of hsa-let-7f-5p, hsa-let-7g-5p, hsa-let- 7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16- 5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir- 451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa- mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir- 449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c- 3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir- 5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa- mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir- 126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa-mir-148a- 3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir- 23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa- mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361- 5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir- 4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa- mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, and hsa-mir-99a-5p, or a fragment thereof.

[0154] According to a twelfth aspect, there is provided a use of a primer for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, wherein the primer is for the amplification of a cancer biomarker, and wherein 24 Docket No. 59888-703.601 the cancer biomarker is selected from the group consisting of hsa-let-7f-5p, hsa-let-7g- 5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa- mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b- 5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa- let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir- 4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir- 3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa- mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir- 126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a- 5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa- mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir- 34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa- mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, and hsa-mir-99a-5p, or a fragment thereof.

[0155] Conveniently, the cancer biomarker is selected from the group consisting of hsa-mir-27b- 5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p, or a fragment thereof.

[0156] Advantageously, the cancer biomarker is selected from the group consisting of hsa-mir- 203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let- 7g-5p, or a fragment thereof.

[0157] Conveniently, the cancer biomarker is selected from the group consisting of hsa-miR-22- 3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0158] Advantageously, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa- miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. 25 Docket No. 59888-703.601

[0159] Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0160] According to a thirteenth aspect, there is provided a use of an antibody and a primer for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, wherein the antibody specifically binds to a polypeptide or a fragment thereof, and wherein the polypeptide is selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, and Phosphatidylserine (PS), and wherein the primer is for the amplification of a cancer biomarker being a miRNA or a fragment thereof, and wherein the miRNA is selected from the group consisting of hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir- 144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir- 320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir- 130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa- mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126- 5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa- mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir- 125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139- 5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a- 5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir- 223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa- mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir- 4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir- 6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, and hsa-mir- 99a-5p. 26 Docket No. 59888-703.601

[0161] Preferably, the polypeptide is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, and Phosphatidylserine (PS), or a fragment thereof, and the miRNA is selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p, or a fragment thereof.

[0162] Advantageously, the polypeptide is selected from the group consisting of GPC-3, CD13, GLLIT1 , or a fragment thereof, and the miRNA is selected from the group consisting of hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g-5p, or a fragment thereof.

[0163] Conveniently, the polypeptide is selected from the group consisting of GPC-3, CD13, GLLIT1 , or a fragment thereof, and the miRNA is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221- 3, or a fragment thereof.

[0164] Preferably, the miRNAs consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa- miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0165] Preferably, the miRNA is hsa-miR-124-3p or a fragment thereof.

[0166] Preferably, the use comprises one or more antibody and one or more primer.

[0167] Preferably, the use comprises three different antibodies and six different primers, or six different pairs of primers, wherein each antibody specifically binds to a polypeptide or a fragment thereof selected from the group consisting of GPC-3, CD13, and GLLIT1 respectively, and wherein each primer, or pair of primers, is for the amplification of a cancer biomarker being a miRNA or a fragment thereof selected from the group consisting of hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g-5p respectively.

[0168] Advantageously, the use comprises three different antibodies and six different primers, or six different pairs of primers, wherein each antibody specifically binds to a polypeptide or a fragment thereof selected from the group consisting of GPC-3, CD13, and GLLIT1 respectively, and 27 Docket No. 59888-703.601 wherein each primer, or pair of primers, is for the amplification of a cancer biomarker being a miRNA or a fragment thereof selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221- 3 respectively.

[0169] Conveniently, cancer biomarker being a miRNA molecule is transcribed into a complementary DNA (cDNA) sequence, and wherein the primer is for the amplification of the cDNA sequence.

[0170] Advantageously, the tissue of the defined category is liver tissue, such that the cancer is liver cancer. The liver tissue can comprise liver cells. The liver cells can comprise hepatocytes, Kupffer cells, stellate cells, liver-resident dendritic cells, or a combination thereof.

[0171] Preferably, the cancer is HCC.

[0172] Conveniently, the cancer biomarker is present on or in EPs which are secreted or released from cells from the target tissue or the tissue of the defined category. In some embodiments, the cancer biomarker is absent on or in EPs which are secreted or released from cells from the target tissue or the tissue of the defined category.

[0173] Advantageously, the cancer biomarker is present on or in EPs in an enriched fraction of EPs which are secreted or released from cells of the target tissue or the tissue of the defined category. In some embodiments, the cancer biomarker is absent on or in EPs in an enriched fraction of EPs which are secreted or released from cells of the target tissue or the tissue of the defined category.

[0174] Preferably, when the tissue of the defined category is liver tissue, the cells from which the EPs are secreted are hepatocytes, Kupffer cells, stellate cells or liver resident dendritic cells.

[0175] EPs secreted from cells from a target tissue (e.g., hepatocytes, Kupffer cells, stellate cells, liver-resident dendritic cells, or a combination thereof) can be enriched, isolated, or separated from circulating EPs derived from a biological sample. The biological sample can comprise a biofluid.. The biological sample (e.g., biofluid) can comprise blood, urine, saliva, lymph, bile, cerebrospinal fluid, phlegm, mucus, tears, 28 Docket No. 59888-703.601

[0176] Bronchoalveolar Lavage (BAL) fluid, earwax, sweat, faeces, breast milk, interstitial fluids, vaginal fluids, semen, gastric juice, blister fluid, cyst fluid, or any combination thereof.

[0177] Conveniently, the EPs are extracellular vesicles (EVs) or non-vesicular extracellular particles (NVEPs), preferably wherein the EPs are exosomes, ectosomes, microvesicles or apoptotic vesicles (apoVs), preferably wherein the NVEPs are lipoprotein particles (LPPs), ribonucleoprotein particles (RNPs), protein aggregates, exomeres or supermeres.

[0178] Conveniently, the tenth, twelfth or thirteenth aspect comprises: a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category, and b) detecting on or in EPs in the enriched fraction of EPs one of the following using the antibody or primer:

[0179] (i) the presence of one or more cancer biomarkers,

[0180] (ii) the absence of one or more cancer biomarkers, or

[0181] (iii) the presence of one or more cancer biomarkers and the absence of one or more cancer biomarkers.

[0182] Advantageously, step (a) comprises capturing EPs which are secreted or released from cells of the tissue of the defined category in the biological sample, wherein the captured EPs display one or more tissue biomarkers.

[0183] Conveniently, the EPs are captured using one or more tissue binding agents being capable of binding specifically to the or each respective tissue biomarker.

[0184] Preferably, step (a) further comprises isolating the EPs displaying the or each tissue biomarker which are captured using the or each tissue biomarker.

[0185] Advantageously, the isolated EPs are intact EPs.

[0186] Conveniently, the use or method further comprises the step of releasing the contents of the captured or isolated EPs. 29 Docket No. 59888-703.601

[0187] Preferably, the contents are released by permeabilising the membrane of captured or isolated EPs.

[0188] Advantageously, the tenth, twelfth or thirteenth aspect comprises: c) quantifying the level of the or each cancer biomarker in order to determine a detected level of the or each cancer biomarker or the combination of the cancer biomarkers.

[0189] Preferably, the tenth, twelfth or thirteenth aspect comprises: d) comparing the detected level of the one or more cancer biomarkers, or the combination thereof, to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level, and e) determining that the individual has cancer of the tissue of the defined category when the detected level of the one or more cancer biomarkers, or the combination thereof, is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the or each cancer biomarker, or the combination thereof, on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0190] Definitions

[0191] The terms “biomarker” is used herein to refer to a naturally occurring molecule, such as a protein (surface and cytosolic), a post-translational modification on a protein, a gene, a nucleic acid molecule (e.g. DNA or RNA, such as micro-RNA and other RNA), an epigenetic modification on a nucleic acid molecule, a sugar, a lipid or a metabolite, or a fragment thereof, which is a measurable indicator of a particular state. Such states include a category of the tissue of origin or a disease such as cancer.

[0192] The term “tissue biomarker” is used herein to refer to a biomarker which is present at elevated levels in tissue of a defined category (e.g. liver tissue). In some embodiments, the tissue biomarker may be used for the identification of the tissue of origin of an EP. In some embodiments, the tissue biomarker may be used for the capture of EPs which derive from the tissue of the defined category. The term “present at elevated levels” as used herein refers to the presence of a tissue biomarker being elevated in the tissue of 30 Docket No. 59888-703.601 the defined category with respect to the presence of the biomarker in at least one different category of tissue and, in some embodiments, all other categories of tissue.

[0193] The term “cancer biomarker” is used herein to refer to a biomarker which is present at different levels (present at reduced or elevated levels) in cancer. In some embodiments, the cancer biomarker is differentially expressed in cancer. In some embodiments, the cancer biomarker is differentially degraded in cancer. In some embodiments, the cancer biomarker is differentially processed in cancer resulting in reduced or elevated levels. The terms “reduced or elevated levels” as used herein refers to the detected level of a cancer biomarker being reduced or elevated in a biological sample from an individual having cancer with respect to the detected level of the or each biomarker from a biological sample from a healthy individual or a biological sample from a condition or disease of the tissue of the defined category other than cancer. Preferably, “reduced or elevated levels” as used herein refers to the detected level of a cancer biomarker being reduced or elevated in an enriched fraction of EPs from a tissue of a defined category which is obtained from a biological sample from an individual having cancer with respect to the detected level of the or each biomarker in an enriched fraction of EPs from a biological sample from a healthy individual or a biological sample from a condition or disease of the tissue of the defined category other than cancer.

[0194] The term “candidate cancer biomarker” is used herein to refer to a putative biomarker which is selected for evaluation as a cancer biomarker which is present at reduced or elevated levels in cancer of tissue of the defined category. The candidate cancer biomarker is preferably present at reduced or elevated levels in an enriched fraction of EPs from a tissue of a defined category which is obtained from a biological sample from an individual having cancer with respect to the detected level of the or each biomarker in an enriched fraction of EPs from a biological sample from a healthy individual or a biological sample from a condition or disease of the tissue of the defined category other than cancer.

[0195] The terms “disease biomarker” and “candidate disease biomarker” are used as for “cancer biomarker” and “candidate cancer biomarker” above, except that the disease is any disease. The disease may be a liver-related disease or cancer.

[0196] The terms “polypeptide” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to amino acid polymers in which one 31 Docket No. 59888-703.601 or more amino acid residues is a modified residue, or a non-naturally occurring residue, such as an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers. In some embodiments, the phrase “transmembrane protein” as used herein refers to a type of integral membrane protein that spans the entirety of the plasma membrane. In some embodiments, the phrase “surface protein” as used herein refers to refers to a type of integral membrane protein that is embedded in the plasma membrane and is exposed to the external side of the plasma membrane.

[0197] The term “amino acid” as used herein refers to naturally occurring and synthetic amino acids, as well as amino acid analogues and amino acid mimetics that have a function that is similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those modified after translation in cells (e.g. hydroxyproline, gamma-carboxyglutamate, and O-phosphoserine).

[0198] The term “post-translational modification” as used herein refers to a biochemical modification of an amino acid in a protein following protein biosynthesis. These post- translational modifications include but are not limited to acetylation, glycosylation, hydroxylation, lipidation, methylation, nitrosylation, phosphorylation, proteolysis, and ubiquitination.

[0199] The term “nucleic acid molecule” is used herein to refer to a polymer of multiple nucleotides. The nucleic acid molecules may comprise naturally occurring nucleic acids (i.e. DNA or RNA) or may comprise artificial nucleic acids such as peptide nucleic acids, morpholin and locked nucleic acid as well as glycol nucleic acid and threose nucleic acid. The terms “oligonucleotide” and “nucleic acid probe” are used interchangeably herein to refer to a single-stranded polymer of nucleotides.

[0200] The term “nucleotide” as used herein refers to naturally occurring nucleotides and synthetic nucleotide analogues that are recognised by cellular enzymes. A nucleotide is the basic building block of nucleic acids (i.e. DNA or RNA) consists of a sugar molecule (either deoxyribose in DNA or ribose in RNA) attached to a phosphate group and a nitrogen-containing base. The bases used in DNA are adenine (A), cytosine (C), guanine (G), and thymine (T) in DNA or uracil (II) in RNA. Double-stranded DNA and RNA is formed from two complementary strands of nucleotides which are held together by hydrogen bonds between pairs of nucleotides (G with C, and A with T or II). In this 32 Docket No. 59888-703.601 specification, the percentage of a sequence which is “complementary” to another sequence (i.e. a target sequence) is determined using sequencing methods such as EMBOSS Needle Pairwise Sequence Alignment (Rice et al., Trends Genet. 2000 Jun;16(6):276-7; Nucleic Acids Res. 2019 Jul 2;47(W1):W636-W641) using default parameters. In particular, EMBOSS Needle can be accessed on the internet using ebi.ac.uk / Tools / psa / emboss_needle / . In particular, it is known in the literature as to how to ascertain an oligonucleotide sequence that is 100% complementary to an oligonucleotide sequence, for example using tools such as EMBOSS revseq which can be accessed online using bioinformatics.nl / cgi-bin / emboss / revseq. Sequencing methods such as EMBOSS Needle Pairwise Sequence Alignment can then be used to ascertain a particular level of percentage sequence identity between the complementary oligonucleotide sequence and the target oligonucleotide sequence.

[0201] The term “individual” or “subject” as used herein refers to an individual animal, including a human.

[0202] The term “tissue” as used herein refers to a group or layer of cells that possess a similar structure and perform a specific function. A tissue may also contain groups or layers of different cells that work together to perform a specific function. The term “tissue” includes but is not limited to connective tissue (including but not limited to loose connective tissue, adipose tissue, dense fibrous connective tissue, elastic connective tissue, cartilage, and / or osseous tissue) blood, epithelial tissue (including but not limited to simple squamous, stratified squamous, simple cuboidal, stratified cuboidal, simple columnar, stratified columnar, pseudostratified columnar and transitional epithelia, and urothelium), muscle tissue (including but not limited to cardiac tissue, smooth tissue, and skeletal tissue), nervous tissue (including but not limited to neurons and neuroglia) and liver tissue (including but not limited to hepatocytes, Kupffer cells, endothelial cells and hepatic stellate cells). The phrase “tissue of the defined category” refers to a tissue found in or obtained from a particular organ or organ system.

[0203] The term “different category of tissue” as used herein refers to a category of tissue other than the tissue of the defined category. For example, when the tissue of the defined category is liver tissue, the different category of tissue may be lung tissue, kidney tissue or breast tissue.

[0204] The term “extracellular particle”, also termed “EP”, as used herein refers to heterogeneous lipid-bilayer-encapsulated particles that are naturally secreted by cells. 33 Docket No. 59888-703.601

[0205] EPs are non-self-replicating and circulate in extracellular spaces in biofluids. The term “extracellular vesicle” consists of variety of subtypes, including but not limited to extracellular vesicles (EVs) or non-vesicular extracellular particles (NVEPs). EPs include exosomes, microvesicles, ectosomes, oncosomes, and apoptotic bodies or vesicles (apoVs). NVEPs include but are not limited to lipoprotein particles (LPPs), ribonucleoprotein particles (RNPs), protein aggregates, exomeres or supermeres.

[0206] The term “biological sample” as used herein refers to a sample obtained from an individual. In one embodiment, the biological sample is a sample of tissue obtained by biopsy (e.g. a tissue biopsy). In one embodiment, the biological sample (e.g. tissue biopsy) is snap frozen. In an alternative embodiment, the biological sample is a biofluid. In one embodiment, the biological sample is a sample of whole blood, plasma or serum. In some embodiments, the biological sample comprises EPs. In embodiments where the biological sample is a biofluid, the biofluid comprises circulating EPs of various tissues of different categories.

[0207] The biological sample can comprise a biofluid. The biological sample or biofluid can comprise blood, urine, saliva, lymph, bile, cerebrospinal fluid, phlegm, mucus, tears, Bronchoalveolar Lavage (BAL) fluid, earwax, sweat, faeces, breast milk, interstitial fluids, vaginal fluids, semen, gastric juice, blister fluid, cyst fluid, or any combination thereof. The biofluid can comprise circulating EPs of various tissues of different categories. EPs from one tissue category can be enriched or isolated. In some embodiments, EPs from two or more tissue categories can be enriched or isolated.

[0208] As used herein, the term “enriched” refers to the state of having been concentrated relative to other components of a biological sample or biofluid. In some cases, enrichment can involve increasing an amount of a target component (e.g., a minority component) of a mixture relative to one or more alternate (e.g., major) components. In some cases, enrichment can involve removal of a bulk component (e.g., albumin from a plasma sample) while leaving a target component in the sample.

[0209] As used herein, the term “isolated” refers to the state of having been separated from other components of a biological sample or biofluid. 34 Docket No. 59888-703.601

[0210] As used herein, the term “enrichment” in the context of EPs refers to the act of concentrating an EP of interest relative to other components of a biological sample or biofluid.

[0211] As used herein, the term “isolation” in the context of EPs refers to the act of separating an EP of interest from other components of a biological sample or biofluid.

[0212] The terms “biofluid” or “biological fluid” are used interchangeably herein to refer to a liquid obtained from an organism, whereby the liquid helps transport cells, particles, vesicles and nutrients and expels waste from cells.

[0213] The term “plasma” as used herein refers to the liquid component of blood excluding the red blood cells, white blood cells and platelets, in the presence of an anticoagulant.

[0214] The term “serum” as used herein refers to the liquid component of blood excluding the red blood cells, white blood cells and platelets, after the blood has clotted.

[0215] The term “binding agent” as used herein refers to a naturally occurring and synthetic molecule (such as an antibody) which is capable of binding specifically to a target molecule on a target entity. In some embodiments, the term “binding agent” refers to a molecule which is capable of binding specifically to a biomarker on or in EPs. In some embodiments, the term “tissue binding agent” refers to a molecule which is capable of binding specifically to a tissue biomarker on or in EPs. In some embodiments, the term “cancer binding agent” refers to a molecule which is capable of binding specifically to a respective cancer biomarker on or in EPs or is for the amplification of a respective cancer biomarker on or in EPs. In some embodiments, the term “disease binding agent” refers to a molecule which is capable of binding specifically to a respective disease biomarker on or in EPs or is for the amplification of a respective disease biomarker on or in EPs. The phrase “binding specifically” or “binds specifically” refers to the preferential binding of the binding agent to the target molecule in comparison to non-target molecules. The term "binds specifically" means that the binding agent has substantially greater affinity for its target biomarker than its affinity for other related biomarkers. By "substantially greater affinity" is meant that there is a measurable increase in the affinity for the target biomarker as compared with the affinity for other related biomarker. In some embodiments, the affinity is at least 1.5-fold, 2-fold, 5-fold, 10-fold, 100-fold, 103-fold, 35 Docket No. 59888-703.601

[0216] 104-fold, 105-fold, 106-fold or greater for the target polypeptide. In some embodiments, the binding agents bind with high affinity, with a dissociation constant of 10’4M or less, 10’7M or less, 10’9M or less; or subnanomolar affinity (0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1 nM or even less). For example, the binding specificity of a binding agent can be tested using an in vitro binding assay using purified proteins.

[0217] The term “antibody” as used herein refers to a Y-shaped protein consisting of four polypeptides, comprising two identical light chains and two identical heavy chains, joined by noncovalent interactions and disulfide bonds. Each of the four chains has a variable region at its amino terminus, which contributes to the antigen-binding site, and a constant region at its carboxy terminus, which determines the isotype. Preferably, the antigen of the antibody is a tissue biomarker, disease biomarker or a cancer biomarker, preferably a tissue biomarker, disease biomarker or a cancer biomarker being a polypeptide.

[0218] The term “antigen binding fragment” of an antibody means a partial fragment of an antibody having an antigen-binding activity and includes Fab, F(ab’)2, scFv and the like. The term also encompasses Fab’ which is a monovalent fragment in a variable region of an antibody obtained by treating F(ab’)2 under reducing conditions. However, the term is not limited to these molecules as long as the fragment has a binding affinity for an antigen. Further, these antigen-binding fragments include not only a fragment obtained by treating a full-length molecule of an antibody protein with an appropriate enzyme, but also a protein produced in an appropriate host cell using a genetically modified antibody gene.

[0219] The term “primer” as used herein refers to a short single-stranded nucleic acid that provides a starting point for DNA or RNA synthesis. The primer comprises a nucleic acid sequence which is complementary, or partially complementary, to the nucleic acid sequence of a tissue biomarker, a disease biomarker or a cancer biomarker being a DNA or RNA oligonucleotide. The primer may be used to amplify the tissue biomarker, the disease biomarker or the cancer biomarker, preferably by polymerase chain reactions (PCR) or reverse transcription polymerase chain reaction (RT-PCR). Usually the primer binds to the DNA or RNA oligonucleotide biomarker itself. However it is also within the scope of this disclosure that the primer binds to a nucleic acid sequence upstream or downstream (or a pair of primers binds to a nucleic acid sequence upstream and downstream) of the DNA or RNA oligonucleotide biomarker to enable its amplification. In some embodiments, the primer binds to a complementary DNA (cDNA) 36 Docket No. 59888-703.601 oligonucleotide which has been synthesized rom a RNA oligonucleotide biomarker to enable amplification of the cDNA oligonucleotide.

[0220] The terms “amplify” or “amplification” are used herein refers to the process of increasing the number of copies of a given nucleic acid. In some embodiments the process exponentially increases the number of copies of a given nucleic acid, such as processes involving PCR.

[0221] The term “capture” as used herein refers to the immobilisation of a target entity relative to a substrate, such as a particle. In some embodiments, the term “capture” refers to the immobilisation of EPs displaying the tissue biomarker in a sample, preferably a biological sample, by the binding agent.

[0222] The term “isolation” as used herein refers to the separation of a target entity from a sample containing at least one other component. In some embodiments, the term “isolation” refers to the separation of EPs displaying the tissue biomarker in a sample, preferably a biological sample, via capture of the EPs using a binding agent.

[0223] The term “enriched fraction” as used herein refers to the separated fraction of a sample following isolation which is primarily composed of the target entity. In some embodiments, the term “enriched fraction” refers to the fraction of a sample, preferably a biological sample, which, following isolation, is primarily composed of EPs. In some embodiments, the term “enriched fraction” refers to the fraction of a sample, preferably a biological sample, which, following isolation, is primarily composed of EPs from the tissue of the defined category.

[0224] The terms “detecting” or “detection” are used herein to refer to a measurable readout. In some embodiments, the term “detection” refers to a measurable readout of the presence of the candidate biomarker. In some embodiments, the detected level of detection of the presence of the candidate biomarker is quantified. In some embodiments, the presence of the candidate biomarker is detected at a desired sensitivity defined by detecting the biomarker at a concentration in units of biomarkers / ml. In some embodiments, the presence of the candidate biomarker is detected at a concentration in units of EPs / mL. In some embodiments, the presence of the disease or cancer biomarker is inferred from the signal output obtained with the technique used for detection. 37 Docket No. 59888-703.601

[0225] The terms “cancer” and “tumor” as used herein refer to the presence of cells in an individual that exhibit new, abnormal and / or uncontrolled proliferation. In one embodiment, the cells have the capacity to invade adjacent tissues and / or to spread to other sites in the body (i.e. the cells are capable of metastasis). In one embodiment, the cancer cells are in the form of a tumor (i.e. an abnormal mass of tissue). The term “tumor” as used herein includes both benign and malignant neoplasms. In one embodiment, the cancer is liver cancer. In some embodiments, the liver cancer is hepatocellular carcinoma (also referred to as “HCC”).

[0226] The term “threshold level” as used herein refers to a determined value of the detected level of the or each disease or cancer biomarker on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease. The threshold level is therefore preferably a reference threshold level.

[0227] The term “healthy individual” as used herein refers to an individual with no known significant health problems.

[0228] The term “an individual having a different condition or disease” as used herein refers to an individual having a condition or disease other than the claimed disease or cancer. In some embodiments, the term “an individual having a different condition or disease” as used herein refers to an individual having a condition or disease of the tissue of the defined category other than the claimed disease or cancer. For example, if the cancer is liver cancer, the different condition or disease is a liver condition or disease other than cancer, for example Cirrhosis.

[0229] The term “an individual having cirrhosis” as used herein refers to an individual exhibiting liver scarring and / or fibrosis, and / or to an individual who has been diagnosed with cirrhosis. The scarring and / or fibrosis can be caused by injury or disease. The scarred and / or fibrotic tissue can replace the healthy tissue. The scarring and / or fibrosis can prevent the liver from properly functioning. The cirrhosis can promote HCC.

[0230] The term “an individual having Hepatitis B” as used herein refers to an individual who has been infected by the Hepatitis B virus, wherein the virus has resulted in liver inflammation, and / or to an individual who has been diagnosed with Hepatitis B. In such individuals, a blood test can reveal the presence of Hepatitis B viral antigens or 38 Docket No. 59888-703.601 antibodies. The infection can be acute or long-term. The inflammation can promote liver scarring, which can promote cirrhosis. The cirrhosis can promote HCC.

[0231] The term “an individual having MASH (Metabolic Dysfunction-Associated Steatohepatitis)” as used herein refers to an individual who exhibits an excess of fat in the liver, and / or an individual who has been diagnosed with MASH. MASLD (Metabolic dysfunction-associated steatotic liver disease) may progress to MASH or to MALFD (Metabolic dysfunction-associated fatty liver disease). The excess fat can result in liver inflammation and liver cell damage. The liver inflammation can promote liver scarring and / or fibrosis, which can promote cirrhosis. The cirrhosis can promote HCC.

[0232] The term “an individual having HCC” as used herein refers to an individual who exhibits dysregulated hepatocyte cell division, and / or an individual who has been diagnosed with HCC. The HCC can comprise a single tumor or multiple tumors. The HCC can be stage I, II, III, or IV. HCC can be promoted by liver inflammation, fibrosis, and / or scarring. The inflammation, fibrosis, and / or scarring can be induced by MASLD, MASH, , MALFD, Hepatitis B, Hepatitis C, and / or cirrhosis.

[0233] The terms “screening for or surveillance of disease” and “screening for or surveillance of cancer” as used herein is used to mean a test for a disease or cancer in a biological sample from an individual. The test may be performed on a biological sample obtained from an individual before the individual displays any symptoms for the disease or cancer, for early cancer diagnosis. Additionally or alternatively, the test may be performed on a biological sample obtained from an individual having the disease or cancer or having had cancer to monitor prognosis or treatment response. The test may be performed at regular intervals, for example as part of a screening programme for a disease or cancer. For example, the test may be performed on individuals having a high risk of developing cancer, for example due to the presence of a genetic modification or a pre-existing medical condition or disease. As an example, patients having cirrhosis have a high risk of developing liver cancer. Such patients are therefore enrolled in screening programmes for the early detection of liver cancer. The test is preferably for identifying disease or cancer of the tissue of the defined category. 39 Docket No. 59888-703.601

[0234] The term “EP enrichment subsystem” as used herein is used to mean a collection of components for obtaining an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category from a biological sample obtained from an individual. The “EP enrichment subsystem” may comprise one or more tissue binding agents.

[0235] The term “biomarker detection subsystem” as used herein is used to mean a collection of components for the detection of one or more disease or cancer biomarkers on or in EPs. The “biomarker detection subsystem” may comprise one or more disease binding agents. The “biomarker detection subsystem” may comprise one or more cancer binding agents.

[0236] The term “non-volatile data carrier” as used herein is used to mean a type of memory device, for example a digital computer memory device, that can retain stored information even after power is removed.

[0237] The term “processor” as used herein is used to mean a component or machine that is operable to process a set of instructions and perform one or more operations based on the instructions. For example, the processor may be a Central Processing Unit (CPU).

[0238] The term “processor control code” as used herein is used to mean a set of instructions that, when read by a processor, cause the processor to perform one or more operations. The term “in vivo” (Latin for "within the living") as used herein refers to experiments performed in a whole, living organism. The term “in vitro" (Latin for “in glass”) as used herein refers to experiments performed on biological material cultured or stored in an artificial receptacle. The term “ex vivo" (Latin for "out of the living") as used herein refers to experiments performed on biological material taken from an organism.

[0239] As used herein, each of the tissue biomarkers and cancer biomarkers have, in preferred embodiments, the amino acid sequence corresponding to the respective Uniprot ID number shown in Table 7 or the nucleic acid sequence corresponding to the respective miRBase accession number shown in Table 8; the respective amino acid and nucleic acid sequences are herein incorporated by reference. The cancer biomarkers being miRNA molecules are in the mature form. 40 Docket No. 59888-703.601

[0240] Brief of the Drawings

[0241] Embodiments will now be described with reference to the following figures in which:

[0242] Figure 1 shows a workflow of a method of screening for or surveillance of hepatocellular carcinoma (HCC). Blood species are collected from patients and the samples are treated for the specific isolation of hepatocyte-derived EPs (h-EPs). The h-EPs are assayed for HCC protein and miRNA biomarkers. The test is complemented by testing HCC- associated serological proteins and all the data is fed into a diagnostic algorithm with the patient’s age and sex, to generate a qualitative (positive / negative) test result.

[0243] Figure 2 shows a flow diagram of the development studies.

[0244] Figure 3 shows a flow diagram of the case control study.

[0245] Figure 4 shows a flow diagram of the biomarker discovery and technology translation study.

[0246] Figure 5 shows a workflow for training and testing with random data partitioning.

[0247] Figure 5 shows the process of scaling, RFE and random forest model training.

[0248] Figure 6 shows data for SLC2A1 .

[0249] Figure 7 shows data for ANPEP.

[0250] Figure 8 shows Receiver Operating Characteristic (ROC) curves for miRNA models, Protein models and multiomics models.

[0251] Figure 9 shows a randomized response variable (RRV) split between HCC and Cirrhosis patients.

[0252] Figure 10 shows the performance of the model when separating HCC samples by stage.

[0253] Figures 11 A and 11 B shows the individual analysis of each sample using various models. 41 Docket No. 59888-703.601

[0254] Figure 12 shows a graph of the detection of the level of specific cancer biomarkers, hsa- let-7g-5p GLLIT1 , CD13, GPC-3, hsa-mir-203a-3p, hsa-mir-486-5p, hsa-mir-27b-5p, hsa-mir-23a-3p, and hsa-mir-122-5p, and serological proteins AFP and DCP, on or in hepatocyte EVs from HCC and cirrhosis samples using an embodiment of the methods provided herein.

[0255] Figures 13A and 13B show the performance, as indicated by true positive rate versus false positive rate, of a multiomics biomarker signature made up of the combination of cancer biomarkers from Figure 12, excluding and including the serological proteins AFP and DCP, in comparison to GAAD, in detecting HCC on or in hepatocyte EVs using an embodiment of the methods provided herein. GAAD stands for an algorithm that uses gender, age, alpha-fetoprotein, and des-gamma-carboxy prothrombin. Figure 13A shows a graph of true positives versus false positives, and Figure 13B shows the area under the curve (AUC) + / - 95% confidence interval (Cl) for these three conditions.

[0256] Figure 14 shows a graph of the performance of a multiomics biomarker signature made up of the combination of cancer biomarkers from Figure 12, including the serological proteins AFP and DCP, in detecting both early and late stages of HCC on or in hepatocyte EVs using an embodiment of the methods provided herein. HCC stage is classified using the Barcelona clinic liver cancer (BCLC) algorithm.

[0257] Figure 15 shows an exemplary workflow of a blood test provided herein.

[0258] Figures 16A to H shows small RNA sequencing data of differentially abundant (DE) microRNAs and distinct pathway enrichments between hepatocyte-enriched and bulk EVs in HCC versus cirrhosis.

[0259] Figures 17A to C show small RNA sequencing data of microRNA reads and distinct pathway enrichments between hepatocyte-enriched and bulk EVs.

[0260] Figures 18A and 18B show example data illustrating miRNA 16-5p (mir16-5p) total read counts (18A) and normalized read counts (18B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals. 42 Docket No. 59888-703.601

[0261] Figures 19A and 19B show example data illustrating miRNA 23 (mir23) total read counts (19A) and normalized read counts (19B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals.

[0262] Figures 20A and 20B show example data illustrating miRNA 27 (mir27) total read counts (20A) and normalized read counts (20B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals.

[0263] Figures 21 A and 21 B show example data illustrating miRNA 203 (mir203) total read counts (21 A) and normalized read counts (21 B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals.

[0264] Figures 22A and 22B show example data illustrating miRNA 486 (mir486) total read counts (22A) and normalized read counts (22B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals.

[0265] Figures 23A and 23B show example data illustrating let-7g-5p total read counts (23A) and normalized read counts (23B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals.

[0266] Figures 24A and 24B show example data illustrating miRNA 122-5p (mir122-5p) total read counts (24A) and normalized read counts (24B) in blood samples from individuals with hepatocellular carcinoma (HCC), individuals with cirrhosis, and healthy individuals.

[0267] Detailed Description of the Disclosure

[0268] A method of identifying one or more cancer or liver disease biomarkers

[0269] In some embodiments, disclosed herein is a method of determining that a subject has or is at risk of having a disease. The method can comprise (a) obtaining a biological sample from the subject, wherein the biological sample comprises extracellular particles secreted or released from a plurality of tissues; (b) selectively enriching, from the biological sample, extracellular particles that are secreted or released from a target tissue among the plurality of tissues; and (c) assaying the selectively enriched extracellular particles to detect one or more disease biomarkers that are indicative of a 43 Docket No. 59888-703.601 presence, an absence, or an increased risk of the disease. The method can further comprise (d) using at least the one or more disease biomarkers detected in (c) to determine that said subject has or is at increased risk of having said disease. The disease can comprise a cancer or liver disease. The cancer can comprise hepatocellular carcinoma (HCC). The liver disease can comprise cirrhosis or Hepatitis B.

[0270] The extracellular particle can be either secreted or released from the target tissue. The secretion or release can occur through many mechanisms, including but not limited to membrane budding, fission, extracellular vesicle or exosome secretion, regulated or constitutive exocytosis, or cell lysis.

[0271] The biological sample can comprise blood, urine, saliva, lymph, bile, cerebrospinal fluid, phlegm, mucus, tears, Bronchoalveolar Lavage (BAL) fluid, earwax, sweat, faeces, breast milk, interstitial fluids, vaginal fluids, semen, gastric juice, blister fluid, cyst fluid, or any combination thereof.

[0272] In some embodiments, the disease biomarker is present on or in extracellular particles which are secreted or released from the target tissue. The target tissue may be present on all extracellular particles which are secreted or released from the target tissue, or a subset of extracellular particles which are secreted or released from the target tissue. The disease biomarker may be present on about 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% of extracellular particles which are secreted or released from the target tissue. The extracellular particle can comprise an extracellular vesicle. In some embodiments, the extracellular particle can comprise non- vesicular extracellular particle (NVEP), exosome, ectosome, microvesicle, apoptotic vesicle, apoptotic body, lipoprotein particle, ribonucleoprotein particle, protein aggregate, exomere, supermere, or any combination thereof.

[0273] The target tissue can comprise liver tissue. The liver tissue can comprise one or more cells. In some embodiments, the liver tissue is or comprises hepatocytes. The one or more cells can be hepatocytes, Kupffer cells, stellate cells, dendritic cells, or a combination thereof. In some embodiments, the cells from which the extracellular particles are secreted or released may be hepatocytes, Kupffer cells, stellate cells, dendritic cells, or a combination thereof. In some embodiments, the target tissue is nonliver tissue (e.g., cardiac, pulmonary, gastrointestinal, hepatic, muscular, or brain tissue). The target tissue can be a single tissue type or a combination of tissue types. In some 44 Docket No. 59888-703.601 embodiments, the determination that the subject has or is at risk for the disease is further based at least in part on one or more disease biomarkers which are not associated with the selectively enriched extracellular particles. The one or more disease biomarkers which are not associated with the selectively enriched extracellular particles can comprise a protein. The protein can comprise alpha-fetoprotein (AFP) or des-gamma carboxyprothrombin (DCP). In some embodiments, the one or more disease biomarkers are not indicative of the disease when assayed in extracellular particles obtained from an alternate tissue type.

[0274] The one or more disease biomarkers comprise a protein or fragment of a protein, a DNA or RNA molecule or a fragment thereof, a lipid, a complex sugar, a post-translational modification, a metabolite, or any combination thereof. The RNA molecule or fragment thereof can comprise a miRNA. In some embodiments, the one or more disease biomarkers comprise a miRNA which is indicative of presence, absence, or severity of the disease in the target tissue. In some embodiments, the miRNA is not indicative of presence, absence, or severity of the disease when assayed in the biological sample prior to the selective enrichment or isolation. In some embodiments, the one or more disease biomarkers comprise two or more miRNAs which are indicative of presence, absence, or severity of the disease in the target tissue, which are not indicative of presence, absence, or severity of the disease when assayed in the biological sample prior to the selective enrichment or isolation. The two or more miRNAs can comprise at Ieast 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more miRNAs.

[0275] In some embodiments, the one or more disease biomarkers comprise a protein which is indicative of presence, absence, or severity of the disease in the target tissue. The one or more disease biomarkers can comprise at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more proteins. In some embodiments, the protein is not indicative of presence, absence, or severity of the disease when assayed in the biological sample prior to the selective enrichment or isolation.

[0276] In some embodiments, the extracellular particles are captured using a binding agent being capable of binding selectively to extracellular particles originating from the target tissue. The binding agent can comprise an antibody or antigen binding fragment thereof, an aptamer, a lectin, a lipid-binding protein or domain, or a DNA or RNA oligonucleotide or a fragment thereof. The antibody or antigen binding fragment thereof can be 45 Docket No. 59888-703.601 configured to target an argonaute protein bound to an miRNA, wherein the one or more disease biomarkers comprises said miRNA.

[0277] The DNA or RNA oligonucleotide can comprise a primer. The primer can be configured to amplify a miRNA. The primer can be configured to amplify one or more of hsa-let-7f- 5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa- mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir- 20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa- let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir- 130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa- mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126- 5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa- mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir- 125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139- 5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a- 5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir- 223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa- mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir- 4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir- 6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, or hsa-mir-99a- 5p.

[0278] In some embodiments, the binding agent is attached to a substrate. The substrate can comprise any suitable material. The substrate can comprise a bead, a nanoparticle, a chromatography column, a microplate, a microfluidics channel, or a biochip. The chromatography column can comprise an affinity chromatography column. The bead can comprise a gold bead, a polystyrene bead, or any combination thereof. The bead can be magnetic. The nanoparticle can comprise a gold nanoparticle. The nanoparticle can be magnetic. The microfluidics channel can comprise gold, silicon oxide, glass, graphene, polystyrene, or any combination thereof. 46 Docket No. 59888-703.601

[0279] In some embodiments, the extracellular particle is intact prior to the assaying. The assaying can comprise releasing the contents of the intact extracellular particle. In some embodiments, all the contents are released. In some embodiments, a subset of the contents is released. In some embodiments, releasing the contents of the intact extracellular particle comprises permeabilising the membrane of the extracellular particle. In some embodiments, releasing the contents of the intact extracellular particle comprises subjecting the extracellular particles to a lysis buffer. The lysis buffer can disrupt the extracellular particle membrane. The lysis buffer can comprise Triton X-100, NP-40, anionic detergents, salts, a buffer (e.g., Tris-HCI) or any combination thereof. In some embodiments, releasing the contents of the intact extracellular particle comprises the addition of protease inhibitors and / or RNAse inhibitors. The addition of said inhibitors can mitigate the degradation of the one or more disease biomarkers prior to analysis.

[0280] In some embodiments, the assaying comprises reverse-transcribing the RNA molecule into a complementary DNA (cDNA) sequence. The RNA molecule can comprise an miRNA molecule. The miRNA molecule can comprise hsa-mir-16-5-p, hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, hsa-mir-486-5p, let-7g-5p, hsa-mir-122-5p, hsa-mir-10a- 5p, or any combination thereof. The binding agent can a DNA oligonucleotide primer for the amplification of the cDNA sequence. The assaying can further comprise quantifying each cDNA sequence. The assaying can comprise any suitable sequencing technique. The sequencing technique can comprise next-generation sequencing. The nextgeneration sequencing can comprise small RNA sequencing. The sequencing technique can comprise direct miRNA sequencing (e.g., nanopore-induced phase-shift sequencing, direct-miR-seq). The direct miRNA sequencing can facilitate the direct reading of miRNA sequences without the need for reverse transcription. The assaying can further comprise quantifying each cDNA or miRNA sequence.

[0281] In some embodiments, the method further comprises comparing the level of the one or more disease biomarkers to a control level. The control level can be that of a healthy patient known to not have the disease. In some embodiments, hsa-mir-16-5-p, hsa-mir- 203a, hsa-mir-486-5p, hsa-let-7g-5p, hsa-mir-10a-5p, or any combination thereof, is indicative of presence, absence, or severity of cirrhosis in the target tissue. In some embodiments, an elevated level of hsa-mir-16-5-p, hsa-mir-203a, hsa-mir-486-5p, hsa- let-7g-5p, hsa-mir-10a-5p, or any combination thereof, is indicative of presence, absence, or severity of cirrhosis in the target tissue. The level of hsa-mir-16-5-p, hsa- mir-203a, hsa-mir-486-5p, hsa-let-7g-5p, hsa-mir-10a-5p, or any combination thereof 47 Docket No. 59888-703.601 can be elevated by at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, or more in biological samples from individuals with cirrhosis. In some embodiments, hsa-mir-23a-3p, hsa-mir- 27a-3p, hsa-mir-203a, hsa-mir-122-5p, or any combination thereof, is indicative of presence, absence, or severity of HCC in the target tissue. In some embodiments, an elevated level of hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, hsa-mir-122-5p, or any combination thereof, is indicative of presence, absence, or severity of HCC in the target tissue. In some embodiments, hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, hsa-mir- 122-5p, or any combination thereof, is indicative of presence, absence, or severity of Hepatitis B in the target tissue. In some embodiments, an elevated level of hsa-mir-23a- 3p, hsa-mir-27a-3p, hsa-mir-203a, hsa-mir-122-5p, or any combination thereof, is indicative of presence, absence, or severity of Hepatitis B in the target tissue. The level of hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, hsa-mir-122-5p, or any combination thereof, can be elevated by at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, or more in biological samples from individuals with HCC.

[0282] In some embodiments, the method further comprises providing treatment to the subject based at least in part on the detected one or more target tissue associated disease biomarkers. Providing the treatment can be further based at least in part on one or more disease biomarkers which are not associated with the target tissue. In some embodiments, the disease is HCC, and providing the treatment comprises liver resection, liver transplant, local ablation, radiotherapy, chemotherapy, immunotherapy, tyrosine kinase inhibitors, or any combination thereof. The tyrosine kinase inhibitors can comprise sorafenib, lenvatinib, regorafenib, cabozantinib, or any combination thereof. The immunotherapy can comprise systemic immunotherapy. The immunotherapy can comprise administration of a biologic. The biologic can comprise atezolizumab, bevacizumab, durvalumab, tremelimumab, or any combination thereof. In some embodiments, the disease is Hepatitis B, and providing the treatment comprises administration of interferon alpha (IFNa) or a nucleoside or nucleotide analog (NA). The IFNa can comprise pegylated IFNa. The NA can comprise lamivudine (LAM), telbivudine, entecavir (ETV), adefovir dipivoxil (ADV), tenofovir disoproxil fumarate (TDF), tenofovir alafenamide fumarate (TAF), or any combination thereof. In some embodiments, the disease is cirrhosis, and providing the treatment comprises managing complications. Managing complications can comprise administration of pharmacologic treatments to facilitate fluid retention (e.g., spironolactone, furosemide), mitigate bleeding from varices 48 Docket No. 59888-703.601

[0283] (e.g., beta- blockers), prevent hepatic encephalopathy (e.g., rifaximin), supplement nutrition (e.g., thiamine, multivitamins), reduce liver inflammation (e.g., steroids), reduce pain (e.g., paracetamol), or any combination thereof.

[0284] In one aspect, provided herein is a system which can facilitate any of the methods disclosed herein.

[0285] In another aspect, the disclosure provides a method of identifying a cancer biomarker for cancer of a tissue of a defined category, comprising: a) obtaining, from a biological sample obtained from an individual having cancer of the tissue of the defined category, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category, b) detecting the presence of a candidate cancer biomarker on or in EPs in the enriched fraction of EPs, and c) selecting the candidate cancer biomarker as a cancer biomarker when the detected level of the candidate cancer biomarker is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the candidate cancer biomarker detected on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0286] The candidate cancer biomarker can be any type of biomarker which is associated with cancer. In some embodiments, the candidate cancer biomarker is a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite, which is detectable on or in EPs. Preferably, the cancer biomarker is a protein or fragment of a protein, or an RNA oligonucleotide or a fragment of an RNA oligonucleotide. In embodiments where the cancer biomarker is an RNA oligonucleotide or a fragment thereof, the cancer biomarker is preferably a microRNA (miRNA) molecule or a fragment thereof. In preferred embodiments, thecancer biomarker is a protein or fragment of a protein, or a miRNA molecule or a fragment of a miRNA molecule. Most preferably, the candidate cancer biomarker is a cancer biomarker as described below.

[0287] In some embodiments, the enriched fraction of EPs which are secreted or released from the tissue of the defined category is enriched from a biological sample comprising a 49 Docket No. 59888-703.601 mixture of EPs. Preferably, the mixture of EPs comprises EPs from the tissue of the defined category and EPs from at least one different category of tissue from the individual. In embodiments where the tissue of the defined category is liver tissue, the enriched fraction of EPs which are secreted or released from cells of liver tissue is enriched from a biological sample comprising a mixture of EPs. Preferably, the mixture of EPs comprises EPs from liver tissue and EPs from at least one different category of tissue from the individual. For example, the mixture of EPs, preferably where the biological sample is a biofluid, may comprise EPs from liver tissue and EPs from at least one other category of tissue, such as breast tissue.

[0288] In some embodiments, the enriched fraction of EPs is obtained by capturing EPs which are secreted or released from cells of the tissue of the defined category in the biological sample, wherein the captured EPs display one or more tissue biomarkers. The step of obtaining the enriched fraction of EPs is preferably performed using one or more tissue binding agents being capable of binding specifically to the respective tissue biomarker. The one or more tissue biomarkers and the one or more tissue binding agents are preferably as described below. Further details of how to obtain an enriched fraction of EPs are provided in International Patent Application No. PCT / GB2024 / 051281 , the contents of which are hereby incorporated by reference.

[0289] In some embodiments, the step of detecting the presence of a candidate cancer biomarker is performed using mass spectrometry or RNA sequencing. For example, where the candidate cancer biomarker is a polypeptide or a fragment thereof, the cancer biomarker may be detected using mass spectrometry. For example, where the candidate cancer biomarker is an RNA oligonucleotide or a fragment thereof, the cancer biomarker may be detected using RNA sequencing.

[0290] The candidate cancer biomarker is selected as a cancer biomarker when the level of the candidate cancer biomarker detected on or in an enriched fraction of EPs obtained from an individual having cancer of the tissue of the defined category is reduced or elevated in comparison to the level of the candidate cancer biomarker detected on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease. 50 Docket No. 59888-703.601

[0291] It is preferred that the level of the candidate cancer biomarker corresponds to the expression level of the candidate cancer biomarker detected in or on EPs. Such that, the candidate cancer biomarker is differentially expressed in cancer.

[0292] In some embodiments, the different condition or disease is a condition or disease other than cancer. In some embodiments, the different condition or disease is a condition or disease of the tissue of the defined category other than cancer. For example, if the cancer is liver cancer, the different condition or disease is a liver condition or disease other than cancer, for example Cirrhosis.

[0293] In preferred embodiments, the method further comprises quantifying the level of the cancer biomarker in order to determine a detected level of the candidate cancer biomarker or the combination of the cancer biomarkers.

[0294] It is preferred that the tissue of the defined category is liver tissue, such that the cancer is liver cancer and the tissue is liver tissue. Preferably, the cells from which the EPs are secreted are hepatocytes, Kupffer cells, stellate cells or liver resident dendritic cells.

[0295] Most preferably, the liver cancer is HCC.

[0296] By preparing a fraction of EPs from a biological sample obtained from an individual which is enriched in EPs from a particular tissue category, the specificity of the detection of cancer is improved. Given that the EPs derive from the particular category of tissue (e.g. liver tissue), it can be confirmed whether or not the individual has cancer of the particular category of tissue. In contrast, if the same detection step is performed directly on the unprocessed biological sample, without an enriching step, it may only be possible to confirm that the individual has cancer of an unspecified category. Furthermore, the enriching step enables the possibility of additional cancer biomarkers which do not act as cancer biomarkers in relation to an unprocessed biological sample (because the levels thereof do not correlate with the presence or absence of cancer on a systemic level).

[0297] Preparing a fraction of EPs which derive from the particular category of tissue also enables a more sensitive detection of the cancer biomarkers. In some cases, candidate cancer biomarkers may be selected which otherwise would not have been identified as a cancer biomarker in a sample which had not been enriched i.e. a sample of complex 51 Docket No. 59888-703.601 biofluid comprising a mixture of EPs from multiple tissue types. This is because biological samples are extremely complex. For example, as discussed above, 99.8% of total EPs in blood are originated from cells naturally residing in the blood tissue, including but not limited to platelets, erythrocytes and lymphocytes, 0.16% from adipose tissue and only 0.03% originate from vital tissues (Li et al., 2020). In a biological sample, the candidate cancer biomarker therefore may not be present at a sufficient concentration to be detectable. However, once an enriched fraction of EPs from a particular vital tissue is obtained from the complex biofluid, the candidate cancer biomarker is present at an increased concentration in the total sample and thus is detectable or is detectable at an increased sensitivity.

[0298] It should be understood that, once a candidate cancer biomarker is identified using the method of identification disclosed herein, the biomarker(s) may be used without performing the identification step in further methods, in particular in the method of screening for cancer and uses described below.

[0299] Further details of the method of identifying are provided below.

[0300] A method of screening for or surveillance of cancer or liver disease

[0301] In one aspect, disclosed herein is a method of determining whether a subject has or is at risk for a disease. The method can comprise (a) assaying extracellular particles from a target tissue among a plurality of tissues obtained from the subject to detect one or more disease biomarkers; and (b) using at least the one or more disease biomarkers detected in (a) to determine that said subject has or is at risk of having said disease, wherein the determining has an accuracy, a sensitivity, or a specificity of at least 60%. The disease can comprise a cancer or liver disease. The cancer can comprise hepatocellular carcinoma (HCC). The liver disease can comprise cirrhosis or Hepatitis B.

[0302] In some embodiments, the method further comprises computer-generating a report indicative of said subject having or being at risk for said disease. Computer-generating the report can comprise processing the detected one or more disease biomarkers using one or more machine learning algorithms. The one or more machine learning algorithms can leverage artificial intelligence. 52 Docket No. 59888-703.601

[0303] The assaying can further comprise classifying using a plurality of disease biomarkers, a disease state of the subject. The disease state can be selected from: a healthy state, a cancer state, a cirrhosis state, a MASH state, or a Hepatitis B state. The determination can have an accuracy, a sensitivity, or a specificity of at least 60%. The determination can have an accuracy, a sensitivity, or a specificity of at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.

[0304] In some embodiments, an area under a receiver operator curve for differentiating a cancer state from a healthy state is at least 0.6. In some embodiments, the area under the receiver operator curve for differentiating of the cancer state from the healthy state is at least 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 0.99. In some embodiments, an area under a receiver operator curve for differentiating a cancer state from a cirrhosis state is at least 0.6. In some embodiments, the area under the receiver operator curve for differentiating of the cancer state from the cirrhosis state is at least 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 0.99. In some embodiments, an area under a receiver operator curve for differentiating a cirrhosis state from a healthy state or a cirrhosis state from a MASH state is at least 0.6. In some embodiments, the area under the receiver operator curve for differentiating of the cirrhosis state from the healthy state is at least 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 0.99. In some embodiments, an area under a receiver operator curve for differentiating a Hepatitis B state from a healthy state is at least 0.6. In some embodiments, the area under the receiver operator curve for differentiating of the Hepatitis B state from the healthy state is at least 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 0.99. In some embodiments, an area under a receiver operator curve for differentiating a Hepatitis B state from a cancer state is at least 0.6. In some embodiments, the area under the receiver operator curve for differentiating of the Hepatitis B state from the cancer state is at least 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 0.99.

[0305] In some embodiments, the tissue biomarker for a given disease exhibits elevated levels in patients having the disease compared to that of healthy patients. In some embodiments, the tissue biomarker can be elevated in patients having the disease compared to healthy patients by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, 350%, 400%, 450%, or 500%.

[0306] Also provided herein is a non-volatile data carrier. The non-volatile data carrier can carry processor control code to implement any of the methods or systems disclosed herein. 53 Docket No. 59888-703.601

[0307] The disclosure provides a method of screening for or surveillance of cancer in a biological sample obtained from an individual, wherein the method comprises: a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category, and b) detecting the presence of one or more cancer biomarkers on or in EPs in the enriched fraction of EPs.

[0308] Detecting the presence of the one or more cancer biomarkers on or in the EPs in the enriched fraction of EPs is indicative of the presence of cancer in the individual from whom the biological sample is obtained.

[0309] In some embodiments, the method further comprises: c) quantifying the level of the cancer biomarker in order to determine a detected level of the cancer biomarker or the combination of the cancer biomarkers.

[0310] In some embodiments, the method further comprises: d) comparing the detected level of the one or more cancer biomarkers, or the combination thereof, to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level, and e) determining that the individual has cancer of a tissue of a defined category when the detected level of the one or more cancer biomarkers, or the combination thereof, is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the cancer biomarker, or the combination thereof, on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0311] The method of screening for or surveillance of cancer may be performed by detecting the levels of one or more cancer biomarkers on or in EPs in the enriched fraction of EPs obtained from a biological sample, and, in some embodiments detecting the level of one or more serological proteins in the biological sample. The level of the two or more cancer biomarkers may be combined to determine the detected level. In some embodiments, the detected level of the one or more cancer biomarkers may be combined, including the detected level of the one or more serological proteins, to determine the detected level. When the combined detection level is elevated in comparison to the threshold level, it is 54 Docket No. 59888-703.601 determined that the individual has cancer (positive result). When the combined detection level is reduced in comparison to the threshold level, it is determined that the individual does not have cancer (negative result).

[0312] In an exemplary embodiment, the method of screening for or surveillance of cancer may be performed by detecting the levels of nine cancer biomarkers (including the h-EV protein biomarkers GPC-3, CD13, GLLIT1 and six h-EV miRNA biomarkers miR-203a, mir-27b-5, mir122-5p, 23a-3p, 486-5p, let-7g-5p) on or in h-EPs in the enriched fraction of h-EPs obtained from a biological sample, and, detecting the level of two soluble serological proteins AFP and DCP in the biological sample. The detected level is calculated by combining the level of the nine cancer biomarkers and the two soluble serological proteins. When the combined detection level is elevated in comparison to a threshold level, it is determined that the individual has HCC (positive result). When the combined detection level is reduced is reduced in comparison to the threshold level, it is determined that the individual does not have HCC (negative result).

[0313] Further details of the method of screening or surveillance are provided below.

[0314] A non-volatile data carrier

[0315] Provided herein is a non-volatile data carrier carrying processor control code to implement the method of screening or surveillance for cancer or liver disease. The nonvolatile data carrier can be configured to implement any of the methods or systems provided herein.

[0316] It should be appreciated that the non-volatile data carrier can be any kind of memory device, for example a digital computer memory device, that can retain the information when powered off.

[0317] The processor control code is preferably a set of instructions or a set of computer- readable instructions. Such that the set of instructions that, when read by a processor, cause the processor to implement the method of screening or surveillance for cancer.

[0318] A system for analysing a biological sample 55 Docket No. 59888-703.601

[0319] Provided herein is a system for processing a biological sample obtained from a subject according to any of the methods provided herein.

[0320] In one aspect the disclosure provides a system for analysing a biological sample obtained from an individual, comprising: a biomarker detection subsystem for detecting a level of one or more cancer biomarkers on or in EPs in the enriched fraction of EPs; and a processor, couplable to the biomarker detection subsystem, configured to: a) compare the detected level of the one or more cancer biomarkers to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level; and b) determine that the individual has cancer of the tissue of the defined category when the detected level of the one or more cancer biomarkers detected in the biological sample from the individual is reduced or elevated in comparison to the threshold level, wherein the threshold level is a detected level of the cancer biomarker on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0321] In preferred embodiments, the biomarker detection subsystem comprises one or more cancer binding agents. The cancer binding agent is capable of binding specifically to a respective cancer biomarker on or in EPs or is for the amplification of a respective cancer biomarker on or in EPs.

[0322] In some embodiments, the system further comprises: an EP enrichment subsystem configured to obtain, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category.

[0323] In preferred embodiments, the EP enrichment subsystem comprise one or more tissue binding agents. The tissue binding agent is capable of binding specifically to a respective tissue biomarker which is displayed on or in the enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category.

[0324] Further details of the system are provided below. 56 Docket No. 59888-703.601

[0325] Use of one or more cancer biomarkers in the detection of cancer or liver disease

[0326] In one aspect, provided herein is a use of one or more cancer biomarkers in the detection of cancer in a biological sample obtained from an individual. The cancer biomarker is selected from the group consisting of TFR1, RPL10A, SLC4A1 , C1S, SLC2A1 , a polypeptide of CD13 (also known as AN PEP) or a fragment thereof, GLUT1 , JAM3, a polypeptide of PIGR or a fragment thereof, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, phosphatidylserine (PS), hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa- mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir- 23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f- 1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa- mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa- mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b- 5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa- mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir- 191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir- 205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a- 5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa- mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir- 7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, and a combination of two or more thereof.

[0327] It is to be understood that the cancer biomarkers may be used in the detection of cancer in various different ways. For example, in one embodiment, the presence of the one or cancer biomarkers is detected in the biological sample and the detection thereof is indicative of the presence of cancer in the individual. In other embodiments, the level of the one or more cancer biomarkers is quantified to determine a detected level thereof 57 Docket No. 59888-703.601 which is compared with a threshold level to determine whether the detected level is reduced or elevated in comparison to a threshold level, which threshold level correlates with the presence or absence of cancer. The results of the comparison are thereby indicative of the presence or absence of cancer in the individual.

[0328] In some embodiments, the use comprises a further cancer biomarker being GBA.

[0329] In preferred embodiments, the cancer biomarker is selected from the group consisting of SLC2A1 , a polypeptide of CD13 (also known as ANPEP) or a fragment thereof, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa- mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two or more thereof.

[0330] More preferably, the cancer biomarker is selected from the group consisting of GPC-3, a polypeptide of CD13 or a fragment thereof, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof.

[0331] In some embodiments, the cancer biomarker is present on or in EPs which are secreted or released from cells from a tissue of a defined category. In some embodiments, the tissue of the defined category is liver tissue. In such embodiments, the cells from which the EPs are secreted may be hepatocytes, Kupffer cells, stellate cells or liver resident dendritic cells.

[0332] In some embodiments the cancer (e.g., HCC) biomarker comprises hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-23b-3p, hsa-miR-203a-3p, hsa-miR-320a, hsa-miR-24-3p, hsa-miR-320d, hsa-miR-320c, hsa-miR-126-3p, hsa-miR-107, hsa-miR- 221-3p, hsa-let-7d-5p, hsa-miR-10a-5p, hsa-miR-130a-3p, hsa-miR-122-5p, or any fragment or combination thereof.

[0333] In some embodiments, the use comprises two or more cancer biomarkers, three or more cancer biomarkers, four or more cancer biomarkers, or five or more cancer biomarkers, which are detectable on or in EPs.

[0334] In some embodiments, the use comprises: a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category, and 58 Docket No. 59888-703.601 b) detecting the presence of the cancer biomarkers on or in EPs in the enriched fraction of EPs.

[0335] In embodiments of the use of one or more cancer biomarkers which comprises step (a) (the step of obtaining an enriched fraction of EPs which are secreted or released from cells of the tissue of the defined category), each cancer biomarker is preferably selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine (PS), hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa- mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir- 23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f- 1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa- mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa- mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b- 5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa- mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir- 191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir- 205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a- 5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa- mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir- 7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, and hsa-mir-99a-5p.

[0336] In preferred embodiments, the cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g- 5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, and hsa-mir- 122-5p. 59 Docket No. 59888-703.601

[0337] More preferably, the cancer biomarker is selected from the group consisting of GPC-3, CD13, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa- mir-486-5p, and hsa-let-7g-5p.

[0338] Alternatively, the cancer biomarker is selected from the group consisting of hsa-miR-22- 3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3.

[0339] Preferably, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124- 3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0340] Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0341] In some embodiments, the use further comprises: c) quantifying the level of the cancer biomarker in order to determine a detected level of the cancer biomarker or the combination of the cancer biomarkers.

[0342] In some embodiments, the use further comprises: d) comparing the detected level of the one or more cancer biomarkers, or the combination thereof, to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level, and e) determining that the individual has cancer when the detected level of the one or more cancer biomarkers, or the combination thereof, is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the cancer biomarker, or the combination thereof, on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0343] Further details of the use are provided below.

[0344] An antibody for the detection of cancer or liver disease, and use thereof the detection of cancer or liver disease.

[0345] The disclosure provides an antibody, or one or more antibodies, for the detection of cancer of a tissue of a defined category in a biological sample obtained from an 60 Docket No. 59888-703.601 individual. The antibody, or one or more antibodies, specifically binds to a cancer biomarker, or one or more cancer biomarkers.

[0346] The disclosure provides a use of an antibody, or one or more antibodies, for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual. The antibody, or one or more antibodies, specifically binds to a cancer biomarker, or one or more cancer biomarkers.

[0347] The cancer biomarker is preferably selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, and PS.

[0348] In preferred embodiments, the cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLUT1 , GPC-3, and PS.

[0349] More preferably, the cancer biomarker is selected from the group consisting of GPC-3, CD13, and GLUT1.

[0350] In some embodiments, the use relates to the use of a plurality of antibodies for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, such as two or more antibodies, three or more antibodies, four or more antibodies, or five or more antibodies. Preferably each antibody specifically binds to a respective cancer biomarker.

[0351] Further details of the antibody and use thereof are provided below.

[0352] A primer for the detection of cancer or liver disease and use thereof the detection of cancer or liver disease.

[0353] The disclosure provides a primer, or one or more primers, for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual. The primer, or one or more primers, is for the amplification of a cancer biomarker, or one or more cancer biomarkers. 61 Docket No. 59888-703.601

[0354] The disclosure provides a use of a primer, or one or more primers, for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual. The primer, or one or more primers, is for the amplification of a cancer biomarker, or one or more cancer biomarkers.

[0355] In some embodiments, the primer binds to a nucleic acid sequence upstream or downstream of the DNA or RNA oligonucleotide biomarker to enable its amplification. In some embodiments, the primer is a pair of primers which binds to a nucleic acid sequence upstream and downstream of the DNA or RNA oligonucleotide biomarker to enable its amplification.

[0356] The cancer biomarker is preferably selected from the group consisting of hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir- 15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir- 374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir- 137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa- mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150- 5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa- mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa- mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir- 146a-5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir- 184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir- 203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir- 34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa- mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, and hsa-mir-99a-5p.

[0357] In preferred embodiments, the cancer biomarker is selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa- mir-203a-3p, and hsa-mir-122-5p. 62 Docket No. 59888-703.601

[0358] More preferably, the cancer biomarker is selected from the group consisting of hsa-mir- 203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let- 7g-5p.

[0359] Alternatively, the cancer biomarker is selected from the group consisting of hsa-miR-22- 3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3.

[0360] Preferably, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124- 3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0361] Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0362] In preferred embodiments, the primer is a DNA primer. It is particularly preferred that a pair of primers is provided which are complementary to sequences upstream and downstream, respectively, of the target sequence and thereby permit amplification of the target sequence such as by PCR amplification. For example, when the cancer biomarker is a miRNA molecule or a fragment of a miRNA molecule, the target sequence may be a complementary DNA (cDNA) sequence which is synthesized from the miRNA molecule or fragment thereof.

[0363] In some embodiments, the use relates to the use of a plurality of primers for the detection of cancer of a tissue of a defined category in a biological sample obtained from an individual, such as two or more primers, three or more primers, four or more primers, or five or more primers. Most preferably, the use comprises a pair of primers which binds to a nucleic acid sequence upstream and downstream of the DNA or RNA oligonucleotide biomarker to enable its amplification.

[0364] Further details of the antibody and use of the antibody are provided below.

[0365] Combined use

[0366] In preferred embodiments, the one or more antibodies and one or more primers, or uses thereof, can be combined for the detection of cancer.

[0367] Preferably, the disclosure provides a use of one or more antibodies and one or more primers for the detection of cancer of a tissue of a defined category in a biological sample 63 Docket No. 59888-703.601 obtained from an individual, wherein the primer is for the amplification of a cancer biomarker. The antibodies and primers are preferably those described above.

[0368] In some embodiments, the use of the antibody and / or use of the primer comprises: a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of the tissue of a defined category, and b) detecting the presence of one or more cancer biomarkers on or in EPs in the enriched fraction of EPs using the antibody or primer.

[0369] In some embodiments, the use of the antibody and / or use of the primer further comprises: c) quantifying the level of the cancer biomarker in order to determine a detected level of the cancer biomarker or the combination of the cancer biomarkers.

[0370] In some embodiments, the use of the antibody and / or use of the primer further comprises: d) comparing the detected level of the one or more cancer biomarkers, or the combination thereof, to a threshold level to determine whether the detected level is reduced or elevated in comparison to the threshold level, and e) determining that the individual has cancer of a tissue of a defined category when the detected level of the one or more cancer biomarkers, or the combination thereof, is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the cancer biomarker, or the combination thereof, on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0371] Further details of the use of the antibody and use of the primer are as discussed below.

[0372] Extracellular particles

[0373] EPs are heterogeneous lipid-bilayer-encapsulated particles that are naturally secreted or released from all types of cells. EPs play a role in removing components from cells that may be erroneously produced or are in excess and / or transporting cellular components either locally or to distant sites via the circulation. These EPs are thus enriched in proteins, nucleic acid molecules (e.g. DNA or RNA), lipids and metabolites 64 Docket No. 59888-703.601 which mirror the molecular composition of their parental cells and / or tissue of origin. As such, the proteins, nucleic acid molecules, lipids and metabolites which are detectable on or in EPs which derive from liver tissue have utility as a biomarker of the liver cells and / or liver tissue from which the EP was secreted. For example, in the case where EPs are secreted or released from liver cells which display cancer, the proteins, nucleic acid molecules, lipids and metabolites which are detectable on or in EPs have utility as a cancer biomarker.

[0374] The EPs are preferably extracellular vesicles (EVs) or non-vesicular extracellular particles (NVEPs). In embodiments where the EPs are EPs, the EPs are preferably exosomes, ectosomes, microvesicles or apoptotic vesicles (apoVs). In embodiments where the EPs are NVEPs, the NVEPs are preferably lipoprotein particles (LPPs), ribonucleoprotein particles (RNPs), protein aggregates, exomeres or supermeres.

[0375] Biological sample

[0376] It is preferred that the biological sample is a sample of tissue of the defined category or a biofluid.

[0377] In one embodiment, the biological sample being a sample of tissue is obtained by biopsy (e.g. a tissue biopsy). In one embodiment, the biological sample (e.g. tissue biopsy) is snap frozen.

[0378] In an alternative embodiment, the biological sample being a biofluid is obtained by liquid biopsy.

[0379] In embodiments where the biological sample is a biofluid, the biofluid comprises circulating EPs. Preferably the biofluid is blood, urine, saliva, lymph, bile, cerebrospinal fluid, phlegm, mucus, tears, Bronchoalveolar Lavage (BAL) fluid, earwax, sweat, faeces, breast milk, interstitial fluids, vaginal fluids, semen, gastric juice, blister fluid or cyst fluid. Most preferably, the biofluid is blood, urine, cerebrospinal fluid or mucus. The blood sample may be whole blood, plasma or serum.

[0380] Cancer 65 Docket No. 59888-703.601

[0381] The reference to cancer used herein includes, for example, pre-cancerous lesions, malignancy potential and different stages of cancer.

[0382] In preferred embodiments, the cancer is liver cancer. Preferably, the liver cancer is hepatocellular carcinoma (HCC). In some embodiments, the liver cancer is HCC, Fibrolamellar carcinoma, Angiosarcoma, Hemangiosarcoma, Hepatoblastoma, or Combined hepatocellular-cholangiocarcinoma.

[0383] In preferred embodiments, the cancer is characterized by shedding low amounts of ctDNA, especially at early-disease stages, Such cancers include all brain tumors, non- small-cell lung cancer (NSCLC) with adenocarcinoma, renal cell carcinoma, prostate, and thyroid cancers, lung cancers, liver cancers including HCC, ovarian cancer, pancreas cancer, breast cancer, esophagus cancer or head and neck cancer at early disease stages; oligometastatic or nodal only disease. In some embodiments, the cancer is bladder cancer, basal cell carcinoma, cervical cancer, colon cancer, colorectal cancer, combined small cell carcinoma, craniopharyngioma, ductal carcinoma, endometrial cancer, ependymoma, gallbladder cancer, gastric cancer, glioblastoma, lobular carcinoma, kidney cancer, large cell carcinoma, leukemia, liver cancer, lung cancer, lymphoma, medulloblastoma, melanoma, meningioma, Merkel cell carcinoma, mesothelioma, multiple myeloma, pinealoma, pituitary adenoma, rectal cancer, renal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, or uterine cancer.

[0384] Disease

[0385] In some embodiments, it is not essential that the disease is cancer. The disease may be any disease. For example, the disease may be a liver-related disease. Preferably, the disease is liver disease. It should be appreciated that, in such embodiments, the term “cancer biomarker” used herein should be understood as “disease biomarker”, and the term “cancer binding agent” used below should be understood as “disease binding agent”.

[0386] It should also be appreciated that in embodiments when the disease is not cancer the methods provided herein are performed substantially in the same way as described in 66 Docket No. 59888-703.601 relation to cancer except that the disease is different and one or more different disease biomarkers may be used.

[0387] In some embodiments, the disease is a disease of the tissue of the defined category.

[0388] In some embodiments, the disease is a liver disease. The liver disease can be Alagille syndrome, alcoholic liver disease, alpha-1 antitrypsin deficiency, autoimmune hepatitis, benign liver tumors, biliary atresia, cancer (including hepatocellular carcinoma and bile duct cancer), cardiac cirrhosis, cholangiocarcinoma, cirrhosis, Crigler-Najjar syndrome, fatty liver disease (including non-alcoholic steatohepatitis), Gilbert syndrome, hemochromatosis, hepatic encephalopathy, hepatic hemangioma, hepatitis A, hepatitis B, hepatitis C, hepatitis D, hepatitis E, hepatoblastoma, hepatorenal syndrome, intrahepatic cholestasis of pregnancy, liver cysts, liver failure, polycystic liver disease, primary biliary cholangitis, primary sclerosing cholangitis, Progressive Familial Intrahepatic Cholestasis (PFIC), Reye's syndrome, or Wilson's disease.

[0389] In preferred embodiments, the disease is characterized by shedding low amounts of cfDNA or ctDNA, especially at early-disease stages.

[0390] Tissue of the defined category

[0391] Preferably, the tissue of the defined category is liver tissue, such that the cancer is liver cancer and the tissue is liver tissue. Preferably, the cells from which the EPs are secreted are hepatocytes, Kupffer cells, stellate cells or liver resident dendritic cells.

[0392] In embodiments where the tissue of the defined category is liver tissue, it is preferred that the cancer biomarker, or the plurality of cancer biomarkers, is detected on or in EPs in an obtained enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category and display one or more tissue biomarkers. Preferably, the tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2, SLCO1 B1 , SLC38A3, TMEM56, UNC93A, SLC22A9, SLC2A2 or FXYD1 , or a combination thereof. Most preferably, the tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2 or SLCO1 B1. In preferred embodiments, the tissue biomarkers consist of ASGR1 , ASGR2, TFR2, and SLCO1 B1. In some embodiments the enriched fraction of EPs are captured using one or more tissue binding agents being capable of binding specifically to the respective tissue biomarker. In embodiments where 67 Docket No. 59888-703.601 the tissue biomarker is ASGR1 , the tissue binding agent is preferably an anti-ASGR1 antibody. In embodiments where the tissue biomarker is ASGR2, the tissue binding agent is preferably an anti-ASGR2 antibody. In embodiments where the tissue biomarker is TFR2, the tissue binding agent is preferably an anti-TFR2 antibody. In embodiments where the tissue biomarker is SLCO1 B1 , the tissue binding agent is preferably an anti-SLCO1 B1 antibody. In embodiments where the tissue biomarker is SLC38A3, the tissue binding agent is preferably an anti-SLC38A3 antibody. In embodiments where the tissue biomarker is TMEM56, the tissue binding agent is preferably an anti-TMEM56 antibody. In embodiments where the tissue biomarker is LINC93A, the tissue binding agent is preferably an anti-UNC93A antibody. In embodiments where the tissue biomarker is SLC22A9, the tissue binding agent is preferably an anti-SLC22A9 antibody. In embodiments where the tissue biomarker is SLC2A2, the tissue binding agent is preferably an anti-SLC2A2 antibody. In embodiments where the tissue biomarker is FXYD1 , the tissue binding agent is preferably an anti-FXYD1 antibody. In the context of these tissue biomarkers, it is preferred that the cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g-5p, hsa- mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p. More preferably, the cancer biomarker is selected from the group consisting of GPC-3, CD13, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa- mir-486-5p, and hsa-let-7g-5p. In some embodiments, the cancer biomarker is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa- miR-23a-3p, and hsa-miR-221-3. Preferably, the cancer biomarkers consist of miR-22- 3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0393] Enriched fraction of EPs

[0394] In some embodiments, the enriched fraction of EPs which are secreted or released from the tissue of the defined category is enriched from a biological sample comprising a mixture of EPs. Preferably, the mixture of EPs comprises EPs from the tissue of the defined category and EPs from at least one different category of tissue from the individual. 68 Docket No. 59888-703.601

[0395] In embodiments where the tissue of the defined category is liver tissue, the enriched fraction of EPs which are secreted or released from cells of liver tissue is enriched from a biological sample comprising a mixture of EPs. Preferably, the mixture of EPs comprises EPs from liver tissue and EPs from at least one different category of tissue from the individual. For example, the mixture of EPs, preferably where the biological sample is a biofluid, may comprise EPs from liver tissue and EPs from at least one other category of tissue, such as breast tissue.

[0396] In some embodiments, the enriched fraction of EPs is obtained by capturing EPs which are secreted or released from cells of the tissue of the defined category in the biological sample, wherein the captured EPs display one or more tissue biomarkers.

[0397] Tissue biomarkers

[0398] In some embodiments, the tissue biomarker is a protein or fragment of a protein, a DNA oligonucleotide, an RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite, which is detectable on or in EPs.

[0399] In some embodiments, the tissue biomarker is detectable on the surface of EPs. In embodiments where the tissue biomarker is a protein, or fragment thereof, preferably, the tissue biomarker is a transmembrane protein, surface protein or a cytosolic protein, or a fragment thereof. Preferably, the tissue biomarker is a transmembrane or surface protein, or a fragment thereof. In embodiments where the tissue biomarker is fragment of a protein, preferably the biomarker is one epitope on the protein. In embodiments where the tissue biomarker is a protein, or a fragment of a protein, the protein may have a post-translational modification, such as acetylation, glycosylation, hydroxylation, lipidation, methylation, nitrosylation, phosphorylation, proteolysis, and ubiquitination. In some embodiments, the tissue biomarker is a post-translational modification on a protein. In some embodiments, the tissue biomarker is an epigenetic modification on a nucleic acid molecule, such as methylation.

[0400] In some embodiments, the tissue biomarker is a molecule present in the cytosol of EPs. In embodiments where the tissue biomarker is a cytosolic molecule, the step of capturing the tissue biomarker comprises a step of permeabilising the membrane of EPs. Preferably, the permeabilisation step comprises the use of detergents, such as Triton-X, NP-40, saponin and others, sonication or electroporation. 69 Docket No. 59888-703.601

[0401] In some embodiments, the step of detecting uses a plurality of tissue biomarkers, such as two or more tissue biomarkers, three or more tissue biomarkers, or four or more tissue biomarkers. Preferably, the step of detecting uses four tissue biomarkers.

[0402] In embodiments where the tissue of the defined category is liver tissue, it is preferred that the tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2, SLCO1 B1 , SLC38A3, TMEM56, UNC93A, SLC22A9, SLC2A2 or FXYD1 , or a combination thereof. Most preferably, the tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2 or SLCO1 B1. In preferred embodiments, the tissue biomarkers consist of ASGR1 , ASGR2, TFR2, and SLCO1 B1.

[0403] In some embodiments, the tissue biomarker can comprise a miRNA or a fragment thereof. The miRNA can comprise hsa-miR-16-5p, hsa-miR-486-5p, hsa-miR-451a, hsa- let-7g-5p, hsa-miR-15b-5p, hsa-let-7b-5p, hsa-let-7f-5p, hsa-miR-26b-5p, hsa-miR-92a- 3p, hsa-miR-320b, hsa-miR-93-5p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-23b-3p, hsa-miR-203a-3p, hsa-miR-320a, hsa-miR-24-3p, hsa-miR-320d, hsa- miR-320c, hsa-miR-126-3p, hsa-miR-107, hsa-miR-221-3p, hsa-let-7d-5p, hsa-miR- 10a-5p, hsa-miR-130a-3p, hsa-miR-122-5p, or any combination thereof.

[0404] The miRNA can comprise any miRNA listed in Table 1A or 1 B. The one or more target tissue-associated biomarkers can comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more biomarkers having a sequence provided in Table 1A. In some embodiments, the one or more target tissue-associated biomarkers comprise a biomarker having at least a 90% sequence match to any sequence listed in Table 1A. The one or more target tissue-associated biomarkers can comprise a biomarker having at least 75%, 76%, 77%, 78%, 79%, 80%, 81 %, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence match to any sequence listed in Table 1A. In some embodiments, the one or more target tissue- associated biomarkers comprise a biomarker having a 100% sequence match to any sequence listed in Table 1A. In some embodiments, the one or more target tissue- associated biomarkers comprise a biomarker differing from a sequence listed in Table 1A by 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 nucleotides.

[0405] Also provided herein is an antibody or antigen-binding fragment thereof configured to target a biomarker listed in Table 1A or 1 B. The biomarker can be an miRNA The 70 Docket No. 59888-703.601 antibody or antigen-binding fragment thereof can be configured to target an argonaute protein which can be associated with the miRNA.

[0406] Further provided herein is an oligonucleotide primer configured to amplify a biomarker listed in Table 1 A or 1 B. In some embodiments, the primer is subjected to quality control analysis prior to use in the methods provided herein. The quality control analysis can involve assessment of one or more parameters. The parameters can comprise melting temperature, GC-content, or temperature at which a hairpin structure would dissociate. In some embodiments, the temperature at which a hairpin structure would dissociate is below 30°C, 25°C, 20°C, 15°C, 10°C, or 5°C.

[0407] Table 1 A. List of candidate miRNA biomarker sequences. 71 Docket No. 59888-703.601 72 Docket No. 59888-703.601 73 Docket No. 59888-703.601 74 Docket No. 59888-703.601 75 Docket No. 59888-703.601 76 Docket No. 59888-703.601 77 Docket No. 59888-703.601 78 Docket No. 59888-703.601 79 Docket No. 59888-703.601 80 Docket No. 59888-703.601 81 Docket No. 59888-703.601 82 Docket No. 59888-703.601 83 Docket No. 59888-703.601 84 Docket No. 59888-703.601 85 Docket No. 59888-703.601 86 Docket No. 59888-703.601 87 Docket No. 59888-703.601 88 Docket No. 59888-703.601 89 Docket No. 59888-703.601 90 Docket No. 59888-703.601 91 Docket No. 59888-703.601 92 Docket No. 59888-703.601

[0408] Table 1 B. List of candidate miRNA biomarker names. 93 Docket No. 59888-703.601

[0409] Tissue binding agents

[0410] Preferably, the EPs are captured using one or more tissue binding agents being capable of binding specifically to the respective tissue biomarker.

[0411] The tissue binding agent is one member of a binding pair capable of binding specifically to a tissue biomarker, which is the other member of the binding pair, and which is detectable on or in of EPs which are secreted or released from tissue of a defined category. In some embodiments, the tissue binding agent, comprises an antibody, an antigen-binding fragment of an antibody (e.g. a Fab fragment of a F(ab’)2 fragment), an aptamer, a lectin, a lipid-binding protein or domain, a DNA oligonucleotide or an RNA oligonucleotide. In some embodiments, the tissue binding agent is a DNA or RNA primer or probe.

[0412] In embodiments where the tissue biomarker is an DNA oligonucleotide or an RNA oligonucleotide, it is preferable that the tissue binding agent is a DNA oligonucleotide or an RNA oligonucleotide, and the tissue binding agent comprises a region having a sequence complementary to the tissue biomarker. Usually the primer binds to the tissue biomarker being a DNA oligonucleotide or an RNA oligonucleotide itself. In some embodiments, the primer binds to a nucleic acid sequence upstream or downstream of the DNA or RNA oligonucleotide biomarker to enable its amplification. In some of these embodiments, the tissue binding agent comprises a region having a nucleic acid sequence which is 100% complementary to the nucleic acid sequence of the tissue biomarker. In some of these embodiments, the tissue binding agent comprises a region having a nucleic acid sequence which is at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 95% complementary to the nucleic acid sequence of the tissue biomarker.

[0413] In embodiments where the tissue biomarker is a protein or fragment of a protein, it is preferable that the tissue binding agent is an antibody or an antigen-binding fragment of 94 Docket No. 59888-703.601 an antibody, and the tissue binding agent binds specifically to the protein or fragment of a protein.

[0414] An antibody, also known as immunoglobulin, consists of four polypeptides, comprising two identical light chains and two identical heavy chains, joined by noncovalent interactions and disulfide bonds to form a flexible Y-shaped structure. Each of the four chains has a variable region at its amino terminus, which contributes to the antigenbinding site, and a constant region at its carboxy terminus, which determines the isotype. Antibodies are divided into five major classes of immunoglobulin, IgM, IgG, Iga, IgD, and IgE, based on their constant region structure and immune function.

[0415] The fragment antigen binding (Fab) region is composed of one constant and one variable domain from each heavy and light chains of the antibody. The variable region is further subdivided into hypervariable and framework regions. Each of the light and heavy chains contain three hypervariable loops, also known as complementarity determining regions (CDRs) and four framework regions. The six CDRs exhibit a hypervariable amino acid composition and are involved in determining the antigen binding specificity of the antibody.

[0416] In embodiments where the tissue binding agent comprises an antigen-binding fragment, preferably, the antigen-binding fragment is a F(ab’)2 fragment, Fab’ fragment, Fab fragment or a variable region.

[0417] Antibodies are normally produced by B cells, which are part of the immune system, when an organism’s immune system encounters a foreign molecule (typically a protein). Antibodies can be generated which are specific for a tissue biomarker by repeated immunisation of a suitable animal, such as rabbit, goat, donkey or sheep with the tissue biomarker. Antigen-binding fragments can then be generated by antibody fragmentation, such as by enzyme-mediated digestion of the antibody.

[0418] In some embodiments, the tissue binding agent is provided attached to a substrate. Preferably, the substrate is a structure or a particle. In embodiments where the substrate is a structure, the structure is immobile with respect to any surrounding sample. In embodiments where the substrate is a particle, the particle is mobile within a surrounding sample. Preferably the substrate comprises a magnetic bead or nanoparticle, a gold bead or nanoparticle, a polystyrene bead, an affinity chromatography column, a 95 Docket No. 59888-703.601 microplate, a microfluidics channel or a biochip with a surface made of gold, silicon oxide, glass, graphene or polystyrene. Most preferably the substrate comprises a magnetic bead. In preferred embodiments, the binding agent comprises an antibody and the substrate comprises a magnetic bead (i.e. antibody-labelled magnetic bead). For example, in some embodiments, the tissue binding agent is provided on a substrate (e.g. a magnetic bead) and the biological sample is contacted with the substrate. The tissue binding agent binds to EPs presenting the biomarker thereby “capturing” the EPs. The unbound biological sample is then washed away, leaving the captured EPs bound to the substrate via the tissue binding agent. In some particular embodiments, the captured EPs are then released from the tissue binding agent in order to provide an enriched fraction of EPs or fully isolated EPs.

[0419] In some embodiments, the EPs are captured using a plurality of tissue binding agents, such as two or more tissue binding agents, three or more tissue binding agents, or four or more tissue binding agents. Preferably, the method comprises using four tissue binding agents. Preferably, the number of tissue binding agents corresponds to the number of tissue biomarkers.

[0420] In some embodiments, the EPs are captured using a plurality of tissue binding agents being capable of binding specifically to a plurality of respective tissue biomarkers and the tissue binding agent is provided in a proportion of 10-50% of the total, wherein the total mixture of tissue binding agents is equal to 100% or wherein each tissue binding agent is provided in equal proportions.

[0421] In some embodiments, the EPs are captured using four tissue binding agents, wherein the first tissue binding agent is provided in a proportion of 30-50% of the total, and each of the other tissue binding agent is provided in a proportion of 10-30% of the total, wherein the total mixture of the tissue binding agents is equal to 100%.

[0422] In some embodiments, the EPs secreted or released from cells of the tissue of the defined category preferentially bind the binding agent compared with EPs from at least 2 different categories of tissue from the organism. For example, the EPs which are secreted or released from cells of liver tissue preferentially bind the binding agent compared with EPs which derive from 2 non-liver tissues, such as kidney tissue and breast tissue, or from 3 non-liver tissues, such as lung tissue, kidney tissue or breast tissue. In some embodiments, the EPs secreted or released from cells of the tissue of 96 Docket No. 59888-703.601 the defined category preferentially bind the binding agent compared with EPs from all other categories of tissue from the organism. For example, the EPs secreted or released from cells of the liver tissue preferentially bind the binding agent compared with EPs from all other categories of tissue from the organism.

[0423] In embodiments where the tissue biomarker is ASGR1 , the tissue binding agent is preferably an anti-ASGR1 antibody. In embodiments where the tissue biomarker is ASGR2, the tissue binding agent is preferably an anti-ASGR2 antibody. In embodiments where the tissue biomarker is TFR2, the tissue binding agent is preferably an anti-TFR2 antibody. In embodiments where the tissue biomarker is SLCO1 B1 , the tissue binding agent is preferably an anti-SLCO1 B1 antibody. In embodiments where the tissue biomarker is SLC38A3, the tissue binding agent is preferably an anti-SLC38A3 antibody. In embodiments where the tissue biomarker is TMEM56, the tissue binding agent is preferably an anti-TMEM56 antibody. In embodiments where the tissue biomarker is LINC93A, the tissue binding agent is preferably an anti-UNC93A antibody. In embodiments where the tissue biomarker is SLC22A9, the tissue binding agent is preferably an anti-SLC22A9 antibody. In embodiments where the tissue biomarker is SLC2A2, the tissue binding agent is preferably an anti-SLC2A2 antibody. In embodiments where the tissue biomarker is FXYD1 , the tissue binding agent is preferably an anti-FXYD1 antibody.

[0424] In preferred embodiments, the EPs are captured using four tissue binding agents consisting of the anti-ASGR1 antibody or antigen binding fragment thereof, the anti- ASGR2 antibody or antigen binding fragment thereof, the anti-SLCO1 B1 antibody or antigen binding fragment thereof and the anti-TFR2 antibody or antigen binding fragment thereof, and wherein the anti-ASGR1 antibody or antigen binding fragment thereof is provided in a proportion of 30-50% of the total, and each of the other antibodies or antigen binding fragments thereof is provided in a proportion of 10-30% of the total, wherein the total mixture of the binding agents is equal to 100%.

[0425] In some embodiments, the method comprises isolating the EPs displaying the tissue biomarker which are captured using the tissue biomarker.

[0426] In some embodiments, the isolated EPs are intact EPs. 97 Docket No. 59888-703.601

[0427] In some embodiments, the method further comprises the step of releasing the contents of the captured or isolated EPs.

[0428] Cancer biomarkers

[0429] The cancer biomarker is preferably a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite, which is detectable on or in EPs.

[0430] Preferably, the cancer biomarker is a protein or fragment of a protein, or an RNA oligonucleotide or a fragment of an RNA oligonucleotide.

[0431] In embodiments where the cancer biomarker is an RNA oligonucleotide, preferably, the cancer biomarker is a small non-coding RNA (ncRNA) molecule. The term small noncoding RNA (ncRNA) molecule refers to RNA that is not translated into protein. Preferably, the small non-coding RNA (ncRNA) molecule is a transfer RNA (tRNA) molecule, a ribosomal RNA (rRNA) molecule, a snoRNAs molecule, a microRNA (miRNA) molecule, a siRNAs molecule, a small nuclear (snRNA) molecule, a Y RNA molecule, a vault RNA molecule, an antisense RNA molecule, a tiRNA (transcription initiation RNA) molecule, a TSSa-RNA (transcriptional start-site associated RNA) molecule and a piwiRNA (piRNA) molecule. Most preferably, the cancer biomarker is a microRNA (miRNA) molecule.

[0432] Most preferably, the cancer biomarker is a protein or fragment of a protein, or a miRNA molecule or a fragment of a miRNA molecule.

[0433] In some embodiments, the cancer biomarker is present on or in an EPs which are secreted or released from cells of the tissue of the defined category. In some embodiments, the cancer biomarker is present on or in an enriched-fraction of EPs which are secreted or released from cells of the tissue of the defined category.

[0434] In some embodiments, the cancer biomarker is detectable on the surface of EPs.

[0435] In some embodiments, the cancer biomarker is a molecule present in the cytosol of EPs. In embodiments where the cancer biomarker is a cytosolic molecule, the step of detecting the presence of the cancer biomarker comprises a step of permeabilising the 98 Docket No. 59888-703.601 membrane of EPs. Preferably, the permeabilisation step comprises the use of detergents, such as Triton-X, NP-40, saponin and others, sonication or electroporation.

[0436] In embodiments where the cancer biomarker is a protein, or fragment thereof, preferably, the cancer biomarker is a transmembrane protein, surface protein or a cytosolic protein, or a fragment thereof. In embodiments where the cancer biomarker is fragment of a protein, preferably the biomarker is one epitope on the protein. In embodiments where the cancer biomarker is a protein, or a fragment of a protein, the protein may have a post-translational modification, such as acetylation, glycosylation, hydroxylation, lipidation, methylation, nitrosylation, phosphorylation, proteolysis, and ubiquitination. In some embodiments, the cancer biomarker is a post-translational modification on a protein. In some embodiments, the cancer biomarker is an epigenetic modification on a nucleic acid molecule, such as methylation.

[0437] The cancer biomarker is preferably selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine (PS), hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa- mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir- 26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir- 126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa- mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100- 5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a- 3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir- 124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa- mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir- 22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa- mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir- 99 Docket No. 59888-703.601

[0438] 4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir- 6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, and a combination of two or more thereof.

[0439] Some embodiments comprise a combination of two or more cancer biomarkers, three or more cancer biomarkers, four or more cancer biomarkers, five or more cancer biomarkers, six or more cancer biomarkers, seven or more cancer biomarkers, eight or more cancer biomarkers, nine or more cancer biomarkers, or ten or more cancer biomarkers.

[0440] In some embodiments, the cancer biomarker is a polypeptide of TFR1, RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, a fragment thereof, or a combination of two or more thereof.

[0441] In some embodiments, the cancer biomarker is a sugar being Heparan sulphate.

[0442] In some embodiments, the cancer biomarker is a phospholipid being PS.

[0443] In some embodiments, the cancer biomarker is a miRNA oligonucleotides of hsa-let-7f- 5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa- mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir- 20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa- let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir- 130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa- mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126- 5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa- mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir- 125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139- 5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a- 5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir- 100 Docket No. 59888-703.601

[0444] 223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa- mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir- 4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir- 6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, or a combination of two or more thereof.

[0445] In preferred embodiments, the cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g- 5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two or more thereof.

[0446] More preferably, the cancer biomarker is selected from the group consisting of GPC-3, CD13, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa- mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof.

[0447] In preferred embodiments, the cancer biomarkers consist of GPC-3, CD13, GLLIT1 , hsa- mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g-5p.

[0448] In some embodiments, the cancer biomarker is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221- 3.

[0449] Most preferably, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR- 124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or fragments thereof.

[0450] Most preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof, hsa- miR-124-3p was detected in EPs following hepatocyte-specific EV isolation but not following bulk EV isolation in Example 4 (Figure 16C).

[0451] In embodiments where the cancer biomarker is a polypeptide or a fragment thereof, it is preferred that the cancer biomarker is selected from the group consisting of TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, 101 Docket No. 59888-703.601

[0452] H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine (PS), a fragment thereof, and a combination of two or more thereof.

[0453] Most preferably, the cancer biomarker is a protein selected from the group consisting of SLC2A1 , CD13, GLLIT1 , and GPC-3, or is a phospholipid being Phosphatidylserine (PS), or is a combination of two or more thereof.

[0454] More preferably, the cancer biomarker is a protein selected from the group consisting of GPC-3, CD13, GLUT 1 , a fragment thereof, and a combination of two or more thereof.

[0455] In preferred embodiments, the cancer biomarkers being a protein or a fragment thereof consists of GPC-3, CD13, and GLUT1.

[0456] In embodiments where the cancer biomarker is a miRNA molecule or a fragment thereof, it is preferred that the cancer biomarker is selected from the group consisting of hsa-let- 7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b-5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa- mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa-mir-26b-5p, hsa-mir-320b, hsa-mir- 320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486-5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa-mir-126-3p, hsa-mir-130a-3p, hsa-mir- 130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa- mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir-100-5p, hsa-mir-10a-5p, hsa-mir-126- 5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21-5p, hsa-mir-223, hsa-mir-27b-3p, hsa- mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir-203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir- 125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa-mir-1307, hsa-mir-133a-1 , hsa-mir-139- 5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir-152-3p, hsa-mir-15a-5p, hsa-mir-181a- 5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a-5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir- 223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir-23a, hsa-mir-23b-3p, hsa-mir-23b, hsa- mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir-3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir- 4492, hsa-mir-449c, hsa-mir-4784, hsa-mir-5193, hsa-mir-6068, hsa-mir-6773, hsa-mir- 6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa-mir-92a-3p, hsa-mir-27b-5p, hsa-mir-99a-5p, a fragment thereof, and a combination of two or more thereof. 102 Docket No. 59888-703.601

[0457] Preferably, the cancer biomarker is a miRNA molecule selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir- 27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two or more thereof.

[0458] More preferably, the cancer biomarker is a miRNA molecule selected from the group consisting of hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa- mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof.

[0459] In preferred embodiments, the cancer biomarkers being a miRNA molecule or a fragment thereof consists of hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g-5p.

[0460] In some embodiments, the cancer biomarker is a miRNA molecule selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, hsa-miR-221-3, a fragment thereof, and a combination of two or more thereof.

[0461] Preferably, the cancer biomarkers consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124- 3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0462] Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0463] In some embodiments, the step of detection uses a plurality of cancer biomarkers, such as two or more cancer biomarkers, three or more cancer biomarkers, four or more cancer biomarkers, five or more cancer biomarkers, six or more cancer biomarkers, seven or more cancer biomarkers, eight or more cancer biomarkers, nine or more cancer biomarkers, or ten or more cancer biomarkers, which are detectable on or in EPs.

[0464] It is preferred that the plurality of cancer biomarkers comprises more than one different type of cancer biomarker being selected from the group consisting of a protein or fragment of a protein, a DNA oligonucleotide, an RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, and a metabolite. Most preferably, the two or more cancer biomarkers comprise one or more cancer biomarker being a protein and one or more cancer biomarker being a miRNA molecule. The two or more different types of cancer biomarker are preferably detected using two different techniques (multiomics 103 Docket No. 59888-703.601 detection). The detection of two or more different types of cancer biomarker thereby provides a multiomics biomarker signature.

[0465] In embodiments where the tissue of the defined category is liver tissue, it is preferred that the cancer biomarker, or the plurality of cancer biomarkers, is detected on or in EPs in an obtained enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category and display one or more tissue biomarkers. Preferably, the tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2, SLCO1 B1 , SLC38A3, TMEM56, UNC93A, SLC22A9, SLC2A2 or FXYD1 , or a combination thereof. Most preferably, the tissue biomarker is selected from the group consisting of ASGR1 , ASGR2, TFR2 or SLCO1 B1. In preferred embodiments, the tissue biomarkers consist of ASGR1 , ASGR2, TFR2, and SLCO1 B1. In some embodiments, the enriched fraction of EPs are captured using one or more tissue binding agents being capable of binding specifically to the respective tissue biomarker. In embodiments where the tissue biomarker is ASGR1 , the tissue binding agent is preferably an anti-ASGR1 antibody. In embodiments where the tissue biomarker is ASGR2, the tissue binding agent is preferably an anti-ASGR2 antibody. In embodiments where the tissue biomarker is TFR2, the tissue binding agent is preferably an anti-TFR2 antibody. In embodiments where the tissue biomarker is SLCO1 B1 , the tissue binding agent is preferably an anti-SLCO1 B1 antibody. In embodiments where the tissue biomarker is SLC38A3, the tissue binding agent is preferably an anti-SLC38A3 antibody. In embodiments where the tissue biomarker is TMEM56, the tissue binding agent is preferably an anti-TMEM56 antibody. In embodiments where the tissue biomarker is LINC93A, the tissue binding agent is preferably an anti-UNC93A antibody. In embodiments where the tissue biomarker is SLC22A9, the tissue binding agent is preferably an anti-SLC22A9 antibody. In embodiments where the tissue biomarker is SLC2A2, the tissue binding agent is preferably an anti-SLC2A2 antibody. In embodiments where the tissue biomarker is FXYD1 , the tissue binding agent is preferably an anti-FXYD1 antibody. In the context of these tissue biomarkers, it is preferred that the cancer biomarker is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , GPC-3, Phosphatidylserine (PS), hsa-mir-27b-5p, hsa-let-7g-5p, hsa- mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, hsa-mir-122-5p, a fragment thereof, and a combination of two or more thereof. More preferably, the cancer biomarker is selected from the group consisting of GPC-3, CD13, GLUT 1 , hsa-mir-203a- 3p, hsa-mir-27b-5p, hsa-mir-122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-let-7g-5p, a fragment thereof, and a combination of two or more thereof. More preferably, the cancer 104 Docket No. 59888-703.601 biomarkers consist of GPC-3, CD13, GLLIT1 , hsa-mir-203a-3p, hsa-mir-27b-5p, hsa-mir- 122-5p, hsa-mir-23a-3p, hsa-mir-486-5p, and hsa-let-7g-5p. In some embodiments, the cancer biomarker is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b- 3p, hsa-miR-124-3p, hsa-miR-23a-3p, hsa-miR-221-3, a fragment thereof, and a combination of two or more thereof. Preferably, the cancer biomarkers consist of miR- 22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. Preferably, the cancer biomarker is hsa-miR-124-3p or a fragment thereof.

[0466] Cancer binding agent

[0467] In some embodiments, the cancer biomarker is detected using one or more cancer binding agents being capable of binding specifically to the respective cancer biomarker.

[0468] The cancer binding agent is one member of a binding pair capable of binding specifically to a cancer biomarker, which is the other member of the binding pair, and which is detectable on or in of EPs which are secreted or released from tissue of a defined category. In some embodiments, the cancer binding agent is an antibody, an antigenbinding fragment of an antibody (e.g. a Fab fragment of a F(ab’)2 fragment), an aptamer, a lectin, a lipid-binding protein or domain, a DNA oligonucleotide or an RNA oligonucleotide. (In this regard, the general discussion herein about the generation and development of antibodies in relation to tissue binding agents is also relevant and is specifically cross-referenced). The cancer binding agent being a DNA oligonucleotide or an RNA oligonucleotide includes a DNA or RNA probe, or a DNA or RNA primer.

[0469] In embodiments where the cancer binding agent is a DNA oligonucleotide or an RNA oligonucleotide, it is preferable that the cancer biomarker is also an DNA oligonucleotide or an RNA oligonucleotide and the cancer binding agent comprises a region having a sequence complementary to the cancer biomarker. Usually the primer binds to the cancer biomarker (being a DNA oligonucleotide or an RNA oligonucleotide) itself. In some embodiments, the primer binds to a nucleic acid sequence upstream or downstream of the DNA or RNA oligonucleotide biomarker to enable its amplification. In some embodiments, the primer is a pair of primers which binds to a nucleic acid sequence upstream and downstream of the DNA or RNA oligonucleotide biomarker to enable its amplification. In some of these embodiments, the cancer binding agent comprises a region having a nucleic acid sequence which is 100% complementary to the 105 Docket No. 59888-703.601 nucleic acid sequence of the cancer biomarker. In some of these embodiments, the cancer binding agent comprises a region having a nucleic acid sequence which is at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 95% complementary to the nucleic acid sequence of the cancer biomarker. In embodiments where the one or more cancer biomarker is a miRNA molecule, the cancer biomarker is preferably detected using reverse transcription polymerase chain reaction (RT-PCR). The fluorescence may be an intercalating due or a fluorescent DNA or RNA probe.

[0470] In some embodiments, the detection step comprises a step of permeabilising the membrane of EPs. Preferably, the permeabilisation step comprises the use of detergents, such as Triton-X, NP-40, saponin and others, sonication or electroporation.

[0471] In embodiments where the cancer binding agent is an antibody or an antigen-binding fragment of an antibody, it is preferable that the cancer biomarker is a protein or fragment of a protein, and the cancer binding agent binds specifically to the protein or fragment of a protein. In embodiments where the one or more cancer biomarker is a protein, the protein is preferably detected using O-NEXOS (see WO2022 / 122768, which is incorporated herein by reference). In some embodiments, this technique is used to detect the presence of the biomarker at the required sensitivity. For example, the presence of the candidate biomarker can be detected using a reporter assay, such as horseradish peroxidase (HRP), a fluorophore conjugated to an antibody or E-NEXOS.

[0472] In some embodiments, the step of detection uses a plurality of cancer binding agents, such as two or more binding agents, three or more binding agents, four or more binding agents, or five or more binding agents. Preferably, the number of cancer binding agents corresponds to the number of cancer biomarkers.

[0473] In some embodiments, the step of detection uses a plurality of cancer binding agents being capable of binding specifically to a plurality of respective tissue biomarkers and the tissue binding agent is provided in a proportion of 10-50% of the total, wherein the total mixture of tissue binding agents is equal to 100% or wherein each tissue binding agent is provided in equal proportions.

[0474] In some embodiments, the EPs are captured using a plurality of cancer binding agents, wherein the first cancer binding agent is provided in a proportion of 30-50% of the total, 106 Docket No. 59888-703.601 and each of the other cancer binding agent is provided in a proportion of 10-30% of the total, wherein the total mixture of the cancer binding agents is equal to 100%.

[0475] The advantage of using a plurality of cancer biomarkers is that, by providing a plurality of binding agents which are capable of binding specifically to the respective cancer biomarkers, it increases the sensitivity and specificity of the detection. As discussed above, EPs are heterogeneous particles that are naturally secreted by cells in tissue in a dynamic fashion. Tissues also contain multiple types of cells. For example, EPs secreted or released from liver cells therefore may present different liver-cancer biomarkers. Targeting a two or more liver-cancer biomarkers using two or more cancer binding agents therefore allows for the detection of heterogeneous EPs secreted or released from liver cells.

[0476] It is further preferred that the plurality of cancer biomarkers comprises more than one type of cancer biomarker, for example one or more cancer biomarker. The advantage of using more than one type of cancer biomarker is that multiple different detection assays can used to target each type of cancer biomarker. The detection of more than one different type of cancer biomarker thereby provides a multiomics biomarker signature. For example, if the plurality of cancer biomarkers one or more proteins and one or more miRNA molecules, the cancer biomarkers can be detected using O-NEXOS and RT-PCR respectively to provide a multi-omics approach. The combination of detection assays improves the sensitivity and specificity of the detection step.

[0477] Obtaining an enriched fraction of EPs

[0478] In some embodiments, the enriched fraction of EPs is obtained by capturing EPs which are secreted or released from cells of the tissue of the defined category in the biological sample, wherein the captured EPs display one or more tissue biomarkers.

[0479] Preferably, the EPs are captured using one or more tissue binding agents being capable of binding specifically to the respective tissue biomarker.

[0480] In some embodiments, the EPs displaying the tissue biomarker which are captured using the tissue biomarker are isolated. In some embodiments, the isolated EPs are intact EPs. 107 Docket No. 59888-703.601

[0481] In some embodiments, the contents of the captured or isolated EPs is released. Preferably, the contents are released by permeabilising the membrane of captured or isolated EPs. Preferably, the permeabilisation step comprises the use of detergents, such as Triton-X, NP-40, saponin and others, sonication or electroporation.

[0482] Detecting the presence of a cancer biomarker

[0483] The cancer biomarker may be detected using one or more cancer binding agents being capable of binding specifically to the respective cancer biomarker.

[0484] In embodiments where the cancer binding agent is a DNA oligonucleotide or an RNA oligonucleotide, it is preferable that the cancer biomarker is also a DNA oligonucleotide or an RNA oligonucleotide and the cancer binding agent comprises a region having a sequence complementary to the cancer biomarker. In some of these embodiments, the cancer binding agent comprises a region having a nucleic acid sequence which is 100% complementary to the nucleic acid sequence of the cancer biomarker. In some of these embodiments, the cancer binding agent comprises a region having a nucleic acid sequence which is at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 95% complementary to the nucleic acid sequence of the cancer biomarker. In embodiments where the one or more cancer biomarker is a miRNA molecule, the cancer biomarker is preferably detected using reverse transcription polymerase chain reaction (RT-PCR) using fluorescence. Preferably, the miRNA molecule is transcribed into a complementary DNA (cDNA) sequence, and the binding agent is a DNA oligonucleotide for the amplification of the cDNA sequence. The fluorescence may be an intercalating dye or a fluorescent DNA or RNA probe.

[0485] In embodiments where the cancer binding agent is an antibody or an antigen-binding fragment of an antibody, it is preferable that the cancer biomarker is a protein or fragment of a protein, and the cancer binding agent binds specifically to the protein or fragment of a protein. In embodiments where the one or more cancer biomarker is a protein, the protein is preferably detected using O-NEXOS (see WO2022 / 122768, which is incorporated herein by reference). In some embodiments, this technique is used to detect the presence of the biomarker at the required sensitivity. For example, the presence of the candidate biomarker can be detected using a reporter assay, such as horseradish peroxidase (HRP), a fluorophore conjugated to an antibody or E-NEXOS. 108 Docket No. 59888-703.601

[0486] In preferred embodiments, the presence or level of the cancer biomarker is detected in samples derived from the same biological sample.

[0487] Quantifying the level of the cancer biomarker

[0488] In some embodiments, the level of the cancer biomarker is quantified in order to determine a detected level of the cancer biomarker or the combination of the disease biomarkers.

[0489] The concentration of each cancer biomarker in the enriched fraction of EPs may be determined by quantifying the number of cancer biomarkers in the enriched fraction of EPs using a reporter assay, such as horseradish peroxidase (HRP), a fluorophore conjugated to an antibody, E-NEXOS, an dye which intercalates into DNA or RNA or a fluorescent DNA or RNA probe via a calibration curve.

[0490] In preferred embodiments, when cancer biomarker is a polypeptide or a fragment thereof, the number of cancer biomarkers in the enriched fraction of EPs is quantified using a reporter assay conjugated to an antibody, such as horseradish peroxidase (HRP), a fluorophore or E-NEXOS.

[0491] In preferred embodiments, when the cancer biomarker is a DNA or RNA oligonucleotide or a fragment thereof, the number of cancer biomarkers in the enriched fraction of EPs is quantified by amplifying the cancer biomarker using a PCR or RT-PCR. The amount of amplification product may be measured in each cycle of amplification using fluorescence.

[0492] In some embodiments, the level of the biomarker is quantified in absolute terms. In other embodiments, the level of biomarker is quantified in relative terms, e.g. relative to other biomarkers or relative to the level of biomarkers in a control, the control being an enriched fraction of EPs from a healthy individual or an individual who does not have cancer.

[0493] Comparing the detected level of the one or more cancer biomarkers to a threshold level

[0494] In some embodiments, the detected level of the one or more cancer biomarkers is compared to a threshold level, wherein the threshold level is the detected level of the 109 Docket No. 59888-703.601 cancer biomarker on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0495] In some embodiments, the detected level of the plurality of cancer biomarkers is compared to a threshold level, wherein the threshold level is the detected level of the plurality of cancer biomarkers on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0496] In some embodiments, the different condition or disease is a condition or disease other than cancer. In some embodiments, the different condition or disease is a condition or disease of the tissue of the defined category other than cancer. For example, if the cancer is liver cancer, the different condition or disease is a liver condition or disease other than cancer, for example Cirrhosis.

[0497] It is preferred that level of the cancer biomarker corresponds to the expression level of the cancer biomarker detected in or on EPs. Such that, the cancer biomarker is differentially expressed in cancer.

[0498] The threshold level does not need to be determined each time the step of determining that the individual has cancer is performed. In some embodiments, the threshold level is a pre-determined value of the detected level of the cancer biomarker on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease. The threshold level is therefore preferably a reference threshold level.

[0499] Determining that the individual has cancer of a tissue of a defined category

[0500] In some embodiments, the individual has cancer of the tissue of the defined category when the detected level of the one or more cancer biomarkers is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the cancer biomarker on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease. 110 Docket No. 59888-703.601

[0501] In some embodiments, the individual is determined as having cancer of the tissue of the defined category when the detected level of the plurality of cancer biomarkers detected in a biological sample from the individual is reduced or elevated in comparison to a threshold level, wherein the threshold level is the detected level of the plurality of cancer biomarkers on or in an enriched fraction of EPs obtained from a biological sample from a healthy individual or a biological sample from an individual having a different condition or disease.

[0502] For example, in one embodiment, the presence of the one or cancer biomarkers is detected in the biological sample and the detection thereof is indicative of the presence of cancer in the individual. In other embodiments, the level of the one or more cancer biomarkers is quantified to determine a detected level thereof which is compared with a threshold level to determine whether the detected level is reduced or elevated in comparison to a threshold level, which threshold level correlates with the presence or absence of cancer. It is preferred that the detected level includes a combination of the individual levels of the one or more cancer biomarkers which are detected. The results of the comparison are thereby indicative of the presence or absence of cancer in the individual. This conclusion can be reported as a positive or negative result.

[0503] In some embodiments, the different condition or disease is a condition or disease other than cancer. In some embodiments, the different condition or disease is a condition or disease of the tissue of the defined category other than cancer. For example, if the cancer is liver cancer, the different condition or disease is a liver condition or disease other than cancer, for example Cirrhosis.

[0504] It is preferred that level of the cancer biomarker corresponds to the expression level of the cancer biomarker detected in or on EPs. Such that, the cancer biomarker is differentially expressed in cancer.

[0505] In some embodiments, the step of detection comprises a plurality of cancer biomarkers and the detected level of the plurality of cancer biomarkers is calculated as the combination of the detected level of each of the cancer biomarkers. In some embodiments, the step of detection comprises a plurality of cancer biomarkers and the detected level of the plurality of cancer biomarkers is calculated as the sum of the detected level of each of the cancer biomarkers. Preferably, the detected level of the 111 Docket No. 59888-703.601 plurality of cancer biomarkers is calculated as the sum of the expression level of each of the cancer biomarkers.

[0506] In some embodiments, the level of one or more of the cancer biomarker is standardized to calculate the detected level of the plurality of cancer biomarkers. For example, when an increased level of one or more cancer biomarkers is indicative of cancer and a reduced level of one or more different cancer biomarkers is indicative of cancer, the individual levels of the cancer biomarkers may be standardized to calculate a combined detected level of the plurality of cancer biomarkers which is indicative of cancer. In such embodiments, when the combined detection level is elevated in comparison to a threshold level, it is determined that the individual has cancer (positive result). When the combined detection level is reduced is reduced in comparison to the threshold level, it is determined that the individual does not have cancer (negative result).

[0507] In some embodiments, the detected level of the plurality of cancer biomarkers is calculated by applying a weighting to the detected level of each cancer biomarker. Preferably, the detected level of each cancer biomarker being a proportion of 10-50% of the total level, wherein the total level of the cancer biomarkers is equal to 100% or wherein the detected level of each cancer biomarker is in equal proportions.

[0508] In some embodiments, the detected level of the cancer biomarker is calculated by applying a weighting based on additional factors. In some embodiments, the detected level of the cancer biomarker is calculated by including the additional factors. Such additional factors include age, sex, or the presence or detected level of one or more serological proteins, or a combination thereof. More preferably, the detected level of the cancer biomarker includes the detected level of one or more serological proteins, preferably, one or more soluble serological proteins. It is preferred that the one or more serological proteins is selected from the group consisting of alpha-fetoprotein (AFP), AFP-L3, and des-y-carboxy prothrombin (DCP), or a combination of two or more thereof, preferably AFP and DCP, or a combination thereof. More preferably, the serological proteins consist of AFP and DCP. In some embodiments, the detected level of the cancer biomarker includes the detected level of the serological protein.

[0509] Preferably, the detected level includes the level of one or more serological proteins detected in the biological sample, wherein the one or more serological proteins is selected from the group consisting of AFP, AFP-L3, DCP, and a combination of two or 112 Docket No. 59888-703.601 more thereof. More preferably, the detected level includes the level of one or more serological proteins detected in the biological sample, wherein the one or more serological proteins is selected from the group consisting of AFP, DCP, and a combination of two or more thereof, preferably, wherein the serological proteins consist of AFP and DCP.

[0510] In preferred embodiments, the presence or level of the serological protein are measured using plasma samples or serum sample (or other blood derivatives) derived from the same biological sample in which the cancer biomarker is detected. The level of the serological protein may be quantified. The level of the serological protein is preferably determined by ELISA assay and the concentrations calculated using a standard curve.

[0511] In some embodiments, one or more control samples are tested along with the sample from the individual (the patient sample) to show the method has been performed appropriately. The control sample may be a negative control or a positive control. The negative control may be a sample from a healthy volunteer and / or a sample taken from a patient having a disease of the tissue of the defined category other than cancer. For example, when the tissue of the defined category is liver tissue, the disease may be Cirrhosis. The positive control may be a sample from an individual having the cancer of the tissue of the defined category.

[0512] In preferred embodiments, the output of the determining step is a qualitative result being positive or negative for cancer of the tissue of the defined category.

[0513] For example, if the detected level of the one or more cancer biomarkers is reduced or elevated in comparison to the threshold level, the individual is determined that the individual has cancer of a tissue of a defined category. Alternatively, if the detected level of the one or more cancer biomarkers is equal to the threshold level, the individual is determined that the individual does not have cancer of a tissue of a defined category.

[0514] Specific embodiment

[0515] A method of identifying one or more liver-cancer biomarkers for liver cancer

[0516] In one aspect, provided herein is a method of identifying a liver-cancer biomarker being a polypeptide or a fragment thereof or a miRNA molecule or a fragment thereof comprising: 113 Docket No. 59888-703.601 a) obtaining, from a blood sample obtained from an individual having liver cancer, an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the EPs display one or more liver-tissue biomarkers present at elevated expression levels in liver tissue, wherein the liver-tissue biomarker is ASGR1 , ASGR2, TFR2, and SLCO1 B1 , b) detecting, the candidate liver-cancer biomarker being a polypeptide or a fragment thereof or a miRNA molecule or a fragment thereof, on or in EPs in the enriched fraction of EPs one of the following:

[0517] (i) the presence of a candidate liver-cancer biomarker, or

[0518] (ii) the absence of a candidate liver-cancer biomarker, and c) selecting the candidate liver-cancer biomarker as a liver-cancer biomarker when the expression level of the candidate liver-cancer biomarker is reduced or elevated in comparison to a threshold expression level, wherein the threshold expression level is the expression level of the candidate liver-cancer biomarker detected on or in an enriched fraction of EPs obtained from a blood sample from a healthy individual or a blood sample from an individual having Cirrhosis.

[0519] In some embodiments, the enriched fraction of EPs which are secreted or released from cells of liver tissue are captured using one or more antibodies, each antibody being capable of binding specifically to a liver-tissue biomarker listed in step (a).

[0520] In some embodiments, the antibody is provided attached to a magnetic bead.

[0521] In some embodiments, the step of capturing EPs in the blood sample displaying the livertissue biomarker further comprises isolating the EPs displaying the liver-tissue biomarker which are captured using the liver-tissue biomarker.

[0522] In some embodiments where the candidate liver-cancer biomarker is a polypeptide or a fragment thereof, the candidate liver-cancer biomarker is detected using mass spectrometry.

[0523] In some embodiments where the candidate liver-cancer biomarker is an RNA oligonucleotide or a fragment thereof, the candidate liver-cancer biomarker may be detected using RNA sequencing. 114 Docket No. 59888-703.601

[0524] A method of screening for or surveillance of liver cancer

[0525] In a specific embodiment, provided herein is a method of screening for or surveillance of liver cancer in a blood sample obtained from an individual, wherein the method comprises: a) obtaining, from the blood sample, an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the EPs display the liver-tissue biomarkers consisting of ASGR1 , ASGR2, TFR2, and SLCO1 B1 , and wherein the enriched fraction of EPs are captured using four antibodies, each antibody being capable of binding specifically to each liver-tissue biomarker listed, and b) detecting on or in EPs in the enriched fraction of EPs, one of the following:

[0526] (i) the presence of two or more liver-cancer biomarkers,

[0527] (ii) the absence of two or more liver-cancer biomarkers, or

[0528] (iii) the presence of one or more cancer biomarkers and the absence of one or more cancer biomarkers, the two or more liver-cancer biomarkers being one or more polypeptides or a fragment thereof and one or more miRNA molecules or a fragment thereof.

[0529] In some embodiments, the enriched fraction of EPs which are secreted or released from cells of liver tissue are captured using one or more antibodies, each antibody being capable of binding specifically to a liver-tissue biomarker listed in step (a).

[0530] In some embodiments, the antibody is provided attached to a magnetic bead.

[0531] In some embodiments, the step of capturing EPs in the blood sample displaying the livertissue biomarker further comprises isolating the EPs displaying the liver-tissue biomarker which are captured using the liver-tissue biomarker.

[0532] In some embodiments, the one or more polypeptides or a fragment thereof is selected from the group consisting of SLC2A1 , CD13, GLUT 1 , and GPC-3, or a fragment thereof.

[0533] In some embodiments, the one or more miRNA molecules or a fragment thereof is selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p, or a fragment thereof. 115 Docket No. 59888-703.601

[0534] In some embodiments, the one or more miRNA molecules or a fragment thereof is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. Preferably, the miRNA molecules consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. Preferably, the miRNA molecule is hsa-miR- 124-3p or a fragment thereof.

[0535] In some embodiments, the step of detecting the presence of two or more liver-cancer biomarkers is performed using one or more antibodies, each antibody being capable of binding specifically to the liver-cancer biomarker being a polypeptide or a part, and one or more primers, each primer being capable of amplifying the liver-cancer biomarker being a miRNA molecule or a fragment thereof.

[0536] In some embodiments, the method further comprises: c) quantifying the expression level of each liver-cancer biomarker in order to determine a detected level of the cancer biomarker or the combination of the disease biomarkers.

[0537] In some embodiments, the method further comprises: d) comparing the detected expression level of the two or more liver-cancer biomarkers to a threshold expression level to determine whether the detected expression level is reduced or elevated in comparison to the threshold level, and e) determining that the individual has liver cancer when the expression level of the two or more liver-cancer biomarkers is reduced or elevated in comparison to a threshold expression level, wherein the threshold expression level is the expression level of the two or more livercancer biomarkers on or in an enriched fraction of EPs obtained from a blood sample from a healthy individual or a blood sample from an individual having Cirrhosis.

[0538] A system for analysing a biological sample

[0539] In a specific embodiment, the disclosure provides a system for analysing a blood sample obtained from an individual, comprising: a biomarker detection subsystem for detecting an expression level of two or more liver-cancer biomarkers on or in EPs in the enriched fraction of EPs comprising one or more antibodies, each antibody being capable of binding specifically to the liver-cancer 116 Docket No. 59888-703.601 biomarker being a polypeptide or a fragment thereof, and one or more primers, each primer being capable of amplifying the liver-cancer biomarker being a miRNA molecule or a fragment thereof; and a processor, couplable to the biomarker detection subsystem, configured to: a) compare the detected expression level of the two or more liver-cancer biomarkers to a threshold expression level to determine whether the detected expression level is reduced or elevated in comparison to the threshold expression level; and b) determine that the individual has liver cancer when the expression level of the two or more liver-cancer biomarkers detected in the biological sample from the individual is reduced or elevated in comparison to the threshold expression level, wherein the threshold expression level is an expression level of the cancer biomarker on or in an enriched fraction of EPs obtained from a blood sample from a healthy individual or a blood sample from an individual having Cirrhosis.

[0540] In some embodiments, the polypeptide or a fragment thereof is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , and GPC-3, or a fragment thereof.

[0541] In some embodiments, the miRNA molecule or a fragment thereof is selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa- mir-27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p, or a fragment thereof.

[0542] In some embodiments, the miRNA molecule or a fragment thereof is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0543] In some embodiments, the miRNA molecules consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0544] In some embodiments, the miRNA molecule is hsa-miR-124-3p or a fragment thereof.

[0545] In some embodiments, the system further comprises: an EP enrichment subsystem configured to obtain, from the blood sample, an enriched fraction of EPs which are secreted or released from cells of liver tissue, comprising four antibodies, each antibody being capable of binding specifically to a livertissue biomarker on or in EPs which are secreted or released from cells of liver tissue consisting of ASGR1 , ASGR2, TFR2, and SLCO1 B1. 117 Docket No. 59888-703.601

[0546] In some embodiments, the antibody is provided attached to a magnetic bead.

[0547] Use of two or more liver-cancer biomarkers in the detection of liver cancer

[0548] In a specific embodiment, the disclosure provides a use of two or more liver-cancer biomarkers present on or in EPs which are secreted or released from cells from liver tissue, wherein the two or more liver-cancer biomarkers comprises one or more polypeptides or a fragment thereof and one or more miRNA molecules or a fragment thereof, in the detection of liver cancer in a blood sample obtained from an individual. The polypeptide or a fragment thereof is selected from the group consisting of SLC2A1 , CD13, GLUT1 , or GPC-3. The miRNA molecule or a fragment thereof is selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p, or a fragment thereof.

[0549] In some embodiments, the miRNA molecule or a fragment thereof is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0550] In some embodiments, the miRNA molecules consist of miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof.

[0551] In some embodiments, the miRNA molecule is hsa-miR-124-3p or a fragment thereof.

[0552] In some embodiments, the use comprises: a) obtaining, from the blood sample, an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the EPs display the liver-tissue biomarkers consisting of ASGR1 , ASGR2, TFR2, and SLCO1 B1 , and wherein the enriched fraction of EPs are captured using four antibodies, each antibody being capable of binding specifically to each liver-tissue biomarker listed, and b) detecting on or in EPs in the enriched fraction of EPs, one of the following:

[0553] (i) the presence of each liver-cancer biomarker,

[0554] (ii) the absence of each liver-cancer biomarker, or

[0555] (iii) the presence of each liver-cancer biomarker and the absence of one or more cancer biomarkers. 118 Docket No. 59888-703.601

[0556] In some embodiments, the enriched fraction of EPs which are secreted or released from cells of liver tissue are captured using one or more antibodies, each antibody being capable of binding specifically to a liver-tissue biomarker listed in step (a).

[0557] In some embodiments, the step of capturing EPs in the blood sample displaying the livertissue biomarker further comprises isolating the EPs displaying the liver-tissue biomarker which are captured using the liver-tissue biomarker.

[0558] In some embodiments, the step of detecting the presence of two or more liver-cancer biomarkers is performed using one or more antibodies, each antibody being capable of binding specifically to the liver-cancer biomarker being a polypeptide or a part, and one or more primers, each primer being capable of amplifying the liver-cancer biomarker being a miRNA molecule or a fragment thereof.

[0559] In some embodiments, the use further comprises: c) quantifying the expression level of each liver-cancer biomarker in order to determine a detected level of the cancer biomarker or the combination of the disease biomarkers.

[0560] In some embodiments, the use further comprises: d) comparing the detected expression level of the two or more liver-cancer biomarkers to a threshold expression level to determine whether the detected expression level is reduced or elevated in comparison to the threshold expression level, and e) determining that the individual has liver cancer when the expression level of the two or more liver-cancer biomarkers is reduced or elevated in comparison to a threshold expression level, wherein the threshold expression level is the expression level of the two or more livercancer biomarkers on or in an enriched fraction of EPs obtained from a blood sample from a healthy individual or a blood sample from an individual having Cirrhosis.

[0561] An antibody and a primer for the detection of liver cancer, and use thereof the detection of cancer.

[0562] In a specific embodiment, the disclosure provides an antibody for the detection of liver cancer in a blood sample obtained from an individual, wherein the antibody specifically 119 Docket No. 59888-703.601 binds to a liver-cancer biomarker which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the liver-cancer biomarker is a polypeptide or a fragment thereof is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , and GPC-3, or is a phospholipid being PS.

[0563] In a specific embodiment, the disclosure provides an primer for the detection of liver cancer in a blood sample obtained from an individual, wherein the primer is for the amplification of a liver-cancer biomarker which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the liver-cancer biomarker is a miRNA molecule or a fragment thereof selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir-27b-3p, hsa- mir-203a-3p, and hsa-mir-122-5p.

[0564] In a specific embodiment, the disclosure provides an primer for the detection of liver cancer in a blood sample obtained from an individual, wherein the primer is for the amplification of a liver-cancer biomarker which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the liver-cancer biomarker is a miRNA molecule or a fragment thereof selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR- 221-3. In some embodiments, the miRNA molecules consist of miR-22-3p, hsa-miR-23b- 3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. In some embodiments, the miRNA molecule is hsa-miR-124-3p or a fragment thereof.

[0565] In a specific embodiment, the disclosure provides a use of one or more antibodies and one or more primers for the detection of liver cancer in a blood sample obtained from an individual. The antibody specifically binds to a respective liver-cancer biomarker being polypeptide or a fragment thereof which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the polypeptide or a fragment thereof is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , and GPC-3, or is a phospholipid being PS. The primer is for the amplification of a respective liver-cancer biomarker being a miRNA molecule or a fragment thereof which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the miRNA molecule or a fragment thereof is selected from the group consisting of hsa-mir-27b-5p, hsa-let-7g-5p, hsa-mir-23a-3p, hsa-mir-486-5p, hsa-mir- 27b-3p, hsa-mir-203a-3p, and hsa-mir-122-5p. 120 Docket No. 59888-703.601

[0566] In a specific embodiment, the disclosure provides a use of one or more antibodies and one or more primers for the detection of liver cancer in a blood sample obtained from an individual. The antibody specifically binds to a respective liver-cancer biomarker being polypeptide or a fragment thereof which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the polypeptide or a fragment thereof is selected from the group consisting of SLC2A1 , CD13, GLLIT1 , and GPC-3, or is a phospholipid being PS. The primer is for the amplification of a respective liver-cancer biomarker being a miRNA molecule or a fragment thereof which is detected on or in an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the miRNA molecule or a fragment thereof is selected from the group consisting of hsa-miR-22-3p, hsa-miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3. In some embodiments, the miRNA molecules consist of miR-22-3p, hsa- miR-23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p, and hsa-miR-221-3, or a fragment thereof. In some embodiments, the miRNA molecule is hsa-miR-124-3p or a fragment thereof.

[0567] In some embodiments, the use comprises: a) obtaining, from the blood sample, an enriched fraction of EPs which are secreted or released from cells of liver tissue, wherein the EPs display the liver-tissue biomarkers consisting of ASGR1 , ASGR2, TFR2, and SLCO1 B1 , and wherein the enriched fraction of EPs are captured using four antibodies, each antibody being capable of binding specifically to each liver-tissue biomarker listed, and b) detecting on or in EPs in the enriched fraction of EPs one of the following using the one or more antibodies or one or more primers:

[0568] (i) the presence of each liver-cancer biomarker,

[0569] (ii) the absence of each liver-cancer biomarker, or

[0570] (iii) the presence of one or more cancer biomarkers and the absence of one or more cancer biomarkers.

[0571] In some embodiments, the enriched fraction of EPs which are secreted or released from cells of liver tissue are captured using one or more antibodies, each antibody being capable of binding specifically to a liver-tissue biomarker listed in step (a).

[0572] In some embodiments, the step of capturing EPs in the blood sample displaying the livertissue biomarker further comprises isolating the EPs displaying the liver-tissue biomarker which are captured using the liver-tissue biomarker. 121 Docket No. 59888-703.601

[0573] In some embodiments, the use further comprises: c) quantifying the expression level of each liver-cancer biomarker in order to determine a detected level of the cancer biomarker or the combination of the disease biomarkers.

[0574] In some embodiments, the use further comprises: d) comparing the detected expression level of the two or more liver-cancer biomarkers to a threshold expression level to determine whether the detected expression level is reduced or elevated in comparison to the threshold expression level, and e) determining that the individual has liver cancer when the expression level of the two or more liver-cancer biomarkers is reduced or elevated in comparison to a threshold expression level, wherein the threshold expression level is the expression level of the two or more livercancer biomarkers on or in an enriched fraction of EPs obtained from a blood sample from a healthy individual or a blood sample from an individual having Cirrhosis.

[0575] 122 Docket No. 59888-703.601

[0576] Examples

[0577] Example 1 : Novel multiomics Biomarker Signature derived from blood circulating Hepatocyte-Extracellular Vesicles for the early detection of HCC

[0578] 1-1 Materials and Methods

[0579] We conducted a case-control study involving retrospective and prospective samples from patients with HCC and cirrhosis, collected from 6 European centers. The samples were divided into two independent cohorts for discovery and validation. H-EVs were isolated and mass-spectrometry as well as small RNA sequencing were used for biomarker discovery. Biomarker validation was performed using NEXOS, a novel EV detection platform, and PCR.

[0580] 1-2 Results

[0581] A total of 464 samples were collected for the study. All HCC patients were treatment- naive at the time of collection and had confirmed cirrhosis from various etiology backgrounds. About 90% had early or intermediate-stage HCC. Cirrhosis samples were matched for different etiologies.

[0582] More than 50 multiomic biomarkers were found to be differentially expressed between HCC and cirrhosis. By applying advanced machine learning methods, a refined set of novel biomarkers was identified and these were validated in the second cohort.

[0583] This approach established an initial multiomics biomarker signature with specificity and sensitivity that significantly surpassed the performance of US and AFP.

[0584] 1-3 Conclusion

[0585] EVs represent a rapidly advancing modality in liquid biopsy diagnostics, capable of tracing back to their parental cells and containing multiomic markers.

[0586] Our method of selectively isolating H-EVs from patients’ blood, enabled the identification of a novel HCC biomarker signature combining proteins & miRNAs. 123 Docket No. 59888-703.601

[0587] This innovative approach has the potential to revolutionize the early detection of HCC during surveillance, reducing the economic burden on healthcare systems and improving patient outcomes.

[0588] Example 2: Biomarker Discovery Study

[0589] 2-1 Materials and Methods

[0590] Sample collection

[0591] Blood samples from Hepatocellular carcinoma with liver cirrhosis patients (denominated from here on as HCC patients for simplicity) and liver cirrhosis (without HCC) patients were collected across 4 different clinical centres: University College London (UCL) and Imperial college London in UK, Novara in Italy and Institute de Medicine Molecular (iMM) in Portugal. Samples were processed to K2-EDTA plasma before being frozen and shipped in dry ice to Mursla Bio.

[0592] All individuals were anonymised, and ethical informed consents were in place for all collections.

[0593] Study design

[0594] 60 samples from HCC patients and 60 samples from Cirrhotic patients were processed for small RNA-sequencing and mass spectrometry (MS) and sent for omics analysis in two separated batches (Table 2):

[0595] • Batch 1 proteomics (20 HCC and 20 Cirrhosis)

[0596] • Batch 2 proteomics (40 HCC and 40 Cirrhosis)

[0597] • Batch 1 RNA-sequencing (33 HCC and 31 Cirrhosis)

[0598] • Batch 2 RNA-sequencing (27 HCC and 30 Cirrhosis)

[0599] Table 2. Demographic and clinical characteristics for the clinical samples used in batch 1 and batch 2. 124 Docket No. 59888-703.601

[0600] The presence of cirrhotic liver disease was a required condition for eligibility in this study, for patients from both groups. All patients in the HCC group were treatment-naive for cancer, as defined by the exclusion criteria.

[0601] SEC isolation and hepatocyte-derived EPs capture Patients’ plasma samples were thawed on ice overnight (ON) and processed using differential centrifugation (10.000 xg for 20 minutes), filtration (0.22 pm Fisherbrand™ Sterile PES Syringe Filter), and SEC with qEV1 35 nm Gen 2 columns (Izon). 125 Docket No. 59888-703.601

[0602] After SEC isolation, magnetic beads (Invitrogen™ Dynabeads™ M-270 Epoxy) coupled to four different antibodies targeting hepatocyte EP markers, which were validated by Mursla Bio and are commercially proprietary, were incubated ON to capture targeted EPs and washed 4 times in PBS with 0.1% Tween-20.

[0603] For MS analysis, 260 pL of 1 * RIPA buffer was incubated for 1 hour at 4 °C to lyse the captured EPs.

[0604] For RNA-sequencing, qEV RNA Extraction Kit (IZON) was used to lyse the capture EPs and isolate the RNA.

[0605] All samples belonging to the same batch were prepared simultaneously for MS analysis:

[0606] Samples were incubated at 95 °C for 5 min, TCA was added up to a final concentration of 6% and incubated ON at 4°C followed by centrifugation and removal of supernatant. Next, acetone washing (2x) and resuspension of the pellet in Evotec buffer.

[0607] Overnight digestion with Trypsin and desalting.

[0608] Total protein concentration was measured by BCA and was estimated to be in the range 0.5-1 pg.

[0609] Data was acquired in a data-independent acquisition (DIA) mode using timsTOF-SCP.

[0610] All raw files acquired in this study were processed with the DIA-NN software suite (version 1 .8.1) for peptide / protein identification and quantification using a curated Uniprot database (Swissprot and varsplic including protein isoforms, version 2021_04). The false discovery rate (FDR) for precursor identifications was set to 1%. A spectral library (DIA Speclib) was generated from single-shot DIA files with the “FASTA digest for library free search” option and “Deep Learning-based spectra, RTs and IMs prediction” enabled. DIA data was reprocessed using the spectral library with the match-between-runs (MBR) enabled. 126 Docket No. 59888-703.601

[0611] The inference of protein groups from DiaNN output was performed using a custom algorithm based on Nesvizhskii & Aebersold (27). For protein quantification the MaxLFQ algorithm from the DIA-NN R package (github.com / vdemichev / diann-rpackage) was applied and the LFQ intensities were Iog2-transformed. Log2-transformed LFQ intensities were further analysed using R.

[0612] MS Data transformation and quality filtering

[0613] After data acquisition, samples with protein groups (PG) < 330 identifications were filtered out of the analysis to improve the statistical significance of the analysis. Immunoglobulins and keratin proteins were treated as contaminants and removed.

[0614] PGs with Missing Values (MVs) > 50 % samples for Batch 1 and > 40 % samples for Batch 2 in both the Cirrhosis and HCC groups were corrected via Variance Stabilizing Normalization (VSN), using: ‘vsn2’ function from package “VSN”. Quantile smooth correction: package “qsmooth” using group / phenotypes as factor was applied.

[0615] MVs with < one Non-MVs for a PG were treated as Missing Not at Random (MNAR) and were imputed with 0 for that respective group. MVs in rows with more than 75% of missing values for a specific group, were treated as MNAR and imputed with left censored imputation. Remaining MVs were treated as Missing at Random (MAR) and imputed with k-nearest neighbors (KNN) using the function impute. wrapper. KN N. Variability between the batches was corrected using “HarmonizR” with Combat (default settings).

[0616] MS-based HCC prediction models

[0617] The predictive power of all the features after preprocessing, was assessed using “Caret” and “caretEnsemble” to train six different predictive model algorithms (random forest, radial kernel support vector machine, neural network, k-nearest neighbors, linear discriminant analysis and elastic net), using repeated cross-validation with 100 repeats and 5 folds on samples from batch 1 and batch 2. The different models were evaluated based on accuracy and kappa metrics. 127 Docket No. 59888-703.601

[0618] Subsequently, “Caret” was used to perform recursive feature elimination (RFE) with the random forest algorithm, again using repeated cross-validation with 100 repeats and 5 folds on samples from batch 1 and batch 2. Accuracy was used as the metric to select the optimal subset of features. Features subsets containing 1 to 20, 25, 30, 35 and 40 features were evaluated during this process.

[0619] Small-RNA sequencing

[0620] Library preparation and data acquisition

[0621] Library preparation was performed using the NEXTFLEX Small RNA Sequencing Kit V4 (revvity) with the tRNA / yRNA blocker supplied with the kit. cDNA was measured by Qubit Fluorometric Quantification (Invitrogen™ Qubit™) and samples were diluted to have the same quantity of cDNA prior to sequencing.

[0622] 1 Cirrhosis patient sample and 1 HCC patient sample were excluded during this step for not having sufficient cDNA.

[0623] NextSeq 2000 with P3 reagents 200 cycles was used for single-paired sequencing with 50 base-pair reads.

[0624] • Batch 1 (33 HCC, 31 Cirrhosis)

[0625] • Batch 2 (26 HCC, 29 Cirrhosis)

[0626] Sample 9 (Cirrhosis) was sequenced twice, once in each sequencing batch.

[0627] Adaptor trimming and alignment

[0628] COMPSRA (COMprehensive Platform for Small RNA-Seq data Analysis) pipeline was used for aligning sequenced data to the human genome.

[0629] The raw sequencing reads were subject to the QC, alignment and annotation modules of COMPSRA. The following arguments were used for the QC module: -ra TGGAATTCTCGGGTGCCAAGG -rh 20 -rt 20 -rr 20 -rlh 8,17, trimming out the 3’ NEXTflow adapter as well as low-quality bases from the reads ends and the low-quality 128 Docket No. 59888-703.601 reads with the average quality score less than 20. Alignment module was run using the STAR aligner with some modification to the default parameter values of COMPSRA, using trimmed reads with the lengths between 17-50 bp as the input.

[0630] Pre-processing and differential expression analysis

[0631] MicroRNA read-counts quantifications adjusted for multi-mapping were further analysed using R27 (version 4.3.2).

[0632] MicroRNAs with > 5 counts in at least 5 samples of one of the groups (Cirrhosis or HCC) were kept for analysis.

[0633] The following packages were used for normalization and differential abundance analysis:

[0634] 1) “EdgeR” with Trimmed Mean of M values (TMM) normalization.

[0635] 2) “DESeq2” with normalization using negative binomial distribution.

[0636] 3) “Seurat V5” with ‘sctransform’ normalization. microRNA-based HCC prediction models

[0637] PVCA was performed to compare and select the normalization to use in prediction models.

[0638] Similarly to the MS-based prediction models, the predictive power of all the features after preprocessing was evaluated using “Caret” and “caretEnsemble” to train the same six different predictive model algorithms, with repeated cross-validation with 100 repeats and 5 folds on samples from batch 1 and batch 2. The different models were evaluated based on accuracy and kappa metrics.

[0639] Caret was used to perform recursive feature elimination (RFE) with the random forest algorithm, using the same parameters of cross-validation.

[0640] AFP ELISA

[0641] Circulating-AFP was measured by ELISA assay (ab108838, Abeam) in the plasma samples analysed for proteomics (for batch 1 and batch 2 samples only) and small-RNA sequencing. 129 Docket No. 59888-703.601

[0642] Multiomics integration and prediction models

[0643] The proteomic data, microRNA data and AFP measured by enzyme-linked immunosorbent assay (ELISA) were merged for multiomics analysis.

[0644] The different sets of data were standardized and the predictive power of various models were evaluated by repeated cross-validation.

[0645] Random data partitioning was applied without replacement, with 75% of the Cirrhosis samples and 75% of the HCC samples selected to a training dataset and the remaining to a testing dataset. Scaling, RFE and random forest model training for the respective optimal subset of selected features was applied to the training subset (using the same cross-validation parameters as previously mentioned) and then used to predict the classes in the testing subset. This process was repeated 10 times (Fig. 5).

[0646] 2-2 Results

[0647] Multiomics analysis

[0648] 29 PGs were selected as differentially expressed between HCC patients and Cirrhosis patients and 58 miRNA fragments were identified to be differentially expressed or to provide predictive power for distinguishing patients with HCC and Cirrhosis when combined with other biomarkers (Tables 3 to 9 below, Fig. 6 and 7). Via Recursive Feature Elimination (RFE), the list of biomarkers was narrowed down and random forests model was selected for deriving Receiver Operating Characteristic (ROC) curves for miRNA models, Protein models and multiomics models. The AFP measured for the various clinical samples was integrated in the models, resulting in superior sensitivity and specificity. Known risk factors for developing HCC were also integrated in the analysis (Fig. 8).

[0649] Table 3. Protein biomarkers 130 Docket No. 59888-703.601

[0650] 131 Docket No. 59888-703.601

[0651] Table 4. DE miRNAs

[0652] Table 5. DE miRNA fragments: Exclusive to fragment analysis

[0653] 132 Docket No. 59888-703.601

[0654] Table 6. miRNA fragments: Exclusive to RFE naive models selection for miRNAs only - no DE

[0655] 133 Docket No. 59888-703.601

[0656] Table 7. miRNA fragments: Exclusive to RFE naive models selection for multiomics - no DE

[0657] 134 Docket No. 59888-703.601

[0658] 135 Docket No. 59888-703.601

[0659] Table 8. Gene names of proteins

[0660] 136 Docket No. 59888-703.601

[0661] 137 Docket No. 59888-703.601

[0662] 138 Docket No. 59888-703.601

[0663] 139 Docket No. 59888-703.601

[0664] 140 Docket No. 59888-703.601

[0665] 141 Docket No. 59888-703.601

[0666] 142 Docket No. 59888-703.601

[0667] 143 Docket No. 59888-703.601

[0668] 144 Docket No. 59888-703.601

[0669] Table 9. miRNAs

[0670] 145 Docket No. 59888-703.601

[0671] 146 Docket No. 59888-703.601

[0672] 147 Docket No. 59888-703.601

[0673] 148 Docket No. 59888-703.601

[0674] 149 Docket No. 59888-703.601

[0675] Model Robustness

[0676] The robustness of the model was validated using a randomized response variable (RRV) split between HCC and Cirrhosis patients. For randomized split resulted in an AUC of 0.55 whereas the original split resulted in ALICs between 0.882 and 0.894 (Fig 9).

[0677] Performance metrics stratified

[0678] The performance of the model was analysed when separating HCC samples by stage (Fig. 10).

[0679] Individual

[0680] Each sample was analysed individually using various models and, it was identified that only 15 out of 109 samples were often misclassified as HCC or Cirrhosis whereas, for the majority, the diagnosis is in agreement with the clinical information of the patients (Fig. 11 , A and B).

[0681] The list of biomarkers with predictive value for detecting HCC are provided in Tables 3 to 9. In summary, more than 50 multiomic biomarkers were found to be differentially expressed between HCC and cirrhosis. By applying advanced machine learning methods, a refined set of novel biomarkers was identified and these were validated in the second cohort.

[0682] Example 3: Validation Study I

[0683] 3-1 Materials and Methods

[0684] Sample collection and Study design

[0685] Samples from HCC patients and samples from Cirrhotic patients were collected and were processed for small RNA-sequencing and mass spectrometry as above.

[0686] Table 10. Demographic and clinical characteristics for the clinical samples used in the training set (n = 138). 150 Docket No. 59888-703.601

[0687] Table 11. Demographic and clinical characteristics for the clinical samples used in the testing set (n = 33). 151 Docket No. 59888-703.601

[0688] Fragment Filtering and Expression Thresholds

[0689] Sample Inclusion Criterion:

[0690] Fragment or merged fragment expression was filtered to include only those expressed in at least 25 samples within one group (e.g., HCC or Cirrhosis). Fragments expressed in fewer than 25 samples in any group were removed from further analysis. For this step, higher priority was given to fragments expressed in HCC and Cirrhosis samples, with fragments in healthy samples being considered less critical.

[0691] Low-Expressing Fragment Removal:

[0692] To exclude low-expressing fragments, a threshold was applied where a minimum of 5 reads had to be detected in at least 3 samples from a given group. Fragments failing to meet this criterion were excluded from the analysis.

[0693] Average Expression Threshold:

[0694] For each fragment, the raw read counts were averaged within the higher-expressing group (HCC or Cirrhosis). Fragments with an average expression below 8 reads were removed. This criterion ensured that only fragments with sufficient expression in the higher-expressing group were retained for further analysis.

[0695] Minimum Expression Difference Between Groups:

[0696] The difference in raw read counts between HCC and Cirrhosis groups was used as a filtering criterion. Fragments were retained if they met the following criteria for the minimum read difference between the two groups:

[0697] 1) When the high-expressing group had <30 reads, the minimum difference had to be >4 reads.

[0698] 2) When the high-expressing group had between 30 and 100 reads, the minimum difference had to be >10 reads. 152 Docket No. 59888-703.601

[0699] 3) When the high-expressing group had >100 reads, the minimum difference had to be >20 reads.

[0700] Correlation with Non-Clinical Metadata:

[0701] To account for potential batch effects and other non-clinical biases, fragments showing strong correlations with non-clinical metadata (e.g., collection site, standard operating procedure) were identified and removed. For this analysis, the first 32 samples were excluded to mitigate batch effect issues.

[0702] Fragments were ranked based on their expression patterns across HCC, Cirrhosis, and healthy samples, with higher rankings assigned to patterns most likely to reflect true biological signals. The following ranking schema was applied:

[0703] Higher likelihood patterns:

[0704] 1) High in HCC, low in Cirrhosis, low in healthy (Pattern Rank A1).

[0705] 2) Low in HCC, high in Cirrhosis, high in healthy (Pattern Rank A2).

[0706] 3) High in HCC, medium in Cirrhosis, low in healthy (Pattern Rank A3).

[0707] 4) Low in HCC, medium in Cirrhosis, high in healthy (Pattern Rank A4).

[0708] Lower likelihood patterns:

[0709] 1) Low in HCC, high in Cirrhosis, low in healthy (Pattern Rank B1).

[0710] 2) High in HCC, low in Cirrhosis, high in healthy (Pattern Rank B2). miRNA Tissue Database

[0711] Tissue Expression Validation:

[0712] Fragments were further validated and ranked based on their expression profiles in liver versus plasma using publicly available miRNA tissue expression databases, such as miTED (dianalab.e-ce.uth.gr / mited / # / expressions) and the miRNA Tissue Atlas (cobweb. cs.uni-saarland.de / tissueatlas2 / ). The difference in log2RPM values between liver 153 Docket No. 59888-703.601 and plasma was used as a ranking metric, with higher liver expression relative to plasma indicating a better liver-specific marker.

[0713] Database Query Parameters:

[0714] 1) Tissue or Cell Line: Tissues.

[0715] 2) Tissues: Liver, Blood, Plasma.

[0716] 3) Data Collections: All.

[0717] 4) Expression Value: Log2RPM.

[0718] 5) Diseases: All.

[0719] 6) Health Status: All. miR-122-5p (Good Liver Marker):

[0720] • Average log2RPM in liver: 14.8.

[0721] • Average log2RPM in plasma: 8.7.

[0722] • Tissue Rank: 6.1. miR-486-5p (Poor Liver Marker):

[0723] • Average log2RPM in liver: 6.6.

[0724] • Average log2RPM in plasma: 19.2.

[0725] • Tissue Rank: -12.6.

[0726] This approach allowed for the selection of biologically relevant and liver-specific fragments with higher potential as biomarkers in the context of HCC and cirrhosis.

[0727] 3-2 Results

[0728] Figure 12 shows that the detected level of individual hepatocyte-EV cancer biomarkers, let-7g-5p, mir-203a-5p, and mir-486-5p, are as significant as AFP and DCP in differentiating between HCC and Cirrhosis (HCC N = 83, Cirrhosis N = 88) in an embodiment of method of the present invention.

[0729] Figure 13 shows that the detection of a combination of hepatocyte-EV cancer biomarkers in a multiomics biomarker signature complement AFP and DCP in differentiating between 154 Docket No. 59888-703.601

[0730] HCC and Cirrhosis, leading to an improved Area under the curve (AUC), in the method of the present invention. The hepatocyte-EV cancer biomarkers detected consist of hsa- let-7g-5p GLLIT1 , CD13, GPC-3, hsa-mir-203a-3p, hsa-mir-486-5p, hsa-mir-27b-5p, hsa-mir-23a-3p, and hsa-mir-122-5p. The multiomics biomarker signature excluding AFP and DCP: AUC = 0.83, 95 % Cl: 0.686 - 0.976. The multiomics biomarker signature including AFP and DCP: AUC = 0.9, 95 % Cl: 0.791 - 1 (Sex and age not used). GAAD performance: AUC = 0.83, 95 % Cl: 0.67 - 0.96 (Sex and age used).

[0731] Figure 14 shows that the detection of a combination of hepatocyte-EV cancer biomarkers in a multiomics biomarker signature including AFP and DCP in the method of the present invention achieves high accuracy in detecting both Early and Late HCC Stages. The hepatocyte-EV cancer biomarkers detected consist of hsa-let-7g-5p GLUT1 , CD13, GPC-3, hsa-mir-203a-3p, hsa-mir-486-5p, hsa-mir-27b-5p, hsa-mir-23a-3p, and hsa- mir-122-5p.

[0732] Example 4: Validation Study II

[0733] 4-1 Materials and Methods

[0734] EXPERIMENTAL MODEL AND STUDY PARTICIPANTS

[0735] Cell lines

[0736] Human breast adenocarcinoma cell line MCF-7 (ATCC, catalog nr HTB-22), human breast ductal carcinoma cell line BT-474 (ATCC, catalog nr HTB-20) and human hepatocarcinoma cell line HepG2 (ATCC, catalog nr HB-8065) were cultured in Advanced-DMEM (Gibco™), supplemented with 5% (v / v) fetal bovine serum (FBS) (Gibco™) and 4 mM glutaMAX (Gibco™), at 37°C with 5% CO2.

[0737] Tissue and plasma samples

[0738] Flash-frozen tumor / post-morten tissues were sourced from various biobanks (TebuBio, Proteogenex, BiolVT, Amsbio and LifeNet). Samples were collected from biopsies. All donor biopsy samples were obtained with informed consent and in accordance with ethical guidelines. All associated data were anonymized and handled in compliance with the UK General Data Protection Regulation (UK-GDPR).

[0739] Frozen human plasma samples, derived from whole blood collected in BD Vacutainer™ K2 EDTA tubes (Cambridge Bioscience), were obtained from a pooled cohort of healthy, 155 Docket No. 59888-703.601 paid volunteers (Research Donors). Whole blood samples from patients with cirrhosis or hepatocellular carcinoma (HCC) with underlying cirrhosis were collected in BD Vacutainer™ K2 EDTA tubes by the BIOBANCO-Molecular Medicine Institute / Santa Maria Hospital, UCL Biobank - Royal Free London NHS Foundation Trust (RFL B-ERC), Imperial College London - Hammersmith Hospital, and Novara University Hospital - Ospedale Maggiore della Carita. All donor samples were obtained with informed consent and in accordance with ethical guidelines. All associated data were anonymized and handled in compliance with the UK General Data Protection Regulation (UK-GDPR).

[0740] METHOD

[0741] Collection of Extracellular Vesicles (EVs) from cultured cells

[0742] Cells at 70% confluency in T175 flasks (Nunc™) were gently washed three times with phosphate-buffered saline (PBS) (Gibco) and cultured in Advanced-DMEM without FBS. After 48 hours, the conditioned medium was collected and sequentially centrifuged for 5 minutes at 300 x g, followed by another centrifugation for 10 minutes at 3000 x g at 4°C to remove cellular debris and other large contaminants. The conditioned medium was filtered through 0.22 pm polyethersulfone (PES) filters (Fisherbrand) and concentrated to 500 pL using Amicon® Ultra-15 100K MWCO centrifugal filters (Merck Millipore). EVs were isolated by size exclusion chromatography (SEC) using either qEV original 70 nm columns (Izon Science) for breast cell lines (MCF-7, BT-474), or qEV1 35 nm (Izon Science) for HepG2 cell line, on an automatic fraction collector (AFC) (Izon Science), according to the supplier’s instructions. A total of 1.5 mL of eluted EV fractions were collected, pooled, aliquoted and stored at -80°C until further use.

[0743] Collection of EVs from tissue samples

[0744] The protocol for tissue EV isolation was adapted from (nature.com / articles / s41596-020- 00466-1). For each 0.2 mg of tissue (cut into smaller pieces), 2 mL of Advanced-DMEM was added in 5 mL LoBind tubes (Eppendorf). Tissue was enzymatically digested with 2 mg / mL of Collagenase D (Roche) and 40U / mL of DNase I (Thermo Fisher Scientific) at 37°C for 30 minutes under gentle rotation. To remove cells or other large debris, the solution was filtered through a 70 pM cell strainer (Fisherbrand) and 20 mL of sterile PBS was used to wash the strainer. The solution was then centrifuged sequentially to remove any cell debris: 500xg for 10 minutes, 3000xg for 20 minutes, 10,000xg for 30 minutes, transferring the supernatant each time, filtered through a 0.22 pM PES filter to remove large vesicles and other contaminants and concentrated to 1 mL using Amicon® Ultra- 15 100K MWCO centrifugal filters. The EVs were isolated using qEV1 GEN2 35 nm SEC columns (Izon Sciences) on an AFC, according to the supplier’s instructions. A total of 156 Docket No. 59888-703.601

[0745] 2.8 mL of eluted EV fractions were collected, pooled, aliquoted and stored at -80°C until further use.

[0746] Collection of EVs from human plasma

[0747] Plasma samples were thawed on ice, then centrifuged at 10,000 g for 20 minutes at 4°C to remove cell debris, large particles, or aggregates. The supernatants were filtered through 0.22 pm PES filters, and 1.8 mL of each sample was split into two aliquots and subjected to two SEC isolations using qEV original 35 nm columns on an AFC. The EV- containing fractions (totaling 5.6mL per sample) were pooled, aliquoted, and stored at - 80°C until further use.

[0748] EV characterization by nanoparticle tracking analysis (NTA)

[0749] EV particle concentration and size distribution were determined by nanoparticle tracking analysis (NTA) using a NanoSight NS300 system (Malvern Panalytical) configured with a 488 nm laser and a high-sensitivity scientific CMOS camera. Samples were diluted in filtered PBS to the optimal detection range (1x108- 2x109particles / mL), analyzed under a constant flow rate, and recorded as three-x 60 seconds videos and captured with a camera level 12. Data analysis was performed using NanoSight NTA software version 3.4 with a detection threshold of 7.

[0750] EV characterization by Western-blotting

[0751] EV preparations from MCF-7, BT-474 and HepG2 EP were normalized to a concentration of 1x1010particles / mL in PBS. One-hundred pL of each sample was lysed in 1 x RIPA lysis buffer (Abeam) supplemented with 1 x Halt Protease Inhibitor Cocktail (Thermo Fisher Scientific) for 1 h on ice, followed by two 30-second sonication in an iced ultrasonic bath. Lysates were mixed with NuPAGE LDS Sample Buffer and Sample Reducing Agent (Invitrogen), then boiled at 70°C for 10 min.

[0752] For gel electrophoresis, 37 pL per sample was loaded onto NuPAGE 4-12%, Bis-Tris, 1.0-1.5 mm, Mini Protein Gels (Invitrogen) and run at 150 V using MES SDS Running Buffer (Invitrogen). Spectra™ Multicolor Broad Range Protein Ladder (Thermo Fisher Scientific) was run alongside the samples as molecular weight marker.

[0753] For transfer and detection, proteins were transferred to PVDF membranes (Invitrogen) using iBIot 2 Dry Blotting System (Invitrogen). Blocking and antibody incubations were performed using an iBind Western Device (Invitrogen), per supplier instructions. The 157 Docket No. 59888-703.601 membranes were incubated sequentially with iBind solution for blocking, primary antibody and secondary antibody. The primary antibodies used were: anti-CD63 (ab59479, Abeam), anti-CD81 (orb506485, Biorbyt), or anti-CD9 (ab58989, Abeam), diluted at 1 :1000. An anti-mouse IgG horseradish peroxidase-conjugated (HAF007, R&D Systems) was used as a secondary antibody, diluted 1 :250 dilution. Membranes were subsequently incubated with SuperSignal™ West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific) and signal detection was visualized and acquired on a G:BOX Chemi XRQ imaging system (Syngene).

[0754] Preparation of antibody-coupled-magnetic beads

[0755] Dynabeads® M-270 Epoxy magnetic beads (Invitrogen) were conjugated with antibodies using the Dynabeads™ Antibody Coupling Kit (Invitrogen™). The following antibodies were used:

[0756] TABCB11 (sc-74500, Santa Cruz)

[0757] - ABCB11 (STJ190611 , St John's Laboratory)

[0758] - ABCB4 (CSB-PA001050LA01 HU, Cusabio)

[0759] - ABCC6 (ab167564, Abeam)

[0760] - ASGR1 (ab127896, Abeam; MAB43941-100, Biotechne)

[0761] - ASGR2 (ab200196, Abeam)

[0762] - CLEC4G (MAB2947, Biotechne)

[0763] - ELFN1 (MAB10644, Biotechne)

[0764] FXYD1 (sc-393415, Santa Cruz; sc-65478, Santa Cruz),

[0765] GHR (STJ97268, St John's Laboratory)

[0766] LRRC3 (MAB5039, Biotechne)

[0767] - PTP4A1 (sc-13054, Santa Cruz)

[0768] - RTP3 (CSB-PA887106LA01 HU, Cusabio)

[0769] - SLC10A1 (sc-518115, Santa Cruz)

[0770] - SLC13A5 (CSB-PA768239LA01 HU, Cusabio)

[0771] - SLC17A4 (sc- 135562, Santa Cruz)

[0772] - SLC22A9 (CSB-PA811607LA01 HU, Cusabio,

[0773] - SLC2A2 (CSB-PA13329A0Rb, Cusabio; sc-518022, Santa Cruz)

[0774] SLC38A3 (sc-398982, Santa Cruz)

[0775] - SLC38A4 (sc-515125, Santa Cruz)

[0776] - SLCO1 B1 (sc-271157, Santa Cruz; CSB-PA896932LA01 HU, Cusabio)

[0777] - SLCO1 B3 (ABIN570800, Antibodies-online)

[0778] - TFR2 (MAB3120, Biotechne) 158 Docket No. 59888-703.601

[0779] - TMEM56 (CSB-PA023857LA01 HU, Cusabio)

[0780] - UNC93A (CSB-PA773046LA01 HU, Cusabio)

[0781] CD9 (ab58989, Abeam)

[0782] - CD63 (orb506484, Biorbyt)

[0783] Magnetic beads (MBs) were prepared following the kit’s instructions. Briefly, 10 pg of capture antibody per mg of beads was used, and the mixture was incubated overnight at 37°C under constant rotation in LoBind microcentrifuge tubes. To enhance washing stringency, 0.05% Tween®20 was added to HB and LB wash buffers. Washing steps were performed using a DynaMag™-Spin Magnet (Invitrogen™).

[0784] Preparation of hepatocyte-EV TOP4 capture antibody cocktail

[0785] To target hepatocyte-derived EVs, four antibodies, anti-ASGR1 , anti-ASGR2, anti-TFR2 and SLCO1 B1 were each conjugated to MBs in separate coupling reactions as previously described. The resulting MBs were then pooled at a 4:2:2:2 ratio to form the TOP4 hepatocyte-EV TOP4 capture antibody cocktail, designed to enhance surface marker coverage and optimize hepatocyte-derived EVs.

[0786] NEXPLOR CD9+EVs capture and elution

[0787] EVs isolated from MCF-7 cells via SEC were diluted to 7.9x109particles / mL in 1300 pL PBS containing 0.05% Tween®20 (PBS 0.05% T20). The diluted EV suspension was incubated overnight at 4°C on a rotating rack with 130 pL of CD9-antibody-coupled Dynabeads. After incubation, the supernatant was collected and stored for potential downstream analysis. The EV-bound MBs were then washed three times with PBS 0.05% T20 to remove unbound material. To elute the CD9+EVs, the MBs were resuspended in 150 pL 0.2M glycine-HCI buffer pH 2.5 (Thermo Fisher Scientific) and incubated for 5 min at room temperature. The supernatant was recovered, and a second elution was performed with an additional 150 pL of glycine buffer under the same conditions. The two eluates were pooled, immediately neutralized with 1M Tris-HCI pH 8.0 (Thermo Fisher Scientific) and diluted with PBS to a final volume of 1300 pL.

[0788] ExoFlow CD9+EVs capture and elution

[0789] The ExoFlow kit (System Biosciences, SBI), a commercial kit for selective capture of exosome subpopulations based on the presence of CD9, was used to isolate CD9+EVs from MCF-7 cells. All procedures were conducted following the supplier instructions. Briefly, biotinylated anti-CD9 antibodies were conjugated to streptavidin-coated MBs. 159 Docket No. 59888-703.601

[0790] The antibody-coupled MBs were then incubated overnight at 4°C on a rotating rack with MCF-7 SEC-derived EVs at a concentration of 7.9x109particles / mL. After incubation, the EV-bound MBs were washed and CD9+EVs were eluted using the kit’s elution buffer for 2 h at 25°C. Both eluted EV fractions and post-capture supernatants were collected and stored for downstream O-NEXOS and NTA analysis.

[0791] O-NEXOS detection of magnetic bead-captured CD9+and CD63+EVs

[0792] For CD9 protein detection, cell line-derived EVs were normalized to 1x108particles / mL in PBS and incubated with anti-CD9 antibody-coupled MBs and biotinylated anti-CD9 antibody (ab28094, Abeam).

[0793] For CD63 protein detection, cell line-derived EVs were normalized to 2x109particles / mL in PBS; tissue-derived EVs were normalized to 1x1010particles / mL in Assay Defender® diluent (Candor Bioscience). Plasma EVs were normalized by input volume and diluted in Assay Defender® diluent. Detection was performed using a biotinylated anti-CD63 antibody (orb506484, Biorbyt).

[0794] O-NEXOS detection of magnetic bead-captured EVs was previously described (An electro-optical platform for the ultrasensitive detection of small extracellular vesicle subtypes and their protein epitope counts - iScience. 2024 Apr 30;27(6): 109866. doi: 10.1016 / j.isci.2024.109866. eCollection 2024 Jun 21.). Briefly, in 96-well plates, 200 pL of each EV samples were incubated with 10 ng / pL of the appropriate biotinylated detection antibody for 45 min at room temperature under rotation. Following antibody incubation, MBs pre-coated with the capture antibodies listed above, were added to the samples - 7.5 pL for cell line-derived EVs and 15 pL for tissue-derived EVs. Incubated occurred for 3 h at room temperature with pipette mixing every 30 min using an Opentrons OT-2 liquid handler robot (Opentrons).

[0795] After incubation, MBs were washed three times PBS 0.05% T20 using a Magnetic Stand- 96 (Invitrogen). Subsequently, 200 pL of Poly-HRP strep (1 :6000 dilution) was added to each well and incubated for 30 minutes at room temperature. MBs were then washed four times with PBS 0.05% T20. Colorimetric signal detection was conducted using TMB ELISA Substrate (High Sensitivity) (Abeam). Briefly, 100 pL of TMB was added to each sample and incubated for 20 minutes. The reactions were stopped by adding 100 pL of Stop Solution for TMB Substrate (Abeam). MBs were removed by transferring 190 pL of the final reaction mixture to a new flat-bottom 96-well plate using a Magnetic Stand-96. 160 Docket No. 59888-703.601

[0796] Absorbance was measured at 450 nm using a SpectroStar Nano microplate reader (BMG Labtech).

[0797] Data mining to identify liver / hepatocyte-specific plasma membrane proteins

[0798] Human protein atlas (HPA) (doi.org / 10.1126 / science.1260419 ) version 20.1 was accessed to identify proteins with liver / hepatocyte-specific expression. To achieve this, genes with liver-elevated expression were initially selected based on mRNA tissue specificity annotations, specifically those classified as tissue enriched, group enriched, or tissue enhanced for liver tissue, generating a preliminary list of candidate genes.

[0799] To refine this list, protein expression data derived from immunohistochemistry(IHC) profiles were retrieved from the same HPA database. Candidates were excluded if they exhibited high or medium expression for any non-liver tissues from the following list: "adipose tissue", "adrenal gland", "appendix", "bone marrow", "breast", "bronchus", "cartilage", "caudate", "cerebellum", "cerebral cortex", "cervix", "choroid plexus", "colon", "dorsal raphe", "duodenum", "endometrium", "epididymis", "oesophagus", "eye", "fallopian tube", "gallbladder", "hair", "heart muscle", "hippocampus", "hypothalamus", "kidney", "lactating breast", "lung", "lymph node", "nasopharynx", "oral mucosa", "ovary", "pancreas", "parathyroid gland", "pituitary gland", "placenta", "prostate", "rectum", "retina", "salivary gland", "skeletal muscle", "skin", "small intestine", "smooth muscle", "sole of foot", "spleen", "stomach", "substantia nigra", "testis", "thymus", "Thyroid gland", "tonsil", "urinary bladder", "vagina", "soft tissue".

[0800] Not detected, low, medium or high IHC expressions are assigned by the HPA based on IHC staining intensity (negative, weak, moderate or strong) and fraction of stained cells (<25%, 25-75% or >75%).

[0801] Further additional gene candidates were included if they met at least one of the following criteria:

[0802] 1. High protein expression in hepatocytes along with not detected?, low and medium expression in other tissues

[0803] 2. Medium protein expression in hepatocytes along with not detected? and low expression in other tissues.

[0804] The protein subcellular location was then retrieved using the UniProt Retrieve / ID mapping tool. Proteins with subcellular localization annotations matching “plasma membrane” or regular expression terms such as (A((cellular\s+)?membrane\b)|(\bcell 161 Docket No. 59888-703.601 membrane\b)|(\bcell surface\b)) were selected to identify candidates likely associated with the cell surface .

[0805] This filtering strategy resulted in a final list of 94 liver / hepatocyte-specific plasma membrane proteins, representing strong candidates for further validation of liver / hepatocytes-derived EV cell surface markers.

[0806] The HPA defines RNA tissue specificity terms as follows: tissue enriched is the average mRNA level being at least four-fold higher in the given tissue compared to any other tissues. Group enriched is defined as the average mRNA level being at least four-fold higher in a group of 2-5 tissues compared to any other tissue. Tissue-enhanced is defined as the mRNA level being at least four-fold higher in the given tissue compared to the average level in all other tissues.

[0807] Verification and ranking of liver / hepatocyte-specific plasma membrane proteins Proteomics data obtained from liquid chromatography tandem mass spectrometry (LC- MS / MS) were sourced from publicly available databases: the Human Proteome Map (Kim et al. 2014 Nature pmc.ncbi.nlm.nih.gov / articles / PMC4403737 / ) and ProteomicsDB (Wilhelm et. Al. 2014 Nature nature.com / articles / nature13319). These databases were then queried to examine the expression levels of the 94 plasma membrane proteins identified.

[0808] For the Human Proteome Map analysis, RefSeq accessions identifiers were first mapped to their corresponding gene names. When multiple RefSeq accession entries corresponded to a single gene, spectral counts were averaged. Entries with zero spectral count values were removed, and the remaining values were log2-transformed. Tissues were grouped into organ systems(e.g., respiratory, heart, digestive, central nervous system (CNS)), while liver and blood cell types were analyzed as distinct categories to preserve resolution. For each gene, the average log2-transformed spectral count was calculated per tissue group. Liver specificity was assessed by dividing the liver spectral count by the sum of spectral counts from all other tissue groups (excluding gallbladder and placenta). Genes were then ranked based on liver specificity, and a heatmap was generated to visualize log2spectral counts across tissue groups, with genes ordered from highest to lowest liver specificity. 162 Docket No. 59888-703.601

[0809] For ProteomicsDB analysis, the "ExpressionHeatmap.csv" file containing MS1 / iBAQ intensity was downloaded and values in the column labelled “Average normalized intensity” was used for analysis. Non-representative or ambiguous tissue types (e.g. breast cancer cell, osteosarcoma cell, placenta, embryonic stem cell, amniocyte, gall bladder, synovia and arachnoid cyst) were excluded from the analysis. Similarly to the Human Proteome Map analysis, tissues were grouped into broader biological categories, while liver and blood-derived cells were analyzed as individual categories. For each protein, intensity values were averaged across each tissue group. To assess liver specificity, the intensity in liver was divided by the sum of average intensities across all other tissue groups (excluding gallbladder). Proteins were subsequently ranked by liver specificity, and a heatmap was generated to visualize the normalized protein intensities across tissue groups, with proteins arranged in descending order of liver specificity. Protein expression data for 19 out of the 94 proteins were unavailable across both databases.

[0810] NEXPLOR of hepatocyte-derived EVs for Mass Spectrometry (MS) and small RNA sequencing

[0811] Plasma samples were obtained from 13 HCC patients, 10 cirrhosis patients and 6 healthy volunteers for MS and 8 HCC patients, 8 cirrhosis patients and 4 healthy volunteers for RNA sequencing (RNAseq)

[0812] SEC-isolated plasma EVs were used at 4.85 mL for MS and 0.7 mL for RNAseq. Samples were diluted in Assay Defender diluent and captured with TOP4 MBs at 260 pL for MS or 30 pL for RNAseq. A KingFisher Apex (Thermo Fisher Scientific) instrument was used to incubate the capture mix for 16 hours at 4°C with mixing every 30 minutes. The MB-bound EVs were then washed with PBS 0.05% T20 to remove any unbound particles on the KingFisher instrument before being lysed for MS or RNAseq.

[0813] EV lysis for MS

[0814] EVs, either bound to antibody-coupled MBs or isolated via SEC, were lysed using RIPA buffer (Thermo Fisher Scientific) by mixing on a rotator for 1 hour at 4 °C. This was followed by two 30-second sonication steps in an iced ultrasonic bath (70% amplitude). Lysates from SEC-isolated EVs were stored directly at -80 °C, whereas lysates from MB- bound EVs were first separated from the magnetic beads before freezing.

[0815] Sample preparation and data acquisition 163 Docket No. 59888-703.601

[0816] LC-MS / MS was outsourced to an external provider, Evotec International GmbH. In short, lysed samples were incubated at 95 °C for 5 min, and proteins were precipitated in trichloroacetic acid (5 % TCA). The pellets were resuspended in a buffer containing TCEP and chloroacetamide for reduction alkylation and also amenable for digestion. Protein digestion was performed overnight at 37°C with trypsin. The digested samples were purified via StageTips , resuspended and transferred to autosampler vials for LC- MS / MS analysis. Data was acquired in a data-independent acquisition (DIA) mode using a timsTOF-SCP (Bruker) instrument.

[0817] Small RNA library preparation and sequencing

[0818] Small RNA libraries were prepared with NEXTFLEX V4 kit (PerkinElmer) and the manufacturer’s instructions were followed. The starting material was 4 pL of isolated RNA as described before with the addition of 1 pL of tRNA / YRNA blocker. The quality of the library construction was accessed with a High Sensitivity D1000 DNA ScreenTape analysis in TapeStation 4200, while its concentration was measured with the Qubit dsDNA High Sensitivity Assay. The libraries were sequenced using a NextSeq 2000 (Illumina) with P3 reagents 200 cycles for single-paired sequencing with 50 base-pair reads.

[0819] DIA-NN-Based Peptide and Protein Identification and Quantification

[0820] All raw files acquired in this study were processed with the DIA-NN software suite (doi.org / 10.1038 / s41592-019-0638-x ) (version 1.8.1) for peptide / protein identification and quantification using a curated Uniprot database (Swissprot and varsplic including protein isoforms, version 2021_04). The FDR for precursor identifications was set to 1%. A spectral library (DIA Speclib) was generated from single-shot DIA files from pilot and cohort data with the “FASTA digest for library free search” option and “Deep Learningbased spectra, RTs and IMs prediction” enabled. DIA data was reprocessed using the spectral library with the match-between-runs (MBR) enabled. The inference of protein groups(PGs) from DiaNN output was performed using a custom algorithm by Nesvizhskii & Aebersold ). For protein quantification, the MaxLFQ algorithm (pubmed. ncbi.nlm.nih.gov / 24942700 / )from the DIA-NN R package

[0821] (github.com / vdemichev / diann-rpackage) was applied and the LFQ intensities were Iog2- transformed.

[0822] Filtering, Imputation, and Differential Abundance Testing of Log2-Transformed Protein Intensities 164 Docket No. 59888-703.601

[0823] Log2-transformed LFQ intensity values of the quantified PGs were further analyzed using RStudio (Posit, R version 4.3.2).

[0824] Protein group counts were calculated per sample, and paired samples in which at least one sample had PG counts below the lower 10th percentile of the total distribution were filtered out.

[0825] Immunoglobulins (IGgs) were considered contaminants and filtered out.

[0826] Protein groups not present in at least 55% of samples in at least one of the experimental groups (SEC or NEXPLOR) were also excluded. Missing values (MVs) were considered Missing Not at Random (MNAR) based on evaluation of sample clustering via heatmaps (pheatmap, version 0.9-7) (github.com / raivokolde / pheatmap ) and intensity distribution patterns using the DEP package (nature.com / articles / nprot.2017.147 ) (version 1.28.0).

[0827] MVs were imputed using zero imputation for PGs lacking any intensity values in one of the groups (applied group-wise), and left-censored imputation using the impute. MinProb() method from the imputeLCMD package (Lazar C BT, 2022 - chrome- extension: / / efaidnbmnnnibpcajpcglclefindmkaj / https: / / cran.r- project.org / web / packages / imputeLCMD / imputeLCMD.pdf) (version 2.1) for remaining missing values.

[0828] Principal component analysis (PCA) was performed using prcomp() on scaled data and visualized with autoplot() from the ggfortify package (Masaaki Horikoshi and Yuan Tang (2016). ggfortify: Data Visualization Tools for Statistical Analysis ResultsCRAN.R- project.org / package=ggfortify) (version 0.4.2).

[0829] Clustering heatmapsand Venn diagrams were generated using the ComplexHeatmap (pubmed.ncbi.nlm.nih.gov / 27207943 / ) (version 2.22.0), and VennDiagram (CRAN.R- project.org / package=VennDiagram ) (version 1.12) packages, respectively.

[0830] Normality was assessed using the Shapiro-Wilk test for each PG. As a considerable proportion of features did not meet the normality assumption, differential abundance testing was performed using the Wilcoxon signed-rank test (doi.org / 10.2307 / 3001968 ). Protein groups with an absolute fold change > 1 and a Benjamin Hochberg adjusted p- value < 0.05 were considered significantly altered. 165 Docket No. 59888-703.601

[0831] The ggplot2 package (Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, ggplot2.tidyverse.org) (version 0.5.1) was used for generating additional illustrative figures.

[0832] Proteomic functional enrichment analysis

[0833] Differentially abundant PGs were divided into those more abundant in the NEXPLOR group and those more abundant in the SEC group.

[0834] Gene symbols were mapped to Entrez IDs using the bitr() function from the clusterProfiler package (pubmed.ncbi.nlm.nih.gov / 22455463 / ) (version 4.8.3), with org.Hs.eg.db as the reference database. KEGG pathway enrichment was performed using the enrichKEGG() function with organism set to Homo sapiens ("hsa"), an adjusted p-value cutoff of 0.1 , a q-value cutoff of 0.2, and gene set size filters ranging from 10 to 200. The Benjamini-Hochberg method

[0835] (academic.oup.com / jrsssb / article / 57 / 1 / 289 / 7035855 ) was used to adjust for multiple testing.

[0836] For GO Term enrichment analysis, FunRich software (doi:10.1002 / pmic.201400515 ) (version 3.1.3) was used and GO Terms of interest selected for comparison between the two groups with Benjamini-Hochberg adjusted p-values being considered.

[0837] Network analysis and functional enrichment using unique identified PGs in liver disases subset was performed using String (Szklarczyk et al. Nucleic acids research 51. D1 (2023): D638-D646).

[0838] String analysis was performed

[0839] Differentially Abundant Marker Annotation of Hepatocyte-Specific and Secreted Proteins To characterize differentially abundant PGs, multiple datasets from the Human Protein Atlas (HPA) were used to identify hepatocyte-specific proteins. Data sources included:

[0840] (i) genes with elevated mRNA expression in hepatocytes compared to other liver cell types, based on single-cell RNA-seq (cell_type_category_rna_Hepatocytes_Cell.tsv),

[0841] (ii) genes with high protein expression in hepatocytes based on immunohistochemistry data, and

[0842] (iii) genes annotated as being secreted to blood, based on the HPA secretome resource (785 genes).

[0843] Protein groups were classified into six categories according to their expression and secretion profiles: 166 Docket No. 59888-703.601

[0844] 1. High protein expression in hepatocytes

[0845] 2. Hepatocyte-specific mRNA expression

[0846] 3. Combined high protein and mRNA expression

[0847] 4. Secreted proteins with high hepatocyte expression

[0848] 5. Secreted hepatocyte-specific mRNA

[0849] 6. Secreted proteins with both high protein and mRNA expression

[0850] These categories were assigned by intersecting each gene list with the set of differentially abundant PGs. Genes falling into multiple categories were consolidated into unified labels (e.g., Secreted Protein & mRNA Hepatocyte). Each PG was also labeled as "Secreted" or "Not secreted" based on the HPA blood secretome annotation.

[0851] Categorized PGs were visualized using volcano plots and heatmaps, with custom colour palettes defined for each annotation category to support biological interpretation.

[0852] Protein content-based EV characterization

[0853] The level of abundance of markers used for EV sample characterization described in table 4 of MISEV2023 was evaluated through categorizing the identified PGs accordingly. Categorized PGs were visualized using volcano plots and heatmaps, with custom colour palettes defined for each annotation category to support biological interpretation.

[0854] The R package EVqualityMS (github.com / ruma1974 / EVqualityMS, accessed July 2024) was used to evaluate expression patterns via heatmaps of extracellular vesicle (EV) subpopulation markers, as described by Kowal et al. (2016) (pubmed.ncbi.nlm.nih.gov / 26858453 / ). The tool utilizes the ComplexHeatmap package for visualization.

[0855] Small RNA sequencing data analysis

[0856] Raw sequencing reads were trimmed of adapters using BBDuk (github.com / Biol nfoTools / BBMap / blob / master / sh / bbduk.sh) prior to alignment. Alignment was done to the hg38 genome+transcriptome using the extra-cellular RNA processing toolkit (exceRpt)(ref) run using default settings

[0857] (github.gersteinlab.org / exceRpt / ). 167 Docket No. 59888-703.601

[0858] MicroRNAs were filtered for low counts considering a minimum of 5 counts in at least 5 HCC samples or 5 Cirrhosis samples or 3 Healthy samples for both hepatocyte EVs and Bulk EVs samples separately.

[0859] Package multiMiR (pmc.ncbi.nlm.nih.gov / articles / PMC4176155 / ) (version 1.32.0) was used to perform functional enrichement of the genes targeted by the microRNAs present in hepatocyte EVs and the microRNAs exclusive to bulk EV samples.

[0860] Read-count normalization and differential abundance analysis between HCC with Cirrhosis and Cirrhosis without HCC patient samples was performed using “DESeq2” (pmc.ncbi.nlm.nih.gov / articles / PMC4302049 / ) (version 1.46) considering a p-value threshold of 0.05 and an absolute Iog2 fold-change threshold of 0.5. Differentially abundant miRNAs were then processed using MiEAA (ccb-compute2.cs.uni- saarland.de / mieaa / user_input / ) (REF) for pathway analysis using Benjamini-Hochberg (BH) adjusted p-value cut-off 0.05% for pathways (miRwalk).

[0861] 4-2 Results

[0862] After aligning reads and assessing the number if mapped reads, a notable difference between the number of mapped reads for microRNA between hepatocyte and bulk EV samples is reported with a considerable high number of microRNA reads for bulk EVs, while the amount of reads for protein coding RNA and tRNA was similar across the two groups (Figure 16B). This is further demonstrated by the microRNA library size on Figure 17A.

[0863] After filtering out microRNA with low counts, 39 different microRNAs remained for hepatocyte EV samples while 250 remained for bulk EV samples, with 212 being found exclusively in bulk EVs. Interestingly, the unique microRNA found exclusively in hepatocyte EVs, has-miR-124-3p has been implicated as a tumor suppressor in HCC (Figure 16C).

[0864] Interestingly, similar functional enrichment profiles were found for the targets of the 39 microRNAs found in hepatocyte samples and the 212 exclusive to bulk EVs, when considering experimentally validated targets to these microRNAs (Figure 17B and 17C). 168 Docket No. 59888-703.601

[0865] Differential abundance with DESeq2 revealed 5 microRNAs (hsa-miR-22-3p, hsa-miR- 23b-3p, hsa-miR-124-3p, hsa-miR-23a-3p and hsa-miR-221-3) in HCC when considering hepatocyte EVs (Figure 16D). Both hsa-miR-124-3p and hsa-miR-23b-3p, which are thought to be tumour suppressors in HCC, are upregulated in HCC samples, which might represent cancer cells export mechanism of tumor suppressors via EVs. For bulk EVs, 6 downregulated and 15 upregulated microRNAs for HCC were found (Figure 16E).

[0866] Boxplots displaying normalized counts across the three phenotype subgroups (HCC, cirrhosis, and healthy) for both hepatocyte EVs and bulk EVs illustrate the expression differences of each differentially expressed microRNA (Figure 16F and G, respectively).

[0867] DE results can be found in Tables 12 and 13 below. Table 12. DESeq2 MicroRNAs Hepatocyte EVs HCC vs Hepatocyte EVs Cirrhosis results 169 Docket No. 59888-703.601

[0868] Table 13. DESeq2 MicroRNAs Bulk EVs HCC vs Bulk EVs Cirrhosis results 170 Docket No. 59888-703.601 171 Docket No. 59888-703.601 172 Docket No. 59888-703.601 173 Docket No. 59888-703.601 174 Docket No. 59888-703.601 175 Docket No. 59888-703.601

[0869] To investigate the biological pathways potentially modulated by these microRNAs, enrichment analysis was performed using the miRNA Enrichment Analysis and Annotation Tool (miEAA), which incorporates both predicted and experimentally supported targets. In the comparison between HCC and cirrhosis, microRNAs upregulated in hepatocyte-derived EVs were uniquely enriched for cancer- and liverrelevant signalling pathways, including glycolysis and gluconeogenesis (WP534), hypoxia response via HIF activation (P00031), hepatocyte growth factor receptor signalling, and the Gq / Go signaling pathway, all of which have been previously implicated in HCC progression. Notably, these enrichments were exclusive to the hepatocyte EVs and not observed in the bulk EV-derived microRNA set. In contrast, pyruvate metabolism was enriched in both hepatocyte and bulk EVs, suggesting some shared metabolic deregulation across EV compartments in liver disease. 176 Docket No. 59888-703.601

[0870] Additional metabolic pathways, including inositol phosphate metabolism, tryptophan metabolism, and galactose metabolism, were also exclusively enriched in hepatocyte- derived EVs. Similarly, the phosphatidylinositol signalling pathway, which feeds into the PI3K / AKT / mTOR axis, is frequently altered in HCC and contributes to tumorigenesis by promoting cell growth, survival, and metabolic shift of Warburg effect. In contrast, the MAPK and TGF beta signalling pathways were uniquely enriched in the bulk EV-derived microRNA profile, suggesting that bulk EVs may capture pathway activity from nonhepatocyte sources or reflect a broader cellular response to liver injury and inflammation (Figure 16H). The exclusive detection of metabolic and signalling pathways such as HIF activation, Hepatocyte growth facto receptor signalling and phosphatidylinositol signalling in hepatocyte EVs underscores the specificity of cell-type-targeted EV isolation in uncovering HCC-associated molecular mechanisms that may be diluted or absent in analyses of heterogeneous bulk EV populations.

[0871] Example 5: mRNA sequencing of extracellular particles secreted or released from liver tissue by patient population

[0872] Platelet-free plasma was isolated from blood samples collected from healthy individuals, individuals with hepatocellular carcinoma, and individuals with cirrhosis. Samples were collected from a combination of male and female individuals with ages ranging from 23- 88. Subsequently, liver-secreted extracellular particles were selectively enriched or isolated from the plasma samples. These extracellular particles were then subjected to mRNA sequencing analysis. The number of total miRNA reads, as well as the average number of miRNA reads for each sample, was compared between healthy individuals, individuals with hepatocellular carcinoma, and individuals with cirrhosis.

[0873] A subset of the miRNAs analyzed was differentially abundant in healthy individuals, individuals with hepatocellular carcinoma, and individuals with cirrhosis. Specifically, miRNAs including hsa-miR-16-5p, hsa-miR-486-5p, hsa-miR-451a, hsa-let-7g-5p, hsa- miR-15b-5p, hsa-let-7b-5p, hsa-let-7f-5p, hsa-miR-26b-5p, hsa-miR-92a-3p, hsa-miR- 320b, and hsa-miR-93-5p were elevated or depleted with statistical significance in individuals with cirrhosis compared to healthy individuals (Table 13). These miRNAs can therefore be considered liver disease cirrhosis biomarkers. In addition, miRNAs including hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-23b-3p, hsa-miR-203a-3p, hsa-miR-320a, hsa-miR-24-3p, hsa-miR-320d, hsa-miR-320c, hsa-miR-126-3p, hsa- miR-107, hsa-miR-221-3p, hsa-let-7d-5p, hsa-miR-10a-5p, hsa-miR-130a-3p, and hsa- 177 Docket No. 59888-703.601 miR-122-5p were elevated or depleted with statistical significance in individuals with HCC compared to healthy individuals (Table 13). These miRNAs can therefore be considered HCC biomarkers. Table 13. miRNA total and average read counts by patient population.

[0874] Example 6: micro-RNA sequencing of extracellular particles from liver tissue by patient population To understand whether specific variants of the miRNAs from Example 5 could be driving the differences in miRNA abundance observed between healthy versus diseased individuals, miRNA sequencing was performed. Platelet-free plasma was isolated from blood samples collected from healthy individuals, individuals with hepatocellular 178 Docket No. 59888-703.601 carcinoma, and individuals with cirrhosis. Samples were collected from a combination of male and female individuals with ages ranging from 23-88. Subsequently, liver-secreted extracellular particles were selectively enriched or isolated from the plasma samples. These extracellular particles were then subjected to miRNA sequencing analysis. The number of total reads for specific variants of hsa-mir-16-5-p, hsa-mir-23a-3p, hsa-mir- 27a-3p, hsa-mir-203a, hsa-mir-486-5p, hsa-let-7g-5p, hsa-mir-122-5p, hsa-mir-10a-5p, miR-15b-5p, hsa-let-7b-5p, hsa-let-7f-1-5p, hsa-miR-26b-5p, hsa-miR-92a-3p, hsa-mir- 27b-3p, hsa-mir-320b, hsa-mir-24-3p, hsa-mir-451a, hsa-mir-93-5p, hsa-miR-320d, hsa- mir-126-3p, and hsa-mir-23b, as well as the average number of reads for each sample, was compared between healthy individuals, individuals with hepatocellular carcinoma, and individuals with cirrhosis.

[0875] The miRNAs miR-15b-5p, hsa-let-7b-5p, hsa-let-7f-1-5p, hsa-miR-26b-5p, hsa-miR-92a- 3p, hsa-mir-27b-3p, hsa-mir-320b, hsa-mir-24-3p, hsa-mir-451a, hsa-mir-93-5p, hsa- miR-320d, hsa-mir-126-3p, and hsa-mir-23b did not exhibit specific variants which were substantially different between healthy individuals, individuals with hepatocellular carcinoma, and individuals with cirrhosis.

[0876] However, a subset of the miRNAs evaluated exhibited variants which were differentially abundant in healthy individuals, individuals with hepatocellular carcinoma, and individuals with cirrhosis. hsa-mir-16-5-p exhibited three variants which were elevated in individuals with cirrhosis (Table 14 and FIGS. 18A-18B). hsa-mir-23a-3p exhibited three variants which were elevated in individuals with HCC (Table 14 and FIGS. 19A-19B). hsa-mir-27a-3p exhibited two variants which were elevated in individuals with HCC (Table 14 and FIGS. 20A-20B). hsa-mir-203a exhibited one variant which was elevated in individuals with HCC and one variant which was elevated in individuals with cirrhosis (Table 14 and FIGS. 21A-21B). hsa-mir-486-5p exhibited six variants which were elevated in individuals with cirrhosis (Table 14 and FIGS. 22A-22B). hsa-let-7g-5p exhibited three variants which were elevated in individuals with cirrhosis (Table 14 and FIGS. 23A-23B). hsa-mir-122-5p exhibited nine variants which were elevated in individuals with HCC (Table 14 and FIGS. 24A-24B). hsa-mir-10a-5p exhibited three variants which were elevated in individuals with cirrhosis (Table 14).

[0877] These results suggest that evaluating the abundance of specific miRNAs in a biological sample from a patient can be a non-invasive but effective means of diagnosis. 179 Docket No. 59888-703.601

[0878] Table 14. Abundance of miRNA variants by patient population. 180 Docket No. 59888-703.601

[0879] Example 7: Identification and treatment of a subject having cirrhosis using the methods provided herein

[0880] A blood sample is collected from a subject, and platelet-free plasma is isolated from the blood sample. Extracellular particles either released or secreted from the liver are selectively enriched or isolated from the platelet-free plasma sample. The contents of the extracellular particles are isolated, purified, and sequenced. The biological sample from the subject is found to exhibit elevated levels of hsa-mir-16-5-p, hsa-mir-203a, hsa- mir-486-5p, hsa-let-7g-5p, and hsa-mir-10a-5p. The subject is diagnosed with cirrhosis and is provided with treatment to manage complications. The treatment involves administration of pharmacologic treatments to facilitate fluid retention (e.g., spironolactone, furosemide), mitigate bleeding from varices (e.g., beta-blockers), prevent hepatic encephalopathy (e.g., rifaximin), supplement nutrition (e.g., thiamine, multivitamins), reduce liver inflammation (e.g., steroids), and reduce pain (e.g., paracetamol).

[0881] Example 8: Identification and treatment of a subject having Hepatitis B using the methods provided herein

[0882] A blood sample is collected from a subject, and platelet-free plasma is isolated from the blood sample. Extracellular particles either released or secreted from the liver are selectively enriched or isolated from the platelet-free plasma sample. The contents of the extracellular particles are isolated, purified, and sequenced. The biological sample from the subject is found to exhibit elevated levels of hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, and hsa-mir-122-5p. The subject is diagnosed with Hepatitis B and is administered treatment to inhibit the viral life cycle. The subject receives weekly doses of subcutaneously administered pegylated IFNa for 48 weeks. Alternatively, the subject is prescribed an orally administered nucleoside / nucleotide analog (NA), to be taken indefinitely on a daily basis. The NA is lamivudine, telbivudine, entecavir, adefovir dipivoxil, tenofovir disoproxil fumarate, or tenofovir alafenamide fumarate. 181 Docket No. 59888-703.601

[0883] Example 9: Identification and treatment of a subject having an early-stage case of HCC using the methods provided herein

[0884] A blood sample is collected from a subject, and platelet-free plasma is isolated from the blood sample. Extracellular particles either released or secreted from the liver are selectively enriched or isolated from the platelet-free plasma sample. The contents of the extracellular particles are isolated, purified, and sequenced. The biological sample from the subject is found to exhibit elevated levels of hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, and hsa-mir-122-5p. The subject is diagnosed with stage I HCC and is administered treatment. The single tumor is locally ablated by radiofrequency ablation or microwave ablation. Alternatively, the single tumor is surgically resected.

[0885] Example 10: Identification and treatment of a subject having an advanced-stage case of HCC using the methods provided herein

[0886] A blood sample collected from a subject, and platelet-free plasma is isolated from the blood sample. Extracellular particles either released or secreted from the liver are selectively enriched or isolated from the platelet-free plasma sample. The contents of the extracellular particles are isolated, purified, and sequenced. The biological sample from the subject is found to exhibit elevated levels of hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, and hsa-mir-122-5p. The subject is diagnosed with stage III HCC and is administered treatment. The treatment involves systemic immunotherapy. The immunotherapy involves administration of a combination of atezolizumab and bevacizumab, or durvalumab and tremelimumab if the patient is at risk of gastrointestinal bleeding. Alternatively, the treatment involves tyrosine kinase inhibitors. The tyrosine kinase inhibitor therapy involves administration of sorafenib or lenvatinib. Regorafenib or cabozantinib are administered as a second-line treatment.

[0887] While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby. 182 Docket No. 59888-703.601

[0888] Literature references

[0889] Singal et al. (2023). HCC Surveillance Improves Early Detection, Curative Treatment Receipt, and Survival in Patients with Cirrhosis: A Systematic Review and Meta-Analysis. J Hepatol. 2022 Jul;77(1):128-139.

[0890] Li et al. (2020). EV-origin: Enumerating the tissue-cellular origin of circulating extracellular vesicles using exLR profile. Computational and Structural Biotechnology Journal, 18, 2851-285.

[0891] 183 Docket No. 59888-703.601

[0892] Numbered Embodiments:

[0893] 1. A method of screening for or surveillance of a disease in a biological sample obtained from an individual, wherein the method comprises: a) obtaining, from the biological sample, an enriched fraction of EPs which are secreted or released from cells of a tissue of a defined category, and b) detecting on or in EPs in the enriched fraction of EPs one of the following:

[0894] (i) the presence of one or more disease biomarkers,

[0895] (ii) the absence of one or more disease biomarkers, or

[0896] (iii) the presence of one or more disease biomarkers and the absence of one or more disease biomarkers.

[0897] 2. The method according to embodiment 1 , wherein the or each disease biomarker is a protein or fragment of a protein, a DNA or RNA oligonucleotide or a fragment of a DNA or RNA oligonucleotide, a lipid, a complex sugar, a post-translational modification, or a metabolite, preferably wherein the RNA molecule or a fragment thereof is a miRNA molecule or a fragment of a miRNA molecule.

[0898] 3. The method according to embodiment 1 or 2, wherein the disease is cancer, such that the disease biomarker is a cancer biomarker, preferably wherein the cancer is hepatocellular carcinoma (HCC).

[0899] 4. The method according to embodiment 3, wherein the cancer is characterized by shedding low amounts of ctDNA.

[0900] 5. The method according to embodiment 1 or 2, wherein the disease is liver disease, such that the disease biomarker is a liver disease biomarker.

[0901] 6. The method according to embodiment 3 or 4, wherein the or each cancer biomarker is selected from the group consisting of RPL10A, SLC4A1 , C1S, SLC2A1, CD13 (also known as ANPEP), GLUT1, JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1, DMBT1, PLBD1 , GGT1, CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1, F5, RPS...

Claims

202 Docket No. 59888-703.601Claims:

1. A method of determining whether a subject has or is at elevated risk of having a disease, comprising: a) obtaining a biological sample from the subject, wherein the biological sample comprises extracellular particles secreted or released from a plurality of tissues; b) selectively enriching, from the biological sample, extracellular particles that are secreted or released from a target tissue among the plurality of tissues; and c) assaying the selectively enriched extracellular particles to detect one or more disease biomarkers that are indicative of a presence, an absence, or an increased risk of the disease.

2. A method of determining whether a subject has or is at increased risk for a disease, the method comprising: a) assaying extracellular particles from a target tissue among a plurality of tissues obtained from the subject to detect one or more disease biomarkers; and b) using at least the one or more disease biomarkers detected in a) to determine that said subject has or is at increased risk of having said disease, wherein the determining has an accuracy, a sensitivity, or a specificity of at least 60%.

3. The method of claim 1 , further comprising: d) using at least the one or more disease biomarkers detected in c) to determine that said subject has or is at increased risk of having said disease.

4. The method of any one of claims 2-3, wherein the determining comprises computer generating a report indicative of said subject having or being at increased risk for said disease.

5. The method of claim 4, wherein computer generating the report comprises processing the detected one or more disease biomarkers using one or more machine learning algorithms.

6. The method of any one of claims 1-5, wherein the target tissue is liver tissue.

7. The method of claim 6, wherein the liver tissue is or comprises hepatocytes.203 Docket No. 59888-703.6018. The method of claim 6 or 7, wherein the disease is liver cirrhosis or Hepatitis B.

9. The method of claim 6 or 7, wherein the disease comprises cancer.

10. The method of claim 9, wherein the cancer comprises liver cancer.

11. The method of claim 9, wherein the liver cancer is hepatocellular carcinoma(HCC).

12. The method of any one of claims 1-11 , wherein the assaying further comprises classifying using a plurality of disease biomarkers, a disease state of the subject.

13. The method of any one of claims 1-12, wherein an accuracy, a sensitivity, or a specificity of the determination is at least 60%.

14. The method of claim 6 or 7, wherein an area under a receiver operator curve for differentiating a cancer state from a healthy state is at least 0.6.

15. The method of claim 6 or 7, wherein an area under a receiver operator curve for differentiating a cancer state from a cirrhosis state is at least 0.

616. The method of claim 6 or 7, wherein an area under a receiver operator curve for differentiating a cirrhosis state from a healthy state or a cirrhosis state from a metabolic dysfunction-associated steatohepatitis (MASH) state is at least 0.6.

17. The method of claim 6 or 7, wherein an area under a receiver operator curve for differentiating a Hepatitis B state from a healthy state is at least 0.6.

18. The method of claim 6 or 7, wherein an area under a receiver operator curve for differentiating a cancer state from a Hepatitis B state is at least 0.6.

19. The method of any one of claims 1-18, wherein the determination that the subject has or is at risk for the disease is further based at least in part on one or more disease biomarkers which are not associated with the selectively enriched extracellular particles.204 Docket No. 59888-703.60120. The method of claim any one of claims 1-19, wherein the one or more disease biomarkers comprise a miRNA which is indicative of presence, absence, or severity of the disease in the target tissue.

21. The method of claim 20, wherein the miRNA is not indicative of presence, absence, or severity of the disease when assayed in the biological sample prior to the selective enrichment.

22. The method of claim 21 , wherein the one or more disease biomarkers comprise two or more miRNAs which are indicative of presence, absence, or severity of the disease in the target tissue, which are not indicative of presence, absence, or severity of the disease when assayed in the biological sample prior to the selective enrichment.

23. The method of claim of claim 22, wherein the two or more miRNAs comprise at least 3 miRNAs.

24. The method of any one of claims 1-23, wherein the one or more disease biomarkers comprise a protein which is indicative of presence, absence, or severity of the disease in the target tissue.

25. The method of claim 24, wherein the protein is not indicative of presence, absence, or severity of the disease when assayed in the biological sample prior to the selective enrichment.

26. The method of any one of claims 1-25, wherein the one or more disease biomarkers are not indicative of the disease when assayed in extracellular particles obtained from an alternate tissue type.

27. The method of any one of claims 1-26, further comprising providing treatment to the subject based at least in part on the detected one or more target tissue associated disease biomarkers.

28. The method of any one of claims 1 or 3-27, wherein the biological sample comprises blood, urine, saliva, lymph, bile, cerebrospinal fluid, phlegm, mucus, tears, Bronchoalveolar Lavage (BAL) fluid, earwax, sweat, faeces, breast milk, interstitial205 Docket No. 59888-703.601 fluids, vaginal fluids, semen, gastric juice, blister fluid, cyst fluid, or any combination thereof.

29. The method of any one of claims 1-28, wherein the extracellular particle comprises an extracellular vesicle (EV), non-vesicular extracellular particle (NVEP), exosome, ectosome, microvesicle, apoptotic vesicle, lipoprotein particle, ribonucleoprotein particle, protein aggregate, exomere, supermere, or any combination thereof.

30. The method of claim 29, wherein the extracellular particle comprises an extracellular vesicle.

31. The method of any one of claims 1-30, wherein the extracellular particle is intact prior to the assaying.

32. The method of claim 31 , wherein the assaying comprises releasing the contents of the intact extracellular particle.

33. The method of claim 32, wherein the contents are released by permeabilising the membrane of the extracellular particle.

34. The method of any one of claims 6-33, wherein the extracellular particles are secreted or released from one or more liver cells comprising hepatocytes, Kupffer cells, stellate cells, dendritic cells, or any combination thereof.

35. The method of any one of claims 1-34, wherein the disease comprises cancer or liver disease.

36. The method of claim 35, wherein the cancer is characterized by shedding low amounts of ctDNA.

37. The method of any one of claims 1-36, wherein the one or more disease biomarkers comprise a protein or fragment of a protein, a DNA or RNA molecule or a fragment thereof, a lipid, a complex sugar, a post-translational modification, or a metabolite.206 Docket No. 59888-703.60138. The method of any one of claims 1-37, wherein the one or more disease biomarker comprises a cancer biomarker.

39. The method of claim 38, wherein the cancer biomarker comprises TFR1 , RPL10A, SLC4A1 , C1S, SLC2A1 , CD13 (also known as ANPEP), GLUT1 , JAM3, PIGR, ATIC, FLNB, DENND1A, RPS9, APOL1 , DMBT1 , PLBD1 , GGT1 , CTSB, SERPINA1 , PSMA1 , TTN, RTN4, TMTC1 , F5, RPS4X, IGHA1 , TFRC, PKD2L1 , C1 R, GDPD3, H4C1 , GBA, GPC-3, HSP70, GS, HepPar-1 , Arg1 , BSEP, CD10, Heparan Sulphate, Phosphatidylserine, hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-106b- 5p, hsa-mir-141-3p, hsa-mir-144-3p, hsa-mir-15b-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-185-5p, hsa-mir-18a-5p, hsa-mir-20a-5p, hsa-mir-23a-3p, hsa-mir-25-3p, hsa- mir-26b-5p, hsa-mir-320b, hsa-mir-320c, hsa-mir-374b-5p, hsa-mir-451a, hsa-mir-486- 5p, hsa-mir-92a-3p, hsa-mir-93-5p, hsa-let-7b-5p, hsa-let-7f-1 , hsa-mir-103a-3p, hsa- mir-126-3p, hsa-mir-130a-3p, hsa-mir-130a, hsa-mir-137, hsa-mir-17, hsa-mir-186-5p, hsa-mir-205-5p, hsa-mir-20b-5p, hsa-mir-22-5p, hsa-mir-4467, hsa-mir-449c, hsa-mir- 100-5p, hsa-mir-10a-5p, hsa-mir-126-5p, hsa-mir-150-5p, hsa-mir-200c-3p, hsa-mir-21- 5p, hsa-mir-223, hsa-mir-27b-3p, hsa-mir-320d, hsa-mir-3616, hsa-mir-5193, hsa-mir- 203a-3p, hsa-let-7a-1 , hsa-let-7d-5p, hsa-mir-1-3p, hsa-mir-10b-5p, hsa-mir-122-5p, hsa-mir-124-3p, hsa-mir-1246, hsa-mir-125b-5p, hsa-mir-126-3p, hsa-mir-126-5p, hsa- mir-1307, hsa-mir-133a-1 , hsa-mir-139-5p, hsa-mir-146a-5p, hsa-mir-148a-3p, hsa-mir- 152-3p, hsa-mir-15a-5p, hsa-mir-181a-5p, hsa-mir-184, hsa-mir-191-5p, hsa-mir-196a- 5p, hsa-mir-200c-3p, hsa-mir-203a-3p, hsa-mir-203a, hsa-mir-205-5p, hsa-mir-22-3p, hsa-mir-22-5p, hsa-mir-221-3p, hsa-mir-223-3p, hsa-mir-223, hsa-mir-23a-3p, hsa-mir- 23a, hsa-mir-23b-3p, hsa-mir-23b, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-27a-3p, hsa-mir-320b, hsa-mir-320c, hsa-mir-320d, hsa-mir-34a-5p, hsa-mir-361-5p, hsa-mir- 3616, hsa-mir-4311 , hsa-mir-4467, hsa-mir-4492, hsa-mir-449c, hsa-mir-4784, hsa-mir- 5193, hsa-mir-6068, hsa-mir-6773, hsa-mir-6777-5p, hsa-mir-7161 , hsa-mir-7703, hsa- mir-92a-3p, hsa-mir-27b-5p, and hsa-mir-99a-5p, or a fragment thereof, or any combination thereof.

40. The method of any one of claims 1-39, wherein the disease biomarker is present on or in EPs which are secreted or released from the target tissue.

41. The method of any one of claims 1-40, wherein the extracellular particles are captured using a binding agent being capable of binding selectively to extracellular particles originating from the target tissue.207 Docket No. 59888-703.60142. The method of claim 41 , wherein the binding agent comprises an antibody or antigen binding fragment thereof, an aptamer, a lectin, a lipid-binding protein or domain, or a DNA or RNA oligonucleotide or a fragment thereof.

43. The method of claim 42 wherein the DNA or RNA oligonucleotide comprises primer.

44. The method of claim 43, wherein the primer is configured to amplify one or more capture markers comprising one or more of ASGR1 , ASGR2, TFR2, SLCO1 B1, SLC38A3, TMEM56, UNC93A, SLC22A9, SLC2A2, and FXYD1.

45. The method of claim 43, wherein the RNA oligonucleotide comprises a miRNA molecule.

46. The method of any one of claims 41-45, wherein the binding agent is attached to a substrate.

47. The method of claim 46, wherein the substrate comprises a magnetic bead, a magnetic nanoparticle, a gold bead, a gold nanoparticle, a polystyrene bead, an affinity chromatography column, a microplate, a microfluidics channel, or a biochip.

48. The method of claim 47, wherein a surface of the microfluidics channel comprises gold, silicon oxide, glass, graphene, polystyrene, or any combination thereof.

49. The method of any one of claims 41-48, wherein the assaying comprises reverse-transcribing the RNA molecule into a complementary DNA (cDNA) sequence, and wherein the binding agent is a DNA oligonucleotide primer for the amplification of the cDNA sequence.

50. The method of any one of claims 1-49, further comprising quantifying a level of the one or more disease biomarkers.

51. The method of claim 50, further comprising comparing the level of the one or more disease biomarkers to a control level.208 Docket No. 59888-703.60152. The method of any one of claims 27-51 , wherein providing the treatment is further based at least in part on one or more disease biomarkers which are not associated with the target tissue.

53. The method of any one of claims 20-52, wherein the miRNA molecule comprises hsa-mir-16-5-p, hsa-mir-23a-3p, hsa-mir-27a-3p, hsa-mir-203a, hsa-mir- 486-5p, let-7g-5p, hsa-mir-122-5p, or hsa-mir-10a-5p.

54. The method of claim 53, wherein one or more of the miRNA molecules is indicative of presence, absence, or severity of cirrhosis in the target tissue.

55. The method of claim 53, wherein one or more of the miRNA molecules is indicative of presence, absence, or severity of HCC in the target tissue.

56. The method of claim 53, wherein one or more of the miRNA molecules is indicative of presence, absence, or severity of Hepatitis B in the target tissue.

57. The method of any one of claims 1-56, wherein the one or more disease biomarkers comprise a biomarker having at least a 90% sequence match to any sequence listed in Table 1A.

58. The method of claim 57, wherein the one or more disease biomarkers comprise two or more biomarkers having a sequence provided in Table 1A.

59. The method of claim 27 or 52, wherein the disease is HCC and providing the treatment comprises one or more of: liver resection, liver transplant, local ablation, radiotherapy, or administration of one or more of: lenvatinib.regorafenib, atezolizumab, bevacizumab, duravalumab, tremelimumab, and / or cabozantinib.

60. The method of claim 27 or 52, wherein the disease is Hepatitis B and providing the treatment comprises administration of a pegylated interferon alpha and / or one or more of lamivudine (LAM), telbivudine, entecavir (ETV), adefovir dipivoxil (ADV), tenofovir disoproxil fumarate (TDF), or tenofovir alafenamide fumarate (TAF).209 Docket No. 59888-703.60161. The method of claim 27 or 52, or 59-60, wherein providing the treatment comprises obtaining a confirmatory magnetic resonance imaging (MRI) or computed tomography (CT) scan prior to administration of one or more therapeutic interventions.

62. The method of any one of claims 43-60, wherein the one or more capture markers comprise ASGR1 , ASGR2, TFR2, and SLCO1 B1.

63. The method of any one of claims 24-62, wherein the protein comprises GPC-3, CD13 and / or GLUT1.

64. The method of any one of the preceding claims, wherein a reduction in an amount of at least one of the one or more disease biomarkers compared to a control is amount of the at least one of the one or more disease biomarkers is indicative of a presence, an absence, or an increased risk of the disease.

65. The method of any one of claims 1-64, wherein an increase in an amount of at least one of the one or more disease biomarkers compared to a control is amount of the at least one of the one or more disease biomarkers is indicative of a presence, an absence, or an increased risk of the disease.

66. The method of any one of claims 9-65, wherein the one or more disease biomarkers are indicative of a severity or a rate of growth of the cancer.

67. The method of claim 66, wherein the method differentiates between a slow growth cancer variant and a fast growth cancer variant.

68. The method of any one of claims 8 or 12-65, wherein the one or more disease biomarkers are indicative of a severity of the liver cirrhosis or the Hepatitis B.

69. The method of any one of the preceding claims, wherein the subject is a patient under treatment for Hepatitis B or cirrhosis.

70. The method of any one of the preceding claims, wherein the one or more disease biomarkers comprise one or more phospholipids.210 Docket No. 59888-703.60171. The method of claim 70, wherein the one or more phospholipids comprise Phosphatidylserine (PS).

72. The method of any one of the preceding claims, wherein the selective enrichment comprises isolation.

73. A system for processing a biological sample obtained from a subject according to the method of any one of claims 1-72.

74. A non-volatile data carrier carrying processor control code to implement the method of any one of claims 1-72 or the system of claim 73.

75. An antibody or antigen-binding fragment thereof configured to target a biomarker listed in Table 1 A or Table 1 B.

76. An oligonucleotide primer configured to amplify a biomarker listed in Table 1A or Table 1 B.

Images