Methods for disease diagnosis using microbial extracellular vesicles (MEV) analytes

Abstract

Presented herein are methods and systems for predicting disease in a subject through a combination of fungal and non-fungal molecular analyte signatures of a biological sample.

Description

[Technical field]

[0001] cross reference This application claims the benefit of U.S. Provisional Application No. 63 / 223,725, filed July 20, 2021, which is incorporated herein by reference. Summary of the Invention

[0002] The present disclosure provides affinity-based methods for isolating microbial extracellular vesicles (MEVs) for molecular analysis and using the results of said molecular analysis to form a diagnosis of a subject's health or disease state. Specifically, the present invention provides methods for affinity-based isolation of MEVs and non-microbial extracellular vesicles (non-microbial EVs) present in a liquid biopsy sample, as well as methods for using the presence or abundance of vesicle-associated analytes (VAAs) to diagnose and classify disease in a subject. In some cases, the subject may be a mammal or a non-mammal.

[0003] The methods of the invention disclosed herein generate diagnostic methods that can diagnose and classify diseases based on disease-specific presence and / or abundance of VAA. In some embodiments, characterization of MEVs present in liquid biological samples (e.g., liquid biopsy samples) may enable diagnosis of conditions that may otherwise be refractory to non-invasive or minimally invasive treatments. For example, 16S rDNA sequencing analysis of blood-derived MEVs in Alzheimer's disease mouse models revealed striking taxonomic differences between Alzheimer's disease mice and wild-type controls (PMID: 29302204), suggesting that MEV-derived DNA can provide disease-specific microbial signatures for pathologies that are traditionally only fully diagnosed via postmortem analysis of brain tissue. Similarly, MEVs isolated from urine samples facilitated bacterial metagenomic analysis of individuals with autism spectrum disorder (PMID: 29093639).

[0004] Aspects disclosed herein provide a method of diagnosing a disease in a subject based on disease-associated MEVs in a liquid biological sample, the method comprising: (a) enriching MEVs with one or more affinity capture reagents; and (b) detecting a disease-associated abundance of a molecular analyte from the MEVs. In some embodiments, (a) may result in separation of the MEVs from non-microbial EVs. In some embodiments, (b) may further comprise quantifying the disease-associated abundance of the molecular analyte. In some embodiments, the MEVs may comprise vesicle-associated cell-free microbial DNA, microbial RNA, non-microbial DNA, non-microbial RNA, microbial proteins, non-microbial proteins, microbial metabolites, or non-microbial metabolites, microbial lipids, non-microbial lipids, microbial glycans, non-microbial glycans, or any combination thereof.

[0005] In some embodiments, the liquid biological sample may be a liquid biopsy sample. In some embodiments, the liquid biopsy sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, lymph, sweat, tears, exhaled breath condensate, or any diluted or processed fraction thereof. In some embodiments, the affinity-based enrichment (a) includes enriching for microbial cell wall molecular motifs or non-microbial cell wall molecular motifs. In some embodiments, the microbial cell wall molecular motifs or non-microbial cell wall molecular motifs may include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the one or more affinity capture reagents may comprise a recombinant innate immune pattern recognition receptor, or a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. In some embodiments, the recombinant innate immune pattern recognition receptor may comprise Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. In some embodiments, the derivatives may include epitope tags and full-length recombinant innate immune pattern recognition receptors or recombinant soluble ectodomains thereof. In some embodiments, the epitope tags include N-terminal or C-terminal 6x histidine tags, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusions, biotin, or any combination thereof. In some embodiments, antibodies, aptamers, single chain variable fragments (scFv), or single chain antibodies may contain epitope tags.In some embodiments, the epitope tag may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some embodiments, the affinity reagent may comprise a region that interacts with a microbial cell wall molecular motif or a non-microbial cell wall molecular motif. In some cases, the region may bind to a microbial cell wall molecular motif or a non-microbial cell wall molecular motif. In some embodiments, the affinity-based enrichment (a) may comprise contacting the liquid biopsy sample with a support comprising a covalently immobilized affinity agent. In some cases, the support may comprise multiple covalently immobilized affinity agents. In some embodiments, the covalently immobilized affinity agent may comprise a region that may interact with a microbial cell wall molecular motif or a non-microbial cell wall molecular motif. In some embodiments, the support may comprise magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymer, or any combination thereof.

[0006] In some embodiments, enriching for MEVs from a liquid biological sample may include: (a.) contacting the liquid biological sample with one or more affinity capture reagents to form a capture reagent-molecular motif interaction complex; (b.) contacting the capture reagent-molecular motif interaction complex with a support; and (c.) separating the support from the liquid biological sample to enrich for the capture reagent-molecular motif interaction complex. In some embodiments, one or more affinity capture reagents may include an epitope tag. In some embodiments, the support may include an epitope tag recognition surface. In some embodiments, the epitope tag recognition surface of the support may interact with an epitope tag of one or more affinity capture reagents. In some embodiments, the epitope tag recognition surface may include streptavidin, or an antibody specific for a 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), biotin, or any combination thereof. In some embodiments, the epitope tag recognition surface may include an anti-species antibody.

[0007] In some embodiments, detecting the disease-associated abundance of a molecular analyte in a MEV may include nucleic acid sequencing, polymerase chain reaction (PCR)-based, or hybridization-based analysis of the nucleic acid analyte. In some embodiments, the disease-associated abundance of a molecular analyte may be further quantified. In some cases, the molecular analyte may be on or within the MEV. In some embodiments, detecting and / or quantifying the molecular analyte may include performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), or any combination thereof. In some embodiments, detecting and / or quantifying the molecular analyte may include performing an immunoassay analysis, such as an enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), fluorescent immunoassay (FIA), chemiluminescence immunoassay (CIA), real-time immunoquantitative PCR (iqPCR), or any combination thereof.

[0008] Aspects disclosed herein provide a method of generating a diagnosis for detecting a disease in a subject based on disease-associated MEVs in a liquid biological sample, the method comprising: (a) providing a liquid biological sample containing microbial extracellular vesicles and non-microbial extracellular vesicles; (b) enriching the microbial extracellular vesicles with one or more first affinity capture reagents; (c) enriching the non-microbial extracellular vesicles with one or more second affinity capture reagents; (d) detecting a disease-associated abundance of a molecular analyte from the microbial extracellular vesicles; (e) detecting a disease-associated abundance of a molecular analyte from the non-microbial extracellular vesicles; and (f) identifying a disease state in the subject by combining the first and second disease-associated abundances of the molecular analytes from the microbial extracellular vesicles and the non-microbial extracellular vesicles to form a signature of the disease state. In some embodiments, the microbial extracellular vesicles may comprise vesicle-associated cell-free microbial DNA, microbial RNA, non-microbial DNA, non-microbial RNA, microbial proteins, non-microbial proteins, microbial metabolites, non-microbial metabolites, microbial lipids, non-microbial lipids, microbial glycans, non-microbial glycans, or any combination thereof. In some embodiments, the liquid biological sample may comprise plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, lymph, sweat, tears, exhaled breath condensate, or any diluted or processed fraction thereof. In some embodiments, the affinity-based enrichment (b) and (c) may comprise enriching for microbial cell wall molecular motifs or non-microbial cell wall molecular motifs. In some embodiments, the microbial or non-microbial cell wall molecular motifs may include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the first and second one or more affinity capture reagents may include recombinant innate immune pattern recognition receptors, or polyclonal antibodies, monoclonal antibodies, recombinant antibodies, aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof.In some embodiments, the recombinant innate immune pattern recognition receptor may comprise Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. In some embodiments, the recombinant innate immune pattern recognition receptor derivative may comprise an epitope tag and a full-length recombinant innate immune pattern recognition receptor or a recombinant soluble ectodomain thereof. In some embodiments, the epitope tag may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some embodiments, the antibody, aptamer, single chain variable fragment (scFv), and single chain antibody may contain an epitope tag. In some embodiments, the epitope tag may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some embodiments, the first and second one or more affinity reagents may comprise a region that interacts with a microbial cell wall molecular motif or a non-microbial cell wall molecular motif. In some cases, the region may bind to a microbial cell wall molecular motif or a non-microbial cell wall molecular motif. In some embodiments, enriching for microbial extracellular vesicles may comprise incubating the liquid biopsy sample with a support comprising a covalently immobilized affinity agent. In some cases, the support may include a plurality of covalently immobilized affinity agents. In some embodiments, the covalently immobilized affinity agents may include a region that interacts with a microbial cell wall molecular motif or a non-microbial cell wall molecular motif. In some cases, the region may bind to a microbial cell wall molecular motif or a non-microbial cell wall molecular motif.In some embodiments, the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some embodiments, the affinity capture reagent for non-microbial extracellular vesicles may comprise polyclonal, monoclonal, recombinant antibodies, aptamers, single-chain variable fragments (scFv), single-chain antibodies, or any combination thereof. In some embodiments, the first and second one or more affinity capture reagents may be specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, and EpCAM. In some embodiments, enrichment of non-microbial extracellular vesicles may comprise incubating the liquid biological sample with a support comprising a covalently immobilized affinity agent. In some embodiments, the covalently immobilized affinity agent may comprise a region capable of interacting with a non-microbial molecular motif. In some cases, the region may bind to a non-microbial molecular motif. In some embodiments, the support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some embodiments, detecting the molecular analyte may include nucleic acid sequencing of the nucleic acid analyte, polymerase chain reaction (PCR) analysis. In some cases, the molecular analyte may be further quantified. In some embodiments, detecting and / or quantifying the molecular analyte may include performing an immunoassay analysis. In some cases, the immunoassay analysis may be, by way of non-limiting examples, an enzyme-linked immunosorbent assay (ELISA), a radioimmunoassay (RIA), a fluorescent immunoassay (FIA), a chemiluminescent immunoassay (CIA), a real-time immunoquantitative PCR (iqPCR), or any combination thereof.

[0009] Aspects disclosed herein provide a method of identifying a disease in a subject, comprising providing a biological sample from a subject comprising one or more microbial extracellular vesicles, enriching the one or more microbial extracellular vesicles with one or more affinity reagents, detecting one or more molecular analytes from the one or more microbial extracellular vesicles, and identifying the disease in the subject based on the one or more molecular analytes from the one or more microbial extracellular vesicles. In some embodiments, the biological sample comprises non-microbial extracellular vesicles. In some embodiments, the method further comprises quantifying the abundance of the one or more molecular analytes. In some embodiments, the one or more molecular analytes of the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. In some embodiments, the one or more molecular analytes outside the one or more non-microbial cells include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some embodiments, the biological sample includes a liquid biological sample, a tissue biological sample, or any combination thereof. In some embodiments, the liquid biological sample includes plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. In some embodiments, the whole blood includes plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some embodiments, the tissue biological sample includes a tissue homogenate. In some embodiments, the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.In some embodiments, the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, wherein the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. In some embodiments, the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. In some embodiments, the derivative comprises an epitope tag and a full-length recombinant innate immune pattern recognition receptor or a recombinant soluble ectodomain thereof. In some embodiments, the epitope tag comprises an N- or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some embodiments, one or more antibodies, aptamers, single chain variable fragments (scFv), or single chain antibodies of the invention comprise an epitope tag. In some embodiments, the epitope tag comprises an N- or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.In some embodiments, one or more affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, enriching comprises contacting the biological sample with a support comprising a covalently immobilized affinity agent. In some embodiments, the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some embodiments, enriching comprises contacting the biological sample with the one or more affinity reagents to form a capture reagent-molecular motif interaction complex, contacting the capture reagent-molecular motif interaction complex with a support, and separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. In some embodiments, one or more affinity reagents comprise an epitope tag. In some embodiments, the support comprises an epitope tag recognition surface. In some embodiments, the epitope tag recognition surface comprises an antibody specific for streptavidin, or a 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), biotin, or any combination thereof. In some embodiments, the epitope tag recognition surface comprises an anti-species antibody. In some embodiments, the detecting comprises nucleic acid sequencing, polymerase chain reaction (PCR), or hybridization-based analysis of one or more molecular analytes. In some embodiments, the nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some embodiments, the detecting comprises performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some embodiments, the detecting comprises performing an immunoassay analysis. In some embodiments, the disease comprises cancer.In some embodiments, the cancer comprises stage I or stage II cancer, hi some embodiments, the cancer comprises bone, breast, lung, colon, brain, or skin. In some embodiments, the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, the cancer comprises one or more of the following types of cancer other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinosarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, identifying the cancer comprises predicting the cancer by a predictive model, the predictive model being trained on one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and a related disease of the other subject. In some embodiments, the predictive model is configured to receive as input the one or more molecular analytes of the subject and output the disease of the subject. In some embodiments, the predictive model is configured to predict one or more cancers, one or more cancer subtypes, one or more anatomical locations of the cancer, or any combination thereof, for the subject.In some embodiments, the predictive model is configured to predict the stage of the cancer, the prognosis of the cancer, the mutation status of the cancer, the future immunotherapy response of the cancer, the optimal therapy for treating the cancer, or any combination thereof. In some embodiments, the predictive model is configured to predict the cancer among one or more cancer types of the subject, and to identify a particular cancer type of the one or more cancer types. In some embodiments, the predictive model includes a machine learning model, and the machine learning model includes a regularized machine learning model, an ensemble of machine learning models, or any combination thereof. In some embodiments, the predictive model includes a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some embodiments, enriching the microbial extracellular vesicles improves the accuracy of the predictive model when predicting the cancer of the subject by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20%. In some embodiments, the subject includes a non-human mammal or a human subject.

[0010] An aspect of the present disclosure provides a method of identifying a disease in a subject, the method comprising: providing a biological sample from a subject comprising one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles; enriching the one or more microbial extracellular vesicles with one or more first affinity reagents and enriching the one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles; detecting a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles; and identifying the disease in the subject from an association between a combination of the first abundance of one or more molecular analytes and the second abundance of the one or more molecular analytes and a model of a disease associated with a third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles and a fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles. In some embodiments, detecting comprises quantifying the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes. In some embodiments, the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. In some embodiments, the one or more molecular analytes of the one or more non-microbial extracellular vesicles comprise cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some embodiments, the biological sample comprises a liquid biological sample, a tissue biological sample, or any combination thereof. In some embodiments, the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination thereof, any dilution, or processed fraction thereof. In some embodiments, the whole blood comprises white blood cells, plasma, red blood cells, platelets, or any combination thereof. In some embodiments, the tissue biosample comprises a tissue homogenate.In some embodiments, enriching comprises enriching for one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some embodiments, the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the one or more first affinity reagents or the one or more second affinity reagents comprise recombinant innate immune pattern recognition receptors or one or more antibodies, and the one or more antibodies comprise polyclonal antibodies, monoclonal antibodies, recombinant antibodies, and aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof. In some embodiments, the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. In some embodiments, the derivative comprises an epitope tag, a full-length recombinant innate immune pattern recognition receptor, or a recombinant soluble ectodomain thereof. In some embodiments, one or more of the antibodies, the aptamers, the single chain variable fragments (scFvs), and the single chain antibodies comprise an epitope tag, hi some embodiments, the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.In some embodiments, the one or more first affinity reagents and the one or more second affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, enriching comprises incubating the biological sample with a support comprising a covalently immobilized affinity agent. In some embodiments, the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some embodiments, the one or more first affinity reagents or the one or more second affinity reagents are specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, EpCAM, or any combination thereof. In some embodiments, detecting comprises performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on the one or more molecular analytes of the one or more microbial extracellular vesicles or the one or more molecular analytes from the one or more non-microbial extracellular vesicles. In some embodiments, the nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some embodiments, the model comprises a predictive model, the predictive model being trained on the third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles, the fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles, and the corresponding disease of the one or more subjects. In some embodiments, the predictive model is configured to receive as input the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes, and output a prediction of the disease of the subject.In some embodiments, the disease comprises cancer. In some embodiments, the cancer comprises stage I or stage II cancer. In some embodiments, the cancer comprises a type of cancer of bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. In some embodiments, the predictive model is configured to predict one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof in the subject. In some embodiments, the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, the cancer comprises one or more of the following types of cancer other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine corpus endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, the predictive model comprises a machine learning model. In some embodiments, the machine learning model comprises one or more machine learning models, regularized machine learning models, ensembles of machine learning models, or any combination thereof.In some embodiments, the predictive model comprises a random forest, a neural network, a naive Bayes, a support vector machine, a linear regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some embodiments, the subject comprises a human or a non-human mammal. In some embodiments, enriching the one or more microbial extracellular vesicles and the one or more non-microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the cancer in the subject. In some embodiments, the area under the receiver operating characteristic curve of the predictive model when predicting the disease in the subject increases by at least 1%, at least 2%, at least 4%, at least 5%, or at least 10% when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the predictive model.

[0011] Aspects of the present disclosure provide a method of identifying a treatment for a disease in a subject, comprising providing a biological sample of a subject comprising one or more microbial extracellular vesicles, enriching the one or more microbial extracellular vesicles with one or more affinity reagents, thereby producing one or more enriched microbial extracellular vesicles, detecting one or more molecular analytes from the one or more enriched microbial extracellular vesicles, and identifying the treatment for the disease in the subject based on the one or more molecular analytes. In some embodiments, the biological sample comprises non-microbial extracellular vesicles and the one or more microbial extracellular vesicles. In some embodiments, the method further comprises quantifying the abundance of the one or more molecular analytes. In some embodiments, the one or more molecular analytes of the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. In some embodiments, the one or more molecular analytes of the non-microbial extracellular vesicles include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some embodiments, the biological sample includes a liquid biological sample, a tissue biological sample, or any combination thereof. In some embodiments, the liquid biological sample includes plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. In some embodiments, the whole blood includes plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some embodiments, the tissue biological sample includes a tissue homogenate. In some embodiments, the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.In some embodiments, the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, wherein the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. In some embodiments, the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. In some embodiments, the derivative comprises an epitope tag and a full-length recombinant innate immune pattern recognition receptor or a recombinant soluble ectodomain thereof. In some embodiments, the epitope tag comprises an N- or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some embodiments, one or more antibodies, aptamers, single chain variable fragments (scFv), or single chain antibodies of the invention comprise an epitope tag. In some embodiments, the epitope tag comprises an N- or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.In some embodiments, one or more affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, enriching comprises contacting the biological sample with a support comprising a covalently immobilized affinity agent. In some embodiments, the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some embodiments, enriching comprises contacting the biological sample with the one or more affinity reagents to form a capture reagent-molecular motif interaction complex, contacting the capture reagent-molecular motif interaction complex with a support, and separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. In some embodiments, one or more affinity reagents comprise an epitope tag. In some embodiments, the support comprises an epitope tag recognition surface. In some embodiments, the epitope tag recognition surface comprises an antibody specific for streptavidin, or a 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), biotin, or any combination thereof. In some embodiments, the epitope tag recognition surface comprises an anti-species antibody. In some embodiments, the detecting comprises nucleic acid sequencing, polymerase chain reaction (PCR)-based, or hybridization-based analysis of the nucleic acid analyte. In some embodiments, the nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some embodiments, the detecting comprises performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some embodiments, the detecting comprises performing an immunoassay analysis. In some embodiments, the disease comprises cancer.In some embodiments, the cancer comprises stage I or stage II cancer. In some embodiments, the cancer comprises bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer thereof. In some embodiments, the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, the cancer comprises one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination of these types of cancer. In some embodiments, identifying the treatment for the disease comprises predicting the treatment by a predictive model, the predictive model being trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and an associated treatment for the disease of the other subject. In some embodiments, the predictive model is configured to receive as input the one or more molecular analytes of the subject and output the treatment for the disease of the subject. In some embodiments, the predictive model comprises a machine learning model, the machine learning model comprising a normalized machine learning model, an ensemble of machine learning models, or any combination thereof.In some embodiments, the predictive model comprises a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some embodiments, enriching the one or more microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the treatment of the subject. In some embodiments, the subject comprises a non-human mammal or a human subject. In some embodiments, the treatment comprises a repurposed treatment that may or may not have originally been approved to target cancer. In some embodiments, the treatment comprises a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. In some embodiments, the probiotic comprises an engineered bacterial strain or an ensemble of engineered bacteria. In some embodiments, the treatment comprises an adjuvant given in combination with the first-line treatment for the cancer to improve the efficacy of the first-line treatment. In some embodiments, the treatment comprises adoptive cell transfer to a target microbial antigen associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises a cancer vaccine that utilizes a microbial antigen associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises a monoclonal antibody to a microbial antigen associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises a multivalent antibody, antibody fragment, or antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises a targeted antibiotic against a specific type of microorganism or a class of functionally or biologically similar microorganisms.In some embodiments, two or more of the following therapeutic types are combined, with at least one type taking advantage of the presence or abundance of a microbe in the cancer to enhance the overall therapeutic effectiveness: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0012] An aspect of the disclosure provides a method of identifying a treatment for a disease in a subject, the method comprising: providing a biological sample from a subject comprising one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles; enriching the one or more microbial extracellular vesicles with one or more first affinity reagents and enriching the one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles; detecting a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles; and identifying the treatment for the disease in the subject from an association between a combination of the first abundance of one or more molecular analytes and the second abundance of the one or more molecular analytes and a model of a disease treatment associated with a third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles and a fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles. In some embodiments, detecting comprises quantifying the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes. In some embodiments, the one or more molecular analytes of the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. In some embodiments, the one or more molecular analytes of the non-microbial extracellular vesicles comprise vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some embodiments, the biological sample comprises a liquid biological sample, a tissue biological sample, or any combination thereof. In some embodiments, the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination thereof, any dilution, or processed fraction thereof. In some embodiments, whole blood comprises white blood cells, plasma, red blood cells, platelets, or any combination thereof.In some embodiments, the tissue biosample comprises a tissue homogenate. In some embodiments, enriching comprises enriching for one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some embodiments, the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharide (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the one or more first affinity reagents or the one or more second affinity reagents comprise recombinant innate immune pattern recognition receptors or one or more antibodies, and the one or more antibodies comprise polyclonal antibodies, monoclonal antibodies, recombinant antibodies, and aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof. In some embodiments, the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. In some embodiments, the derivative comprises an epitope tag, a full-length recombinant innate immune pattern recognition receptor, or a recombinant soluble ectodomain thereof. In some embodiments, one or more of the antibodies, the aptamers, the single chain variable fragments (scFvs), and the single chain antibodies comprise an epitope tag, hi some embodiments, the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.In some embodiments, the one or more first affinity reagents and the one or more second affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, enriching comprises incubating the liquid biological sample with a support comprising a covalently immobilized affinity agent. In some embodiments, the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some embodiments, the one or more second affinity reagents comprise polyclonal, monoclonal, recombinant antibodies, aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof. In some embodiments, the one or more first affinity reagents or the one or more second affinity reagents are specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, EpCAM, or any combination thereof. In some embodiments, detecting comprises performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on the one or more molecular analytes of the one or more microbial extracellular vesicles or the one or more molecular analytes from the one or more non-microbial extracellular vesicles. In some embodiments, nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof.In some embodiments, the model comprises a predictive model, the predictive model being trained on the third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles, the fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles, and a corresponding treatment for the disease in the one or more subjects. In some embodiments, the predictive model is configured to receive as input the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes, and output a prediction of the treatment for the disease in the subject. In some embodiments, the disease comprises cancer. In some embodiments, the cancer comprises stage I or stage II cancer. In some embodiments, the cancer comprises a type of cancer of the bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. In some embodiments, the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, the cancer comprises one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination of these types of cancer.In some embodiments, the predictive model comprises a machine learning model. In some embodiments, the machine learning model comprises one or more machine learning models, regularized machine learning models, an ensemble of machine learning models, or any combination thereof. In some embodiments, the predictive model comprises a random forest, a neural network, a naive Bayes, a support vector machine, a linear regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some embodiments, the subject comprises a human or a non-human mammal. In some embodiments, enriching the one or more microbial extracellular vesicles and the one or more non-microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the treatment for the cancer of the subject. In some embodiments, the area under the receiver operating characteristic curve of the predictive model predicting the treatment for the disease in the subject is increased by at least 1%, at least 2%, at least 4%, at least 5%, or at least 10% when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the predictive model. In some embodiments, the treatment comprises a repurposed treatment that may or may not have been originally approved to target cancer. In some embodiments, the treatment comprises a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. In some embodiments, the probiotic comprises an engineered bacterial strain or an ensemble of engineered bacteria. In some embodiments, the treatment comprises an adjuvant given in combination with the first-line treatment for the cancer to improve efficacy of the first-line treatment. In some embodiments, the treatment comprises adoptive cell transfer to a target microbial antigen associated with the cancer or a cancer microenvironment. In some embodiments, the treatment comprises a cancer vaccine that utilizes a microbial antigen associated with the cancer or a cancer microenvironment.In some embodiments, the treatment comprises a monoclonal antibody against a microbial antigen associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises a multivalent antibody, antibody fragment, or antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or cancer microenvironment. In some embodiments, the treatment comprises a targeted antibiotic against a specific type of microbe or a class of functionally or biologically similar microbes. In some embodiments, the treatment comprises the following types of treatment: small molecules, biologics, engineered host-derived cell types, prostaglandins, etc., where at least one type utilizes the presence or abundance of a microbe in the cancer to enhance overall therapeutic efficacy. Two or more of biotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages are combined.

[0013] Aspects of the present disclosure provide a method of training a predictive model, comprising: providing a biological sample of one or more subjects, the biological sample comprising one or more microbial extracellular vesicles and associated health classifications of the one or more subjects; enriching the one or more microbial extracellular vesicles with one or more affinity reagents, thereby producing one or more enriched microbial extracellular vesicles; detecting one or more molecular analytes from the enriched one or more microbial extracellular vesicles; and training the predictive model with the one or more molecular analytes and associated health classifications of the one or more subjects. In some embodiments, the biological sample comprises non-microbial extracellular vesicles. In some embodiments, the method further comprises quantifying the abundance of the one or more molecular analytes. In some embodiments, the one or more molecular analytes of the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. In some embodiments, the one or more molecular analytes of the non-microbial extracellular vesicles include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some embodiments, the biological sample includes a liquid biological sample, a tissue biological sample, or any combination thereof. In some embodiments, the liquid biological sample includes plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. In some embodiments, the whole blood includes plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some embodiments, the tissue biological sample includes a tissue homogenate. In some embodiments, the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.In some embodiments, the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some embodiments, the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, wherein the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. In some embodiments, the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. In some embodiments, the derivative comprises an epitope tag and a full-length recombinant innate immune pattern recognition receptor or a recombinant soluble ectodomain thereof. In some embodiments, the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.

[0014] The method of claim 191, wherein the one or more antibodies, aptamers, single chain variable fragments (scFv), or the single chain antibodies comprise an epitope tag. In some embodiments, the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some embodiments, the one or more affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, enriching comprises contacting the liquid biological sample with a support comprising a covalently immobilized affinity agent. In some embodiments, the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. In some embodiments, the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymer, or any combination thereof. In some embodiments, enriching comprises contacting the liquid biological sample with the one or more affinity reagents to form a capture reagent-molecular motif interaction complex, contacting the capture reagent-molecular motif interaction complex with a support, and separating the support from the liquid biological sample to enrich the capture reagent-molecular motif interaction complex. In some embodiments, the one or more affinity reagents comprise an epitope tag. In some embodiments, the support comprises an epitope tag recognition surface. In some embodiments, the epitope tag recognition surface comprises streptavidin, or an antibody specific for a 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), biotin, or any combination thereof. In some embodiments, the epitope tag recognition surface comprises an anti-species antibody. In some embodiments, detecting comprises nucleic acid sequencing, polymerase chain reaction (PCR)-based, or hybridization-based analysis of the nucleic acid analyte.In some embodiments, the nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some embodiments, the detecting comprises performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some embodiments, the detecting comprises performing immunoassay analysis. In some embodiments, the trained predictive model is configured to receive as input one or more molecular analytes of the subject and output a predicted disease in the subject, a treatment for the disease in the subject, or any combination thereof. In some embodiments, the disease comprises cancer. In some embodiments, the cancer comprises stage I or stage II cancer. In some embodiments, the cancer comprises a type of cancer of the bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. In some embodiments, the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof.In some embodiments, the cancer comprises one or more of the following types of cancer other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinosarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some embodiments, the trained predictive model is configured to predict one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof, for the subject. In some embodiments, the trained predictive model is configured to predict the stage of the cancer, the prognosis of the cancer, the mutation status of the cancer, the future immunotherapy response of the cancer, the optimal therapy for treating the cancer, or any combination thereof. In some embodiments, the trained predictive model is configured to predict the cancer among one or more cancer types of the subject, and to identify a particular cancer type of the one or more cancer types. In some embodiments, the trained predictive model comprises a machine learning model, and the machine learning model comprises a regularized machine learning model, an ensemble of machine learning models, or any combination thereof. In some embodiments, the trained predictive model comprises a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some embodiments, enriching the microbial extracellular vesicles improves the accuracy of the trained predictive model when predicting the cancer of the subject by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20%. In some embodiments, the subject comprises a non-human mammal or a human subject. In some embodiments, the health classification includes cancerous, non-cancerous disease, or non-cancerous non-disease.

[0015] An aspect of the present disclosure provides a computer-implemented method for training a predictive model, the method including receiving from a database sequencing data of biological samples of one or more subjects and corresponding health classifications, the sequencing data of the biological samples including sequences of one or more analytes of one or more microbial extracellular vesicles, and training the predictive model with the sequencing data of the biological samples of the one or more subjects and the corresponding health classifications.

[0016] 1. A computer system configured to identify a disease in a subject, the computer system comprising: one or more processors; and a non-transitory computer-readable storage medium comprising software executable instructions that, when executed, cause the one or more processors of the computer system to: (i) receive a biological sample from a subject comprising one or more microbial extracellular vesicles; (ii) enrich the one or more microbial extracellular vesicles with one or more affinity reagents; (iii) detect one or more molecular analytes from the one or more microbial extracellular vesicles; and (iv) identify the disease in the subject based on the one or more molecular analytes obtained from the one or more microbial extracellular vesicles.

[0017] Incorporation by Reference All publications, patents, and patent applications mentioned in this application 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. To the extent that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained in the specification, it is intended that the present specification supersede and / or take precedence over any such conflicting material.

[0018] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (herein referred to as "Figure" and "FIG.") in which: [Brief description of the drawings]

[0019] [Figure 1] 1 illustrates an exemplary liquid biopsy diagnostic development workflow according to embodiments herein. [Diagram 2] 1 shows an exemplary MEV enrichment workflow as described in embodiments herein. [Diagram 3] 1 shows an exemplary MEV enrichment workflow as described in embodiments herein. [Figure 4] 1 illustrates an exemplary diagnostic workflow according to embodiments herein. [Diagram 5] 1 illustrates an exemplary predictive model training scheme according to an embodiment of the present specification. [Figure 6] FIG. 1 shows a scheme of an exemplary trained predictive model for providing a diagnosis or prediction based on one or more feature data of a subject and optionally clinical metadata of the subject, as described in embodiments herein. [Figure 7] FIG. 1 shows a workflow diagram of a method for identifying a disease in a subject with one or more molecular analytes of one or more MEVs as described in embodiments herein. [Figure 8] FIG. 1 shows a workflow diagram of a method for identifying a disease in a subject with one or more molecular analytes of one or more MEVs and / or non-microbial EVs as described in embodiments herein. [Figure 9] FIG. 1 shows a workflow diagram of a method for identifying a treatment for treating a disease in a subject with one or more analytes of one or more MEVs as described in embodiments herein. [Figure 10] FIG. 1 shows a workflow diagram of a method for identifying a therapy for treating a disease in a subject with one or more analytes of one or more MEVs and / or non-microbial EVs as described in embodiments herein. [Figure 11]FIG. 1 shows a workflow diagram of a method for training a predictive model with one or more analytes of one or more MEVs as described in embodiments herein. [Figure 12] FIG. 1 shows a workflow diagram of a method for training a predictive model with one or more analytes of one or more MEVs and / or non-microbial EVs as described in embodiments herein. [Figure 13] FIG. 1 shows a workflow diagram of a method for training a predictive model with sequencing data of one or more analytes from one or more MEVs of one or more subjects and corresponding health classifications from a database described in an embodiment herein. [Figure 14] FIG. 1 shows a workflow diagram of a method for training a predictive model with sequencing data of one or more analytes from one or more MEVs and / or non-microbial EVs of one or more subjects from a database described in embodiments herein and corresponding health classifications. [Figure 15] 1 shows a diagram of a system configured to perform the methods described elsewhere herein according to embodiments herein. [Figure 16] FIG. 1 shows a workflow diagram for a method for isolating fraction components of plasma from small cell lung cancer patients as described in embodiments herein. [Figure 17A] 1 shows a graph of the microbiome analysis of a fraction of plasma from a patient with small cell lung cancer as described in embodiments herein. [Figure 17B] 1 shows a graph of the microbiome analysis of a fraction of plasma from a patient with small cell lung cancer as described in embodiments herein. [Figure 17C] 1 shows a graph of the microbiome analysis of a fraction of plasma from a patient with small cell lung cancer as described in embodiments herein. [Figure 17D] 1 shows a graph of the microbiome analysis of a fraction of plasma from a patient with small cell lung cancer as described in embodiments herein. [Figure 18A] 1 shows a graph of the analysis of the microbiome between two independent sets of SCLC patient groups. [Figure 18B] 1 shows a graph of the analysis of the microbiome between two independent sets of SCLC patient groups. [Figure 18C] 1 shows a graph of the analysis of the microbiome between two independent sets of SCLC patient groups. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] The present invention provides, in some embodiments, a method for diagnosing disease in an animal or human subject using microbial extracellular vesicles derived from a liquid biological sample (e.g., a liquid biopsy sample). This may be accomplished, in some embodiments, by providing a novel, high-throughput method for MEV isolation that does not require low-throughput and time-consuming high-speed density gradient centrifugation or size-exclusion chromatography. Furthermore, high-speed density gradient centrifugation and size-exclusion chromatography may not be suitable for clinical implementation. In some embodiments, MEV enrichment may be achieved using agents that can interact with molecular features of MEVs. In some cases, the agents may be capable of recognizing or binding to molecular features of MEVs. In some cases, the molecular features of MEVs may be present on an external, solvent-accessible surface of the MEVs. Anchoring these agents to a solid phase may, in some embodiments, facilitate the removal of the formed agent-MEV complexes, either directly or indirectly. In some instances, the recovered agent-MEV complexes may be isolated and made available for molecular analysis. Once isolated, the molecular content (e.g., linear nucleic acid, plasmid, circular ribonucleic acid, metabolites, proteins, glycoproteins, lipids, glycolipids, etc.) can be detected and / or quantified. In some embodiments, detection and / or quantification of this molecular content can be used to establish a diagnostic relationship between the presence and / or abundance of the analyte(s) and disease. It should be noted that although the methods described herein may be presented in the context of disease diagnosis, other uses of such methods may be reasonably conceivable and readily implementable by those skilled in the art.

[0021] The invention disclosed herein, in some embodiments, may use analytes from MEVs to diagnose a condition (e.g., cancer). In some embodiments, the invention disclosed herein may provide a better clinical outcome compared to a pathology report, which may include one or more of observed histology, cellular atypia, or other subjective measures that may be used to diagnose disease. In some embodiments, the methods disclosed herein may focus on the presence of microbial analytes. In some cases, the methods disclosed herein may provide greater sensitivity by focusing on microbial analytes rather than modified mammalian (e.g., cancer-derived) analytes, which may be present at low frequency in the background of "normal" (i.e., non-cancerous) mammalian sources. In some embodiments, the methods disclosed herein may achieve such outcomes in liquid biological samples (e.g., liquid biopsy samples), which may require minimal sample preparation and may be minimally invasive.

[0022] method In some embodiments, the disclosure herein provides a disease-specific diagnostic method, as can be seen in FIG. 1, that may include: (a) assembling a collection of liquid biopsy samples from a known healthy subject 101 and a known diseased subject 102, where the liquid biopsy samples may contain a mixture of MEVs and non-microbial EVs 103; (b) performing affinity-based enrichment of MEVs from the collection of liquid biopsy samples 104; (c) detecting or quantifying MEV-derived analytes 105-107; and (d) formalizing a relationship between the presence and / or abundance of the detected analytes and the investigated disease state 108. In some embodiments, the formalized relationship may include a mathematical representation of the abundance of the analyte (e.g., its concentration) that may include a relationship to the disease state. In some embodiments, the relationship may be a correlation. In some embodiments, the formalized relationship may include a set of features derived from one or more analytical methods 105-107 that may be computationally ranked. In some cases, the features may be ranked via statistical analysis or statistical learning (e.g., machine learning). In some cases, statistical analysis or statistical learning (e.g., machine learning) may be used to form diagnostic feature sets indicative of healthy subjects 101 and diagnostic feature sets indicative of diseased subjects 102. In some embodiments, MEV analytes 109 from subjects with unknown disease states may be analyzed with respect to formalized relationships to arrive at a diagnosis 110 of the disease state.

[0023] In some cases, the statistical analysis or statistical learning may include training 500 a predictive model, as shown in FIG. 5. In some cases, the predictive model 504 may include one or more machine learning models. In some cases, the one or more machine learning models may include linear regression, logistic regression, decision tree, support vector machine (SVM), naive Bayes, k-nearest neighbor (kNN), k-means, random forest machine learning model, or any combination thereof. In some cases, the machine learning model may be trained using a supervised, unsupervised, or any combination thereof training method. In some cases, the predictive model may be trained to provide a diagnosis or prediction of a disease or condition by one or more features of the liquid biopsy sample described elsewhere herein. In some cases, the predictive model 504 may be trained with one or more training and test features (501-503, and 505-507), which may include MEV-derived nucleic acid analyte data, MEV-derived non-nucleic acid data, or any combination thereof, as described elsewhere herein. In some cases, the one or more training and test features may include training and test features for features of control (e.g., healthy, disease-free, etc.) subjects (502, 506), diseased subjects (503, 507), or any combination thereof. In some cases, the training and test features may be labeled by the subject's disease or lack thereof in disease state. In some cases, subject clinical metadata (501, 505) may be used as training and test set training data. In some cases, the subject clinical metadata may include the subject's height, age, weight, sex, past medical history, current prescription medications, or any combination thereof. In some cases, training of the predictive model may include supervised or unsupervised training. In some cases, test and training split ratios of 10:90, 20:80, 30:70, 40:60, 50:50, or any combination thereof, of the training and test split data may be utilized when training one or more predictive models. In some cases, the predictive models may be trained with at least one k-fold. In some cases, the predictive models may include previously trained predictive models that may be retrained (e.g., via transfer learning).In some cases, retraining a predictive model may enable retraining the predictive model with a smaller amount of data than would be required to train an untrained predictive model.

[0024] In some embodiments, as can be seen in FIG. 6, after the predictive model 504 is trained, the trained predictive model 604 may predict and / or diagnose whether one or more subjects have the presence or absence of a disease 600. In some cases, the input features of the trained predictive model may include feature data 602 of subjects with an unknown disease or condition, optionally corresponding subject metadata 601 as described elsewhere herein, or any combination thereof. In some cases, the feature data of subjects with an unknown disease or condition may include nucleic acid analyte data from MEV, non-nucleic acid data from MEV, or any combination thereof, the analysis of which is described elsewhere herein. In some cases, the trained predictive model may process feature data 602 of subjects with an unknown disease or condition, optionally corresponding subject metadata 601, and provide a prediction and / or diagnosis 606 of the presence or absence of a disease in one or more subjects.

[0025] In some embodiments, MEV-derived nucleic acid analytes may be analyzed by sequencing 105, such as next generation sequencing (NGS), long read sequencing (e.g., nanopore sequencing), polymerase chain reaction (PCR), nucleic acid hybridization-based methods, or any combination thereof. In some embodiments, MEV-derived non-nucleic acid analytes may be quantified via physicochemical analysis 106 or via immunoassay 107.

[0026] In some embodiments, affinity-based enrichment 104 of MEVs as shown in FIG. 1 may be performed by incubating a liquid biological sample 201 containing one or more MEVs and non-microbial EV compositions with a solid phase having one or more MEV capture reagents as shown in FIG. 2. In some embodiments, the solid phase may be functionalized with recombinant pattern recognition receptors that interact with microbial or non-microbial cell wall molecular motifs, which may generate a MEV affinity matrix 202. In some cases, the recombinant pattern recognition receptors may be immobilized on the solid phase by non-covalent interactions, e.g., passive adsorption, or through electrostatic interactions, or through covalent interactions between the solid phase and the receptor functional groups. In some embodiments, the solid phase may include a mixture of non-covalently immobilized and / or covalently immobilized recombinant pattern recognition receptors. In some embodiments, the solid phase may be functionalized with antibodies and / or aptamers with specificity for microbial or non-microbial cell wall molecular motifs, which may generate a MEV affinity matrix 203. In some embodiments, after incubation of the liquid biological sample 201 with the MEV affinity matrix, the MEV affinity matrix may be separated 204 from the liquid biological sample and some or all of the bound MEVs may be collected. In some embodiments, following collection of the MEV affinity matrix-bound MEVs, MEV analyte analysis 205 may be performed according to the methods shown in Figures 1, 105-107.

[0027] In some embodiments, affinity-based enrichment 104 of MEVs as shown in FIG. 1 may be performed by incubating a liquid biological sample 301 containing one or more MEVs and non-microbial EV compositions with a MEV capture reagent as shown in FIG. 3. In some embodiments, the MEV capture reagent may be a solution phase epitope tag bearing MEV capture reagent. In some embodiments, the epitope tag bearing MEV capture reagent may include a recombinant pattern recognition receptor that interacts with a microbial cell wall molecular motif or a non-microbial cell wall molecular motif 302. In some cases, the recombinant pattern recognition receptor may be specific for a microbial cell wall molecular motif or a non-microbial cell wall molecular motif 302. In some embodiments, the epitope tag bearing MEV capture reagent may include an antibody and / or an aptamer that interacts with a microbial cell wall molecular motif or a non-microbial cell wall molecular motif 303. In some cases, the antibody and / or aptamer may have specificity for a microbial cell wall molecular motif or a non-microbial cell wall molecular motif 303. In some embodiments, following incubation 302 and / or 303 of the liquid biological sample with one or more epitope tags with MEV capture reagents, the sample may be incubated with a solid phase epitope tag affinity matrix to bind capture reagent-molecular motif interaction complexes 304. In some embodiments, following incubation 304 of the liquid biological sample with the epitope tag affinity matrix, the epitope tag affinity matrix may be separated 305 from the liquid biological sample and some or all of the bound MEVs may be collected. In some embodiments, following collection of epitope tag affinity matrix bound MEVs, MEV analyte analysis 306 may be performed according to the methods shown in FIG. 1 , 105-107.

[0028] In some embodiments, disease-specific diagnosis resulting from the invention disclosed herein may include a combination of EV analyte data obtained from MEVs and non-microbial EVs, as shown in FIG. 4. In some cases, microbial analytes may be detected in non-microbial EVs. In some embodiments, affinity-based enrichment of MEVs may be performed by incubating a liquid biological sample containing one or more MEVs and non-microbial EV compositions 401 with one or more solid phases having MEV capture reagents. In some embodiments, the solid phase may be functionalized with recombinant pattern recognition receptors that may interact with microbial or non-microbial cell wall molecular motifs, which may generate a MEV affinity matrix 402. In some cases, the recombinant pattern recognition receptors may be specific for microbial and / or non-microbial cell wall molecular motifs, which may generate a MEV affinity matrix 402. In some embodiments, the solid phase may be functionalized with antibodies and / or aptamers that interact with microbial or non-microbial cell wall molecular motifs, which may generate a MEV affinity matrix 403. In some cases, the antibodies and / or aptamers may have specificity for microbial and / or non-microbial cell wall molecular motifs or non-microbial cell wall molecular motifs 403. In some embodiments, after incubation of the liquid biological sample 401 with the MEV affinity matrix, the MEV affinity matrix may be separated 404 from the liquid biological sample and some or all of the bound MEVs may be collected. In some embodiments, collection of the MEV affinity matrix-bound MEVs may be followed by MEV analyte analysis 405 according to the methods shown in Figure 1, 105-107. In some embodiments, separating 404 the MEV affinity matrix from the liquid biological sample may produce a MEV-depleted supernatant 406 that contains non-microbial EVs. In some embodiments, the non-microbial EVs present in the MEV-depleted supernatant 406 may be isolated 407 by incubating the MEV-depleted supernatant 406 with a solid phase having antibodies and / or aptamers that interact with non-microbial vesicle surface antigens. In some cases, the antibodies and / or aptamers may be specific for non-microbial vesicle surface antigens407.In some embodiments, after incubation of the MEV-depleted supernatant 406 with the non-microbial EV affinity matrix, the non-microbial EV affinity matrix may be separated 408 from the sample to collect some or all of the bound non-microbial EVs. In some embodiments, collection of the non-microbial EV affinity matrix-bound EVs may be followed by a non-microbial EV analyte analysis 409, according to the methods shown in Figures 1, 105-107. In some embodiments, data from the MEV analyte specific analysis 405 and the non-microbial EV analyte specific analysis 409 may be combined. In some cases, by combining the data from the MEV analyte specific analysis 405 and the non-microbial EV analyte specific analysis 409, a signature of a disease state may be formed, which may be a disease-specific EV analyte diagnostic signature 410.

[0029] In some embodiments, the disclosure provided herein describes a method 700 for identifying a disease in a subject, as seen in FIG. 7. In some cases, the method may include (a) providing a biological sample from a subject comprising one or more microbial extracellular vesicles 702, (b) enriching the one or more microbial extracellular vesicles with one or more affinity reagents 704, (c) detecting one or more molecular analytes from the one or more microbial extracellular vesicles 706, and (d) identifying a disease in the subject based on the one or more molecular analytes from the one or more microbial extracellular vesicles 708. In some cases, the biological sample may comprise one or more non-microbial extracellular vesicles and one or more microbial extracellular vesicles. In some cases, the method may include quantifying the abundance of the one or more molecular analytes. The one or more molecular analytes of the one or more microbial extracellular vesicles may comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. The one or more molecular analytes of the one or more non-microbial extracellular vesicles may include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. The biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. The whole blood may include plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some cases, the subject may include a non-human mammal or a human subject. In some cases, the tissue biological sample may include a tissue homogenate.

[0030] In some cases, the one or more affinity reagents can be configured to couple to one or more microbial cell wall molecular motifs and / or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs can include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof.

[0031] In some cases, the one or more affinity reagents may comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, the one or more antibodies comprising a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. The recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. The derivative may include epitope tag and full-length recombinant innate immune pattern recognition receptor or recombinant soluble ectodomain thereof. The epitope tag may include an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some cases, the one or more affinity reagents may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.

[0032] In some cases, one or more of the antibodies, aptamers, single chain variable fragments (scFVs), or single chain antibodies may comprise an epitope tag, which may include an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.

[0033] In some cases, enriching may include contacting the biological sample with a support comprising a covalently immobilized affinity agent. The covalently immobilized affinity agent may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof.

[0034] In some cases, enriching may include (a) contacting the biological sample with one or more affinity reagents to form a capture reagent-molecular motif interaction complex; (b) contacting the capture reagent-molecular motif interaction complex with a support; and (c) separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. In some cases, one or more affinity reagents may include an epitope tag. In some cases, the support may include an epitope tag recognition surface. The epitope tag recognition surface may include an antibody specific for streptavidin, 6x histidine tag, GFP, myc, HA, biotin, or any combination thereof. The epitope tag recognition surface may include an anti-species antibody.

[0035] In some cases, the detecting may include nucleic acid sequencing, polymerase chain reaction (PCR), or hybridization-based analysis of one or more molecular analytes. Nucleic acid sequencing may include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some cases, the detecting may include performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some cases, the detecting may include performing immunoassay analysis.

[0036] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. The cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. The cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, invasive breast carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following cancer types other than the intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinosarcoma, uterine endometrial cancer, uveal melanoma, or any combination of these types of cancer.

[0037] In some cases, identifying the cancer may include predicting the cancer by a predictive model, where the predictive model is trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and an associated disease of another subject. The predictive model may be configured to receive one or more molecular analytes of the subject as input and output a disease of the subject. The predictive model may be configured to predict one or more cancers, one or more subtypes of the cancer, one or more anatomical locations of the cancer, or any combination thereof. The predictive model may be configured to predict a stage of the cancer, a prognosis of the cancer, a mutation status of the cancer, a future immunotherapy response of the cancer, an optimal therapy for treating the cancer, or any combination thereof. The predictive model may be configured to predict the cancer among one or more cancer types of the subject and identify a particular cancer type of the one or more cancer types.

[0038] In some cases, the predictive model may include a machine learning model, which may include a regularized machine learning model, an ensemble of machine learning models, or any combination thereof. The predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof.

[0039] In some embodiments, the disclosure provided herein describes a method 800 for identifying a disease in a subject, as seen in FIG. In some cases, the method may include (a) providing a biological sample from a subject comprising one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles 802; (b) enriching the one or more microbial extracellular vesicles with one or more first affinity reagents and enriching the one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles 804; (c) detecting a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles 806; and (d) identifying a disease in the subject from an association between a combination of the first abundance of the one or more molecular analytes and the second abundance of the one or more molecular analytes and a model of the disease associated with a third abundance of the one or more molecular analytes from the one or more microbial extracellular vesicles and a fourth abundance of the one or more molecular analytes from the one or more non-microbial extracellular vesicles 808. In some cases, the third abundance and the fourth abundance may be different from the first abundance and the second abundance of the one or more molecular analytes. In some cases, detecting may include quantifying the first abundance of the one or more molecular analytes and the second abundance of the one or more molecular analytes. The one or more molecular analytes of the one or more microbial extracellular vesicles may include cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. The one or more molecular analytes of the one or more non-microbial extracellular vesicles may include non-microbial cell-free DNA, non-microbial cell-free RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof.

[0040] In some cases, the biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination thereof, any dilution, or processed fraction thereof. Whole blood may include white blood cells, plasma, red blood cells, platelets, or any combination thereof. In some cases, the tissue biological sample may include a tissue homogenate. In some cases, the subject may include a human or non-human mammal.

[0041] In some cases, enriching can include concentrating one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs can include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof.

[0042] In some cases, the one or more first affinity reagents or the one or more second affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, which may include a polyclonal antibody, a monoclonal antibody, a recombinant antibody, and an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. The recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. The derivative may include an epitope tag, a full-length recombinant innate immune pattern recognition receptor, or a recombinant soluble ectodomain thereof. One or more of the antibodies, aptamers, single-chain variable fragments (scFVs), and single-chain antibodies may include an epitope tag. The epitope tag may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some cases, the one or more first affinity reagents and the one or more second affinity reagents may comprise a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the one or more first affinity reagents or the one or more second affinity reagents may be specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, EpCAM, or any combination thereof.

[0043] In some cases, enriching may include incubating the biological sample with a support comprising a covalently immobilized affinity agent. The covalently immobilized affinity agent may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof.

[0044] In some cases, detecting can include performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on one or more molecular analytes of the one or more microbial extracellular vesicles or one or more molecular analytes from the one or more non-microbial extracellular vesicles. Nucleic acid sequencing can include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof.

[0045] In some cases, the model may include a prediction mode, and the predictive model may be trained on a third abundance of one or more molecular analytes from one or more microbial extracellular vesicles, a fourth abundance of one or more molecular analytes from one or more non-microbial extracellular vesicles, and a corresponding disease of one or more subjects. In some cases, the disease may include a cancer described elsewhere herein. In some cases, the predictive model may be configured to receive as input a first abundance of one or more molecular analytes and a second abundance of one or more molecular analytes and output a prediction of the disease of the subject. In some cases, the predictive model may be configured to predict one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof in the subject.

[0046] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. In some cases, the cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. In some cases, the cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung cancer, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinoma sarcoma, uterine endometrial cancer, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following cancer types other than the intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinosarcoma, uterine endometrial cancer, uveal melanoma, or any combination of these types of cancer.

[0047] In some cases, the predictive model may include a machine learning model. The machine learning model may include one or more machine learning models, normalized machine learning models, ensembles of machine learning models, or any combination thereof. In some cases, the predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a linear regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some cases, enriching one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles may improve the accuracy of a predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting cancer in a subject. In some cases, the area under the receiver operating characteristic curve of a predictive model when predicting a disease in a subject is increased by at least 2%, at least 4%, at least 5%, or at least 10% at 1% when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the predictive model.

[0048] In some embodiments, the disclosure provided herein describes a method 900 for identifying a treatment for a disease in a subject, as seen in FIG. 9. In some cases, the method may include (a) providing a biological sample from a subject containing one or more microbial extracellular vesicles 902, (b) enriching the one or more microbial extracellular vesicles with one or more affinity reagents 904, (c) detecting one or more molecular analytes from the one or more microbial extracellular vesicles 906, and (d) identifying a treatment for the disease in the subject based on the one or more molecular analytes from the one or more microbial extracellular vesicles 908. In some cases, the biological sample may include one or more non-microbial extracellular vesicles and one or more microbial extracellular vesicles. In some cases, the method may include quantifying the abundance of the one or more molecular analytes. The one or more molecular analytes of the one or more microbial extracellular vesicles may include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. The one or more molecular analytes of the one or more non-microbial extracellular vesicles may include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some cases, the biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. The whole blood may include plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some cases, the subject may include a non-human mammal or a human subject. In some cases, the tissue biological sample includes a tissue homogenate.

[0049] In some cases, the one or more affinity reagents can be configured to couple to one or more microbial cell wall molecular motifs and / or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs can include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof.

[0050] In some cases, the one or more affinity reagents may comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, the one or more antibodies comprising a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. The recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. The derivative may include epitope tag and full-length recombinant innate immune pattern recognition receptor or recombinant soluble ectodomain thereof. The epitope tag may include an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some cases, the one or more affinity reagents may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.

[0051] In some cases, one or more of the antibodies, aptamers, single chain variable fragments (scFVs), or single chain antibodies may comprise an epitope tag, which may include an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.

[0052] In some cases, enriching may include contacting the biological sample with a support comprising a covalently immobilized affinity agent. The covalently immobilized affinity agent may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof.

[0053] In some cases, enriching may include (a) contacting the biological sample with one or more affinity reagents to form a capture reagent-molecular motif interaction complex; (b) contacting the capture reagent-molecular motif interaction complex with a support; and (c) separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. In some cases, one or more affinity reagents may include an epitope tag. In some cases, the support may include an epitope tag recognition surface. The epitope tag recognition surface may include an antibody specific for streptavidin, 6x histidine tag, GFP, myc, HA, biotin, or any combination thereof. The epitope tag recognition surface may include an anti-species antibody.

[0054] In some cases, the detecting may include nucleic acid sequencing, polymerase chain reaction (PCR), or hybridization-based analysis of one or more molecular analytes. Nucleic acid sequencing may include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some cases, the detecting may include performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some cases, the detecting may include performing immunoassay analysis.

[0055] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. The cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. The cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, invasive breast carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following cancer types other than the intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinosarcoma, uterine endometrial cancer, uveal melanoma, or any combination of these types of cancer.

[0056] In some cases, identifying a treatment for the disease can include predicting the treatment by a predictive model, the predictive model being trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and an associated treatment for the disease of another subject. The predictive model can be configured to receive as input one or more molecular analytes of the subject and output a treatment for the disease of the subject.

[0057] In some cases, the predictive model may include a machine learning model, which may include a regularized machine learning model, an ensemble of machine learning models, or any combination thereof. The predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof.

[0058] In some cases, enriching for one or more microbial extracellular vesicles may improve the accuracy of a predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the treatment of the subject.

[0059] In some cases, the treatment may include a repurposed treatment that may or may not have been originally approved to target cancer. In some cases, the treatment may include a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. The probiotic may include an engineered bacterial strain or an ensemble of engineered bacteria. In some cases, the treatment may include an adjuvant given in combination with a first-line treatment for the subject's cancer to improve the efficacy of the first-line treatment. In some cases, the treatment may include adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a multivalent antibody, an antibody fragment, or an antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Treatments may include targeted antibiotics against specific types of microbes or classes of functionally or biologically similar microbes. In some cases, two or more of the following types of treatments are combined, with at least one type taking advantage of the presence or abundance of the cancer microbe to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0060] In some embodiments, the disclosure provided herein describes a method 1000 for identifying a treatment for a disease in a subject, as seen in FIG. In some cases, the method may include (a) providing a biological sample from a subject comprising one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles 1002; (b) enriching the one or more microbial extracellular vesicles with one or more first affinity reagents and enriching the one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles 1004; (c) detecting a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles 1006; and (d) identifying a treatment for the disease in the subject from an association between a combination of the first abundance of the one or more molecular analytes and the second abundance of the one or more molecular analytes and a model of a disease treatment associated with a third abundance of the one or more molecular analytes from the one or more microbial extracellular vesicles and a fourth abundance of the one or more molecular analytes from the one or more non-microbial extracellular vesicles 1008. In some cases, the third abundance and the fourth abundance may be different from the first abundance and the second abundance of the one or more molecular analytes. In some cases, detecting may include quantifying the first abundance of the one or more molecular analytes and the second abundance of the one or more molecular analytes. The one or more molecular analytes of the one or more microbial extracellular vesicles may include cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. The one or more molecular analytes of the one or more non-microbial extracellular vesicles may include non-microbial cell-free DNA, non-microbial cell-free RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof.

[0061] In some cases, the biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination thereof, any dilution, or processed fraction thereof. Whole blood may include white blood cells, plasma, red blood cells, platelets, or any combination thereof. In some cases, the tissue biological sample may include a tissue homogenate. In some cases, the subject may include a human or non-human mammal.

[0062] In some cases, enriching can include concentrating one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs can include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof.

[0063] In some cases, the one or more first affinity reagents or the one or more second affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, which may include a polyclonal antibody, a monoclonal antibody, a recombinant antibody, and an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. The recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. The derivative may include an epitope tag, a full-length recombinant innate immune pattern recognition receptor, or a recombinant soluble ectodomain thereof. One or more of the antibodies, aptamers, single-chain variable fragments (scFVs), and single-chain antibodies may include an epitope tag. The epitope tag may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some cases, the one or more first affinity reagents and the one or more second affinity reagents may comprise a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the one or more first affinity reagents or the one or more second affinity reagents may be specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, EpCAM, or any combination thereof.

[0064] In some cases, enriching may include incubating the biological sample with a support comprising a covalently immobilized affinity agent. The covalently immobilized affinity agent may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof.

[0065] In some cases, detecting can include performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on one or more molecular analytes of the one or more microbial extracellular vesicles or one or more molecular analytes from the one or more non-microbial extracellular vesicles. Nucleic acid sequencing can include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof.

[0066] In some cases, the model may include a predictive mode, where the predictive model may be trained with a third abundance of one or more molecular analytes from one or more microbial extracellular vesicles, a fourth abundance of one or more molecular analytes from one or more non-microbial extracellular vesicles, and a corresponding treatment for a disease of one or more subjects. In some cases, the disease may include a cancer described elsewhere herein. In some cases, the predictive model may be configured to receive as input a first abundance of one or more molecular analytes and a second abundance of one or more molecular analytes and output a prediction of a treatment for a disease of the subject. In some cases, the predictive model may be configured to predict one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof in a subject.

[0067] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. In some cases, the cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. In some cases, the cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung cancer, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinoma sarcoma, uterine endometrial cancer, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following cancer types other than the intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinosarcoma, uterine endometrial cancer, uveal melanoma, or any combination of these types of cancer.

[0068] In some cases, the predictive model may include a machine learning model. The machine learning model may include one or more machine learning models, normalized machine learning models, an ensemble of machine learning models, or any combination thereof. In some cases, the predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a linear regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. In some cases, enriching one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles may improve the accuracy of a predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting cancer in a subject. In some cases, the area under the receiver operating characteristic curve of a predictive model when predicting a treatment for a disease in a subject is increased by at least 2%, at least 4%, at least 5%, or at least 10% at 1% when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the predictive model.

[0069] In some cases, the treatment may include a repurposed treatment that may or may not have been originally approved to target cancer. In some cases, the treatment may include a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. The probiotic may include an engineered bacterial strain or an ensemble of engineered bacteria. In some cases, the treatment may include an adjuvant given in combination with a first-line treatment for the subject's cancer to improve the efficacy of the first-line treatment. In some cases, the treatment may include adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a multivalent antibody, an antibody fragment, or an antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Treatments may include targeted antibiotics against specific types of microbes or classes of functionally or biologically similar microbes. In some cases, two or more of the following types of treatments are combined, with at least one type taking advantage of the presence or abundance of the cancer microbe to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0070] In some embodiments, the disclosure provided herein describes a method 1100 of training a predictive model, as seen in FIG. 11. In some cases, the method may include (a) providing a biological sample from one or more subjects, including one or more microbial extracellular vesicles and associated health classifications of the one or more subjects 1102, (b) enriching the one or more microbial extracellular vesicles with one or more affinity reagents, thereby generating one or more enriched microbial extracellular vesicles 1104, (c) detecting one or more molecular analytes from the one or more enriched microbial extracellular vesicles 1106, and (d) training a predictive model with the one or more molecular analytes and associated health classifications of the one or more subjects 1108. In some cases, the trained predictive model may be configured to receive one or more molecular analytes as input and output a predicted disease of the subject, a treatment for the disease of the subject, or any combination thereof. In some cases, the associated health classifications of the one or more subjects may include cancerous, non-cancerous disease, non-cancerous, and not diseased (e.g., healthy).

[0071] In some cases, the biological sample may include one or more non-microbial extracellular vesicles and one or more microbial extracellular vesicles. In some cases, the method may include quantifying the abundance of one or more molecular analytes. The one or more molecular analytes of the one or more microbial extracellular vesicles may include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. The one or more molecular analytes of the one or more non-microbial extracellular vesicles may include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. In some cases, the biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. The whole blood may comprise plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some cases, the subject may comprise a non-human mammal or a human subject. In some cases, the tissue biosample comprises a tissue homogenate.

[0072] In some cases, the one or more affinity reagents can be configured to couple to one or more microbial cell wall molecular motifs and / or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs can include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof.

[0073] In some cases, the one or more affinity reagents may comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, the one or more antibodies comprising a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. The recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. The derivative may include epitope tag and full-length recombinant innate immune pattern recognition receptor or recombinant soluble ectodomain thereof. The epitope tag may include an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some cases, the one or more affinity reagents may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.

[0074] In some cases, one or more of the antibodies, aptamers, single chain variable fragments (scFVs), or single chain antibodies may comprise an epitope tag, which may include an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.

[0075] In some cases, enriching may include contacting the biological sample with a support comprising a covalently immobilized affinity agent. The covalently immobilized affinity agent may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof.

[0076] In some cases, enriching may include (a) contacting the biological sample with one or more affinity reagents to form a capture reagent-molecular motif interaction complex; (b) contacting the capture reagent-molecular motif interaction complex with a support; and (c) separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. In some cases, one or more affinity reagents may include an epitope tag. In some cases, the support may include an epitope tag recognition surface. The epitope tag recognition surface may include an antibody specific for streptavidin, 6x histidine tag, GFP, myc, HA, biotin, or any combination thereof. The epitope tag recognition surface may include an anti-species antibody.

[0077] In some cases, the detecting may include nucleic acid sequencing, polymerase chain reaction (PCR), or hybridization-based analysis of one or more molecular analytes. Nucleic acid sequencing may include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some cases, the detecting may include performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some cases, the detecting may include performing immunoassay analysis.

[0078] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. The cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. The cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, invasive breast carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following cancer types other than the intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinosarcoma, uterine endometrial cancer, uveal melanoma, or any combination of these types of cancer.

[0079] In some cases, the trained predictive model may be configured to predict one or more cancers, one or more cancer subtypes, one or more anatomical locations of cancer, or any combination thereof for one or more subjects. The trained predictive model may be configured to predict a stage of cancer, a prognosis of cancer, a mutational status of cancer, a future immunotherapy response of cancer, an optimal therapy for treating cancer, or any combination thereof. The trained predictive model may be configured to predict cancer among one or more cancer types for one or more subjects and identify a specific cancer type for one or more cancer types. In some cases, the trained predictive model may include a machine learning model, the machine learning model including a regularized machine learning model, an ensemble of machine learning models, or any combination thereof. The trained predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, gradient boosting, or any combination thereof.

[0080] In some cases, enriching for one or more microbial extracellular vesicles may improve the accuracy of a trained predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the treatment of the subject or the disease of the subject.

[0081] In some cases, the treatment may include a repurposed treatment that may or may not have been originally approved to target cancer. In some cases, the treatment may include a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. The probiotic may include an engineered bacterial strain or an ensemble of engineered bacteria. In some cases, the treatment may include an adjuvant given in combination with a first-line treatment for the subject's cancer to improve the efficacy of the first-line treatment. In some cases, the treatment may include adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a multivalent antibody, an antibody fragment, or an antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Treatments may include targeted antibiotics against specific types of microbes or classes of functionally or biologically similar microbes. In some cases, two or more of the following types of treatments are combined, with at least one type taking advantage of the presence or abundance of the cancer microbe to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0082] In some embodiments, the disclosure provided herein describes a method 1200 of training a predictive model, as seen in FIG. 12. In some cases, the method may include (a) providing 1202 one or more subject biological samples containing one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles and an associated health classification of one or more subjects, (b) enriching 1204 one or more microbial extracellular vesicles with one or more first affinity reagents and enriching one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles, (c) detecting 1206 a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles, and (d) training 1208 a predictive model with the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes and the associated health classification of one or more subjects. In some cases, the detecting may include quantifying a first abundance of one or more molecular analytes and a second abundance of one or more molecular analytes. In some cases, the trained predictive model may be configured to receive as input the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes and output a predicted subject disease, a treatment for the subject disease, or any combination thereof. In some cases, the one or more subject associated health classifications may include cancerous, non-cancerous disease, non-cancerous, and not diseased (e.g., healthy).

[0083] The one or more molecular analytes of the one or more microbial extracellular vesicles may include cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. The one or more molecular analytes of the one or more non-microbial extracellular vesicles may include non-microbial cell-free DNA, non-microbial cell-free RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof.

[0084] In some cases, the biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination thereof, any dilution, or processed fraction thereof. Whole blood may include white blood cells, plasma, red blood cells, platelets, or any combination thereof. In some cases, the tissue biological sample may include a tissue homogenate. In some cases, the subject may include a human or non-human mammal.

[0085] In some cases, enriching can include concentrating one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs can include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof.

[0086] In some cases, the one or more first affinity reagents or the one or more second affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, which may include a polyclonal antibody, a monoclonal antibody, a recombinant antibody, and an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. The recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. The derivative may include an epitope tag, a full-length recombinant innate immune pattern recognition receptor, or a recombinant soluble ectodomain thereof. One or more of the antibodies, aptamers, single-chain variable fragments (scFVs), and single-chain antibodies may include an epitope tag. The epitope tag may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. In some cases, the one or more first affinity reagents and the one or more second affinity reagents may comprise a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the one or more first affinity reagents or the one or more second affinity reagents may be specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, EpCAM, or any combination thereof.

[0087] In some cases, enriching may include incubating the biological sample with a support comprising a covalently immobilized affinity agent. The covalently immobilized affinity agent may include a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the support may include magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof.

[0088] In some cases, detecting can include performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on one or more molecular analytes of the one or more microbial extracellular vesicles or one or more molecular analytes from the one or more non-microbial extracellular vesicles. Nucleic acid sequencing can include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof.

[0089] In some cases, the predictive model may be configured to predict one or more subjects of one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof.

[0090] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. In some cases, the cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. In some cases, the cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung cancer, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinoma sarcoma, uterine endometrial cancer, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following cancer types other than the intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinosarcoma, uterine endometrial cancer, uveal melanoma, or any combination of these types of cancer.

[0091] In some cases, the trained predictive model may be configured to predict one or more cancers, one or more cancer subtypes, one or more anatomical locations of cancer, or any combination thereof for one or more subjects. The trained predictive model may be configured to predict a stage of cancer, a prognosis of cancer, a mutational status of cancer, a future immunotherapy response of cancer, an optimal therapy for treating cancer, or any combination thereof. The trained predictive model may be configured to predict cancer among one or more cancer types for one or more subjects and identify a specific cancer type for one or more cancer types. In some cases, the trained predictive model may include a machine learning model. The machine learning model may include one or more machine learning models, regularized machine learning models, ensembles of machine learning models, or any combination thereof. In some cases, the trained predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a linear regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof predictive model. In some cases, enriching one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles may improve the accuracy of a trained predictive model when predicting cancer in a subject by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20%. In some cases, the area under the receiver operating characteristic curve of a trained predictive model when predicting treatment for one or more subject diseases is increased by at least 2%, at least 4%, at least 5%, or at least 10% at 1% when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the trained predictive model.

[0092] In some cases, the treatment may include a repurposed treatment that may or may not have been originally approved to target cancer. In some cases, the treatment may include a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. The probiotic may include an engineered bacterial strain or an ensemble of engineered bacteria. In some cases, the treatment may include an adjuvant given in combination with a first-line treatment for the subject's cancer to improve the efficacy of the first-line treatment. In some cases, the treatment may include adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a multivalent antibody, an antibody fragment, or an antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Treatments may include targeted antibiotics against specific types of microbes or classes of functionally or biologically similar microbes. In some cases, two or more of the following types of treatments are combined, with at least one type taking advantage of the presence or abundance of the cancer microbe to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0093] In some embodiments, the present disclosure describes a computer-implemented method 1300 of training a predictive model, as shown in FIG. 13. In some cases, the method includes (a) receiving 1302 sequencing data of one or more subjects' biological samples and corresponding health classifications from a database, where the biological sample sequencing data includes sequences of one or more molecular analytes of one or more microbial extracellular vesicles, and (b) training 1304 a predictive model with the biological sample sequencing data of one or more molecular analytes of one or more microbial extracellular vesicles of the one or more subjects and corresponding health classifications. In some cases, the corresponding health classifications may include cancerous, non-cancerous disease, non-cancerous non-disease (e.g., healthy). The biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. The liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. The whole blood may include plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some cases, the tissue biosample may include a tissue homogenate.

[0094] In some cases, the one or more molecular analytes of the one or more microbial extracellular vesicles include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof.

[0095] In some cases, the method may further include decontaminating the sequencing data of the one or more subject biological samples, which may be completed in silico or using experimental controls.

[0096] In some cases, the method may further include aligning the sequencing data of the one or more subject biological samples to a human genome reference library and retaining one or more sequencing reads that do not align to the human genome reference library, thereby generating one or more microbial sequencing reads for one or more molecular analytes of the one or more microbial extracellular vesicles. In some cases, the method may further include quantifying the abundance of one or more molecular analytes of the one or more microbial extracellular vesicles.

[0097] In some cases, the sequencing data of one or more subject biological samples may be generated by whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof.

[0098] In some cases, the cancer health classification may include stage I or stage II cancer. The cancer health classification may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. The cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, invasive breast carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. The cancer may include one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination of these types of cancer.

[0099] In some cases, the trained predictive model may be configured to receive as input sequencing data of one or more subjects' biological samples, abundances of one or more molecular analytes of one or more microbial extracellular vesicles of one or more subjects, or any combination thereof, and output predicted disease of one or more subjects, treatment for disease of one or more subjects, or any combination thereof. In some cases, the trained predictive model may be configured to predict one or more cancers, one or more cancer subtypes, one or more anatomical locations of cancer, or any combination thereof, for one or more subjects. The trained predictive model may be configured to predict a stage of cancer, a prognosis of cancer, a mutation status of cancer, a future immunotherapy response of cancer, an optimal therapy for treating cancer, or any combination thereof. The trained predictive model may be configured to predict cancer among one or more cancer types in one or more subjects and identify a specific cancer type of one or more cancer types. In some cases, the trained predictive model may include a machine learning model. The machine learning model may include one or more machine learning models, a normalized machine learning model, an ensemble of machine learning models, or any combination thereof. In some cases, the trained predictive model may include a predictive model of random forest, neural network, naive Bayes, support vector machine, linear regression, k-nearest neighbors, k-means, decision tree, logistic regression, gradient boosting, or any combination thereof. In some cases, decontaminating the sequencing data of the one or more subjects' biological samples may improve the accuracy of the trained predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting cancer in one or more subjects. In some cases, decontaminating the sequencing data of the one or more subjects' biological samples may increase the area under the receiver operating characteristic curve by at least 1%, at least 2%, at least 4%, at least 5%, or at least 10% when the trained predictive model predicts disease in one or more subjects or treatment for disease in one or more subjects.

[0100] In some cases, the treatment may include a repurposed treatment that may or may not have been originally approved to target cancer. In some cases, the treatment may include a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. The probiotic may include an engineered bacterial strain or an ensemble of engineered bacteria. In some cases, the treatment may include an adjuvant given in combination with a first-line treatment for the subject's cancer to improve the efficacy of the first-line treatment. In some cases, the treatment may include adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a multivalent antibody, an antibody fragment, or an antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Treatments may include targeted antibiotics against specific types of microbes or classes of functionally or biologically similar microbes. In some cases, two or more of the following types of treatments are combined, with at least one type taking advantage of the presence or abundance of the cancer microbe to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0101] In some embodiments, the present disclosure describes a computer-implemented method 1400 of training a predictive model, as shown in FIG. 14. In some cases, the method includes (a) receiving 1402 sequencing data of a biological sample of one or more subjects and a corresponding health classification from a database, where the sequencing data of the biological sample includes sequences of one or more first molecular analytes of one or more microbial extracellular vesicles and one or more second molecular analytes of one or more non-microbial extracellular vesicles, and (b) training 1404 a predictive model with the sequencing data of the biological sample of one or more first molecular analytes of one or more microbial extracellular vesicles, one or more second molecular analytes of one or more non-microbial extracellular vesicles, and the corresponding health classification of the one or more subjects. In some cases, the corresponding health classification may include cancerous, non-cancerous diseased, non-cancerous non-disease (e.g., healthy). The biological sample may include a liquid biological sample, a tissue biological sample, or any combination thereof. The liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any diluted or processed fraction thereof. Whole blood may include plasma, white blood cells, red blood cells, platelets, or any combination thereof. In some cases, the tissue biological sample may include a tissue homogenate.

[0102] In some cases, the one or more molecular analytes of the one or more microbial extracellular vesicles include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. In some cases, the one or more molecular analytes of the one or more non-microbial extracellular vesicles include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof.

[0103] In some cases, the method may further include decontaminating the sequencing data of the one or more subject biological samples, which may be completed in silico or using experimental controls.

[0104] In some cases, the method may further include quantifying the abundance of one or more molecular analytes of the one or more microbial extracellular vesicles and / or the abundance of one or more molecular analytes of the one or more non-microbial extracellular vesicles.

[0105] In some cases, the sequencing data of one or more subject biological samples may be generated by whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof.

[0106] In some cases, the cancer health classification may include stage I or stage II cancer. The cancer health classification may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. The cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, invasive breast carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. The cancer may include one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination of these types of cancer.

[0107] In some cases, the trained predictive model may be configured to receive as input sequencing data of a biological sample of one or more subjects, abundance of one or more molecular analytes of one or more microbial extracellular vesicles of one or more subjects, abundance of one or more molecular analytes of one or more non-microbial extracellular vesicles of one or more subjects, or any combination thereof, and output a predicted disease of one or more subjects, a treatment for the disease of one or more subjects, or any combination thereof. In some cases, the trained predictive model may be configured to predict one or more cancers, one or more cancer subtypes, one or more anatomical locations of cancer, or any combination thereof for one or more subjects. The trained predictive model may be configured to predict a stage of cancer, a prognosis of cancer, a mutation status of cancer, a future immunotherapy response of cancer, an optimal therapy for treating cancer, or any combination thereof. The trained predictive model may be configured to predict cancer among one or more cancer types in one or more subjects and identify a specific cancer type of one or more cancer types. In some cases, the trained predictive model may include a machine learning model. The machine learning model may include one or more machine learning models, a normalized machine learning model, an ensemble of machine learning models, or any combination thereof. In some cases, the trained predictive model may include a random forest, neural network, naive Bayes, support vector machine, linear regression, k-nearest neighbors, k-means, decision tree, logistic regression, gradient boosting, or any combination thereof. In some cases, decontaminating the sequencing data of the one or more subject biological samples may improve the accuracy of the trained predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when the trained predictive model is provided as input with a combined dataset of the abundance of one or more first molecular analytes of one or more microbial extracellular vesicles and the abundance of one or more second molecular analytes of one or more non-microbial extracellular vesicles.

[0108] In some cases, the area under the receiver operating characteristic curve of a predictive model for predicting a disease or a treatment for a disease may increase by at least 2%, at least 4%, at least 5%, or at least 10% when the trained predictive model is provided as input with a combined dataset of the abundance of one or more first molecular analytes of one or more microbial extracellular vesicles and the abundance of one or more second molecular analytes of one or more non-microbial extracellular vesicles.

[0109] In some cases, the treatment may include a repurposed treatment that may or may not have been originally approved to target cancer. In some cases, the treatment may include a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. The probiotic may include an engineered bacterial strain or an ensemble of engineered bacteria. In some cases, the treatment may include an adjuvant given in combination with a first-line treatment for the subject's cancer to improve the efficacy of the first-line treatment. In some cases, the treatment may include adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or the cancer microenvironment. The treatment may include a multivalent antibody, an antibody fragment, or an antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Treatments may include targeted antibiotics against specific types of microbes or classes of functionally or biologically similar microbes. In some cases, two or more of the following types of treatments are combined, with at least one type taking advantage of the presence or abundance of the cancer microbe to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0110] While the above steps of the methods disclosed herein are illustrative of various embodiments of the present disclosure, one of ordinary skill in the art will recognize many variations based on the teachings set forth herein. In some cases, steps may be completed in a different order. In some instances, steps may be added or omitted. In some embodiments, some of the steps may include sub-steps. Many of these steps may be repeated as often as is beneficial.

[0111] Predictive Model The methods and systems of the present disclosure may utilize or access external capabilities of artificial intelligence, predictive models, and / or machine learning techniques to identify features of one or more analytes from MEVs that may predict cancer. In some cases, the artificial intelligence techniques may identify features of one or more analytes from MEVs and one or more analytes of non-microbial EVs that may predict cancer in one or more subjects. In some cases, the features may be used to train one or more predictive models described elsewhere herein. These features may be used to accurately predict a disease or disorder. In some cases, the disease or disorder may include cancer as described elsewhere herein. Using such predictive capabilities, health care providers (e.g., physicians) may make informed and accurate risk-based decisions, thereby improving the quality of care and monitoring provided to patients.

[0112] The methods and systems of the present disclosure may analyze the presence and abundance of the microbiome of a sample via one or more analytes of MEVs, or one or more analytes of MEVs in combination with one or more non-microbial EVs, to determine one or more microbial and / or non-microbial features that may predict disease in one or more subjects. In some cases, the methods and systems described elsewhere herein may train a predictive model with one or more microbial and / or non-microbial features indicative of cancer in a subject. In some cases, the trained predictive model may then be used to generate a likelihood (e.g., prediction) of cancer for one or more subjects that are different from the one or more subjects utilized to train the predictive model. The trained predictive model may include an artificial intelligence-based model, such as a machine learning-based classifier, configured to process one or more analytes of one or more MEVs, or one or more analytes of one or more MEVs and one or more non-microbial EVs combined to generate a likelihood that the subject has a disease or disorder. The model may be trained using the presence or abundance of one or more analytes from one or more cohorts of patients, such as cancer patients, patients with a non-cancerous disease, disease-free and cancer-free patients, cancer patients undergoing treatment for cancer, patients undergoing treatment for a non-cancerous disease, or any combination thereof. In some cases, the predictive model may be trained to provide treatment predictions for treating cancer for one or more patients that are not part of the training dataset of the predictive model. Such a predictive model, when provided with an input of the presence and abundance of one or more analytes from one or more MEVs of a patient, or the presence and abundance of a combination of one or more analytes from one or more MEVs and non-microbial EVs, may output a treatment recommendation for one or more patients that are not part of the training dataset.

[0113] The model may include one or more predictive models. The model may include one or more machine learning algorithms. Examples of machine learning algorithms may include support vector machines (SVMs), naive Bayes classification, random forests, neural networks (e.g., deep neural networks (DNNs), recurrent neural networks (RNNs), deep RNNs, long short-term memory (LSTM) recurrent neural networks (RNNs), gated recurrent units (GRUs), gradient boosting machines, random forests, or other supervised learning algorithms or unsupervised machine learning, statistics, linear regression, k-nearest neighbors, k-means, decision trees, logistic regression, or any combination thereof. The model may be used for classification or regression. The model may also include estimation of an ensemble model composed of multiple predictive models, and may utilize techniques such as gradient boosting in the construction of gradient boosting decision trees. The model may be trained using one or more training datasets corresponding to patient data, such as the patient's medical history, family medical history, blood pressure, pulse, temperature, oxygen saturation, or any combination thereof.

[0114] The training dataset may be generated, for example, from one or more cohorts of patients with a diagnosis of a common clinical disease or disorder. The training dataset may include a set of features of MEVs, non-microbial EVs, or any combination thereof, in the form of the presence and / or abundance of one or more analytes of MEVs or non-microbial EVs of one or more subjects' biological samples. The features may include one or more subjects' corresponding cancer diagnoses for the aforementioned MEVs, non-microbial EVs, or any combination thereof. In some cases, the features may include patient information such as the patient's age, the patient's medical history, other medical conditions, current or past medications, clinical risk scores, and time since last observation. For example, a set of features collected from a given patient at a given time point may collectively function as a signature that may be indicative of the patient's health state or status at the given time point.

[0115] The label can include, for example, a clinical outcome, such as the presence, absence, diagnosis, or prognosis of a disease or disorder in a subject (e.g., a patient). A clinical outcome can include therapeutic efficacy (e.g., whether the subject is a positive responder to a cancer-based therapy).

[0116] The input features may be structured by aggregating the data into bins, or alternatively by using one-hot encoding. The input may also include feature values ​​or vectors derived from the aforementioned inputs, such as cross-correlations.

[0117] A training record may be constructed from a signature of the presence and / or abundance of one or more analytes in MEVs, or a combination of one or more analytes in MEVs and one or more analytes in non-microbial EVs.

[0118] The model may process the input features to generate output values ​​including one or more classifications, one or more predictions, or a combination thereof. For example, such classifications or predictions may include a binary classification of whether a subject has cancer or not (e.g., absence of disease or disorder), a classification between groups of categorical labels (e.g., "no disease or disorder," "apparent disease or disorder," and "likely disease or disorder"), the likelihood (e.g., relative likelihood or likelihood) of developing a particular disease or disorder, a score indicating the presence of a disease or disorder, a "risk factor" for the likelihood of the patient's death, and a confidence interval for any numerical prediction. Various machine learning techniques may be cascaded such that the output of the machine learning techniques may be used as input features to subsequent layers or subsections of the model.

[0119] To train the model (e.g., by determining model weights and correlations) to generate real-time classifications or predictions, the model may be trained using a dataset described elsewhere herein. Such a dataset may be large enough to generate statistically significant classifications or predictions. For example, a dataset may include a database of data including the presence and / or abundance of fungal and / or non-fungal microorganisms in one or more subject biological samples.

[0120] The dataset may be divided into subsets (e.g., separate or overlapping), such as a training dataset, a development dataset, and a test dataset. For example, the dataset may be divided into a training dataset that includes 80% of the dataset, a development dataset that includes 10% of the dataset, and a test dataset that includes 10% of the dataset. The training dataset may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. The development dataset may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. The test dataset may include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. In some embodiments, leave-one-out cross-validation may be used. The training set (e.g., training data set) may be selected by random sampling of a set of data corresponding to one or more patient cohorts to ensure independence of sampling. Alternatively, the training set (e.g., training data set) may be selected by proportional sampling of a set of data corresponding to one or more patient cohorts to ensure independence of sampling.

[0121] To improve the accuracy of model predictions and reduce overfitting of the model, the data set may be expanded to increase the number of samples in the training set. For example, data expansion may include rearranging the order of observations in the training records. To accommodate a data set with missing observations, methods of imputing missing data such as forward filling, backfilling, linear interpolation, and multitask Gaussian processes may be used. The data set may be filtered or batch corrected to remove or reduce confounding factors. For example, within the database, a subset of patients may be excluded.

[0122] The model may include one or more neural networks, such as a neural network, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), or a deep RNN. The recurrent neural network may include units that may be long short-term memory (LSTM) units or gated recurrent units (GRUs). For example, the model may include an algorithmic architecture that includes a neural network with a set of input features, such as vital signs and other measurements, a patient's medical history, and / or a patient's demographics. Neural network techniques, such as shedding or regularization, may be used during training the model to prevent overfitting. The neural network may include multiple sub-networks, each of which is configured to generate classifications or predictions of different types of output information (e.g., which may be combined to form an overall output of the neural network). The machine learning model may alternatively utilize statistical or related algorithms, including random forests, classification and regression trees, support vector machines, discriminant analysis, regression techniques, and ensembles and gradient boosted variations thereof.

[0123] If the model generates a classification or prediction of a disease or disorder, a notification (e.g., an alert or alarm) may be generated and sent to a health care provider, such as a doctor, nurse, or other member of the patient's treatment team in a hospital. The notification may be sent via an automated phone call, a Short Message Service (SMS) or Multimedia Message Service (MMS) message, an email, or an alert in a dashboard. The notification may include output information such as a prediction of the disease or disorder, a predicted likelihood of the disease or disorder, an expected time to onset of the disease or disorder, a confidence interval of the likelihood or time, or a recommended course of treatment for the disease or disorder.

[0124] To validate the performance of the model, different performance metrics can be generated. For example, the area under the receiver operating characteristic curve (AUROC) can be used to determine the diagnostic ability of the model. For example, the model can use an adjustable classification threshold, such that the specificity and sensitivity are adjustable, and the receiver operating characteristic curve (ROC) can be used to identify different operating points corresponding to different values ​​of specificity and sensitivity.

[0125] In some cases, such as when the dataset is not large enough, cross-validation may be performed to assess the robustness of the model across different training and testing datasets.

[0126] The following definitions may be used to calculate performance metrics such as sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), area under the precision recall curve (AUPR), AUROC, or the like. A "false positive" may refer to an outcome in which a positive outcome or result is generated erroneously or prematurely (e.g., before or without the actual onset of a disease or disorder). A "true positive" may refer to an outcome in which a positive outcome or result is correctly generated if the patient has a disease or disorder (e.g., the patient exhibits symptoms of a disease or disorder or the patient's records indicate the disease or disorder). A "false negative" may refer to an outcome in which a negative outcome or result is generated but the patient has a disease or disorder (e.g., the patient exhibits symptoms of a disease or disorder or the patient's records indicate the disease or disorder). A "true negative" may refer to an outcome in which a negative outcome or result is generated (e.g., before or without the actual onset of a disease or disorder).

[0127] The model may be trained until certain predefined conditions for accuracy or performance are met, such as having a minimum desired value corresponding to a diagnostic accuracy measure. For example, a diagnostic accuracy measure may correspond to a prediction of the likelihood of occurrence of a disease or disorder in a subject. As another example, a diagnostic accuracy measure may correspond to a prediction of the likelihood of aggravation or recurrence of a disease or disorder for which a subject has previously been treated. Examples of diagnostic accuracy measures may include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, AUPR, and AUROC, which correspond to the diagnostic accuracy of detecting or predicting a disease or disorder.

[0128] For example, such a predetermined condition can be that the sensitivity of predicting a disease or disorder includes values, e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0129] As another example, such a predetermined condition may be that the specificity of predicting a disease or disorder includes values, for example, of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0130] As another example, such a predetermined condition can be that the positive predictive value (PPV) of predicting the disease or disorder includes values, e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0131] As another example, such a predetermined condition can be that the negative predictive value (NPV) of predicting the disease or disorder includes values, e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0132] As another example, such a predetermined condition may be that the area under the curve (AUC) of a receiver operating characteristic (ROC) curve for predicting the disease or disorder (AUROC) comprises a value of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0133] As another example, such a predetermined condition may be that the area under the precision recall curve (AUPR) predicting the disease or disorder includes a value of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0134] In some embodiments, the trained model may be trained or configured to predict a disease or disorder with a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0135] In some embodiments, the trained model may be trained or configured to predict a disease or disorder with a specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0136] In some embodiments, the trained model may be trained or configured to predict a disease or disorder with a positive predictive value (PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0137] In some embodiments, the trained model may be trained or configured to predict a disease or disorder with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0138] In some embodiments, the trained model may be trained or configured to predict a disease or disorder with an area under the curve (AUC) of a receiver operating characteristic (ROC) curve (AUROC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0139] In some embodiments, the trained model may be trained or configured to predict a disease or disorder with an area under the precision recall curve (AUPR) of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0140] A training dataset may be collected from training subjects (e.g., humans), each having a diagnosis state indicating either a diagnosed biological condition or an undiagnosed biological condition.

[0141] In some embodiments, the model is a neural network or a convolutional neural network, see Vincent et al., 2010, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, “Exploring strategies for training deep neural networks,” J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology, each of which is incorporated herein by reference.

[0142] In some embodiments, the data is de-dimensionalized using independent component analysis (ICA), such as that described in Lee, T.-W. (1998): Independent component analysis: Theory and applications, Boston, Mass: Kluwer Academic Publishers, ISBN 0-7923-8261-7, and Hyvaerinen, A.; Karhunen, J.; Oja, E. (2001): Independent Component Analysis, New York: Wiley, ISBN 978-0-471-40540-5, which are incorporated herein by reference in their entireties.

[0143] In some embodiments, the data is de-dimensionalized using principal component analysis (PCA), such as that described in Jolliffe, IT (2002). Principal Component Analysis. Springer Series in Statistics. New York: Springer-Verlag. doi:10.1007 / b98835. ISBN 978-0-387-95442-4, which are incorporated herein by reference in their entireties.

[0144] SVM is an SVM, Cristianini and Shawe-Taylor. Theory, Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259, 262-265, and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al. al., 2000, Bioinformatics 16, 906-914, each of which is incorporated herein by reference in its entirety. When used for classification, SVMs separate a given set of binary labeled data using a hyperplane that is maximally far from the labeled data. When linear separation is not possible, SVMs can work in conjunction with the technique of "kernels" that automatically achieve a nonlinear mapping to the feature space. The hyperplane found by the SVM in the feature space corresponds to a nonlinear decision boundary in the input space.

[0145] Decision trees are generally described by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is incorporated herein by reference. Decision tree based methods divide the feature space into a set of rectangles and fit a model (such as a constant) to each. In some embodiments, the decision tree is a random forest regression. One particular algorithm that may be used is Classification and Regression Trees (CART). Other particular decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forest. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 396-408 and pp. 411-412, which are incorporated herein by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, which is incorporated herein by reference in its entirety. Random forests are described in Breiman, 1999, "Random Forests-Random Features," Technical Report 567, Statistics Department, UC Berkeley, September 1999, which is incorporated herein by reference in its entirety.

[0146] Clustering (e.g., unsupervised and supervised clustering model algorithms) are described in Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York (hereinafter "Duda 1973"), pages 211-256, which is incorporated herein by reference in its entirety. As described in section 6.7 of Duda 1973, the clustering problem is described as one of finding natural groupings in a data set. To identify natural groupings, two problems are addressed. First, determine how to measure the similarity (or dissimilarity) between two samples. This metric (similarity measure) is used to ensure that samples in one cluster are more similar to each other than samples in the other cluster. Second, determine a mechanism for splitting the data into clusters using the similarity measure. Similarity measures are discussed in section 6.7 of Duda 1973, which states that one way to begin a clustering study is to define a distance function and calculate a matrix of distances between all pairs of samples in the training set. If distance is a good measure of similarity, the distance between reference entities in the same cluster will be significantly smaller than the distance between reference entities in different clusters. However, as described on page 215 of Duda 1973, clustering does not require the use of a distance metric. For example, a non-metric similarity function s(x,x') may be used to compare two vectors x and x'. Traditionally, s(x,x') is a symmetric function whose value is large if x and x' are "similar" in some way. An example of a non-metric similarity function s(x,x') is given on page 218 of Duda 1973. Once a method for measuring "similarity" or "dissimilarity" between points in a data set has been selected, clustering requires a criterion function that measures the clustering quality of any partition of the data. A partition of the data set that extremizes a criterion function is used to cluster the data. See Duda 1973, page 217. Criterion functions are discussed in Duda 1973, section 6.8.More recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York has been published. Pages 537-563 describe clustering in detail. Details of clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, NY; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, NY; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, New Jersey, each of which is incorporated herein by reference. Certain exemplary clustering techniques that may be used in the present disclosure include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest neighbor, farthest neighbor, average linkage, centroid, or sum of squares algorithms), k-means clustering, fuzzy k-means clustering, and Jarvis-Patrick clustering. In some embodiments, the clustering includes unsupervised clustering, where no preconceived notions are imposed of what clusters should form when a training set is clustered.

[0147] Regression models, such as those of the multi-category logit model, are described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, which is incorporated herein by reference in its entirety. In some embodiments, the model utilizes the regression model disclosed in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, which is incorporated herein by reference in its entirety. In some embodiments, gradient boosting models are used, for example, for the classification algorithms described herein, and these gradient boosting models are described in Boehmke, Bradley; Greenwell, Brandon (2019). "Gradient Boosting". Hands-On Machine Learning with R. Chapman & Hall. pp. 221-245. ISBN 978-1-138-49568-5., which is incorporated herein by reference in its entirety. In some embodiments, ensemble modeling techniques are used, and these ensemble modeling techniques are described in the implementation of the classification models herein and in Zhou Zhihua (2012). Ensemble Methods: Foundations and Algorithms. Chapman and Hall / CRC. ISBN 978-1-439-83003-1, which is incorporated herein by reference in its entirety.

[0148] In some embodiments, the machine learning analysis is performed by a device executing one or more programs (e.g., one or more programs stored in non-persistent or persistent memory) that include instructions for performing the data analysis. In some embodiments, the data analysis is performed by a system that includes at least one processor (e.g., a processing core) and a memory (e.g., one or more programs stored in non-persistent or persistent memory) that includes instructions for performing the data analysis.

[0149] system The present disclosure provides a computer system programmed to implement the methods of the present disclosure. Figure 15 shows a computer system 1501 programmed or otherwise configured to predict cancer, train a predictive model, generate a recommended treatment, or perform any combination thereof, as described elsewhere herein. The computer system 1501 may be a user's electronic device, or a computer system located remotely relative to the electronic device. The electronic device may be a mobile electronic device.

[0150] The computer system 1501 includes a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 1505, which may be a single-core or multiple-core processor, or multiple processors for parallel processing. The computer system 1501 also includes memory or memory locations 1504 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 1506 (e.g., hard disk), a communication interface 1508 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1507, such as cache, other memory, data storage devices, and / or electronic display adapters. The memory 1504, the storage unit 1506, the interface 1508, and the peripheral devices 1507 are in communication with the CPU 1505 via a communication bus (solid lines), such as a motherboard. The storage unit 1506 may be a data storage unit (or data repository) for storing data. The computer system 1501 may be operatively coupled to a computer network ("network") 1500 using the communication interface 1508. The network 1500 may be the Internet, an Internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet. In some cases, the network 1500 is a telecommunications and / or data network. The network 1500 may include one or more computer servers that may enable distributed computing, such as cloud computing. The network 1500 may implement a peer-to-peer network in some cases using computer system 1501, which may enable devices coupled to computer system 1501 to operate as clients or servers.

[0151] CPU 1505 may execute sequences of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as memory 1504. The instructions may be directed to CPU 1505, which may then program or configure CPU 1505 to implement the methods of the present disclosure described elsewhere herein. Examples of operations performed by CPU 1505 may include fetch, decode, execute, and writeback.

[0152] The CPU 1505 may be part of a circuit, such as an integrated circuit. One or more other components of the system 1501 may be included in the circuit. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0153] The storage unit 1506 may store files such as drivers, libraries, and saved programs. The storage unit 1506 may store user data, such as user preferences and user programs. The computer system 1501 may in some cases include one or more additional data storage units that are external to the computer system 1501, such as located on a remote server in communication with the computer system 1501 via an intranet or the Internet.

[0154] Computer system 1501 may communicate with one or more remote computer systems via network 1500. For example, computer system 1501 may communicate with a remote computer system of a user. Examples of remote computer systems may include a personal computer (e.g., a portable PC), a slate or tablet PC (e.g., an Apple® iPad, a Samsung® Galaxy Tab), a phone, a smartphone (e.g., an Apple® iPhone, an Android-enabled device, a Blackberry®), or a personal digital assistant. A user may access computer system 1501 via network 1500.

[0155] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 1501, such as, for example, on the memory 1504 or the electronic storage unit 1506. The machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by the processor 1505. In some cases, the code may be retrieved from the storage unit 1506 and stored on the memory 1504 for immediate access by the processor 1505. In some circumstances, the electronic storage unit 1506 may be excluded and the machine executable instructions are stored in the memory 1504.

[0156] The code may be pre-compiled and configured for use on a machine having a processor adapted to execute the code, or may be compiled during run-time. The code may be provided in a programming language that may be selected to enable the code to be executed in a pre-compiled or as-compiled manner.

[0157] Aspects of the systems and methods provided herein, such as the computer system 1501, may be embodied in programming. Various aspects of the technology may be thought of as a "product" or "article of manufacture," typically in the form of machine (or processor) executable code and / or associated data carried or embodied in some type of machine-readable medium. The machine-executable code may be stored in an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory) or hard disk. A "storage" type medium may include any or all of the tangible memory of a computer, processor, etc., or their associated modules, such as various semiconductor memories, tape drives, disk drives, etc., and may provide non-transitory storage at any time for software programming. All or a portion of the software may be communicated, at times, over the Internet or various other communications networks. Such communication may, for example, enable loading of the software from one computer or processor to another, for example, from a management server or host computer to a computer platform of an application server. Thus, other types of media that may carry software elements include optical, electrical, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical terrestrial communications networks, and across various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., may also be considered software-bearing media. As used herein, unless limited to non-transitory tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0158] Thus, a machine-readable medium such as a computer executable code may take many forms, including but not limited to tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media include optical or magnetic disks, such as any of the storage devices of any computer(s) such as may be used to implement the databases, etc., shown in the figures. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or a DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium having a pattern of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, a cable or link transporting such a carrier wave, or any other medium from which a computer may read programming code or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0159] The computer system 1501 may include or be in communication with an electronic display 1502 including a user interface (UI) 1503 for providing, for example, a display for visualization of prediction results or an interface for training a predictive model. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0160] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software upon execution by the central processing unit 1505. The algorithms may, for example, predict cancer in a subject(s), determine tailored treatments and / or therapeutics for treating cancer in a subject(s), or any combination thereof.

[0161] In some cases, the computer system may include a computer system configured to identify a disease in a subject. In some cases, the computer system may include (a) one or more processors; and (b) a non-transitory computer-readable storage medium including software, the non-transitory computer-readable storage medium including executable instructions that, when executed, cause the one or more processors of the computer system to: (i) receive a liquid biological sample of a subject including one or more microbial extracellular vesicles; (ii) enrich the one or more microbial extracellular vesicles with one or more affinity reagents; (iii) detect one or more molecular analytes from the one or more microbial extracellular vesicles; and (iv) identify a disease in the subject based on the one or more molecular analytes obtained from the one or more microbial extracellular vesicles. In some cases, the liquid biological sample may further include one or more non-microbial extracellular vesicles. In some cases, the executable instructions may further include quantifying the abundance of the one or more molecular analytes. In some cases, the one or more molecular analytes may include vesicle-associated cell-free microbial DNA, microbial RNA, non-microbial DNA, non-microbial RNA, microbial protein, non-microbial protein, microbial metabolite, non-microbial metabolite, microbial lipid, non-microbial lipid, microbial glycan, non-microbial glycan, or any combination thereof. In some cases, the subject may include a non-human mammal or a human subject.

[0162] In some cases, the liquid biological sample may include plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymphatic fluid, exhaled breath condensate, or any diluted or processed fraction thereof. In some cases, the whole blood includes plasma, white blood cells, red blood cells, platelets, or any combination thereof.

[0163] In some cases, the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. The one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs may include canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. In some cases, the one or more affinity reagents may include a recombinant innate immune pattern recognition receptor or one or more antibodies, and the one or more antibodies may include a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. In some cases, the recombinant innate immune pattern recognition receptor may include Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. The derivatives may include epitope tags and full-length recombinant innate immune pattern recognition receptors or recombinant soluble ectodomains thereof. Epitope tags can include N- or C-terminal 6x histidine tags, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusions, biotin, or any combination thereof.

[0164] In some cases, one or more of the antibodies, aptamers, single chain variable fragments (scFVs), or single chain antibodies of the invention may comprise an epitope tag, which in some cases may comprise an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof.

[0165] In some cases, one or more affinity reagents may comprise a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, one or more affinity reagents may comprise an epitope tag.

[0166] In some cases, enriching for one or more microbial extracellular vesicles may include contacting the liquid biological sample with a support comprising a covalently immobilized affinity agent. In some cases, the covalently immobilized affinity agent may comprise a region that interacts with one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. In some cases, the support may comprise magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. In some cases, the support may comprise an epitope tag recognition surface. In some cases, the epitope tag recognition surface may comprise an antibody specific for streptavidin, 6x histidine tag, GFP, myc, HA, biotin, or any combination thereof. In some cases, the epitope tag recognition surface may comprise an anti-species antibody.

[0167] In some cases, enriching may include (a) contacting the liquid biological sample with one or more affinity reagents to form a capture reagent-molecular motif interaction complex, (b) contacting the capture reagent-molecular motif interaction complex with a support, and (c) separating the support from the liquid biological sample to concentrate the capture reagent-molecular motif complex.

[0168] In some cases, detecting the one or more analytes may include nucleic acid sequencing, polymerase chain reaction (PCR), or hybridization-based analysis of the nucleic acid analytes. In some cases, nucleic acid sequencing may include whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. In some cases, detecting the one or more analytes may include performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). In some cases, detecting the one or more analytes may include performing an immunoassay analysis.

[0169] In some cases, the disease may include cancer. The cancer may include stage I or stage II cancer. In some cases, the cancer may include bone, breast, lung, colon, brain, skin, ovarian, pancreatic, or any combination of types of cancer. In some cases, the cancer may include the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung cancer, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine carcinoma sarcoma, uterine endometrial cancer, uveal melanoma, or any combination thereof. In some cases, the cancer may include one or more of the following types of cancer other than the bowel of the subject: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof.

[0170] In some cases, identifying a disease in a subject based on one or more molecular analytes may include predicting cancer by a predictive model, where the predictive model may be trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and an associated disease of one or more subjects. In some cases, the associated disease of one or more subjects may include cancer as described elsewhere herein. In some cases, the predictive model may be configured to receive as input one or more molecular analytes of a subject and output a disease in a subject. In some cases, the predictive model may be configured to predict one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof, in the subject. In some cases, the predictive model may be configured to predict a stage of cancer, a prognosis of cancer, a mutation status of cancer, a future immunotherapy response of cancer, an optimal therapy for treating cancer, or any combination thereof. In some cases, the predictive model may be configured to predict cancer among one or more cancer types in a subject and identify a particular cancer type of one or more cancer types.

[0171] In some cases, the predictive model may include a machine learning model described elsewhere herein, which may include a regularized machine learning model, an ensemble of machine learning models, or any combination thereof. In some cases, the predictive model may include a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof.

[0172] In some cases, enriching one or more microbial extracellular vesicles with one or more affinity reagents may improve the accuracy of a predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting cancer in a subject.

[0173] definition Unless otherwise defined, all technical terms, notations, and other technical and scientific or terminology used herein are intended to have the same meaning as commonly understood by one of ordinary skill in the art to which the claimed subject matter pertains. In some cases, terms having commonly understood meanings are defined herein for clarity and / or ease of reference, and the inclusion of such definitions herein should not necessarily be construed as representing a substantial difference from what is commonly understood in the art.

[0174] Throughout this application, various embodiments may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Thus, the description of a range should be considered to specifically disclose all possible subranges as well as individual values ​​within that range. For example, the description of a range such as 1-6 should be considered to specifically disclose subranges such as 1-3, 1-4, 1-5, 2-4, 2-6, 3-6, etc., as well as individual numbers within that range, such as 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0175] As used in this specification and claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. For example, the term "a sample" includes multiple samples, including mixtures thereof.

[0176] The terms "determining," "measuring," "evaluating," "assessing," "assaying," and "analyzing" are often used interchangeably herein to refer to forms of measurement. These terms include determining whether an element is present or not (e.g., detecting). These terms can include quantitative, qualitative, or quantitative and qualitative determinations. Evaluating can be relative or absolute. "Detecting the presence of" can include determining the amount of something present in addition to determining whether it is present or absent, depending on the context.

[0177] The terms "subject," "individual," or "patient" are used interchangeably herein. A "subject" may be a biological entity that contains expressed genetic material. The biological entity may be, for example, a plant, an animal, or a microorganism, including bacteria, viruses, fungi, and protozoa. A subject may be tissues, cells, and their progeny of a biological entity obtained in vivo or cultured in vitro. A subject may be a mammal. A mammal may be a human. A subject may be diagnosed or suspected of being at high risk for a disease. In some cases, a subject is not necessarily diagnosed or suspected of being at high risk for a disease.

[0178] The term "microbial extracellular vesicles" ("MEVs") generally describes non-replicating, membrane-enclosed structures derived from parent microbial cells. MEVs may contain molecular components derived from the parent cells, and in the case of microbial colonization or organization of non-microbial cells, MEVs may also contain molecular components derived from their host cells or host tissues. "Microbial extracellular vesicles" ("MEVs") may also generally describe any non-replicating, microbial-derived structure, such as a soluble or insoluble aggregate, that contains molecular features (e.g., lipopolysaccharides, microbial glycoproteins, or lipids). MEVs may be affinity enriched by the methods of the invention.

[0179] The terms "microorganism" or "microbial" are used interchangeably herein to refer to a type of microorganism. A microorganism may include, for example, a virus, a bacterium, a fungus, an archaea, or any combination thereof. Thus, a "microorganism" may include a single microorganism of a plurality of microorganisms.

[0180] The term "non-microbial extracellular vesicles" ("non-microbial EV(s)") generally describes non-replicating lipid bilayer membrane-enclosed structures derived from a parent target cell. Non-microbial EVs can be approximately 30-2,000 nm in diameter. Non-microbial EVs may contain molecular components derived from the parent target cell. Non-microbial EVs may also contain molecular components derived from a microbial source (e.g., in the case of tissue colonization by microorganisms).

[0181] The term "extracellular vesicles" ("EVs") generally describes non-replicating membrane-enclosed structures. EVs can be derived from microbial and / or non-microbial parent cells.

[0182] The term "in vivo" generally describes events that take place inside a subject's body.

[0183] The term "ex vivo" generally describes an event that occurs outside of a subject's body. An ex vivo assay is generally not performed on a subject. Rather, it may be performed on a sample separate from the subject. An example of an ex vivo assay performed on a sample may be an "in vitro" assay.

[0184] The term "in vitro" generally describes events that occur outside the biological context of a subject. In some cases, the context may be a contained area in a laboratory such that the material being studied may be separated from a biological source. In some cases, the contained area may be a fume hood, glove box, petri dish, test tube, flask, etc. In vitro assays may include cell-based assays in which live or dead cells may be used. In vitro assays may also include cell-free assays in which intact cells may not be used.

[0185] As used herein, the term "about" a number refers to that number plus or minus 10% of that number. The term "about" a range refers to that range minus 10% of its minimum value and plus 10% of its maximum value.

[0186] The use of absolute or sequential terms, such as "will," "will not," "shall," "shall not," "must," "must not," "first," "initially," "next," "consequently," "before," "after," "lastly," and "finally" are not intended to limit the scope of the embodiments disclosed herein, but are exemplary.

[0187] Any systems, methods, software, compositions, and platforms described herein are modular and not limited to sequential steps, and thus, terms such as "first" and "second" do not necessarily imply a priority, order of importance, or order of actions.

[0188] As used herein, the term "treatment" or "treating" is used in reference to a pharmaceutical or other intervention regimen to obtain a beneficial or desired result in a recipient. Beneficial or desired results include, but are not limited to, therapeutic benefit and / or prophylactic benefit. Therapeutic benefit may refer to the eradication or amelioration of the condition or underlying disease being treated. Therapeutic benefit may also be achieved with the eradication or amelioration of one or more of the physiological symptoms associated with the underlying disease such that an improvement is observed in the subject, although the subject may still be afflicted with the underlying disease. Prophylactic benefits include delaying, preventing, or eliminating the appearance of the disease or condition, delaying or eliminating the onset of symptoms of the disease or condition, slowing, halting, or reversing the progression of the disease or condition, or any combination thereof. For prophylactic benefit, subjects at risk of developing a particular disease or who report one or more of the physiological symptoms of the disease may be treated, even though they may not have been diagnosed with the disease.

[0189] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0190] Embodiment Numbered embodiment 1 includes a method of identifying a disease in a subject, the method comprising providing a biological sample from a subject comprising one or more microbial extracellular vesicles, enriching the one or more microbial extracellular vesicles with one or more affinity reagents, detecting one or more molecular analytes from the one or more microbial extracellular vesicles, and identifying the disease in the subject based on the one or more molecular analytes from the one or more microbial extracellular vesicles. Numbered embodiment 2 includes the method of embodiment 1, wherein the biological sample comprises non-microbial extracellular vesicles. Numbered embodiment 3 includes the method of embodiment 1 or 2, further comprising quantifying the abundance of the one or more molecular analytes. Numbered embodiment 4 includes any of the methods of embodiments 1-3, wherein the one or more molecular analytes of the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. Numbered embodiment 5 includes the method of any of embodiments 1-4, wherein the one or more molecular analytes outside the one or more non-microbial cells comprise vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. Numbered embodiment 6 includes the method of any of embodiments 1-5, wherein the biological sample comprises a liquid biological sample, a tissue biological sample, or any combination thereof. Numbered embodiment 7 includes the method of any of embodiments 1-6, wherein the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any dilution or processed fraction thereof. Numbered embodiment 8 includes the method of any of embodiments 1-7, wherein the whole blood comprises plasma, white blood cells, red blood cells, platelets, or any combination thereof. Numbered embodiment 9 includes the method of any of embodiments 1-6, wherein the tissue biological sample comprises a tissue homogenate.Numbered embodiment 10 includes the method of any of embodiments 1-9, wherein the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. Numbered embodiment 11 includes the method of any of embodiments 1-10, wherein the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. Numbered embodiment 12 includes the method of any of embodiments 1-11, wherein the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, wherein the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. Numbered embodiment 13 includes the method of any of embodiments 1 to 12, wherein the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. Numbered embodiment 14 includes the method of any of embodiments 1 to 13, wherein the derivative thereof comprises an epitope tag and a full-length recombinant innate immune pattern recognition receptor or a recombinant soluble ectodomain thereof.Numbered embodiment 15 includes the methods of any of embodiments 1-14, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 16 includes the methods of any of embodiments 1-12, wherein the one or more antibodies, aptamers, single chain variable fragments (scFv), or single chain antibodies comprise an epitope tag. Numbered embodiment 17 includes the methods of any of embodiments 1-12 or 16, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 18 includes the method of any of embodiments 1-17, wherein the one or more affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. Numbered embodiment 19 includes the method of any of embodiments 1-18, wherein the enriching comprises contacting the biological sample with a support comprising a covalently immobilized affinity agent. Numbered embodiment 20 includes the method of any of embodiments 1-19, wherein the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. Numbered embodiment 21 includes the method of any of embodiments 1-20, wherein the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymer, or any combination thereof. Numbered embodiment 22 includes the method of any of embodiments 1-20, wherein the enriching comprises contacting the biological sample with a support comprising a covalently immobilized affinity agent.

[0191] The method of any of embodiments 1-21 includes contacting the biological sample with the one or more affinity reagents to form a capture reagent-molecular motif interaction complex, contacting the capture reagent-molecular motif interaction complex with a support, and separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. Numbered embodiment 23 includes the method of any of embodiments 1-22, wherein the one or more affinity reagents comprise an epitope tag. Numbered embodiment 24 includes the method of any of embodiments 1-23, wherein the support comprises an epitope tag recognition surface. Numbered embodiment 25 includes the method of any of embodiments 1-24, wherein the epitope tag recognition surface comprises streptavidin, a 6x histidine tag, an antibody specific for green fluorescent protein (GFP), myc, hemagglutinin (HA), biotin, or any combination thereof. Numbered embodiment 26 includes the method of any of embodiments 1-25, wherein the epitope tag recognition surface comprises an anti-species antibody. Numbered embodiment 27 includes the method of any of embodiments 1-26, wherein the detecting comprises nucleic acid sequencing, polymerase chain reaction (PCR), or hybridization-based analysis of one or more molecular analytes. Numbered embodiment 28 includes the method of any of embodiments 1-27, wherein the nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. Numbered embodiment 29 includes the method of any of embodiments 1-28, wherein the detecting comprises performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). Numbered embodiment 30 includes the method of any of embodiments 1-29, wherein the detecting comprises performing an immunoassay analysis. Numbered embodiment 31 includes the method of any of embodiments 1-30, wherein the disease comprises cancer. Numbered embodiment 32 includes the methods of any of embodiments 1-31, wherein the cancer includes stage I or stage II cancer.Numbered embodiment 33 includes the methods of any of embodiments 1-32, wherein the cancer includes a type of cancer of the bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. Numbered embodiment 34 includes the methods of any of embodiments 1-33, wherein the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. Numbered embodiment 35 includes any of the methods of embodiments 1-34, wherein the cancer comprises one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination of the above types of cancer. Numbered embodiment 36 includes the method of any of embodiments 1-35, wherein the identifying the cancer includes predicting the cancer with a predictive model, the predictive model being trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and an associated disease of the other subject. Numbered embodiment 37 includes the method of any of embodiments 1-36, wherein the predictive model is configured to receive as input the one or more molecular analytes of the subject and output the disease of the subject.Numbered embodiment 38 includes the method of any of embodiments 1-37, wherein the predictive model is configured to predict one or more cancers, one or more subtypes of cancer, one or more anatomical locations of cancer, or any combination thereof, in the subject. Numbered embodiment 39 includes the method of any of embodiments 1-38, wherein the predictive model is configured to predict a stage of the cancer, a prognosis of the cancer, a mutational status of the cancer, a future immunotherapy response of the cancer, an optimal therapy for treating the cancer, or any combination thereof. Numbered embodiment 40 includes the method of any of embodiments 1-39, wherein the predictive model is configured to predict the cancer among one or more cancer types in the subject and identify a specific cancer type of the one or more cancer types. Numbered embodiment 41 includes the method of any of embodiments 1-40, wherein the predictive model includes a machine learning model, wherein the machine learning model includes a normalized machine learning model, an ensemble of machine learning models, or any combination thereof. Numbered embodiment 42 includes any of the methods of embodiments 1-41, wherein the predictive model comprises a random forest, a neural network, a naive Bayesian, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. Numbered embodiment 43 includes any of the methods of embodiments 1-42, wherein enriching the microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the cancer in the subject. Numbered embodiment 44 includes any of the methods of embodiments 1-43, wherein the subject comprises a non-human mammal or a human subject.

[0192] Numbered embodiment 45 includes a method of identifying a disease in a subject, the method comprising providing a biological sample from a subject comprising one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles; enriching the one or more microbial extracellular vesicles with one or more first affinity reagents and enriching the one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles; detecting a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles; and identifying the disease in the subject from an association between a combination of the first abundance of one or more molecular analytes and the second abundance of the one or more molecular analytes and a model of a disease associated with a third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles and a fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles. Numbered embodiment 46 includes the method of embodiment 45, wherein detecting comprises quantifying the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes. Numbered embodiment 47 includes the method of embodiment 45 or 46, wherein the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. Numbered embodiment 48 includes the method of any of embodiments 45-47, wherein the one or more molecular analytes of the one or more non-microbial extracellular vesicles comprise cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. Numbered embodiment 49 includes the method of any of embodiments 45-48, wherein the biological sample comprises a liquid biological sample, a tissue biological sample, or any combination thereof.Numbered embodiment 50 includes the method of any of embodiments 45-49, wherein the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination, any dilution, or processed fraction thereof. Numbered embodiment 51 includes the method of any of embodiments 45-50, wherein the whole blood comprises white blood cells, plasma, red blood cells, platelets, or any combination thereof. Numbered embodiment 52 includes the method of any of embodiments 45-49, wherein the tissue biological sample comprises a tissue homogenate. Numbered embodiment 53 includes the method of any of embodiments 45-52, wherein enriching comprises enriching one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. Numbered embodiment 54 includes the methods of any of embodiments 45-53, wherein the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. Numbered embodiment 55 includes the methods of any of embodiments 45-54, wherein the one or more first affinity reagents or the one or more second affinity reagents comprise recombinant innate immune pattern recognition receptors or one or more antibodies, wherein the one or more antibodies comprise polyclonal antibodies, monoclonal antibodies, recombinant antibodies, and aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof.Numbered embodiment 56 includes the methods of any of embodiments 45 to 55, wherein the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose-binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. Numbered embodiment 57 includes the methods of any of embodiments 45 to 56, wherein the derivative thereof comprises an epitope tag, a full-length recombinant innate immune pattern recognition receptor, or a recombinant soluble ectodomain thereof. Numbered embodiment 58 includes the method of any of embodiments 45-57, wherein the one or more antibodies, the aptamers, the single chain variable fragments (scFv), and the single chain antibodies comprise an epitope tag. Numbered embodiment 59 includes the method of any of embodiments 45-58, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 60 includes the method of any of embodiments 45-59, wherein the one or more first affinity reagents and the one or more second affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. Numbered embodiment 61 includes the method of any of embodiments 45-60, wherein enriching comprises incubating the biological sample with a support comprising a covalently immobilized affinity agent. Numbered embodiment 62 includes any of the methods of embodiments 45 to 61, wherein the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs.Numbered embodiment 63 includes the methods of any of embodiments 45-62, wherein the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, a pH-sensitive polymer, or any combination thereof. Numbered embodiment 64 includes the methods of any of embodiments 45-63, wherein the one or more first affinity reagents or the one or more second affinity reagents are specific for a mammalian antigen CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, a human epidermal growth factor receptor (HER) family member, EpCAM, or any combination thereof. Numbered embodiment 65 includes the methods of any of embodiments 45-64, wherein detecting comprises performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on the one or more molecular analytes of the one or more microbial extracellular vesicles or the one or more molecular analytes from the one or more non-microbial extracellular vesicles.Numbered embodiment 66 includes the methods of any of embodiments 45-65, wherein nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. Numbered embodiment 67 includes the method of any of embodiments 45-66, wherein the model includes a predictive model, the predictive model being trained on the third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles, the fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles, and a corresponding disease of the one or more subjects. Numbered embodiment 68 includes the method of any of embodiments 45-67, wherein the predictive model is configured to receive as input the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes, and output a prediction of the disease of the subject. Numbered embodiment 69 includes the method of any of embodiments 45-68, wherein the disease includes cancer.Numbered embodiment 70 includes the methods of any of embodiments 45-69, wherein the cancer comprises stage I or stage II cancer. Numbered embodiment 71 includes the methods of any of embodiments 45-70, wherein the cancer comprises a type of cancer of bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. Numbered embodiment 72 includes the methods of any of embodiments 45-71, wherein the predictive model is configured to predict one or more cancers, one or more cancer subtypes, one or more anatomical locations of cancer, or any combination thereof in the subject. Numbered embodiment 73 includes the methods of any of embodiments 45-72, wherein the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. Numbered embodiment 74 includes the methods of any of embodiments 45-73, wherein the cancer comprises one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinosarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. Numbered embodiment 75 includes the methods of any of embodiments 45-74, wherein the predictive model comprises a machine learning model.Numbered embodiment 76 includes the method of any of embodiments 45-75, wherein the machine learning model includes one or more machine learning models, normalized machine learning models, an ensemble of machine learning models, or any combination thereof. Numbered embodiment 77 includes the method of any of embodiments 45-76, wherein the predictive model includes a predictive model of random forest, neural network, naive Bayes, support vector machine, linear regression, k-nearest neighbors, k-means, decision tree, logistic regression, gradient boosting, or any combination thereof. Numbered embodiment 78 includes the method of any of embodiments 45-77, wherein the subject includes a human or non-human mammal. Numbered embodiment 79 includes the method of any of embodiments 45-78, wherein enriching the one or more microbial extracellular vesicles and the one or more non-microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the cancer in the subject. Numbered embodiment 80 provides that the area under the receiver operating characteristic curve of the predictive model for predicting the disease in the subject is at least 1%, at least 2%, when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the predictive model. Including any of the methods of embodiments 45-79, wherein the increase is at least 4%, at least 5%, or at least 10%.

[0193] Numbered embodiment 81 includes a method of identifying a treatment for a disease in a subject, comprising providing a biological sample of a subject comprising one or more microbial extracellular vesicles, enriching the one or more microbial extracellular vesicles with one or more affinity reagents, thereby producing one or more enriched microbial extracellular vesicles, detecting one or more molecular analytes from the one or more enriched microbial extracellular vesicles, and identifying the treatment for the disease in the subject based on the one or more molecular analytes. Numbered embodiment 82 includes the method of embodiment 81, wherein the biological sample comprises non-microbial extracellular vesicles and the one or more microbial extracellular vesicles. Numbered embodiment 83 includes the method of embodiment 81 or 82, further comprising quantifying the abundance of the one or more molecular analytes. Numbered embodiment 84 includes the method of any of embodiments 81-83, wherein the one or more molecular analytes of the one or more microbial extracellular vesicles include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. Numbered embodiment 85 includes the method of any of embodiments 81-84, wherein the one or more molecular analytes of the non-microbial extracellular vesicles include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. Numbered embodiment 86 includes the method of any of embodiments 81-85, wherein the biological sample includes a liquid biological sample, a tissue biological sample, or any combination thereof. Numbered embodiment 87 includes the methods of any of embodiments 81-86, wherein the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymphatic fluid, exhaled breath condensate, or any dilution or processed fraction thereof. Numbered embodiment 88 includes the methods of any of embodiments 81-87, wherein the whole blood comprises plasma, white blood cells, red blood cells, platelets, or any combination thereof. Numbered embodiment 89 includes the methods of any of embodiments 81-86, wherein the tissue biological sample comprises a tissue homogenate.Numbered embodiment 90 includes the methods of any of embodiments 81-89, wherein the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. Numbered embodiment 91 includes the methods of any of embodiments 81-90, wherein the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components, such as lipopolysaccharide (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. Numbered embodiment 92 includes any of the methods of embodiments 81-91, wherein the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, and the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. Numbered embodiment 93 includes any of the methods of embodiments 81-92, wherein the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. Numbered embodiment 94 includes the methods of any of embodiments 81 to 93, wherein the derivatives thereof comprise epitope-tagged and full-length recombinant innate immune pattern recognition receptors or recombinant soluble ectodomains thereof.Numbered embodiment 95 includes the methods of any of embodiments 81-94, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 96 includes the methods of any of embodiments 81-92, wherein the one or more antibodies, aptamers, single chain variable fragments (scFv), or single chain antibodies comprise an epitope tag. Numbered embodiment 97 includes the methods of any of embodiments 81-92 or 96, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 98 includes the method of any of embodiments 81-97, wherein the one or more affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. Numbered embodiment 99 includes the method of any of embodiments 81-98, wherein the enriching comprises contacting the biological sample with a support comprising a covalently immobilized affinity agent. Numbered embodiment 100 includes the method of any of embodiments 81-99, wherein the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. Numbered embodiment 101 includes the method of any of embodiments 81-100, wherein the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymer, or any combination thereof.

[0194] Numbered embodiment 102 includes the method of any of embodiments 81-101, wherein the enriching comprises contacting the biological sample with the one or more affinity reagents to form a capture reagent-molecular motif interaction complex, contacting the capture reagent-molecular motif interaction complex with a support, and separating the support from the biological sample to enrich the capture reagent-molecular motif interaction complex. Numbered embodiment 103 includes the method of any of embodiments 81-102, wherein the one or more affinity reagents comprise an epitope tag. Numbered embodiment 104 includes the method of any of embodiments 81-103, wherein the support comprises an epitope tag recognition surface. Numbered embodiment 105 includes the method of any of embodiments 81-104, wherein the epitope tag recognition surface comprises streptavidin, a 6x histidine tag, an antibody specific for green fluorescent protein (GFP), myc, hemagglutinin (HA), biotin, or any combination thereof. Numbered embodiment 106 includes the method of any of embodiments 81 to 105, wherein the epitope tag recognition surface comprises an anti-species antibody. Numbered embodiment 107 includes the method of any of embodiments 81 to 106, wherein the detecting comprises nucleic acid sequencing, polymerase chain reaction (PCR)-based, or hybridization-based analysis of the nucleic acid analyte. Numbered embodiment 108 includes the method of any of embodiments 81 to 107, wherein the nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. Numbered embodiment 109 includes the method of any of embodiments 81 to 108, wherein the detecting comprises performing mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), or high performance liquid chromatography (HPLC). Numbered embodiment 110 includes the method of any of embodiments 81 to 109, wherein the detecting comprises performing an immunoassay analysis. Numbered embodiment 111 includes the methods of any of embodiments 81-110, wherein the disease includes cancer.Numbered embodiment 112 includes the methods of any of embodiments 81-111, wherein the cancer comprises stage I or stage II cancer. Numbered embodiment 113 includes the methods of any of embodiments 81-112, wherein the cancer comprises a type of cancer of the bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. Numbered embodiment 114 includes the methods of any of embodiments 81-113, wherein the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. Numbered embodiment 115 includes any of the methods of embodiments 81-114, wherein the cancer comprises one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. Numbered embodiment 116 includes any of the methods of embodiments 81-115, wherein identifying the treatment for the disease includes predicting the treatment by a predictive model, the predictive model being trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and associated treatments for the disease of the other subject.Numbered embodiment 117 includes the method of any of embodiments 81-116, wherein the predictive model is configured to receive the one or more molecular analytes of the subject as input and output the treatment for the disease of the subject. Numbered embodiment 118 includes the method of any of embodiments 81-117, wherein the predictive model includes a machine learning model, wherein the machine learning model includes a normalized machine learning model, an ensemble of machine learning models, or any combination thereof. Numbered embodiment 119 includes the method of any of embodiments 81-118, wherein the predictive model includes a random forest, a neural network, a naive Bayes, a support vector machine, a learning regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. Numbered embodiment 120 includes the method of any of embodiments 81-119, wherein enriching the one or more microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the treatment of the subject. Numbered embodiment 121 includes any of the methods of embodiments 81-120, wherein the subject comprises a non-human mammal or a human subject. Numbered embodiment 122 includes any of the methods of embodiments 81-121, wherein the treatment comprises a repurposed treatment that may or may not have been originally approved to target cancer. Numbered embodiment 123 includes any of the methods of embodiments 81-122, wherein the treatment comprises a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. Numbered embodiment 124 includes any of the methods of embodiments 81-123, wherein the probiotic comprises an engineered bacterial strain or an ensemble of engineered bacteria. Numbered embodiment 125 includes any of the methods of embodiments 81-122, wherein the treatment comprises an adjuvant given in combination with a first-line treatment for the cancer to improve the efficacy of the first-line treatment.Numbered embodiment 126 includes the methods of any of embodiments 81-122, wherein the treatment includes adoptive cell transfer to a target microbial antigen associated with the cancer or cancer microenvironment. Numbered embodiment 127 includes the methods of any of embodiments 81-122, wherein the treatment includes a cancer vaccine that utilizes a microbial antigen associated with the cancer or cancer microenvironment. Numbered embodiment 128 includes the methods of any of embodiments 81-122, wherein the treatment includes a monoclonal antibody against a microbial antigen associated with the cancer or cancer microenvironment. Numbered embodiment 129 includes the methods of any of embodiments 81-122, wherein the treatment includes an antibody-drug conjugate designed to at least partially target a microbial antigen associated with the cancer or cancer microenvironment. Numbered embodiment 130 includes the methods of any of embodiments 81-122, wherein the treatment includes a multivalent antibody, antibody fragment, or antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or cancer microenvironment. Numbered embodiment 131 includes any of the methods of embodiments 81-122, where the treatment includes targeted antibiotics against a specific type of microorganism or a class of functionally or biologically similar microorganisms. Numbered embodiment 132 includes any of the methods of embodiments 81-122, where two or more of the following types of treatments are combined, where at least one type utilizes the presence or abundance of a microorganism in the cancer to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0195] Numbered embodiment 133 includes a method of identifying a treatment for a disease in a subject, the method comprising: providing a biological sample from a subject comprising one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles; enriching the one or more microbial extracellular vesicles with one or more first affinity reagents and enriching the one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby generating one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles; detecting a first abundance of one or more molecular analytes from the enriched one or more microbial extracellular vesicles and a second abundance of one or more molecular analytes from the enriched one or more non-microbial extracellular vesicles; and identifying the treatment for the disease in the subject from an association between a combination of the first abundance of one or more molecular analytes and the second abundance of the one or more molecular analytes and a model of a disease treatment associated with a third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles and a fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles. Numbered embodiment 134 includes the method of embodiment 133, wherein detecting includes quantifying the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes. Numbered embodiment 135 includes the method of embodiment 133 or 134, wherein the one or more molecular analytes of the one or more microbial extracellular vesicles include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. Numbered embodiment 136 includes the method of any of embodiments 133 to 135, wherein the one or more molecular analytes of the non-microbial extracellular vesicles include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. Numbered embodiment 137 includes the methods of any of embodiments 133-136, wherein the biological sample includes a liquid biological sample, a tissue biological sample, or any combination thereof.Numbered embodiment 138 includes the methods of any of embodiments 133-137, wherein the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymph, exhaled breath condensate, or any combination, any dilution, or processed fraction thereof. Numbered embodiment 139 includes the methods of any of embodiments 133-138, wherein the whole blood comprises white blood cells, plasma, red blood cells, platelets, or any combination thereof. Numbered embodiment 140 includes the methods of any of embodiments 133-137, wherein the tissue biological sample comprises a tissue homogenate. Numbered embodiment 141 includes the methods of any of embodiments 133-140, wherein enriching comprises enriching one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. Numbered embodiment 142 includes the methods of any of embodiments 133-141, wherein the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, glycosylphosphatidylinositol (GPI)-anchored proteins, or any combination thereof. Numbered embodiment 143 includes the methods of any of embodiments 133-142, wherein the one or more first affinity reagents or the one or more second affinity reagents comprise recombinant innate immune pattern recognition receptors or one or more antibodies, and the one or more antibodies comprise polyclonal antibodies, monoclonal antibodies, recombinant antibodies, and aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof.Numbered embodiment 144 includes any of the methods of embodiments 133-143, wherein the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, or any derivative thereof. Numbered embodiment 145 includes the methods of any of embodiments 133-144, wherein the derivatives thereof comprise an epitope tag, a full-length recombinant innate immune pattern recognition receptor or a recombinant soluble ectodomain thereof. Numbered embodiment 146 includes the methods of any of embodiments 133-145, wherein the one or more antibodies, the aptamers, the single chain variable fragments (scFvs), and the single chain antibodies comprise an epitope tag. Numbered embodiment 147 includes the methods of any of embodiments 133-146, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 148 includes the method of any of embodiments 133-147, wherein the one or more first affinity reagents and the one or more second affinity reagents comprise a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs. Numbered embodiment 149 includes the method of any of embodiments 133-148, wherein enriching comprises incubating the liquid biological sample with a support comprising a covalently immobilized affinity agent. Numbered embodiment 150 includes the method of any of embodiments 133-149, wherein the covalently immobilized affinity agent comprises a region that interacts with the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs.Numbered embodiment 151 includes the methods of any of embodiments 133-150, wherein the support comprises magnetic beads, agarose beads, non-magnetic latex, functionalized sepharose, pH-sensitive polymers, or any combination thereof. Numbered embodiment 152 includes the methods of any of embodiments 133-151, wherein the one or more second affinity reagents comprise polyclonal, monoclonal, recombinant antibodies, aptamers, single chain variable fragments (scFv), single chain antibodies, or any combination thereof. Numbered embodiment 153 includes the methods of any of embodiments 133-152, wherein the one or more first affinity reagents or the one or more second affinity reagents are specific for mammalian antigens CD9, CD63, CD81, glypican 1 (GPC1), Mart-1, TYRP2, human epidermal growth factor receptor (HER) family members, EpCAM, or any combination thereof. Numbered embodiment 154 includes the methods of any of embodiments 133-153, wherein detecting comprises performing nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), immunoassay analysis, or any combination thereof, on the one or more molecular analytes of the one or more microbial extracellular vesicles or the one or more molecular analytes from the one or more non-microbial extracellular vesicles. Numbered embodiment 155 includes the methods of any of embodiments 133-154, wherein nucleic acid sequencing comprises whole genome sequencing, shotgun sequencing, next generation sequencing, targeted sequencing, RNA sequencing, methylation sequencing, or any combination thereof. Numbered embodiment 156 includes any of the methods of embodiments 133-155, wherein the model includes a predictive model, the predictive model being trained on the third abundance of one or more molecular analytes from the one or more microbial extracellular vesicles in one or more subjects, the fourth abundance of one or more molecular analytes from the one or more non-microbial extracellular vesicles, and a corresponding treatment for the disease in the one or more subjects.Numbered embodiment 157 includes the method of any of embodiments 133-156, wherein the predictive model is configured to receive as input the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes, and output a prediction of the treatment for the disease in the subject. Numbered embodiment 158 ​​includes the method of any of embodiments 133-157, wherein the disease comprises cancer. Numbered embodiment 159 includes the method of any of embodiments 133-158, wherein the cancer comprises stage I or stage II cancer. Numbered embodiment 160 includes the method of any of embodiments 133-159, wherein the cancer comprises a type of cancer of the bone, breast, lung, colon, brain, skin, ovary, pancreas, or any combination thereof. Numbered embodiment 161 includes any of the methods of embodiments 133-160, wherein the cancer comprises the following types of cancer: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, duodenal carcinoma, esophageal carcinoma, glioblastoma multiforme, small cell lung carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof. Numbered embodiment 162 includes any of the methods of embodiments 133-160, wherein the cancer includes one or more of the following cancer types other than intestine: adrenocortical carcinoma, bladder urothelial carcinoma, brain low-grade glioma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, glioblastoma multiforme, lung small cell carcinoma, head and neck squamous cell carcinoma, kidney chromophobe cell, kidney clear cell carcinoma, kidney papillary cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoma diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin cutaneous melanoma, testicular germ cell tumor, thymoma, thyroid carcinoma, uterine carcinoma sarcoma, uterine endometrial carcinoma, uveal melanoma, or any combination thereof.Numbered embodiment 163 includes the method of any of embodiments 133 to 162, in which the predictive model comprises a machine learning model. Numbered embodiment 164 includes the method of any of embodiments 133 to 163, in which the machine learning model comprises one or more machine learning models, regularized machine learning models, an ensemble of machine learning models, or any combination thereof. Numbered embodiment 165 includes the method of any of embodiments 133 to 164, in which the predictive model comprises a random forest, a neural network, a naive Bayes, a support vector machine, a linear regression, a k-nearest neighbor, a k-means, a decision tree, a logistic regression, a gradient boosting, or any combination thereof. Numbered embodiment 166 includes the method of any of embodiments 133 to 165, in which the subject comprises a human or a non-human mammal. Numbered embodiment 167 is any of embodiments 133 to 166, wherein enriching the one or more microbial extracellular vesicles and the one or more non-microbial extracellular vesicles improves the accuracy of the predictive model by at least 1%, at least 5%, at least 10%, at least 15%, or at least 20% when predicting the treatment for the cancer in the subject. Numbered embodiment 168 includes any of the methods of embodiments 133-167, wherein the area under the receiver operating characteristic curve of the predictive model predicting the treatment for the disease in the subject is increased by at least 1%, at least 2%, at least 4%, at least 5%, or at least 10% when the combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes is provided as the input to the predictive model. Numbered embodiment 169 includes any of the methods of embodiments 133-168, wherein the treatment includes a repurposed treatment that may or may not have been originally approved for targeting cancer. Numbered embodiment 170 includes any of the methods of embodiments 133-169, wherein the treatment includes a small molecule, a biologic, a probiotic, a virus, a bacteriophage, an immunotherapy, a broad spectrum antibiotic, or any combination thereof. Numbered embodiment 171 includes the methods of any of embodiments 133-170, wherein the probiotic comprises an engineered bacterial strain or an ensemble of engineered bacteria. Numbered embodiment 172 includes the methods of any of embodiments 133-169, wherein the treatment comprises an adjuvant given in combination with the first-line treatment for the cancer to improve efficacy of the first-line treatment. Numbered embodiment 173 includes the methods of any of embodiments 133-169, wherein the treatment comprises adoptive cell transfer to a target microbial antigen associated with the cancer or the cancer microenvironment. Numbered embodiment 174 includes the methods of any of embodiments 133-169, wherein the treatment comprises a cancer vaccine that utilizes a microbial antigen associated with the cancer or the cancer microenvironment. Numbered embodiment 175 includes the methods of any of embodiments 133-169, wherein the treatment comprises a monoclonal antibody against a microbial antigen associated with the cancer or the cancer microenvironment. Numbered embodiment 176 includes any of the methods of embodiments 133-169, wherein the treatment includes an antibody-drug conjugate designed to target, at least in part, a microbial antigen associated with the cancer or the cancer microenvironment.Numbered embodiment 177 includes any of the methods of embodiments 133-169, in which the treatment includes a multivalent antibody, antibody fragment, or antibody derivative thereof designed to at least partially target one or more microbial antigens associated with the cancer or the cancer microenvironment. Numbered embodiment 178 includes any of the methods of embodiments 133-169, in which the treatment includes a targeted antibiotic against a specific type of microorganism or a class of functionally or biologically similar microorganisms. Numbered embodiment 179 includes any of the methods of embodiments 133-169, in which two or more of the following types of treatment are combined, with at least one type taking advantage of the presence or abundance of a microorganism in the cancer to enhance the overall efficacy of the treatment: small molecules, biologics, engineered host-derived cell types, probiotics, engineered bacteria, natural but selective viruses, engineered viruses, and bacteriophages.

[0196] Numbered embodiment 180 includes a method of training a predictive model, comprising providing a biological sample of one or more subjects comprising one or more microbial extracellular vesicles and associated health classifications of the one or more subjects, enriching the one or more microbial extracellular vesicles with one or more affinity reagents, thereby producing one or more enriched microbial extracellular vesicles, detecting one or more molecular analytes from the enriched one or more microbial extracellular vesicles, and training the predictive model with the one or more molecular analytes and associated health classifications of the one or more subjects. Numbered embodiment 181 includes the method of embodiment 180, wherein the biological sample comprises non-microbial extracellular vesicles. Numbered embodiment 182 includes the method of embodiment 180 or 181, further comprising quantifying the abundance of the one or more molecular analytes. Numbered embodiment 183 includes the method of any of embodiments 180-182, wherein the one or more molecular analytes of the one or more microbial extracellular vesicles include vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof. Numbered embodiment 184 includes the method of any of embodiments 180-183, wherein the one or more molecular analytes of the non-microbial extracellular vesicles include vesicle-associated cell-free non-microbial DNA, cell-free non-microbial RNA, non-microbial DNA, non-microbial RNA, non-microbial proteins, non-microbial metabolites, non-microbial lipids, non-microbial glycans, or any combination thereof. Numbered embodiment 185 includes the method of any of embodiments 180-184, wherein the biological sample includes a liquid biological sample, a tissue biological sample, or any combination thereof. Numbered embodiment 186 includes the methods of any of embodiments 180-185, wherein the liquid biological sample comprises plasma, serum, whole blood, urine, cerebrospinal fluid, saliva, sweat, tears, lymphatic fluid, exhaled breath condensate, or any dilution or processed fraction thereof. Numbered embodiment 187 includes the methods of any of embodiments 180-186, wherein the whole blood comprises plasma, white blood cells, red blood cells, platelets, or any combination thereof.Numbered embodiment 188 includes the methods of any of embodiments 180-185, wherein the tissue biosample comprises a tissue homogenate. Numbered embodiment 189 includes the methods of any of embodiments 180-188, wherein the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs. Numbered embodiment 190 includes the methods of any of embodiments 180-189, wherein the one or more microbial cell wall molecular motifs or the one or more non-microbial cell wall molecular motifs comprise canonical cell wall components such as lipopolysaccharides (LPS), lipoproteins, lipopeptides, lipoteichoic acid (LTA), lipoarabinomannan, chitin, beta-glucan, zymosan, and glycosylphosphatidylinositol (GPI) anchored proteins, or any combination thereof. Numbered embodiment 191 includes any of the methods of embodiments 180-190, wherein the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, and the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single chain variable fragment (scFv), a single chain antibody, or any combination thereof. Numbered embodiment 192 includes the methods of any of embodiments 180-191, wherein the recombinant innate immune pattern recognition receptor comprises Toll-like receptor 1 (TLR1, CD281), Toll-like receptor 2 (TLR2, CD282), Toll-like receptor 4 (TLR4, CD284), Toll-like receptor 5 (TLR5), Toll-like receptor 6 (TLR6, CD286), Toll-like receptor 10 (TLR10, CD290), CD14, lipopolysaccharide binding protein, Dectin-1, Dectin-2, Mannose receptor (CD206), DC-SIGN, SIGNR1, Langerin, Mannose binding lectin, Ficolin-1, Ficolin-2, Ficolin-3, any combination thereof, or any derivative thereof. Numbered embodiment 193 includes the methods of any of embodiments 180 to 192, wherein the derivatives thereof include epitope-tagged and full-length recombinant innate immune pattern recognition receptors or recombinant soluble ectodomains thereof.Numbered embodiment 194 includes the methods of any of embodiments 180-193, wherein the epitope tag comprises an N-terminal or C-terminal 6x histidine tag, green fluorescent protein (GFP), myc, hemagglutinin (HA), Fc fusion, biotin, or any combination thereof. Numbered embodiment 195 includes the methods of any of embodiments 180-191, wherein the one or more antibodies, aptamers, single chain variable fragments (scFv), or single chain antibodies comprise an ep...

Claims

1. 1. A method for identifying a disease in a subject, comprising: (a) enriching one or more microbial extracellular vesicles in a biological sample from a subject containing one or more microbial extracellular vesicles with one or more affinity reagents; (b) detecting one or more molecular analytes from the one or more microbial extracellular vesicles; and (c) identifying the disease in the subject based on the one or more molecular analytes derived from the one or more microbial extracellular vesicles.

2. The method of claim 1, wherein the one or more molecular analytes of the one or more microbial extracellular vesicles comprise vesicle-associated cell-free microbial DNA, cell-free microbial RNA, microbial DNA, microbial RNA, microbial proteins, microbial metabolites, microbial lipids, microbial glycans, or any combination thereof.

3. The method described in claim 1, wherein the one or more affinity reagents are configured to couple to one or more microbial cell wall molecular motifs or one or more non-microbial cell wall molecular motifs.

4. The method of claim 1, wherein the one or more affinity reagents comprise a recombinant innate immune pattern recognition receptor or one or more antibodies, and the one or more antibodies comprise a polyclonal antibody, a monoclonal antibody, a recombinant antibody, an aptamer, a single-chain variable fragment (scFv), a single-chain antibody, or any combination thereof.

5. The enrichment is (a) contacting the biological sample with the one or more affinity reagents to form a capture reagent-molecular motif interaction complex; (b) contacting the capture reagent-molecular motif interaction complex with a support; 10. The method of claim 1, comprising: (c) separating the support from the biological sample to concentrate the capture reagent-molecular motif interaction complex.

6. The method of claim 1, wherein the one or more affinity reagents comprise an epitope tag.

7. The method of claim 1, wherein the detecting comprises nucleic acid sequencing, polymerase chain reaction (PCR), hybridization-based analysis, or immunoassay analysis of the one or more molecular analytes.

8. The method of claim 1, wherein the disease includes cancer.

9. The method of claim 1, wherein identifying the disease includes predicting the disease by a predictive model, the predictive model being trained with one or more molecular analytes obtained from one or more microbial extracellular vesicles of one or more subjects and associated diseases of the one or more subjects.

10. A method for identifying a disease in a subject, comprising: (a) enriching one or more microbial extracellular vesicles with one or more first affinity reagents and one or more non-microbial extracellular vesicles in a subject's biological sample containing one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby producing one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles; (b) detecting a first abundance of one or more molecular analytes from the one or more enriched microbial extracellular vesicles and a second abundance of one or more molecular analytes from the one or more enriched non-microbial extracellular vesicles; (c) identifying the disease in the subject from an association between a combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes and a model of disease associated with a third abundance of one or more molecular analytes derived from one or more microbial extracellular vesicles and a fourth abundance of one or more molecular analytes derived from one or more non-microbial extracellular vesicles.

11. A method for identifying a treatment for a disease in a subject, comprising: (a) enriching one or more microbial extracellular vesicles in a subject's biological sample containing one or more microbial extracellular vesicles with one or more affinity reagents, thereby producing one or more enriched microbial extracellular vesicles; (b) detecting one or more molecular analytes from the one or more enriched microbial extracellular vesicles; and (c) identifying the treatment for the disease in the subject based on the one or more molecular analytes.

12. A method for identifying a treatment for a disease in a subject, comprising: (a) enriching one or more microbial extracellular vesicles with one or more first affinity reagents and one or more non-microbial extracellular vesicles in a subject's biological sample containing one or more microbial extracellular vesicles and one or more non-microbial extracellular vesicles with one or more second affinity reagents, thereby producing one or more enriched microbial extracellular vesicles and one or more enriched non-microbial extracellular vesicles; (b) detecting a first abundance of one or more molecular analytes from the one or more enriched microbial extracellular vesicles and a second abundance of one or more molecular analytes from the one or more enriched non-microbial extracellular vesicles; (c) identifying the treatment for the disease in the subject from an association between a combination of the first abundance of one or more molecular analytes and the second abundance of one or more molecular analytes and a model of disease treatment associated with a third abundance of one or more molecular analytes derived from one or more microbial extracellular vesicles and a fourth abundance of one or more molecular analytes derived from one or more non-microbial extracellular vesicles.

13. A method for training a predictive model, comprising: (a) enriching one or more microbial extracellular vesicles in a biological sample of one or more subjects, the one or more microbial extracellular vesicles containing one or more microbial extracellular vesicles and associated health classifications of the one or more subjects, with one or more affinity reagents, thereby producing one or more enriched microbial extracellular vesicles; (b) detecting one or more molecular analytes from the one or more enriched microbial extracellular vesicles; and (c) training the predictive model with the one or more molecular analytes and the associated health classifications of the one or more subjects.

14. A computer-implemented method for training a predictive model, comprising: (a) receiving from a database sequencing data of one or more subject biological samples and corresponding health classifications, wherein the biological sample sequencing data includes sequences of one or more analytes of one or more microbial extracellular vesicles; (b) training the predictive model with sequencing data of the biological samples of the one or more subjects and the corresponding health classifications.

15. A computer system configured to identify a disease in a subject, comprising: (a) one or more processors; (b) a non-transitory computer-readable storage medium containing software, the software causing the one or more processors of the computer system to: (i) receiving a biological sample from a subject containing one or more microbial extracellular vesicles; (ii) enriching the one or more microbial extracellular vesicles with one or more affinity reagents; (iii) detecting one or more molecular analytes from the one or more microbial extracellular vesicles; and (iv) a non-transitory computer-readable storage medium containing executable instructions for identifying the disease in the subject based on the one or more molecular analytes obtained from the one or more microbial extracellular vesicles.

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