Biomarkers for cancer detection

Abstract

The present disclosure relates to biomarker sets for cancer detection, as well as machine learning and methods for identifying the biomarkers sets, and machine learning and algorithmic methods for using the biomarkers sets for cancer detection.

Description

Attorney Docket No. NXON-014 / 04WO 346247-2095 BIOMARKERS FOR CANCER DETECTIONCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U. S. Provisional Patent Application No. 63 / 720,158 filed on November 13, 2024, U. S. Provisional Patent Application No. 63 / 730,415 filed on December 10, 2024, U. S. Provisional Patent Application No. 63 / 794,283 filed on April 24, 2025, and U. S. Provisional Patent Application No. 63 / 815,446 filed on May 30, 2025, the contents of which are hereby incorporated in their entirety by this reference.BACKGROUND

[0002] Microparticles are small, typically nano-scale (sub-micron), vesicular bodies released from cells, which contain various biomolecules such as proteins, lipids, and nucleic acids.Microparticles are found generally in all biological fluids including blood, urine, and saliva. Microparticles may be of different cellular origins, and may include, by way of example, extracellular vesicles secreted by cells (e.g., released into the extracellular space through fusion of multivesicular bodies with the plasma membrane), exosomes, lipid rafts, or portions of cell membrane from degraded, damaged, or dying cells. Microparticles can be isolated or enriched from a biological sample through various methods, such as but not limited to size-exclusion chromatography or centrifugation.

[0003] Microparticles were first discovered in the 1980s and were initially thought to be cellular debris. However, they are now understood to be involved in intercellular communication and play a role in various physiological and pathological processes. Microparticles can transfer biomolecules such as proteins and nucleic acids between cells, thereby influencing the recipient cell’s behavior. In the case of cancer, it has been shown that cancerous cells can release microparticles that contain oncogenic proteins and RNA, which can be taken up by neighboring cells and contribute to the development and progression of cancer. It has also been shown that microparticles released from cancerous cells and associated myeloid cells in a tumor microenvironment can be derived from multiple biological fluids.

[0004] It has been the hope that microparticle-derived biomarkers can provide diagnostic, prognostic and stratification markers of cancer and drug responsiveness thereof. However, inAttorney Docket No. NXON-014 / 04WO 346247-2095 practice, the usefulness of such biomarkers has been limited by an inability to isolate microparticles and detect microparticle-derived biomarkers with sufficient yield and reproducibility. A number of approaches have been utilized to recover and assess the presence of biomarkers in isolated microparticles. However, to date, such efforts have been limited by relatively large background signals and an inability to evaluate signal beyond the most abundant proteins.

[0005] Thus, there is a need for refined biomarker sets for the diagnosis, prognosis, and stratification of cancer states, as well as computational methods related to the same. Provided herein are machine learning and algorithmic methods and biomarker sets that address this need.

[0006] Patents, patent applications, patent application publications, journal articles and protocols referenced herein are incorporated by reference.SUMMARY

[0007] The present disclosure relates to biomarker sets (panels) for determining presence or stage of a cancer, machine learning and algorithmic methods for identifying the biomarkers sets, and machine learning and algorithmic methods for using the biomarkers sets determining the presence or the stage of the cancer.

[0008] In one aspect, provided herein is a method for determining presence or stage of a cancer in a subject.

[0009] In certain embodiments, the method comprises (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 3.2; and (c) based on the quantification of the two or more proteins, determining the presence or the stage of the stage 1 uterine cancer in the subject.

[0010] In certain embodiments, the method comprises: (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) assaying the expression level of two or more proteins selected from Table 3.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set toAttorney Docket No. NXON-014 / 04WO 346247-2095 at least one trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 uterine cancer, optionally at an accuracy of at least 95%, al least 96%, at least 97%, or at least 98%; and (d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 uterine cancer, to determine the presence or the stage of the stage 1 uterine cancer.

[0011] In certain embodiments, the method comprises (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 4.2; and (c) based on the quantification of the two or more proteins, determining the presence or the stage of the stage 1 breast cancer in the subject.

[0012] In certain embodiments, the method comprises: (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) assaying the expression level of two or more proteins selected from Table 4.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 breast cancer, optionally at an accuracy of at least 95%. at least 96%, at least 97%, or at least 98%; and (d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 breast cancer, to determine the presence or the stage of the stage 1 breast cancer.

[0013] In certain embodiments, the method comprises: (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) quantifying two or more proteins in the microparticle preparation; and (c) based on the quantification of the two or more proteins, determining the presence or the stage of thtage CRC in the subject. Optionally, the two or more proteins are selected from Table 5.2, 6.2, 7.2, or combinations thereof.

[0014] In certain embodiments, the method comprises: (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) assaying the expression level of two or more proteins fromAttorney Docket No. NXON-014 / 04WO 346247-2095 the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins; (c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for an early stage CRC, optionally at an accuracy of at least 95%, at least 96%, at least 97%, or at least 98%: and (d) electronically outputting a report that identifies said classification of the sample as positive or negative for the early stage CRC, to determine the presence or the CRC in a subject. Optionally, the two or more proteins are selected from Table 5.2, 6.2, 7.2, or combinations thereof.Optionally, the at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC, Optionally, the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of: a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 CRC or not at an accuracy of at least 98%; a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 0 CRC or not at an accuracy of at least 98%; and a third trained binary classifier configured to, based on the test data, classify the subject as having an advanced adenoma or not at an accuracy of at least 98%.[0015| In certain embodiments, the method comprises: (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) quantifying two or more proteins in the microparticle preparation; and (c) based on the quantification of the two or more proteins, determining the presence or the stage of the NSCLC in the subject, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC Optionally, the two or more proteins are selected from Table 9.2, 10.2, 11.2, 12.2, 13.2, or combinations thereof.[0016 | In certain embodiments, the method comprises: (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles; (b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the NSCLC at an accuracy of at least 98%; and (d) electronically outputting a report that identifies said classification of the sample as positive or negative for the NSCLC, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC, to determine theAttorney Docket No. NXON-014 / 04WO 346247-2095 presence or the stage of the NSCLC in the subject. Optionally, the two or more proteins are selected from Table 9.2, 10.2, 11,2, 12.2, 13.2, or combinations thereof. Optionally, the at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC. Optionally, the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 NSCLC or not at an accuracy of at least 98%; a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 2 NSCLC or not at an accuracy of at least 98%; a third trained binary classifier configured to, based on the test data, classify the subject as having a stage 3 NSCLC or not at an accuracy of at least 98%; and a fourth trained binary classifier configured to, based on the test data, classify the subject as having a stage 4 NSCLC or not at an accuracy of at least 98%.

[0017] In another aspect, provided herein is a method for monitoring cancer treatment in a subject.

[0018] In certain embodiments the method comprises: (a) assessing a biological fluid sample from a subject that previously was receiving a cancer therapy, in accordance with a method for determining presence or stage of a cancer in a subject as provided above, to determine the presence or the stage of the cancer; and (b) selecting the subject to be a candidate to receive at least one additional administration of the cancer therapy based on the presence or stage of the cancer.

[0019] In certain embodiments the method comprises: (a) assessing a biological fluid sample from a subject that previously was administered a therapeutic agent for treating a cancer, in accordance with a method for determining presence or stage of a cancer in a subject as provided above, to determine the presence or the stage of the cancer; and (b) selecting the subject to be a candidate to receive at least one dose of a different therapeutic agent based on the classification.

[0020] In another aspect, provided herein is a computer system with at least one trained classifier for determining a presence or a stage of a cancer.

[0021] In certain embodiments, the computer system comprises (a) a processor; and (b) a memory, coupled to the processor, the memory storing: (i) test data for a sample from a subject,Attorney Docket No. NXON-014 / 04WO 346247-2095 the test data comprising values indicating a quantitative measure of two or more proteins in a microparticle preparation from a biological fluid sample; and (ii) at least one trained classifier configured to, based on the test data, classify the subject as having a cancer or not having the cancer, optionally at an accuracy of at least 95%, at least, 96%, at least 97%, or at least 98%; and (iii) computer executable instructions for implementing the at least one trained classifier on the test data.

[0022] Optionally, the cancer is a stage 1 uterine cancer, and the two or more proteins are selected from Table 3.2.

[0023] Optionally, the cancer is a stage 1 breast cancer, and the two or more proteins are selected from Table 4.2,

[0024] Optionally, the cancer is an early stage colorectal cancer, and the two or more proteins are selected from Table 5.2, 6.2, 7,2, or combinations thereof.

[0025] Optionally, the cancer is a non-small cell lung cancer, and the two or more proteins are selected from Table 9.2, 10.2, 11.2, 12.2, 13.2, or combinations thereof.

[0026] Optionally, the at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the cancer. Optionally, the multiclass classifier is based on two or more trained binary classifiers.

[0027] In another aspect, provided herein is a panel of two or more biomarkers for use in determining a presence or a stage of a cancer.

[0028] In certain embodiments, the cancer is a stage 1 uterine cancer, and the two or more biomarkers are selected from Table 3.2.

[0029] In certain embodiments, the cancer is a stage 1 breast cancer, and the two or more biomarkers are selected from Table 4,2,

[0030] In certain embodiments, the cancer is an early stage colorectal cancer, and the two or more biomarkers are selected from Table 5.2, 6.2, 7.2, or a combination thereof.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0031] In certain embodiments, the cancer is a non-small cell lung cancer, and the two or more biomarkers are selected from Table 9,2, 10.2, 11.2, 12,2, or 13.2, or a combination thereof,

[0032] In another aspect, provided herein is a kit comprising reagents for detecting the biomarkers of the any one of the panels provided herein, for use in determining the presence or the stage of a cancer.BRIEF DESCRIPTION OF THE DRAWINGS

[0033] FIG. 1 shows a partial least squares (PLS) plot, with each plot representing a sample, demonstrating significant differences in biomarker expression levels between non-cancer, early stage (stages 1 / 2) non-small cell lung cancer (NSCLC), and late stage (stages 3 / 4) NSCLC samples.

[0034] FIGS. 2A-2B shows the mapping of biomarkers, which were significantly differentially expressed between stage 1 CRC and non-cancer, to their respective tissues of origin (FIG, 2A) and biological processes (FIG. 2B).

[0035] FIGS. 3A-3C show comparisons of ELISA-based quantification of biomarkers from microparticle preparations between control (non-cancer) samples and stage 1 colorectal cancer (CRC samples.

[0036] FIG. 3D is a three-dimensional plot of the samples shown in FIGS, 3A-3C plotted against the quantification of each of the three biomarkers.

[0037] FIGS. 4A-4B show's the mapping of biomarkers, which w ere significantly differentially expressed between stage 1 breast cancer and non-cancer, to their respective biological pathways (FIG. 4A) and biological processes (FIG. 4B),

[0038] FIGS. 5A-5B shows the mapping of biomarkers, which w ere significantly differentially expressed between stage 1 non-small cell lung cancer ( SCLC) and non-cancer, to their respective tissues of origin (FIG. 5 A) and biological processes (FIG. 5B).

[0039] FIGS. 6A-6C show comparisons of ELISA-based quantification of biomarkers from microparticle preparations between control (non-cancer) samples and stage 1 NSCLC samples.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0040] FIG. 6D is a three-dimensional plot of the samples shown in FIGS. 6A-6C plotted against the quantification of each of the three biomarkers,

[0041] FIGS. 6E-6F are three-dimensional plots of samples plotted against the quantification of additional three-biomarker panels for detecting stage 1 NSCLC

[0042] FIG. 7 A shows comparisons of ELISA-based quantification of a biomarker from microparticle preparations between control (non-cancer) samples and advanced adenoma samples.

[0043] FIG. 7B shows a table listing results of ELISA-based quantification of different exemplary' biomarker panels for detecting advanced adenoma.

[0044] FIG. 8A shows comparisons of MS-based quantification of 5 biomarker from microparticle preparations between control (non-cancer) samples and advanced adenoma samples.

[0045] FIG. 8B shows a table listing parameters of different exemplary biomarker panels for detecting advanced adenoma, based on the 5 biomarkers shown in FIG. 8A.

[0046] FIG. 8C shows a 2 dimensional scatter plot with predictive algorithms plotted of the best 2-plex proteomics model selected from the 5 biomarkers shown in FIG. 8A.

[0047] FIG. 8D shows a 3-dimensional scatter plot with predictive algorithms plotted of the best 3-plex proteomics model selected from the 5 biomarkers shown in FIG. 8A.

[0048] FIG. 8E shows comparisons of ELISA-based quantification of 2 of the 5 biomarkers, from microparticle preparations between control (non-cancer) samples and advanced adenoma samples.

[0049] FIG. 8F shows an ROC curve of the two biomarkers shown in FIG. 8E, with AUC values of 0.912 and 0.752, respectively.DETAILED DESCRIPTION

[0050] There is provided herein improved panels, computer systems, and methods for detection, determination, diagnosis, or prognostication of one or more aspects of a cancer in aAttorney Docket No. NXON-014 / 04WO 346247-2095 subject, based on multiplexed proteomics of microparticle-associated biomarkers. “Multiplexed proteomics” as used herein refers to the analysis of a quantitative measure of differential expression of two or more biomarkers in patients with cancer versus a non-cancerous population. The biomarkers may be proteins or fragments thereof. The aspects of cancer may include one or a combination of: cancer type, cancer stage, cancer presence prognostication of a pre- cancerous state, stratification of patient populations for assigning to a therapeutic regime or a therapeutic trial, longitudinal monitoring of cancer progression, and longitudinal monitoring of patient response to a therapeutic regime.

[0051] The following description is presented to enable a person of ordinary skill in the art to make and use the various embodiments. Descriptions of specific devices, techniques, and applications are provided only as examples. Various modifications to the examples described herein will be readily apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other examples and applications without departing from the spirit and scope of the various embodiments. Thus, the various embodiments are not intended to be limited to the examples described herein and shown, but are to be accorded the scope consistent with the claims.

[0052] Applicant discloses herein methods of isolating microparticles from a subject and analyzing proteomic information from the isolated microparticles to determine one or more aspects of a cancer in the subject, such as the presence or recurrence of a cancer. Such analysis of the isolated microparticles may also be informative with regard to various clinical indications such as, for example, cancer diagnosis, classi fication, monitoring, and assessmen t of therapeutic efficacy.

[0053] In some embodiments, the analysis of proteomic information may involve a computational analysis, machine learning and / or use of a computer system comprising a processor and a memory operably connected to the processor and or the use of cloud based computation. The memory or cloud based system may store a component comprising test data for a sample from a subject, or test data for a plurality of samples, respectively, from each of a plurality of subjects. The component may further comprise a trained algorithm (e.g., a classifier) configured to classify the subject, or a plurality of subjects, as having a cancer or not having the cancer based on the test data. The component may further comprise computer executable instructions for implementing the classifier on the test data. In some embodiments, the computerAttorney Docket No. NXON-014 / 04WO 346247-2095 system may be implemented as a distributed cloud network, comprises a plurality of interconnected nodes, each node comprising a processor and a memory' operably connected to the processor, that are configured to collaboratively execute computational tasks. In some embodiments, the computer system may' be embodied as a standalone laptop or desktop computer, each comprising a processor and a memory operably connected the processor, as well as input / output interfaces for user interaction and peripheral connectivity.

[0054] “Microparticles” as used herein refers to small nano-scale (sub-micron) vesicular bodies released from cells and containing various biomolecules, such as proteins, lipids, and nucleic acids. Microparticles may include, for example, endosome-derived exosomes, plasma membrane-derived shedding vesicles, microvesicles, extracellular particles, extracellular vesicles (EVs), exosomes, exomeres, small EVs, large EVs, supermeres, apoptotic bodies, proteosomes, P2 and P4 particles, and outer membrane vesicles (OMVs).

[0055] “Microparticle-associated proteins” (MAPs) as used herein include proteins associated with microparticles in one of a variety' of ways. A MAP refer to any protein that has been contained within a microparticle (also referred to as intra-vesicular protein), located on the surface of a microparticle, or trapped between aggregated microparticles (also referred to as an inter-vesicular protein). Some MAPs may be “intrinsic MAPs” that were originally found and / or expressed in the cells (“source cells”) from which the microparticle was released. Intrinsic MAPs may include membrane-bound proteins bound to a membrane of the microparticle, which is typically a portion of a membrane from the source cell. If the microparticles are vesicles with a lumen, the intrinsic MAPs may include intra-vesicular proteins comprised in the lumen of the vesicle, which may' be, for example, a sampling of the intracellular environment of the source cell. Some MAPs may be “corona proteins” defining a microparticle’s “microenvironment”, which become associated with the microparticle after release from the source cell through external macromolecular interactions, e.g., protein-protein or receptor-ligand interactions, in one or more microenvironments. As such, corona proteins may be proteins that are not from the source cells of the microparticles, but rather “host proteins” found in the local microenvironments in which the microparticles may have resided, or have traversed, within the subject (i.e. host) after being released from the source cell. By way' of example, and without being limited by theory, if the microparticles are purified from the subject’s plasma in a way (for example using methods provided herein) that preserves or retains the corona proteins, the MAPsAttorney Docket No. NXON-014 / 04WO 346247-2095 may include a sampling of proteins found m the host’s bloodstream, thus reflecting not simply the state of the source cells of the microparticles, but also reflecting an overall disease state of the host. In such a case, the host protein may be considered a host disease response protein. As such, for example, if the subject is suffering from a disease, e.g., cancer, the corona proteins may include proteins that reflect the subject’s response to the cancer even if none or only a subset of the microparticles were released from cancer cells. A MAP includes both a protein while it is associated with a microparticle, as well as after the protein has been dissociated from the microparticle.Methods - General

[0056] In one aspect, the disclosure herein provides for methods of identifying cancer biomarkers based on the expression level of one or more MAPs from biological samples from cancer patients and non-cancer subjects. In another aspect, the disclosure herein also provides for methods of determining an aspect of a cancer in a subject (e.g., diagnosing or prognosticating the presence of a cancer), using cancer biomarkers quantified from a microparticle-enriched fraction, which cancer biomarkers may have been identified using the biochemical and computational methods described herein. As such, the methods of both aspects of the disclosure may include any one of: processes for preparing a microparticle-enriched fraction from a biological sample; isolating MAPs or fragments thereof from the microparticle-enriched fraction; quantifying one or more of the MAPs; and obtaining or receiving quantification data of the one or more MAPs or fragments thereof in the biological sample.

[0057] The methods of the disclosure may comprise providing a microparticle-enriched fraction from a biological sample from the subject, quantifying two or more proteins in the fraction, and determining an aspect of the cancer in the subject based on the quantification of the two or more proteins. The two or more proteins used for determining an aspect of a cancer in a subject may be referred to herein as a “cancer biomarker”.Samples Containing a Bodily Fluid

[0058] The methods of the disclosure may comprise extracting, obtaining, or providing a sample containing a bodily fluid of a subject. In some embodiments, the bodily fluid may be extracted from the subject directly. In some embodiments, the bodily fluid may have been extracted from a subject and optionally processed by a third party, which is then stored, forAttorney Docket No. NXON-014 / 04WO 346247-2095 example in frozen storage, and the bodily fluid may be obtained from storage, or received from the third party. The bodily fluid sample may then be used as a source of microparticles, as described herein below.

[0059] V arious samples containing a bodily fluid from a subject will be apparent to one of skill in the art and may be used in the methods disclosed herein. A bodily fluid may refer to, for example, a sample of fluid isolated from anywhere in the body of the subject, for example a peripheral location, including but not limited to, for example, blood or a fraction thereof (e.g., plasma, serum), urine, sputum, spinal fluid, pleural fluid, interstitial fluid, bile, glandular fluid, exudate, nipple aspirates, lymph fluid, respiratory droplets, intestinal, and genitourinary tracts, tears, saliva, breast milk, lacrimal fluid, fluid from the lymphatic system, semen, cerebrospinal fluid, intra-organ system fluid, ascitic fluid, tumor cyst fluid, synovial fluid, amniotic fluid, ocular fluid, ascites, bronchoalveolar lavage, and combinations thereof. The method of extraction or storage depends on the bodily fluid, and many such methods are known in the art. In some embodiments, the bodily fluid may be a dried bodily fluid that is reconstituted. In some embodiments, the bodily fluid may undergo various processing step prior to isolation or enrichment of microparticles. By way of example, the bodily fluid may be processed to remove cells, or macroscale solids through, e.g., filtration or centrifugation. In exemplary embodiments, the sample is blood, plasma or urine. If the sample is blood, the sample may be centrifuged to remove cellular material and debris such that a plasma or serum fraction is generated, which is then further process to enrich for microparticles, for example as described herein below.Enrichment of Microparticles from a Sample

[0060] The methods of the present disclosure may comprise enriching or isolating microparticles from a biological sample. For example, a population of microparticles may be isolated from the sample according to any methods known to one of skill in the art (see, for example, Cocucci et al. Traffic 8, 2007:742-757; Simpson et al, Proteomics 8, 2008: 4083-4099; Diaz et al., J. Vis. Exp. (134), e57467, doi: 10.3791 / 57467 (2018). In some embodiments, isolating microparticles may comprise isolating or enriching a given sub-population of microparticles, such as microparticles within a given range of diameters or molecular weights, or microparticles having a specific marker indicating, e.g., a specific class or source of the microparticles.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0061] In certain embodiments, an exemplary method of isolating or enriching for microparticles involves size exclusion chromatography, although other methods of enrichment may be used alternative or in combination, including but not limited to serial centrifugation or ultracentrifugation (Raposo et al., J Exp Med 183, 1996: 1161-72), density gradients (e.g. sucrose density gradients), alternating current (AC) electrokinetic separation; electrophoresis (e.g. organelle electrophoresis), electroporation, anion exchange and / or gel permeation chromatography, magnetic activated sorting (e.g., using magnetic beads), filtration (e.g., microfiltration, nanomembrane ultrafiltration concentration), and microchips with microfluidic technology. Microparticles may also be, in the alternative or in combination, isolated from a sample using affinity capture or affinity capture methods in solution or solid phase. For example, these affinity methods may be immunoaffinity methods (e.g., immunoprecipitation), but in other embodiments, such methods employ other reagents which bind specifically to proteins. Various methods for the isolation of microparticles can be found, for example, in U. S, Patent Nos.6,899,863, 6,812,023, Taylor and Gercel-Taylor, Gynecol Oncol 110, 2008: 13-21, Cheruvanky et al, Am J Physiol Renal Physiol 292, 2007: F1657-61, and Nagrath et al, Nature 450, 2007: 1235-9. A sample that has undergone microparticle enrichment or isolation may be referred to herein as a “■microparticle preparation”.

[0062] In certain embodiments, microparticles, either in enriched form or as found natively in the sample, can be contacted with a tissue-specific reagent to isolate microparticles derived from a specific tissue. Exemplary tissues of interest from which microparticles can be derived, and isolated in a tissue-specific manner, may include, for example, brain, adrenal gland, endocrine gland, pituitary, hypothalamus, parathyroid, uterus, heart, blood vessel, stomach, trachea, pharynx, gums, hair, scalp, subcutaneous tissue, fallopian tube, reproductive tract, urethra, skin, bone, stem cell, umbilical cord, placenta, lymphocyte, monocyte, macrophage, formed blood cell, smooth muscle, skeletal muscle, connective tissue, spinal cord, kidney, bladder, anus, bone, breast, prostate, lung, cervix, colon, rectum, uterus, esophagus, skin, liver, pharynx, mouth, neck, ovary, pancreas, lung, eye, intestine, mouth, thyroid, GI tract, and endometrium.

[0063] In certain embodiments, microparticles may be isolated from the sample with an organelle-specific reagent to isolate the microparticles derived from organelles of cells in a specific tissue. Particular organelles of interest may include, for example, plasma membrane,Attorney Docket No. NXON-014 / 04WO 346247-2095 peroxisome, smooth ER, rough ER, lysosome, mitochondria, and nucleus, insome embodiments, each step is performed using multiple microparticle-specific reagents.

[0064] In some embodiments, the entire population of microparticles from the sample may be contacted with a reagent that binds to microparticles derived from multiple tissue types rather than one that binds microparticles derived from a specific tissue.

[0065] Once a population of microparticles has been isolated or enriched from a sample, the microparticles in the microparticle preparation may be subjected to further selection steps to isolate a subpopulation of microparticles from the more general microparticle population isolated from the sample. For example, a population of microparticles isolated from a sample may comprise cancer cell-derived and non-cancer cell-derived microparticles. In some embodiments, the population of microparticles may be subjected to a further selection step to isolate cancer cell-derived microparticles. Conversely, the population of microparticles may be subjected to a further selection step to isolate non-cancer cell-derived microparticles.Exemplary enrichment process: Size exclusion chromatography

[0066] In certain embodiments, the microparticles may be enriched from a biological sample using size exclusion chromatography (SEC). In certain embodiments, use of a SEC column to isolate microparticles allows for suspension of the resin beads, such as a mobile bead column or other resin beads, during washing.

[0067] In certain embodiments, the SEC column includes: a lower end including an outflow opening; a lower porous support; a layer of resin on the lower porous support, the resin having specific size exclusion for a population of microparticles; an upper porous support; and an upper end including an inflow opening, wherein the resin between the lower porous support and the upper porous support is structured and arranged to permit removal of the upper porous support from the column without substantial removal of the resins.

[0068] In other embodiments, the resin may be fixed between two semi-porous frits such that the resin beads may be suspended and such that the upper frit may be removed during w ashing of the resin to remove background compounds.

[0069] The volume of the resin in the column may vary but is typically less than the total packed volume of the column between the lower porous support and the upper porous support.Attorney Docket No. NXON-014 / 04WO 346247-2095 Preferably, the volume of the resin in the column is no greater than 50%, no greater than 40%, no greater than 30%, no greater than 25%, or no greater than 20% of the total packed volume of the column between the lower porous support and the upper porous support.

[0070] SEC columns can be either large or small. In some embodiments, the SEC column contains an agarose / sepharose slurry. In exemplary embodiments, columns may be single use for individual patient samples. For example, purification of the microparticles for preparing the microparticle preparation may involve the use of a qEV original Gen235mm columns (Izon labs; Medford MA) packed with commercial grade Sepharose and / or agarose beads for size exclusion chromatography (SEC). The columns may be washed and allowed to equilibrate to room temperature before being loaded with a sample.

[0071] In the purification step, water may be used as a mobile phase. In some embodiments, the water may be distilled water, e.g., double distilled water, deionized water, or deionized and distilled water. In some embodiments, a microparticle-containing sample such as plasma, for example, may be added to the column and water may be used as the mobile phase. Without desiring to be limited by theory, SEC columns provide enrichment of MAPs as follows: smaller size material or soluble proteins not associated with microparticles remain associated with the column, while other larger components of the sample such as microparticles (along with the associated MAPs) are eluted from the column through a series of washing and elution steps. In embodiments where the sample contains plasma, the water washes may be configured such that high abundance proteins are eluted in later time collected fractions from the column while microparticles elude in the early fractions, e.g., < 10 minutes, and smaller materials remain associated with the column. In some embodiments, the use of water, e.g., distilled water, as the elution buffer offers certain advantages, for example higher yield and improved retention of corona proteins associated with microparticles, including host proteins.

[0072] Various modifications to the described purification protocol will be readily apparent to one skilled in the art in view of the present disclosure. For example, the number of elution steps may be modified or adjusted to meet specific purposes. The column may be decorated with reagents that assist in microparticle capture, e.g., for affinity-based purifications.

[0073] The enrichment process as described above may allow for microparticle enrichment that is easily accessible for downstream microparticle analysis. This purification process mayAttorney Docket No. NXON-014 / 04WO 346247-2095 serve to remove excess background protein and lipid from serum, plasma, and other microparticle- containing samples. The purification may be non-denaturing to yields an enriched sample of microparticles and MAPs, with or without protein inhibitors. Without desiring to be limited by theory, the enriched microparticle fraction may be further analyzed for elements of specific origin without the general problem of steric inhibition by lipids and high abundance proteins. Further, the purification allows for bench top methods such as enzyme-linked immunosorbent assay (ELISA), and magnetic beads, in addition to more sophisticated but high throughput technologies such as protein mass spectrometry and immuno-analysis to be deployed,

[0074] Once the microparticle preparations are made, they can be further processed to quantify MAPs associated with the enriched microparticles for, as noted above and described in further detail below, methods of identifying cancer biomarkers, or methods of determining an aspect of a cancer in a subject.Analysis of expression levels of MAPs for identification of cancer biomarkers

[0075] The present disclosure provides methods of identifying cancer biomarkers based on the expression status or expression level of a plurality of MAPs associated with microparticles from cancer and non-cancer subjects.

[0076] In certain embodiments, the method may comprise receiving or obtaining quantification data of a plurality of MAPs in the biological sample obtained from at least one cancer cohort comprising a plurality of cancer patients and at least one non-cancer cohort comprising a plurality of non-cancer subjects. Hie quantification data may be based on a proteomic analysis of MAPs from microparticle preparations from different cohorts of cancer patients and non-cancer subjects.

[0077] In certain embodiments, a microparticle preparation may be processed to isolate MAPs and remove non-protein components, such as cell membranes and other lipid components, or non-protein components of a microparticle lumen. By way of example, the microparticles may be subjected to vesicle lysis using standard methods (e.g. urea or guanidinium buffer extractions).Attorney Docket No. NXON-014 / 04WO 346247-2095

[0078] Once MAPs or fragments thereof are isolated from microparticles, they can be quantified using one of various proteomic analysis methods and platforms known in the art. For example, proteomic analysis may be performed by a mass spectrometer (MS). In some embodiments, the MS may be Liquid Chromatography with tandem mass spectrometry (‘ LC-MS / MS”).

[0079] MS quantification data may be analyzed by various methods known in the art. For example, software tools may be used for Data Dependent Acquisition (DDA) spectral library construction and subsequent Data Independent Acquisition (DIA) analysis. The analysis uses raw data as input files and set corresponding parameters based on human database, then perform identification and quantitative analysis. By way of example, identified peptides that satisfy a condition of False Discovery' Rate (FDR) <=1% may be used to construct the final spectral library. One or more of Gene Ontology (GO), Clusters of Orthologous Groups of proteins (COG), and Pathway functional annotation analysis may be also performed in above pipeline. MSstats, which core algorithm is linear mixed effect model, may be used to process DIA quantification result data according to the predefined comparison group, and then a significance test may be performed based on the model. Thereafter, differential protein screening may be performed based on, e.g., Fold Change and statistical significance, e.g., p-value or adjusted p-value (q-value) that is adjusted through, e.g., a Benjamini-Hochberg correction or other correction methods. In some embodiments, a protein may be designated as differentially expressed if the calculated fold change of an expression level of a protein between cancer and non-cancer cohorts is greater than about 1.5, greater than about 1.6, greater than about 1,8, greater than about 2, greater than about 2.2, greater than about 2.4, greater than about 2.5, greater than about 2.6, greater than about 2.8, or greater than about 3. greater than about 1.5. In some embodiments, a protein may be designated as significantly differentially expressed if the calculated p-value between an expression level of a protein between cancer and non-cancer cohorts is less than 0.5, less than 0.4, less than 0.3, less than 0.2, less than 0.1, or less than 0.05. In some embodiments, a protein may be designated as significantly differentially expressed if the calculated q-value (which may be, e.g., an adjusted p-value using a Benjamini-Hochberg correction or other correction methods) between an expression level of a protein between cancer and non-cancer cohorts is less than 0.5, less than 0.4, less than 0.3, less than 0.2, less than 0.1, or less than 0.05.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0080] By way of example, a mass spectrometer such as Eclipse™ may be used to acquire mass spectrometry (MS) data from samples, optionally in Data Independent Acquisition (DIA) mode. A statistical software package such as MSstats may be used to apply intra-system error correction and / nomialization for each sample. Then based on the predefined comparison groups and the linear mixed effect model, the significance of differentially expressed proteins (DEPs) may be evaluated. Filtration criteria (e.g., Fold change (increase or decrease) > 2 and p-value < 0.05) may be used to determine significant differential proteins that are then analyze by various methods such as volcano plots.

[0081] Principal component analysis (PCA) may also be applied to the analysis of expression levels of MAPs, PCA is a method of dimension reduction that combines multiple variables to a new set of integrated variables, and then selects several (usually 2-3) to represent as much original information as possible, to achieve the purpose of dimension reduction. PCA is mainly used to observe the trend of separation between groups in the experimental model, and whether there are exceptional value points, and reflect the inter- and intra- group variations from the original data.

[0082] Analysis of the expression levels of MAPs may allow for identification of cancer biomarkers (biomarker clusters) that can be used for diagnosis of cancer, or determination of prognosis of a test subject with cancer, etc. Certain biomarker clusters may be better suited for diagnostic methods, compared to prognostic methods (or vice versa), or be better for some cancers, than for others, or be suited as a pan cancer biomarker cluster. Accordingly, the expression profiles of multiple biomarker clusters may be analyzed in order to make an accurate diagnosis, determination of prognosis, etc.

[0083] Such expression level analysis methods may include comparing the expression level of two or more MAPs from microparticles from the test subject (i.e. the subject from which the microparticles were isolated) with the expression level of the two or more proteins in samples from a plurality (cohort) of non-cancer subjects or cohort of cancer subjects. The expression levels from samples from the plurality of control subjects may be simultaneously obtained with the test subject expression levels or may constitute a set of numerical values stored on a computer or on computer readable medium. In certain embodiments, the control subjects may be of the same sex, disease stage and of a similar age as the test subject. Control subjects may also be of a similar racial background as the test subject, but need not necessarily be the same.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0084] Comparison of the expression levels of the two or more MAPs in samples from the test subject and from a plurality of control subjects may be performed manually or automatically by a computer program. The expression level of the two or more MAPs in the sample from the test subject may be compared individually to the expression level of the two or more MAPs from samples in each control subject, or the expression level of the two or more microparticle-associated proteins in the sample from the test subject may be compared to an average of the expression levels from samples from the plurality of control subjects. In certain embodiments, the values for the expression levels of the two or more proteins in samples from both the test subject and the plurality of control subjects may be transformed. For example, the expression levels may be transformed by taking the logarithm of the value. Moreover, the expression levels may be normalized by, for example, dividing by the median expression level among all of the samples.

[0085] In certain embodiments, the expression level of the two or more microparticle- associated proteins in samples from test cohort (e.g., a cohort of cancer patients) may be increased relative to the expression level of the two or more MAPs in samples from a plurality of control cohort (e.g., a cohort of non-cancer subjects). In other embodiments, the expression level of the two or more MAPs in the samples from the test cohort may be decreased relative to the expression level of the two or more MAPs in samples from the control cohort. Typically, an expression level is said to be increased or decreased relative to a second expression level if the difference between the two expression levels is statistically significant. The difference between two levels is considered to be statistically significant if it was unlikely to have occurred by chance. Statistical significance may be measured by any means known in the art, such as, for example, Fisherian statistical hypothesis testing or the Neyman-Pearson lemma. In certain embodiments, the two or more proteins may not be expressed in samples from the plurality of controls but will be expressed in the sample from the test subject. In other embodiments, the two or more proteins may not be expressed in the sample from the test subject but will be expressed in samples from the plurality of controls.

[0086] In certain embodiments, a MAP may be designated as a cancer biomarker based on one or more computational analyses, e.g. based on one or more machine learning-based analyses of the MAP’s differential expression between cancer and non-cancer cohorts. Examples of machine learning based analysis includes but are not limited to Receiver operating characteristic (ROC)Attorney Docket No. NXON-014 / 04WO 346247-2095 curve analysis, random forest (RF) modeling, logistical regression modeling, Exhaustive Feature Selection (EFS), and Recursive Feature Elimination (RFE),

[0087] In certain embodiments, a MAP may be designated as a cancer biomarker based on an evaluation of a Receiver operating characteristic (ROC) curve derived from the MAP’s differential expression data comparing cancer and non-cancer cohorts. ROC curves are constructed based upon the sensitivity and specificity of the protein of interest, and the area under the curved (AUC) of such ROC curves can be compared to the control data (historical or concurrent) and utilized to define the significance of the observations allowing for an evaluation of sensitivity, specificity, positive and negative predictive values of relevance of the protein signal to the disease state. In an ideal situation, a quantitative cutoff would exist that will perfectly distinguish cancer from non-cancer samples. In this ideal situation, the area under the curve (AUC) of the ROC curve would be calculated to be 1. By contrast, a random analyte which has no predictive value may be calculated to have an AUC of 0.5. As such, in a case for example where a ROC curve is generated based differential expression of a protein biomarker between cancer and non-cancer cohorts, a biomarker having an AUC of the ROC curve that is closer to 1 would be considered to have a higher predictive value for distinguishing cancer from non-cancer samples. In some embodiments, a MAP that is differentially expressed between cancer and non-cancer cohorts may be designated as a cancer biomarker if it has an AUC of the ROC curve of greater than about 0.8, greater than about 0.85, greater than about 0.9, greater than about 0.95, greater than about 0.96, greater than about 0.97, greater than about 0.98, greater than about 0.99, or about 1.

[0088] A RF model iteratively builds decision trees by selecting random subsets of features and data points. During this process, it calculates the importance of each feature by measuring how much the tree nodes using that feature reduce impurity. Features with higher impurity reduction are considered more important and receives a higher feature importance metric, to be selected for inclusion in the final feature set. In certain embodiments, one or more MAPs may be designated as a cancer biomarker based on an RF model evaluating of the differential expression data comparing cancer and non-cancer cohorts of a plurality of MAPs. In certain embodiments, a feature importance metric of a given MAP may be based on the MAP’s contribution to the Mean Decrease Impurity (Gini importance) of the RF algorithm.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0089] In certain embodiments, one or more MAPs may be designated as a cancer biomarker based on Logistic Regression. Logistic regression is a statistical model used for binary classification tasks, where the outcome variable is categorical and has two possible outcomes, e.g. for classifying cancer vs. non-cancer. The model estimates the probability that a given instance (e.g. a microparticle preparation from a subject suspected of cancer) belongs to a particular category (e.g., cancer vs. non-cancer) based on one or more independent variables, which may be the respective expression level of a plurality of MAPs. Logistic Regression model trained on the differential expression levels of a plurality of MAPs may be used to identify MAPs that provide a high degree of accuracy for predicting cancer vs. non-cancer. A given model may be validated with cross validation, which is used to assess how well a model will generalize to an independent dataset. In cross validation, the dataset is divided into multiple subsets, or "folds". A given fold is designated as a validation set and the remaining folds are designated as a training set for training a model. For example, if the dataset is divided up into 5 folds, then the model may be trained on the 4 folds of the training set, then tested against the remaining fold that is used as a validation set. In certain embodiments, the dataset may be divided up in to between 3 and 10 folds, 3 folds, 4 folds, 5 folds, 6 folds, 7 folds, 8 folds, 9 folds, or 10 folds, This process may be repeated several times, each time with a different fold designated as the validation set. In some embodiments, the cross validation may be stratified. In stratified cross validation, when splitting the data into folds, each fold is made to preserve the same proportion of the target classes as the original dataset. For example, if the dataset contains 80% cancer samples and 20% non-cancer samples, each fold may also contain roughly the same proportions of these classes.

[0090] In certain embodiments, methods for identifying cancer biomarker may include identifying a panel of biomarkers, e.g, identifying a panel of a given number of biomarkers, which may be referred to as a “multiplex”, whose combined expression levels are especially predictive of a cancer in a subject. In some embodiments, a multiplex may comprise between 2 and 20 biomarkers, 2 biomarkers, 3 biomarkers, 4 biomarkers, 5 biomarkers, 6 biomarkers, 7 biomarkers, 8 biomarkers, 9 biomarkers, 10 biomarkers, 11 biomarkers, 12 biomarkers, 13 biomarkers, 14 biomarkers, 15 biomarkers, 16 biomarkers, 17 biomarkers, 18 biomarkers, 19 biomarkers, or 20 biomarkers. As used herein, a multiplex consisting of 3 biomarkers may be referred to herein as a “3plex”, a multiplex consisting of 4 biomarkers may be referred to herein as a “4plex”, and so on.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0091] In certain embodiments, multiplexes of MAPs predictive of a cancer may be identified computationally using Recursive Feature Elimination (RFE). RFE systematically removes less important features by recursively training a model and ranking features based on their contribution to model performance. The process continues until the desired number of features remains or until a specified performance metric is optimized. In some embodiments, a plurality of MAPs may be evaluated with an RFE algorithm to identify a predefined number, which may be referred to as a multiplex, of MAPs that most contribute to model performance. An RFE algorithm may identify different multiplexes from a given plurality of MAPs.

[0092] In certain embodiments, multiplexes of MAPs predictive of a cancer may be identified using Exhaustive Feature Selection (EFS). EFS may be used to identify the most predictive combination of biomarkers for distinguishing between cancer and non-cancer cases based on their expression levels. Starting from a dataset with a large set of biomarkers and corresponding expression levels for both cancer and non-cancer samples, to determine the best combination of biomarkers for predicting cancer, an EFS algorithm may systematically evaluate all possible subsets (e.g., all possible 3plexes of a set of biomarkers) from the larger set of biomarkers. For each subset, a predictive model, such as logistic regression or a decision tree, may be trained and evaluated using performance metrics tailored to binary classification, such as the area under the receiver operating characteristic (ROC) curve. The subsets that yield the highest predictive performance may then be selected as a set of optimal multiplexes for distinguishing between cancer and non-cancer samples based on the expression levels of the constituent biomarkers.

[0093] In some embodiments, two or more of the above-noted analytical and machine learning method may be combined to identify cancer biomarker multiplexes. By way of examples, the differential expression data of a plurality of MAPs obtained from cancer and non-cancer cohorts may be analyzed to select a first subset of MAPs to be designated as candidate biomarkers based on fold change and p-value or q-value, for example by eliminating MAPs having less than a minimum fold change threshold value and eliminating MAPs having a p-value or q-value that is more than a maximum threshold value. The first subset of candidate biomarkers may then be further analyzed with ROC curve AUC analysis, a RF model, or Logistic Regression to generate a further narrowed second subset of MAPs designated as cancer biomarkers. This second set of cancer biomarkers may then be analyzed with RFE or EFS to identify multiplexes of cancer biomarkers that are especially predictive.TriAttorney Docket No. NXON-014 / 04WO 346247-2095

[0094] In some embodiments, the method of identifying cancer biomarkers may comprise identifying cancer biomarkers based on ROC curve AUC analysis, then identifying cancer biomarker multiplexes with RFE.

[0095] In some embodiments, the method of identifying cancer biomarkers may comprise identifying cancer biomarkers based on RF analysis, then identifying cancer biomarker multiplexes with RFE.

[0096] In some embodiments, the method of identifying cancer biomarkers may comprise identifying cancer biomarkers based on ROC curve AUC analysis, then identifying cancer biomarker multiplexes with EFS.

[0097] In some embodiments, the method of identifying cancer biomarkers may comprise identifying cancer biomarkers based on RF analysis, then identifying cancer biomarker multiplexes with EFS.

[0098] In some cases, when a plurality of predictive multiplexes are identified, some individual biomarkers may be overrepresented within the set of identified predictive multiplexes, and thus represent biomarkers that are particularly useful in predicting cancer as part of a multiplex. Such biomarkers may be referred to herein as “key” biomarkers. In some embodiments, the method of identifying cancer biomarkers may comprise identifying a plurality of cancer biomarker multiplexes, then identifying one or more key biomarkers based on the cancer biomarker multiplexes.Types of Cancers

[0099] Cancer, with respect to methods disclosed in the present application may include, but are not limited, colorectal cancer (CRC), breast cancer, uterine cancer, and non-small cell lung cancer (NLCLC). In certain embodiments, the CRC may be a colorectal adenocarcinoma. In certain embodiments, the breast cancer may be an infiltrating ductal carcinoma or an infiltrating lobular carcinoma. In certain embodiments, the uterine cancer may be an endometrial adenocarcinoma. In certain embodiments, the NSCLC may be a lung adenocarcinoma or a squamous cell carcinoma.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0100] Cancers may be grouped into stages, ranging from Stage 0 to Stage 4: Stage 0 (Carcinoma in situ - Cancer is in its earliest stage, has not spread, and is usually highly treatable); Stage 1 (Localized Cancer - Cancer is small and localized to one area; often referred to as early stage cancer); Stage 2 and 3 (Regional Spread - Cancer grown larger and may have spread to nearby lymph nodes or tissues but not too distant parts of the body); Stage 4 (Distant Spread - Cancer has spread to distant parts of the body; often referred to as advanced or metastatic cancer). In some cases, a pre-cancerous stage may be included, in which there are cells not yet cancerous, but show abnormal changes and could develop into cancer cells. For example, in a CRC, a pre-cancerous stage may be a presence of precancerous colorectal polyps in the colon or rectum. In some embodiments, the method of determining an aspect of a cancer in a subject may be diagnosing, determining, or prognosticating the presence in a subject of a pre-cancerous stage, stage 0 cancer, a stage 1 cancer, a stage 2 cancer, a stage 3 cancer, a stage 4 cancer, or distinguishing between one or more stages.Summary of cancer biomarkers

[0101] During development of the present disclosure, numerous MAPs were determined to be differentially expressed in samples from cohorts of subjects having one of a plurality of cancer types compared to samples from non-cancer subjects. The differentially expressed MAPs were further analyzed to identify cancer biomarkers and multiplexes of cancer biomarkers that were determined to be predictive of cancer states in a subject. These differentially expressed MAPs and multiplexes thereof have been provided herein in various tables, referred to in the detailed description, examples and claims, and may be utilized in various methods as provided herein, or be comprised in a panel for use in determining a presence or stage of a cancer as provided herein. There is also provided herein embodiments of a kit comprising reagents for detecting the biomarkers of the any one of the panels for use in determining the presence or the stage of a cancer. Below is a brief summary of each table referenced in the detailed description, examples and claims:

[0102] Table 3.2 provides stage 1 uterine cancer biomarkers.

[0103] Table 3.3 provides stage 1 uterine cancer biomarker 3plexes based on the biomarkers of Table 3.2.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0104] Table 3.4 provides the most common biomarkers in the stage 1 uterine cancer biomarker 3plexes of Table 3.3.

[0105] Table 4.2 provides stage 1 breast cancer biomarkers,

[0106] Table 4.3 provides stage 1 breast cancer biomarker 3plexes based on the biomarkers of Table 4.2.

[0107] Table 4.4 provides the most common biomarkers in the stage 1 breast cancer biomarker 3plexes of Table 4.3.

[0108] Table 5.2 provides stage 1 colorectal cancer (CRC) biomarkers.

[0109] Table 5.3 provides stage 1 CRC biomarker 3plexes based on the biomarkers of Table 5.2.

[0110] Table 5.4 provides the most common biomarkers in the stage 1 CRC biomarker 3plexes of Table 5.3.

[0111] Table 6.2 provides stage 0 colorectal cancer (CRC) biomarkers.

[0112] Table 6.3 provides stage 0 CRC biomarker 3plexes based on the biomarkers of Table 6.2.

[0113] Table 6.4 provides the most common biomarkers in the stage 0 CRC biomarker 3plexes of Table 6,3.

[0114] Table 7,2 provides advanced adenoma biomarkers.

[0115] Table 7.3 provides advanced adenoma biomarker 3plexes based on the biomarkers of Table 7.2.

[0116] Table 7.4 provides the most common biomarkers in the advanced adenoma biomarker 3plexes of Table 7.3.

[0117] Table 9.2 provides stage 1 non-small cell lung cancer (NSCLC) biomarkers.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0118] Table 9.3 provides stage 1 NSCLC biomarker 3plexes based on the biomarkers of Table 9.2.

[0119] Table 9,4 provides the most common biomarkers in the stage 1 NSCLC biomarker 3plexes of Table 9.3.

[0120] Table 10.2 provides stage 2 NSCLC biomarkers.

[0121] Table 10.3 provides stage 2 NSCLC biomarker 3plexes based on the biomarkers of Table 10.2.

[0122] Table 10.4 provides the most common biomarkers in the stage 2 NSCLC biomarker 3plexes of Table 10.3.

[0123] Table 11.2 provides stage 3 NSCLC biomarkers,

[0124] Table 11.3 provides stage 3 NSCLC biomarker 3plexes based on the biomarkers of Table 11.2.

[0125] Table 11.4 provides the most common biomarkers in the stage 3 NSCLC biomarker 3plexes of Table 11.3.

[0126] Table 12.2 provides stage 4 NSCLC biomarkers.

[0127] Table 12.3 provides stage 4 NSCLC biomarker 3plexes based on the biomarkers of Table 12.2.

[0128] Table 12,4 provides the most common biomarkers in the stage 4 NSCLC biomarker 3plexes of Table 12.3.

[0129] Table 13.2 provides stage agnostic NSCLC biomarkers.

[0130] Table 13.3 provides stage agnostic SCLC biomarker 3plexes based on the biomarkers of Table 13.2.

[0131] Table 13.4 provides the most common biomarkers in the stage agnostic NSCLC biomarker 3pl exes of Table 13.3,Attorney Docket No. NXON-014 / 04WO 346247-2095

[0132] The contents of each table listed above are provided for in the Examples section, but is incorporated by reference herein, in the Detailed Description. In certain embodiments, a protein provided in the tables above, when used as a biomarker for cancer detection or prognostication, may be a fragment, a variant, a homolog, a congener, a phosphorylated modification or a post- translational modification of the protein.Cancer type and cancer stage specific biomarkers

[0133] During development of the present disclosure, numerous MAPs were determined to be differentially expressed in samples from cancer patients with different cancers at different stages compared to samples from non-cancer subjects. These differentially expressed MAPs were further analyzed for example using the machine learning and other computational methods provided herein to identify cancer type- and stage-sensitive biomarkers and multiplexes of said biomarkers that were determined to be predictive of a given cancer type or stage thereof cancer state in a subject. In certain embodiments, the cancer biomarkers or multiplexes thereof may be predictive of a given cancer type or stage thereof at an accuracy of at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, more than 99%, or 100%. In some embodiments, an accuracy of a biomarker or a multiplex of biomarkers may be calculated, e.g., as a ratio of the number of instances correctly predicted by a trained classifier based on a quantitative measure of the biomarker or multiplex or biomarkers, to the total number of instances tested with a validation set.Uterine Cancer

[0134] As such the present disclosure provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 1 uterine cancer biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 1 uterine cancer in a subject. In certain embodiments, the two or more biomarkers are selected from Table 3.2. In certain embodiments, the two or more proteins comprise a multiplex selected from Table 3,3. In certain embodiments, the multiplex comprises at least one protein selected from Table 3.4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, MINP1, PIGR, UGGG1, and ADEC1.Attorney Docket No. NXON-014 / 04WO 346247-2095 Breast Cancer

[0135] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 1 breast cancer biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 1 breast cancer in a subject. In certain embodiments, the two or more biomarkers are selected from Table 4.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 4.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 4.4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, ADEC1, LMGG1, and L140.Colorectal Cancer

[0136] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 1 colorectal cancer (CRC) biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 1 CRC in a subject. In certain embodiments, the two or more biomarkers are selected from Table 5.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 5.3. In certain embodiments, the multiplex comprises at least one biomarker selected from ’Table 5.4 In certain embodiments, the multiplex comprises one, two, or three out of FCGBP, TREE, CO8B (alternatively CO8 without specifying a subunit thereof), AMD, and MINP1. In certain embodiments, the multiplex comprises or consists of FETUA, FCN2, and ITLNI. In certain embodiments, the multiplex comprises or consists of CO8 and ITLNI.

[0137] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 0 colorectal cancer (CRC) biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 0 CRC in a subject. In certain embodiments, the two or more biomarkers are selected from Table 6.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 6.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 6.4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, NOE2, MINP1, DQBL, PTPRJ, and ACY1.

[0138] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as biomarkers for advanced adenoma, as well as multiplexes thereof, in detection or prognostication of one or more aspects of the colorectal pre-cancer state.Attorney Docket No. NXON-014 / 04WO 346247-2095 Advanced adenoma as used herein refers to a subset of colorectal polyps characterized by histologic and morphologic features associated with an elevated risk of malignant transformation, up to and including carcinoma in situ (i.e., stage 0 CRC). Advanced adenomas are distinguished from non-neoplastic or low-risk colorectal polyps by one or more of the following criteria: i) presence of carcinoma in situ of any size (i.e., stage 0 CRC); ii) presence of high-grade dysplasia of any size; iii) presence of villous architecture of any size comprising at least 25% of the lesion; iv) presence of a tubular adenoma at least I cm in length; v) presence of a serrated lesion of at least 1 cm in diameter. While advanced adenomas represent a stage in the adenoma-carcinoma sequence, they remain non-invasive and are therefore categorically distinct from stage I CRC. Accordingly, advanced adenoma occupies an intermediate position in the neoplastic continuum for CRC: more clinically significant than mere polyp presence, yet not meeting the diagnostic threshold for stage 1 CRC.

[0139] In certain embodiments, the two or more biomarkers are selected from Table 7.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 7.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 7.4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, PTPRJ, MINP1, NOE2, SNEDI, and TRI75 In certain embodiments, the multiplex comprises or consists of ESTI, Cl 63 A, and FCN2. In certain embodiments, the multiplex comprises or consists of ESTI, CI 63 A, and ITLN1. In certain embodiments, the multiplex comprises or consists of ESTI, FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of C163A, FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of ESTI and CI63A. In certain embodiments, the multiplex comprises or consists of ESTI and FCN2. In certain embodiments, the multiplex comprises or consists of ESTI and ITLN1, In certain embodiments, the multiplex comprises or consists of Cl 63 A and FCN2. In certain embodiments, the multiplex comprises or consists of Cl 63 A and ITLN1. In certain embodiments, the multiplex comprises or consists of FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of ESTI, C163A, FCN2, and ITLN1.Non-Small Cell Lung Cancer

[0140] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 1 non-small cell lung cancer (NSCLC) biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 1 NSCLCAttorney Docket No. NXON-014 / 04WO 346247-2095 in a subject. In certain embodiments, the two or more biomarkers are selected from Table 9.2. in certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 9.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 9.4. In certain embodiments, the multiplex comprises one, two, or three out of MMP9, UD2B7, BASP1, NUCB1, ALDOA, BMP 1, RAC 1, ANGL8, and CFAD. In certain embodiments, the multiplex comprises or consists of TRFL, ELNE, and C1Q. In certain embodiments, the multiplex comprises or consists of CFAD, BMP1, and FCN1. In certain embodiments, the multiplex comprises or consists of HEPS, UD2B7, and COR1A.

[0141] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 2 NSCLC biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 2 NSCLC in a subject. In certain embodiments, the two or more biomarkers are selected from Table 10.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 10.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 10.4. In certain embodiments, the multiplex comprises one, two, or three out of BASP1, MIME, PYGM, KSYK. FLOT2, and ARL8. A.

[0142] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 3 NSCLC biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 3 NSCLC in a subject. In certain embodiments, the two or more biomarkers are selected from Table 11.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 11.3. In certain embodiments, the multiplex comprises at least one protein selected from Table 11.4. In certain embodiments, the multiplex comprises one, two, or three out of PYGM, KV37, IJCB1, BAM-ti, and C1QC.

[0143] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may be useful as stage 4 NSCLC biomarkers, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage 4 NSCLC in a subject. In certain embodiments, the two or more biomarkers are selected from Table 12.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 12.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 12.4.Attorney Docket No. NXON-014 / 04WO 346247-2095 In certain embodiments, the multiplex comprises one, two, or three out of PYGM, ANGL8, BMPL C FAD, ANGI, RNAS4, and IF4A1

[0144] The present disclosure also provides two or more microparticle-associated proteins (MAPs) that may- be useful as stage agnostic NSCLC (“pan-NSCLC”) biomarkers for NSCLC at any stage, as well as multiplexes thereof, in detection or prognostication of one or more aspects of stage agnostic NSCLC in a subject. In certain embodiments, the two or more biomarkers are selected from Table 13.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 13.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 13.4. In certain embodiments, the multiplex comprises one. two. or three out of PYGM, H. XK3, HS71A. HS71B, and ANGL8.Analysis of expression levels of MAPs for cancer diagnosis

[0145] The present disclosure provides methods of analyzing microparticles to determine the respective expression status or expression level of a plurality of microparticle associated proteins (MAPs) for detection, determination, diagnosis or prognostication of one or more aspects of a cancer in a subject. The plurality of MAPs may be cancer biomarkers provided herein and identified, e.g., by methods described herein.

[0146] In certain embodiments, expression level of a protein (e.g., a cancer biomarker of the present application) may include an absolute amount of a protein from a microparticle, or may¬ be an indication of presence or absence of a protein in a sample. In certain embodiments, the expression level may be a relative amount compared to microparticles from a different condition (e.g. microparticles derived from cancer patients compared with those derived from non-cancer subjects or a different timepoint in a same patient). The expression level may also be compared to a reference standard. The expression level of the microparticle-associated protein may be detected by any methods known to one of skill in the art, which may in certain embodiments be an immunoassay (see, for example: Coligan et al, Unit 9, Current Protocols m Immunology, Wiley Interscience, 1994). Examples of immunoassays include: antibody detection, immunohistochemistry7(Microscopy, Immunohisto chemistry and Antigen Retrieval Methods for Light and Electron Microscopy, M. A. Hayat (Author), Kluwer Academic Publishers, 2002; Brown C: “Antigen retrieval methods for immunohistochemistry / ’ Toxicol Pathol 1998; 26(6): 830-1), ELIS A(Onorato et al., “Immunohistochemical and ELISA assays for biomarkers ofAttorney Docket No. NXON-014 / 04WO 346247-2095 oxidative stress in aging and disease,” Ann NY Acad Sci 199820; 854: 277-90), Western blotting (Laemmeli UK: “Cleavage of structural proteins during the assembly of the head of a bacteriophage T4,” Nature 1970; 227: 680-685; Egger & Bienz, “Protein (western) blotting”, Mol Biotechnol 1994; 1(3): 289-305), and antibody microarray (Huang, “Detection of multiple proteins in an antibody-based protein microarray system,” Immunol Methods 2001 1; 255 (1-2): 1-13) as well as novel affinity readouts of protein presence using Proximity extension assay protein profiling of liquid biopsy samples using commercial or custom-made immunoaffinity readouts (Olink Proteomics AB, Uppsala, Sweden; Alamar, Inc.). Other examples include a proximity ligation assay using a selected antibody with nucleic acid tag that can be amplified by primers for detection of small protein quantities. In certain embodiment, the expression of protein may be quantified using an affinity capture assay that utilizes a capture agent where, said capture agent is selected from the group consisting of an antibody or fragment thereof, a nucleic acid-based protein binding reagent (e.g., a, and a small molecule). Other protein quantification methods include mass spectrometry, aptamer-based detection, single-molecule array assay (SIMOA), a proximity extension assay, protein identification by short epitope mapping, and protein sequencing,

[0147] Various approaches may be used in preparation a sample for detecting the expression levels of the MAPs, as cancer biomarkers, for detection, determination, diagnosis or prognostication of one or more aspects of a cancer in a subject. In one approach, the proteins may be dissociated from microparticles. For example, the microparticles may be lysed, and the proteins in, or associated with, the microparticles may be extracted, precipitated, and reconstituted for analysis. In another approach, the microparticles may be kept intact so that the protein remains associated, and the microparticles are attached to a column, resin, or bead. The reconstituted protein or the microparticles attached to a column, resin, or bead may be used in the detection step. For example, the reconstituted protein or the microparticles attached to a column, resin, or bead may be contacted with an antibody specific to the protein biomarker.

[0148] In certain embodiments, detecting the expression level includes detecting binding of the protein to an antibody specific to the protein. Antibodies may be monoclonal or polyclonal, included fragments, and they may be obtained from a commercial source or generated for use in the methods described herein. Methods for producing and evaluating antibodies are well known in the art, see, e.g., Coligan, (1997) Current Protocols in Immunology, John Wiley & Sons, Inc;Attorney Docket No. NXON-014 / 04WO 346247-2095 and Harlow and Lane (1989) Antibodies: A Laboratory Manual, Cold Spring Harbor Press, NY (“Harlow and Lane”).

[0149] The antibody may be covalently bound to a bead or fixed on a solid surface, such as glass, plastic, or silicon chip. Typically, MAPs are contacted with an antibody specific to at least one protein biomarker, such that the corresponding biomarkers present in the sample will bind to the specific antibody. The mixture is washed, and the antibody-protein biomarker complexes can be detected.

[0150] Ihis detection can be achieved by contacting the washed antibody -protein biomarker complexes with a detection reagent. Tins detection reagent may be, for example, a secondary7antibody which is labeled with a detectable label. Exemplary7detectable labels include magnetic beads (e.g., DYNABEADS™), fluorescent dyes, radiolabels, enzymes (e.g., horseradish peroxide, alkaline phosphatase, and others commonly used in ELISA), and colorimetric labels such as colloidal gold, colored glass, or plastic beads.

[0151] Methods for measuring the amount or presence of antibody-biomarker complexes may include, for example, detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence, or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method, or interferometry7). Optical methods include microscopy (both confocal and non-confocal), imaging methods, dynamic light scattering, fluorescent NanoSight Tracking Analysis (NanoSight Ltd., Wiltshire UK) and non-imaging methods. Electrochemical methods include voltammetry and amperometry7methods. Radio frequency methods include multipolar resonance spectroscopy. Methods for performing these assays are readily known in the art. Useful assays may include, for example, an enzyme immune assay (EIA) such as enzyme-linked immunosorbent assay (ELISA), a radioimmune assay (RIA), a Western blot assay, immuno-PCR using proximal ligation assay (PLA) or proximity extension assays (PEA) in the form of pre-conjugated kits or customized designed protein detecting kits (Life Technologies, Carlsbad, C A, Olink Bioscience, Uppsala, Sweden) and high sensitivity7protein immunoassay (Life Technologies ProQuantum). or slot blot assay. These methods are also described in, e.g.: Methods in Cell Biology: Antibodies in Cell Biology7, volume 37 ( Asai, ed. 1993); Basic and Clinical Immunology (Stites & Terr, eds., 7th ed. 1991). In certain embodiments, detecting binding of the protein biomarker to an antibody specific to the biomarker may include detecting fluorescence or other methods of quantification.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0152] Throughout the assays, incubation and / or washing steps may be required after each combination of reagents. Incubation steps may vary' from about 5 seconds to several hours, preferably from about 5 minutes to about 24 hours. However, the incubation time will depend upon the assay format, marker, the volume of solution, concentrations, and the like. Usually the assays will be carried out at ambient temperature, although they can be conducted over a range of temperatures, such as 10°C to 40°C,

[0153] Immunoassays may also be used to determine the presence or absence of a MAP as well as the quantity of the MAP. The amount of an antibody-biomarker complex can be determined by comparing to a standard. A standard may be, for example, a known compound or another protein known to be present in a sample. As noted above, the test amount of marker need not be measured in absolute units, as long as the unit of measurement can be compared to a reference value.

[0154] In some embodiments, the methods of detecting the expression levels of the MAPs involve detecting the expression level of clusters or panels of a plurality of MAPs. Detecting the expression level of multiple protein biomarkers can be achieved, for example, with a protein microarray such as an antibody microarray. The production of such microarrays can be carried out essentially as described in Schweitzer & Kingsmore, “Measuring proteins on microarrays,” Curr Opin Biotechnol 2002; 13(1): 14-9; Avseenko et al., “Immobilization of proteins in immunochemical microarrays fabricated by electrospray deposition,” Anal Chem 2001 15; 73(24): 6047-52; Huang, “Detection of multiple proteins in an antibody-based protein microarray system,” Immunol Methods 2001 1; 255 (1-2): 1-13. In some embodiments, protein microarrays may be produced essentially as described in, e.g.: Schena et al., “Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes,” Proc. Natl. Sci. USA (1996) 93, 10614-10619; U. S. Pat. Nos. 6,291,170 and 5,807,522; U. S. Pat. No. 6,037,186 (Stimpson, inven tor) “Parallel production of high density' arrays,”; WO 99 / 13313 (Genovations Inc (US), applicant) “Method of making high density arrays,”; or WO 02 / 05945 (Max Delbruck Center for Molecular Medicine (Germany), applicant) “Method for producing microarray chips with nucleic acids, proteins or other test substrates.”

[0155] Protein or antibody microarray hybridization may be carried out as described in, e.g., Ekins et al. J Pharm Biomed Anal 1989. 7: 155; Ekins and Chu, Clin Chem 1991. 37: 1955;Attorney Docket No. NXON-014 / 04WO 346247-2095 Ekins and Chu, Trends in Biotechnology, 1999, 17, 217-218; or MacBeath and Schreiber, Science 2000; 289(5485): p. 1760-1763,

[0156] In certain embodiments, once two or more biomarkers, e.g., in a microparticle preparation from a subject, have been quantified, e.g., using one or more of the methods noted above, a dataset comprising respective quantitative measures of the two or more biomarkers may be used to determine or predict an aspect of a cancer in the subject, e.g., determine whether or not the subject has the cancer.

[0157] It will be appreciated that any number of biomarkers may be used for the analyses provided herein. The two or more biomarkers may be between 2 and 20 biomarkers, between 4 and 10 biomarkers, between 2 and 8 biomarkers, between 3 and 5 biomarkers, 2 biomarkers, 3 biomarkers, 4 biomarkers, 5 biomarkers, 6 biomarkers, 7 biomarkers, 8 biomarkers, 9 biomarkers, 10 biomarkers, 11 biomarkers, 12 biomarkers, 13 biomarkers, 14 biomarkers, 15 biomarkers, 16 biomarkers, 17 biomarkers, 18 biomarkers, 19 biomarkers, 20 biomarkers, or more than 20 biomarkers.

[0158] The present disclosure includes trained classifiers based on pattern recognition methods such as logistic regression, random forest, support vector machine (SVM), k-nearest neighbor, neural network, XGBoost, lightGBM, gradient boosting classifier, and AdaBoost classifier. Additional pattern recognition algorithms are also contemplated by these methods.

[0159] In some embodiments, a dataset containing quantitative measures of two or more biomarkers from a subject may be classified using a trained algorithm. This algorithm, trained with reference data, may act as a classifier to identify patterns in the data set for classification purposes. In certain embodiments, the trained classifier may determine a cancer-related state of the subject based on quantitative measures of two or more biomarkers from the subject.

[0160] In certain embodiments, the classification may be binary, for example, distinguishing between a cancer state and a non-cancer state. In some embodiments, the cancer state may be that of a type of cancer, e.g., a uterine cancer, a breast cancer, a colorectal cancer, or a lung cancer. In some embodiments, the cancer state may be that of a cancer at a certain stage, e.g., a precancerous stage, stage 0, stage 1, stage 2, stage 3 or stage 4. In some embodiments, the cancerAttorney Docket No. NXON-014 / 04WO 346247-2095 state may be a combination of stages, for example an early stage cancer state combining stages 1-2 and a late stage cancer state combining stages 3-4.Multiclass classifiers

[0161] In certain examples, the classification may be a multiclass classification, allowing for the identification of multiple cancer stages beyond a simple binary outcome. In certain embodiments, the multiclass classifier may be configured to provide an output of 3 or more states, for example, an output of a non-cancer state, and an output for each of cancer stages 1 -4. In some embodiments, the cancer state may be a combination of stages, for example an early stage cancer state combining stages 1 -2 and a late stage cancer state combining stages 3-4. In an example, the outputs of a multiclass classifier may be a non-cancer state, an early stage cancer state combining for example stages 1 and 2, and a late stage cancer state combining for example stages 3 and 4. In another example, the outputs may be a non-cancer state, an early stage cancer state combining for example a precancerous state (for example presence of precancerous polyps preceding colorectal cancer), stage 0 cancer (for example presence of high grade dysplasia preceding colorectal cancer), and stage 1 cancer, and a late stage cancer state combining for example stage 2 through stage 4 cancer.

[0162] In certain embodiments, the input for a multiclass classifier may be a compound panel that comprise multiple panels of biomarkers, each panel defining a stage of cancer. For example, if each panel used for determining one of 4 stages of a cancer is a 3plex of 3 biomarkers, the compound panel may comprise or consist of 12 biomarkers, or less, e.g., 7, 8, 9, 10, or 11 biomarkers, depending on whether some of the panels have overlapping biomarkers. For example, the respective panels for defining stages 1-4 of NSCLC may comprising the following biomarker 3plexes: UD2B7, BASP1, and ALDOA selected from Table 9.3 for determining stage 1 NSCLC; PYGM, MMP9, and BASP1 selected from Table 10.3 for determining stage 2 NSCLC; RAC1, BASP1, and PYGM selected from Table 11,3 for determining stage 3 NSCLC; and BASP1, IMBl, and PYGM selected from Table 12.3 for determining stage 4 NSCLC. It will be appreciated that, given the overlap in the above selection of biomarker 3plexes, the compound panel used for determining each of stages 1-4 of NSCLC would only require 7 biomarkers (UD2B7, BASP1,. ALDO A, PYGM, MMP9, RAC1, and IMBl).Attorney Docket No. NXON-014 / 04WO 346247-2095

[0163] A multiclass classifier may be based on binary classifiers, e.g., SVM, logistic regression, and neural networks, configured for multiclass classification task by employing strategies such as one-vs-one or one-vs-rest.

[0164] In certain embodiments, the niulticlass classifier is based on a plurality of binary classifiers configured to utilize a one-vs-one strategy. Under this strategy, a separate binary classifier is trained for each unique pairwise combination of classes (e.g., cancer stages). For a classification problem involving n distinct classes, this results in the construction of n(n-l) / 2 individual binary classifiers. Each binary classifier is responsible for distinguishing between two specific classes, and during inference, a voting mechanism or aggregation scheme may be employed to determine the final predicted class based on the outputs of all pairwise classifiers.

[0165] In certain embodiments, the multiclass classifier is based on a plurality of binary¬ classifiers configured to utilize a one-vs-many strategy. This strategy' involves training n binary classifiers, where each classifier is designed to distinguish one class from all other classes combined. Specifically, for each classifier, the positive class corresponds to one of the n classes (e.g., stage I NCSLC), while the negative class encompasses the union of the remaining n l classes (e.g., stages 2-4 NSCLC). The final classification decision may be made by selecting the class whose corresponding classifier yields the highest confidence metric or decision function value.

[0166] In certain embodiments, a multiclass classifier of the disclosure is applied to the staging of early-stage colorectal cancer (CRC), yvhich may be subdivided into advanced adenoma, stage 0 CRC, and stage 1 CRC. To enable classification among these three stages using binary classifiers, either a one-vs-one or one-vs-rest strategy may be employed. In the one-vs-one configuration, three binary classifiers are constructed, each trained to distinguish bet veen a unique pair of stages, e.g., advanced adenoma vs stage 0, advanced adenoma vs stage 1, and stage 0 vs stage 1. In the one-vs-rest configuration, three binary classifiers are constructed, each trained to distinguish one stage from the remaining two combined, e.g., advanced adenoma vs [stage 0, stage 1], rhe final classification decision may be made by selecting the class yvhose corresponding classifier yields the highest confidence metric or decision function value. In both configurations, the input features may comprise a compound biomarker panel formed by aggregating multiple stage-specific biomarker panels.Attorney Docket No. NXON-014 / 04WO 346247-2095[001671 In certain embodiments, a multiclass classifier of the disclosure is applied to the staging of non-small cell lung cancer (NSCLC), wherein the stages 1 through 4 represent distinct diagnostic categories. To enable classification among these four stages using a binary classifier, either a one-vs-one or one-vs-rest strategy may be employed. In the one-vs-one configuration, six binary classifiers are constructed, each trained to distinguish between a unique pair of stages, e.g., Stage 1 vs Stage 2, Stage 1 vs Stage 3, etc. In the one-vs-rest configuration, four binary classifiers are constructed, each trained to distinguish one stage from the remaining three combined, e.g.. Stage 1 vs [Stage 2, 3, 4[. In both configurations, the input features may comprise a compound biomarker panel formed by aggregating multiple stage-specific biomarker panels.Classifier training

[0168] The training of a trained classifier typically involves using a labeled reference dataset, where the outcomes (e.g., cancer or non-cancer) are already known. This dataset may be divided into a training set and a validation set. The training set may be used to teach the model by allowing it to identify patterns and correlations between the biomarkers and the known outcomes. Various techniques, such as cross-validation and hyperparameter tuning, may be employed to optimize the model's performance. The validation set may then be used to evaluate the model's accuracy and generalization capability. In some embodiments, the model may become more proficient at classifying new, unseen data (that is, data not presented during training) based on the patterns it has learned by iteratively adjusting the model and testing its predictions. In some embodiments, the trained model (e.g., a classifier) may be predictive of cancer states in a subject based on unseen data at an accuracy of at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, more than 99%, or 100%. Accuracy of a trained model may be calculated, e.g., as a ratio of the number of instances correctly predicted by the classifier, to the total number of instances tested with a validation set.

[0169] The training process of the model may involve adjusting weights to optimize its predictive accuracy. This adjustment is typically performed through iterative models. For example, during each iteration, the model may calculate prediction error by comparing the predicted outcomes to the actual outcomes in the training dataset. It may then update the weights in a direction that reduces this error. For example, in gradient descent, the weights may beAttorney Docket No. NXON-014 / 04WO 346247-2095 adjusted in proportion to the negative gradient of the error with respect to each weight, effectively minimizing the error function. This process is repeated until the model converges to a set of weights that result in maximally accurate predictions. Regularization techniques may also be applied to prevent overfitting, ensuring that the model performs well on both training and unseen data.

[0170] In some embodiments, the training data used to train the model may be based on, or include the same data (e.g. MAP quantification data from mass spectrometry) that was used to identify the biomarkers used for the classification, in some embodiments, aspects of the results of the training process used to identify the biomarker, e.g., classification rules and coefficients (e.g., beta coefficients or weight coefficients depending on the training method used), may be applied to the model used to perform classifications (e.g., cancer vs. non-cancer) with new and unseen data.

[0171] In certain embodiments, the trained model may be a Support Vector Machine (SVM). In the context of logistic regression, the training process may involve adjusting the weight coefficients assigned to respective biomarkers to best fit the model to the training data. This process may start with initializing the weight coefficients, which may be to small random values. The model then makes predictions on the training data using these initial weight coefficients, computing the probability that each instance belongs to the positive class (e.g., cancer). The training process may include iterations of making predictions, computing a loss, and updating the weight coefficients, until the weight coefficients converge to values that minimize a loss function.

[0172] In certain embodiments, a final trained Support Vector Machine (SVM)with its weight coefficients may be a mathematical model that provides a classification predicting a cancer- related outcome based on the input features (quantitative measures of biomarkers). In certain embodiments, the classification may be binary, for example, distinguishing between e.g., cancer and non-cancer, stage I and stage 2 cancer, or stage 2 and stage 3 cancer. In some embodiments, the cancer state may be a combination of stages, for example an early stage cancer state combining stages 1 -2 and a late stage cancer state combining stages 3-4. In certain examples, the classification may be multi class, providing an output of 3 or more states, for example, an output of a non-cancer state, and an output for each of cancer stages 1-4. In some embodiments, a cancer state may be a combination of stages, for example an early stage cancer state combiningAttorney Docket No. NXON-014 / 04WO 346247-2095 stages 1-2 and a late stage cancer state combining stages 3-4. In an example, the outputs of a multiclass classifier may be a non-cancer state, an early stage cancer state combining for example stages 1 and 2, and a late stage cancer state combining for example stages 3 and 4. In another example, the outputs may be a non-cancer state, an early stage cancer state combining for example a precancerous state (for example presence of precancerous polyps preceding colorectal cancer), stage 0 cancer (for example presence high grade dysplasia preceding colorectal cancer), and stage 1 cancer, and a late stage cancer state combining for example stage 2 through stage 4 cancer.In certain embodiments, the mathematical model may be represented by the function applied to a linear combination of the input features and their corresponding weight coefficients, i.e., a linear SVM model.

[0173] For example, a linear SVM model for classifying cancer vs. non-cancer based on the quantitative measures of three biomarkers may be expressed as:Classifier (target:::‘Cancer’)::::bias + weightl*Proteinl + weight2*Protein2 +weights *Protein3)where Protein!, Protein2 and ProteinS are the quantitative measures, respectively of each biomarker, bias is an intercept, weightl is a weight coefficient for Protein!, weight2 is a weight coefficient for Protem2, and weights is a weight coefficient for Protem3. SVM models based on two proteins, or more than 3 proteins, e.g., 4 proteins, 5 proteins, 6 proteins, 7 proteins, etc. are also contemplated. The decision function or classifier may be set to provide a binary' outcome where, e.g., classifier > 0 is cancer and classifier <= 0 is normal (non-cancer).

[0174] SVM is inherently a binary' classifier, but can be effectively adapted for multiclass classification tasks to, e.g., determine the subject as having one of a plurality of cancer types, or having a given cancer type a given stage, through the implementation of methodologies that extend classifiers’ binary' nature, as provided herein, for example by utilizing one-vs-one and one-vs-rest strategies.

[0175] In some embodiments, the analysis of quantitative measures of biomarkers may involve use of a computer system comprising a processor and a memory coupled to the processor. The memory' may store a component comprising test data for a sample from a subject,Attorney Docket No. NXON-014 / 04WO 346247-2095 or test data for a plurality of samples, respectively, from each of a plurality of subjects. The component may further comprise a trained model as described above (e.g., a classifier) configured to classify the subject or plurality of subjects as having a cancer or not having the cancer based on the test data. The component may further comprise computer executable instructions for implementing the classifier on the test data. In some embodiments, the computer system may be implemented as a distributed cloud network, comprises a plurality of interconnected nodes, each node comprising a processor and a memory operably connected to the processor, that are configured to collaboratively execute computational tasks. In some embodiments, the computer system may be embodied as a standalone laptop or desktop computer, each comprising a processor and a memory operably connected the processor, as well as input / output interfaces for user interaction and peripheral connectivity.Clinical applications

[0176] The cancer type-specific and stage-specific biomarkers of the present disclosure (for examples as provided herein above) may be used in clinical applications to inform various aspects related to cancer in a subject from which the microparticles originated.

[0177] The methods of the present disclosure may comprise determining a presence or a stage of a cancer in a subject based on the quantification of two or more biomarkers of the present disclosure (for examples as provided herein above) from a microparticle preparation from a biological sample from the subject. In certain embodiments, the determination of the presence of the cancer may be performed by a trained machine learning model, e.g., a classifier. In certain embodiments, determining the presence of a cancer in a subject may involve assaying the expression level of a two or more proteins from a microparticle preparation prepared from a biological fluid sample from a subject, to yield a data set comprising respective quantitative measures of each of the two or more proteins, inputting the data set to a trained machine learning model that is configured to generate a classification of said sample as positive or negative for the cancer, and electronically outputting a report that identifies said classification of the sample as positive or negative for the cancer.

[0178] In certain embodiments, determining the presence of a cancer may be determining the presence in a subject of a specific cancer type, for example uterine cancer, breast cancer, CRC, or NSCLC.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0179] As noted herein, cancers may be staged by two or more levels of severity, from precancerous, through stage 0 (carcinoma in situ), stage 1 (localized cancer), stage 2 (regional spread - limited), stage 3 (regional spread - extensive), and stage 4 (distant spread). As such, certain embodiments, determining the stage of a cancer may be determining whether a cancer present is a subject is two or more of precancerous, a stage 1 cancer, a stage 2 cancer, a stage 3 cancer, or a stage 4. In certain embodiments, the stage may be a combination of stages, for example an early stage combining precancerous and stage 0, combining stages 0-1, or combining stages 1-2, or a late stage cancer state combining stages 3-4.

[0180] In certain embodiments, determining the presence or the stage of a cancer may be determining that the cancer is a stage 1 uterine cancer based on an expression level of two or more biomarkers selected from Table 3.2 In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 3.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 3.4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, MINP1. PIGR, UGGG1, and ADEC1

[0181] In certain embodiments, determining the presence or the stage of a cancer may be determining the presence of a stage 1 breast cancer based on an expression level of two or more biomarkers selected from Table 4.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 4.3, In certain embodiments, the multiplex comprises at least one biomarker selected from Table 4 4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN L ADEC1, UGGG1, and LV140.

[0182] In certain embodiments, determining the presence or the stage of a cancer may be determining the presence of an early stage CRC.

[0183] In certain embodiments, determining the presence or the stage of the early stage CRC may be determining that the early stage CRC is a stage 1 CRC based on an expression level of two or more biomarkers selected from Table 5.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 5.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 5.4 In certain embodiments, the multiplex comprises one, two, or three out of FCCGBP, TRFE, CO8B (alternatively CO8 without specifying a subunit thereof), AMD, and MINP1. In certain embodiments, the multiplexAttorney Docket No. NXON-014 / 04WO 346247-2095 comprises or consists of FETUA, FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of CO8 and ITLN1.

[0184] In certain embodiments, determining the presence or the stage of the early stage CRC may be determining that the early stage CRC is a stage 0 CRC based on an expression level of two or more biomarkers selected from Table 6.2. in certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 6.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 6,4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, NOE2, M1NP1, DQB1, PTPRJ, and ACY1.

[0185] In certain embodiments, determining the presence or the stage of the early stage CRC may be determining that the early stage CRC is an advanced adenoma based on an expression level of two or more biomarkers selected from Table 7.2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 7.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 7.4. In certain embodiments, the multiplex comprises one, two, or three out of ITLN1, PTPRJ, MINP1, NOE2, SNED1, and TRI75. In certain embodiments, the multiplex comprises or consists of EST1, C163A, and FCN2. In certain embodiments, the multiplex comprises or consists of EST1, C163A, and ITLN1. In certain embodiments, the multiplex comprises or consists of EST1, FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of C163A, FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of EST1 and C163A. In certain embodiments, the multiplex comprises or consists of EST1 and FCN2. In certain embodiments, the multiplex comprises or consists of EST1 and ITLN1. In certain embodiments, the multiplex comprises or consists of C163A and FCN2. In certain embodiments, the multiplex comprises or consists of C163A and ITLN1. In certain embodiments, the multiplex comprises or consists of FCN2, and ITLN1. In certain embodiments, the multiplex comprises or consists of EST1, C163A, FCN2, and ITLN1.

[0186] In certain embodiments, determining the presence or the stage of a cancer may be determining the presence or the stage of an NSCLC.

[0187] In certain embodiments, determining the presence of the NSCLC may be based on an expression level of two or more pan-NSCLC biomarkers selected from Table 13.2. In certainAttorney Docket No. NXON-014 / 04WO 346247-2095 embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 13.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 13.4 In certain embodiments, the multiplex comprises one, two, or three out of PYGM, HXK3, HS71A, HS71B, and ANGLS.

[0188] In certain embodiments, determining the presence or the stage of the NSCLC may be determining that the NSCLC is a stage 1 NSCLC based on two or more biomarkers selected from Table 9.2. In certain embodiments, the two or more of biomarkers comprise a multiplex of biomarkers selected from Table 9.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 9.4. In certain embodiments, the multiplex comprises one. two, or three out of MMP9, UD2B7, B ASP 1, NUCB1, ALDOA, BMP1, RAC1, ANGL8. and CFAD. In certain embodiments, the multiplex comprises or consists of TRFL, ELNE, and C1Q. In certain embodiments, the multiplex comprises or consists of CFAD, BMP1, and FCN1. In certain embodiments, the multiplex comprises or consists of HEPS, UD2B7, and COR1A.

[0189] In certain embodiments, determining the presence or the stage of the NSCLC may be determining that the NSCLC is a stage 2 NSCLC based on two or more biomarkers selected from Table 10.2. In certain embodiments, the two or more of biomarkers comprise a multiplex of biomarkers selected from Table 10.3. In certain embodiments, the multiplex comprises at least one biomarker selected from ’Table 10.4 In certain embodiments, the multiplex comprises one. two, or three out of BASP1, MIME, PYGM, KSYK, FLOT2, and ARL8A.

[0190] In certain embodiments, determining the presence or the stage of the N SCLC may be determining that the NSCLC is a stage 3 NSCLC based on two or more biomarkers selected from Table 11,2. In certain embodiments, the two or more biomarkers comprise a multiplex of biomarkers selected from Table 11.3. In certain embodiments, the multiplex comprises at least one biomarker selected from Table 11.4. In certain embodiments, the multiplex comprises one, two, or three out of PYGM, KV37, NUCB1, BASP1, and C1QC.

[0191] In certain embodiments, determining the presence or the stage of the NSCLC may be determining that the NSCLC is a stage 4 NSCLC based on two or more biomarkers selected from Table 12.2. In certain embodiments, the two or more biomarkers may comprise a multiplex of biomarkers selected from Table 12.3. In certain embodiments, the multiplex comprises at leastAttorney Docket No. NXON-014 / 04WO 346247-2095 one biomarker selected from Table 12.4. In certain embodiments. the multiplex comprises one, two, or three out oi PY GM, ANGLE. BMP I, CFAD, ANG1, RNAS4, and IF4A I.

[0192] Once the presence or the stage of a cancer in a subject is determined, that information can be used in various clinically relevant ways.

[0193] In some embodiments, the determination of whether or not a subject has a cancer may be used to select the subject to be a candidate for receiving a cancer therapy. As such, in certain embodiments, the methods of the disclosure may comprise determining whether the subject has a cancer and thereby be a candidate for receiving a cancer therapy. Optionally, methods of the disclosure may further comprise treating the selected subject with the cancer therapy.

[0194] Certain cancer therapies are more appropriate for certain cancer types, and / or certain cancer stages. In some embodiments, the determination of whether or not a subject has a cancer may be used to select an appropriate cancer therapy for the determined cancer type and / or cancer stage.

[0195] In some embodiments, the subject may have been previously diagnosed with a cancer and be undergoing ongoing cancer treatment, and the determination of the presence or the stage of the cancer may be used to monitor the ongoing cancer treatment. In some embodiments, the determination of presence or the stage of the cancer may be used to determine whether to continue or change a cancer therapy that the subject has been receiving.

[0196] In some embodiments, a maintained presence or stage of the cancer may indicate that the current cancer therapy requires more time, and thereby be selected to receive an additional administration of the cancer therapy. For example, if a subject previously determined to have a stage 1 NSCLC based on a method of the disclosure is later determined to still have stage 1 NSCLC the subject may be selected to receive an additional administration of the cancer therapy.

[0197] In some embodiments, the a maintained presence or stage of the cancer, or the cancer being determined to have progressed to a more advanced stage (e.g., from a stage 1 NSCLC to a stage 2 NSCLC) may indicate that the current cancer therapy is inadequate, and the subject may be selected to receive the same cancer therapy at a higher dose, and the subject may optionally be administered the same cancer therapy at the higher dose. In some embodiments, theAttorney Docket No. NXON-014 / 04WO 346247-2095 maintained presence or stage of the cancer, or the cancer being determined to have progressed to a more advanced stage may indicate that the current cancer therapy is inadequate, and the subject may be selected to receive a different cancer therapy, and the subject may optionally be administered the different cancer therapy.

[0198] In some embodiments, the cancer being determined, with a method of the disclosure, to have progressed to a less advanced stage (e.g., from a stage 1 CRC to a stage 0 CRC) may indicate that the current cancer therapy is working, and the subject may be selected to continue receiving the same cancer therapy, optionally at a same or reduced dose.

[0199] In some embodiments, if a subject previously diagnosed was a cancer is determined, using a method of the disclosure, to no longer have cancer (e.g., a subject, previously determined to have stage 1 NLCSC is tested with a method of the disclosure and is not determined to be negative for stage 1 NLCSC, and is optionally further negative for any or all of stage 2, NSCLC, stage 3 NSCLC, and stage 4 NSCLC), subject may be selected to have the cancer therapy terminated.

[0200] In certain embodiments, the subject may be in remission, and a determination that the subject has cancer may be determined to be a recurrence of the cancer.

[0201] In some embodiments, a cancer therapy may be chemotherapy, hormone therapy, combination therapy, immunotherapy, vaccine therapy, cell-based therapy, radiation therapy, electromagnetic stimulation and / or surgery. In some embodiments, cancer therapy may be administration of a therapeutic agent. The therapeutic agent may be a chemotherapeutic agent, or an immunotherapeutic agent (e.g. a checkpoint inhibitor, a CAR-T, or a cytokine). In certain embodiments, the therapeutic agent may be a small molecule or a biologic, e.g., an antibody, or an engineered cell. In certain embodiments, the therapeutic agent may be a cancer vaccine. Other examples of cancer therapies include radiation therapy (e.g. X-ray, alpha particles emission). Examples of specific therapeutic agents include, for example, cyclophosphamide, chlorambucil, melphalan, methotrexate, cytarabine, fludarabine, 6-mercaptopurine, 5-fluorouracil, vincristine, paclitaxel, vinorelbine, docetaxel, doxorubicin, irinotecan, cisplatin, carboplatin, oxaliplatin, tamoxifen, bicalutarmde, anastrozole, exemestane, letrozole, imatimb, gefitimb, erlotinib, rituximab, trastuzumab, gemtuzumab ozogamicin, interferon -alpha, tretinoin, arsenic trioxide, bevacizumab, sorafinib, and sunitinib.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0202] In certain embodiments, a therapeutic agent for cancer therapy may be an immune response modulator. The immune response modulator may be a checkpoint inhibitors, which may include PD-1 inhibitors such as pembrolizumab and nivolumab, and PD-L1 inhibitors such as atezolizumab and durvalumab. In certain embodiments, the immune response modulator may be a cytokine, such as IL-2 and interferon-alpha. Other examples of immune response modulators include CAR-T cell therapies such as tisagenlecleucel and axicabtagene ciloleucel, and monoclonal antibodies such as rituximab (targeting CD20) and trastuzumab (targeting HER2).

[0203] It will also be appreciated that an “administration” of a given cancer therapy make take one of various forms depending on the cancer therapy. For example, if the cancer therapy is a surgery, then the administration may be the performance of a surgical procedure. If the cancer therapy is a therapeutic agent, then the administration may be, e.g., oral, subcutaneous, parenteral, intravenous, intracranial, etc. If the cancer therapy is a radiation therapy, then the administration may be a session to receive an emission of a radiation.Clinically relevant analyses of biomarkers

[0204] The methods of the present disclosure may be used in clinical applications to inform various aspects related to cancer in a subject from which the microparticles originated. In certain embodiments, such clinical application methods may involve comparison of protein expression in microparticles from a test subject with microparticles from one or more control subjects. One of skill in the art would readily recognize appropriate control microparticles from control subjects for various clinical applications.Monitoring Progression of Cancer

[0205] In certain aspects, the invention includes methods of monitoring the progression of cancer in a test subject. “Monitoring progression” as used herein may refer to the use of expression levels of protein biomarkers to provide useful information about a test subject or a test subject's health or disease status. The methods of monitoring the progression of cancer as described herein may be used once or multiple times, at irregular or regular intervals, in the treatment and management of cancer in a test subject.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0206] Monitoring progression may include, for example, determination of prognosis, risk¬ stratification, selection of drug therapy or other treatment, assessment of ongoing drug therapy, determination of effectiveness of treatment, prediction of outcomes, determination of response to therapy, diagnosis of a disease or disease complication, following of progression of a disease or providing any information relating to a test subject’s health status over time, selecting test subjects most likely to benefit from experimental therapies with known molecular mechanisms of action, selecting test subjects most likely to benefit from approved drugs with known molecular mechanisms where that mechanism may be important in a small subset of a disease for which the medication may not have a label, screening a population of test subjects to help decide on a more invasive / expensive test, for example, a cascade of tests from a non-invasive blood test to a more invasive option such as biopsy, or testing to assess side effects of drugs used to treat another indication. In certain embodiments, monitoring the progression of cancer can refer to distinguishing between necrotic tissue and cancerous growth after the administration of radiation therapy to a test subject. In particular, monitoring progression may refer to making a determination that cancer in a test subject has progressed from a less advanced to a more advanced stage of cancer between two time points or making a determination that cancer in a test subject has not progressed from a less advanced to a more advanced stage of cancer between two time points.

[0207] Monitoring the progression of cancer may include the use of one or more standard clinical techniques such as ultrasound, magnetic resonance imaging, computed tomography scan, single-photon emission computerized tomography, biopsy, or positron emission tomography scan. Results from these tests may be used to supplement or confirm the information gleaned from the expression levels of the MAP biomarkers from microparticles in the test subject for monitoring the progression of cancer,

[0208] In certain embodiments, determining the stage monitoring the progression of cancer in the test subject includes comparing the expression level of two or more microparticle- associated proteins from microparticles in the sample from the test subject with the expression level of one or more MAPs in samples from a plurality of comparator subjects. The plurality of comparator subjects may include subjects known to have cancer at different levels of progression. In certain embodiments, the different levels of progression are different stages of cancer, including Stage 0, Stage 1, Stage 2, Stage 3, and Stage 4. In other embodiments, the different levels of progressionAttorney Docket No. NXON-014 / 04WO 346247-2095 may be different grades of tumor or different levels of other pathological classifications known in the art.EXEMPLARY EMBODIMENTSSet I

[0209] Embodiment 1-1. A method for determining presence of a stage 1 uterine cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 3.2; and(c) based on the quantification of the two or more proteins, determining the presence of the stage 1 uterine cancer in the subject.

[0210] Embodiment 1-2. A method for analyzing a biological fluid sample of a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins selected from Table 3.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins,(c) inputting the data set to a trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 uterine cancer at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 uterine cancer.

[0211] Embodiment 1-3. The method of embodiment 1-1 or 1-2, wherein the trained classifier was trained with training data obtained from a plurality of training samples, and wherein theAttorney Docket No. NXON-014 / 04WO 346247-2095 training samples are microparticle preparations obtained from biological fluid samples from known stage 1 uterine cancer patients and known non-cancer subjects.

[0212] Embodiment 1-4. The method of embodiment 1-3, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of stage 1 uterine cancer or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0213] Embodiment 1-5. The method of any one of embodiments 1-1 to 1-4, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 3.3.

[0214] Embodiment 1-6. The method of embodiment 1-5, wherein the panel of proteins comprises at least one protein selected from Table 3.4.

[0215] Embodiment 1-7. A method for determining presence of a stage 1 breast cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 4.2; and(c) based on the quantification of the two or more proteins, determining the presence of the stage 1 breast cancer in the subject.

[0216] Embodiment 1-8. A method for analyzing a biological fluid sample of a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins selected from Table 4.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins:Attorney Docket No. NXON-014 / 04WO 346247-2095 (c) inputting the data set to a trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 breast cancer at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 breast cancer.

[0217] Embodiment 1-9. The method of embodiment 1-7 or 1-8, wherein the trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known stage 1 breast cancer patients and known non-cancer subjects.

[0218] Embodiment 1-10. The method of embodiment 1-9, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of stage 1 breast cancer or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0219] Embodiment 1-11. The method of any one of embodiments 1-7 to I- 10, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 4.3.

[0220] Embodiment 1-12. The method of embodiment 1-11, wherein the multiplex of proteins comprises at least one protein selected from Table 4.4.

[0221] Embodiment 1-13, A method for determining presence of an early-stage colorectal cancer (CRC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation; and(c) based on the quantification of the two or more proteins, determining the presence of the early phase CRC in the subject.

[0222] Embodiment 1-14. A method for analyzing a biological fluid sample of a subject, the method comprising:Attorney Docket No. NXON-014 / 04WO 346247-2095 (a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to a trained classifier that is configured to generate a classification of said sample as positive or negative for an early phase CRC at w accuracy of al least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the early phase CRC.

[0223] Embodiment 1-15. The method of embodiment 1-13 or 1-14, wherein the trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known early phase CRC patients and known non-cancer subjects.

[0224] Embodiment 1-16. The method of embodiment 1-15, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of early phase CRC or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0225] Embodiment 1-17, The method of any one of embodiments 1-13 to 1-16, wherein the early phase colorectal cancer is stage 1 CRC, and the two or more proteins are selected from Table 5.2.

[0226] Embodiment 1-18. The method of embodiment 1-17, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 5,3.

[0227] Embodiment 1-19. The method of embodiment 1-18, wherein the multiplex of proteins comprises at least one protein selected from Table 5.4.

[0228] Embodiment 1-20. The method of any one of embodiments 1-13 to 1-16, wherein the early phase colorectal cancer is stage 0 CRC, and the two or more proteins are selected from Table 6.2,Attorney Docket No. NXON-014 / 04WO 346247-2095

[0229] Embodiment 1-21. The method of embodiment 1-20, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 6,3.

[0230] Embodiment 1-22. The method of embodiment 1-21, wherein the multiplex of proteins comprises at least one protein selected from Table 6.4.

[0231] Embodiment 1-23. The method of any one of embodiments 1-13 to 1-16, wherein the early phase colorectal cancer is a precancerous colorectal polyps or a stage 0 CRC, and the two or more proteins are selected from Table 7.2.

[0232] Embodiment 1-24, The method of embodiment 1-23, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 7.3.

[0233] Embodiment 1-25. The method of embodiment 1-24, wherein the multiplex of proteins comprises at least one protein selected from Table 7.4.

[0234] Embodiment 1-26. A method for determining presence of a non-small cell lung cancer (NSCLC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation: and(c) based on the quantification of the two or more proteins, determining the presence of the NSCLC in the subject, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC.

[0235] Embodiment 1-27, A method for analyzing a biological fluid sample of a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins:Attorney Docket No. NXON-014 / 04WO 346247-2095 (c) inputting the data set to a trained classifier that is configured to generate a classification of said sample as positive or negative for a non-small cell lung cancer (NSCLC) at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the NSCLC, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC.

[0236] Embodiment 1-28. The method of embodiment 1-13 or 1-14, wherein the trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known NSCLC and known non-cancer subjects.

[0237] Embodiment 1-29. The method of embodiment 1-15, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of NSCLC or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0238] Embodiment 1-30. The method of any one of embodiments 1-26 to 1-29, wherein the NSCLC is the stage 1 NSCLC, and the two or more proteins are selected from Table 9.2.

[0239] Embodiment 1-31. The method of embodiment 1-30, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 9.3.

[0240] Embodiment 1-32, The method of embodiment 1-31, wherein the multiplex of proteins comprises at least one protein selected from Table 9.4.

[0241] Embodiment 1-33. The method of any one of embodiments 1-26 to 1-29, wherein the NSCLC is the stage 2 NSCLC, and the two or more proteins are selected from Table 10.2.

[0242] Embodiment 1-34. The method of embodiment 1-30, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 10.3.

[0243] Embodiment 1-35. The method of embodiment 1-31, wherein the multiplex of proteins comprises at least one protein selected from Table 10.4.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0244] Embodiment 1-36. The method of any one of embodiments 1-26 to 1-29, wherein the NSCLC is the stage 3 NSCLC, and the two or more proteins are selected from Table 11,2.

[0245] Embodiment 1-37. The method of embodiment 1-36, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 11.3.

[0246] Embodiment 1-38. The method of embodiment 1-37, wherein the multiplex of proteins comprises at least one protein selected from Table 11.4.

[0247] Embodiment 1-39. The method of any one of embodiments 1-26 to 1-29, wherein the NSCLC is the stage 4 NSCLC, and the two or more proteins are selected from Table 12.2.

[0248] Embodiment 1-40, The method of embodiment 1-42, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 12.3.

[0249] Embodiment 1-41. The method of embodiment 1-43, wherein the multiplex of proteins comprises at least one protein selected from Table 12.4.

[0250] Embodiment 1-42. The method of any one of embodiments 1-26 to 1-29, w herein the NSCLC is any one of the stage 1 NSCLC, the stage 2 NSCLC, the stage 3 NSCLC, or the stage 4 NSCLC, and the two or more proteins are selected from Table 13.2.

[0251] Embodiment 1-43. The method of embodiment 1-42, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 13.3.

[0252] Embodiment 1-44. The method of embodiment 1-43, wherein the multiplex of proteins comprises at least one protein selected from Table 13.4.

[0253] Embodiment 1-45, The method of any one of embodiments 1-1 to 1-44, w herein the trained classifier is an algorithm comprising a plurality of coefficients, each of the plurality of the coefficients being associated w ith one of the two or more proteins, and wherein the algorithm is configured to generate the classification based on the data set comprising the respective quantitative measures of the two or more proteins and the plurality of coefficients.

[0254] Embodiment 1-46. The method of any one of embodiments 1-1 to 1-44, wherein the two or more proteins comprise between 2 and 20 proteins.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0255] Embodiment 1-47. The method of any one of embodiments 1-1 to 1-44, wherein at least one of the two or more proteins is a fragment thereof, a variant thereof, a homolog thereof, a congener thereof, a phosphorylated modification thereof or a post-translational modification thereof.

[0256] Embodiment 1-48. The method of any one of embodiments 1-1 to 1-44, wherein the providing of the microparticle preparation comprises a use of one or more enrichment processes selected from the group consisting of: centrifugation, ultracentrifugation, density gradients, affinity purification, filtration, electroporation, affinity binding in solution or solid phase, magnetic activated sorting, immunoprecipitation, microfiltration, size-exclusion chromatography, and alternating current (AC) electrokinetic separation.

[0257] Embodiment 1-49. The method of embodiment 1-48, wherein the providing of the microparticle preparation comprises use of size-exclusion chromatography, and the microparticles are eluted from a size exclusion chromatography column comprising a solid phase, using water as a mobile phase.

[0258] Embodiment 1-50, The method of embodiment 1-49, wherein the water is distilled water.

[0259] Embodiment 1-51. The method of embodiment 1-50, wherein the distilled water is double distilled water.

[0260] Embodiment 1-52. The method of any one of embodiments 1-1 to 1-51, wherein the biological fluid is or is obtained from: blood or a fraction thereof, interstitial fluid, synovial fluid, bile, breast milk, lacrimal fluid, menstrual fluid, lymph fluid, urine, cerebrospinal fluid, ascites, saliva, lavage, semen, glandular fluid, vaginal fluid, exudate, contents of cysts, or feces.

[0261] Embodiment 1-53. The method of embodiment 1-52, wherein the biological fluid is a fraction of the blood, and the fraction of the blood is serum or plasma.

[0262] Embodiment 1-54. The method of any one of embodiments 1-1 to 1-53, wherein the two or more proteins are quantified using an affinity capture assay, mass spectrometry, single molecule array assay (SIMOA), a proximity extension assay, and protein identification by short epitope mapping, or combinations thereof.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0263] Embodiment 1-55. The method of embodiment 1-54, wherein the two or more proteins are quantified using the affinity capture assay, and the affinity capture utilizes a capture agent selected from the group consisting of an antibody, an antibody fragment, a nucleic acid-based protein binding reagent, and a small molecule.

[0264] Embodiment 1-56. The method of any one of embodiments 1-1 to 1-53, wherein the two or more proteins are quantified using an immunoassay.

[0265] Embodiment 1-57. The method according to embodiment 1-56, wherein the immunoassay is selected from the group consisting of: enzyme-linked immunosorbent assay (ELISA), enzyme immunoassay (EIA), radioimmunoassay (RIA), antibody detection, immunohistochemistry, western blot, antibody microarray assay, and a proximity ligation assay using a selected antibody with nucleic acid tag that can be amplified by primers for detection of small protein quantities, or a combination thereof.

[0266] Embodiment 1-58. The method of embodiment 1-57, wherein the immunoassay is selected from the group consisting of ELISA, EIA, and RIA.

[0267] Embodiment 1-59. The method of any one of embodiments 1-1 to 1-58, the method further comprising: determining whether the subject is a candidate for receiving a cancer therapy based on the classification.

[0268] Embodiment 1-60. The method of embodiment 1-59, wherein the subject is the candidate, and the method further comprises treating the subject with the cancer therapy.

[0269] Embodiment 1-61. A method of monitoring cancer treatment in a subject, the method comprising:(a) assessing a biological fluid sample from a subject that previously was receiving a cancer therapy, in accordance with any one of embodiments 1-1 to 1-60 to receive a classification of said sample as positive or negative for the cancer; and(b) selecting the subject to be a candidate to receive at least one additional administration of the cancer therapy based on the classification.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0270] Embodiment 1-62. The method according to embodiment 1-61, further comprising administering the at least one additional administration of the cancer therapy to the subject.

[0271] Embodiment 1-63. The method of embodiment 1-61 or 1-62, wherein the at least one additional administration is characterized by an increased dose of the cancer therapy.

[0272] Embodiment 1-64. A method of monitoring cancer treatment in a subject, the method comprising:(a) assessing a biological fluid sample from a subject that previously was administered a therapeutic agent for treating a stage 1 uterine cancer, in accordance with any one of embodiments 1-1 to 1-60 to receive a classification of said sample as positive or negative for the cancer; and(b) selecting the subject to be a candidate to receive at least one dose of a different therapeutic agent based on the classification.

[0273] Embodiment 1-65. The method according to embodiment 1-64, further comprising administering the different therapeutic agent to the subject in an amount effective to treat the stage 1 uterine cancer

[0274] Embodiment 1-66. A computer system comprising:(a) a processor; and(b) a memory, coupled to the processor, the memory storing a module comprising:(i) test data for a sample from a subject, the test data including values indicating a quantitative measure of two or more proteins in a microparticle preparation from a biological fluid sample;(ii) a trained classifier configured to, based on the test data, classify the subject as having a cancer or not having the cancer at an accuracy of at least 98%; and(iii) computer executable instructions for implementing the classifier on the test data, wherein:the cancer is a stage 1 uterine cancer, and the two or more proteins are selected from Table 3.2,the cancer is a stage 1 breast cancer, and the two or more proteins are selected from Table 4.2,Attorney Docket No. NXON-014 / 04WO 346247-2095 the cancer is an early phase colorectal cancer, and the two or more proteins are selected from Table 5.2, 6.2, or 7.2,the cancer is a non-small cell lung cancer, and the two or more proteins are selected from Table 9.2, 10.2, 11.2, 12.2, or 13.2.Set 11

[0275] Embodiment 11-1. A method for determining presence or stage of a stage 1 uterine cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 3.2; and(c) based on the quantification of the two or more proteins, determining the presence or the stage of the stage 1 uterine cancer in the subject.

[0276] Embodiment II-2. The method of embodiment II-1, wherein the determining of the presence or stage of the stage 1 uterine cancer is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins; andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the stage 1 uterine cancer at an accuracy of at least 98%.

[0277] Embodiment II-3 A method for determining presence or stage of a stage 1 uterine cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;Attorney Docket No. NXON-014 / 04WO 346247-2095 (b) assaying the expression level of two or more proteins selected from Table 3.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 uterine cancer at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 uterine cancer, to determine the presence or the stage of the stage 1 uterine cancer.

[0278] Embodiment II-4. The method of embodiment II-2 or II-3, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known stage 1 uterine cancer patients and known non-cancer subjects.

[0279] Embodiment 11-5. The method of embodiment 11-4, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of stage 1 uterine cancer or non-cancer; and (b) a quantitative measure of at least the two or more proteins,

[0280] Embodiment 11-6, The method of any one of embodiments II- 1 to 11-5, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 3.3.

[0281] Embodiment II-7. The method of embodiment II-6, wherein the multiplex of proteins comprises at least one protein selected from Table 3.4.

[0282] Embodiment II-8. A method for determining presence or stage of a stage 1 breast cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 4.2; andAttorney Docket No. NXON-014 / 04WO 346247-2095 (c) based on the quantification of the two or more proteins, determining the presence of the stage 1 breast cancer in the subject,

[0283] Embodiment II-9. The method of embodiment II-8, wherein the determining of the presence or the stage of the stage 1 breast cancer is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins; andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the stage 1 breast cancer at an accuracy of at least 98%, to determine the presence or the stage of the stage 1 breast cancer,

[0284] Embodiment II- 10. A method for determining presence or stage of a stage 1 breast cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins selected from Table 4.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 breast cancer at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 breast cancer, to determine the presence or the stage of the stage 1 breast cancer.

[0285] Embodiment II- 11. The method of embodiment II-9 or II- 10, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known stage 1 breast cancer patients and known non-cancer subjects.Attorney Docket No. NXON-014 / 04WO 346247-2095[00286| Embodiment 11-12. The method of embodiment II- 11, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of stage 1 breast cancer or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0287] Embodiment 11-13. The method of any one of embodiments II-8 to 11-12, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 4.3.

[0288] Embodiment 11-14. The method of embodiment 11-13, wherein the multiplex of proteins comprises at least one protein selected from Table 4.4.

[0289] Embodiment 11-15. A method for determining a presence or a stage of an early stage colorectal cancer (CRC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation; and(c) based on the quantification of the two or more proteins, determining the presence or the stage of the early stage CRC in the subject.

[0290] Embodiment 11-16. The method of embodiment 11-15, wherein the determining of the presence of the early stage CRC is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins, andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the early stage CRC at an accuracy of at least 98%, to determine the presence or the CRC in a subject.

[0291] Embodiment II- 17. A method for determining a presence or a stage of an early stage colorectal cancer (CRC) in a subject the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;Attorney Docket No. NXON-014 / 04WO 346247-2095 (b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins:(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for an early stage CRC at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the early stage CRC, to determine the presence or the CRC in a subject.

[0292] Embodiment 11-18. The method of embodiment 11-16 or 11-17, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known early stage CRC patients and known non-cancer subjects,

[0293] Embodiment II- 19. The method of embodiment II- 18, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of early stage CRC or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0294] Embodiment 11-20. The method of any one of embodiments II- 15 to 11-19, wherein the early stage colorectal cancer is stage I CRC, and the two or more proteins are selected from Table 5.2.

[0295] Embodiment 11-21. The method of embodiment 11-20, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 5.3.

[0296] Embodiment 11-22. The method of embodiment 11-21, wherein the mul tiplex of proteins comprises at least one protein selected from Table 5.4.

[0297] Embodiment 11-23. The method of any one of embodiments II- 15 to 11-19, wherein the early stage colorectal cancer is stage 0 CRC, and the two or more proteins are selected from Table 6.2.Attorney Docket No. NXON-014 / 04WO 346247-2095[00298| Embodiment 11-24. The method of embodiment 11-23, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 6.3.

[0299] Embodiment 11-25. The method of embodiment 11-24, wherein the multiplex of proteins comprises at least one protein selected from Table 6.4.

[0300] Embodiment 11-26. The method of any one of embodiments 11-15 to 11-19, wherein the early stage colorectal cancer is an advanced adenoma, and the two or more proteins are selected from Table 7.2.

[0301] Embodiment 11-27. The method of embodiment 11-26, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 7.3.

[0302] Embodiment 11-28. The method of embodiment 11-27, wherein the multiplex of proteins comprises at least one protein selected from Table 7,4.

[0303] Embodiment 11-29. The method of any one of embodiments 11-16 to 11-19, wherein the two or more proteins are selected from Table 5.2, 6.2, or 7.2.

[0304] Embodiment 11-30. The method of any one of embodiments 11-16 to 11-19, and 11-29 wherein at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC

[0305] Embodiment 11-31, The computer system of embodiment 11-30, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 CRC or not having the cancer at an accuracy of at least 98%;a second trained binary' classifier configured to, based on the test data, classify the subject as having a stage 0 CRC or not having the cancer at an accuracy of at least 98%; andAttorney Docket No. NXON-014 / 04WO 346247-2095 a third trained binary classifier configured to, based on the test data, classify the subject as having an advanced adenoma or not having the cancer at an accuracy of at least 98%.

[0306] Embodiment 11-32. The computer system of embodiment 11-30 or II-31, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

[0307] Embodiment 11-33. The computer system of embodiment 11-30 or 11-30, wherein the trained multiclass classifier utilizes a one-vs-rest strategy.

[0308] Embodiment 11-34. A method for determining a presence or a stage of a non-small cell lung cancer (NSCLC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation; and(c) based on the quantification of the two or more proteins, determining the presence or the stage of the NSCLC in the subject, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC.

[0309] Embodiment 11-35. The method of embodiment 11-34, wherein the determining of the presence of the NSCLC is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins; andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the NSCLC at an accuracy of at least 98%, to determine the presence or the stage of NSCLC in the subject.

[0310] Embodiment 11-36. A method of determining a presence or a stage of a non-small cell lung cancer (NSCLC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;Attorney Docket No. NXON-014 / 04WO 346247-2095 (b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins:(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the NSCLC at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the NSCLC, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC, to determine the presence or the stage of the NSCLC in the subject.

[0311] Embodiment 11-37. The method of embodiment 11-35 or 11-36, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known NSCLC and known non-cancer subjects.

[0312] Embodiment 11-38. The method of embodiment 11-37, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of NSCLC or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

[0313] Embodiment 11-39. The method of any one of embodiments 11-34 to 11-38, wherein the NSCLC is the stage 1 NSCLC, and the two or more proteins are selected from Table 9.2.

[0314] Embodiment 11-40. The method of embodiment 11-39, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 9.3.

[0315] Embodiment 11-41. The method of embodiment 11-40, wherein the multiplex of proteins comprises at least one protein selected from Table 9.4.

[0316] Embodiment 11-42. The method of any one of embodiments 11-34 to 11-38, wherein the NSCLC is the stage 2 NSCLC, and the two or more proteins are selected from Table 10.2.Attorney Docket No. NXON-014 / 04WO 346247-2095 [00317| Embodiment 11-43. The method of embodiment 11-42, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 10.3.

[0318] Embodiment 11-44. The method of embodiment 11-43, wherein the multiplex of proteins comprises at least one protein selected from Table 10.4.

[0319] Embodiment 11-45. The method of any one of embodiments 11-34 to 11-38, wherein the NSCLC is the stage 3 NSCLC, and the two or more proteins are selected from Table 11.2.

[0320] Embodiment 11-46, The method of embodiment 11-45, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 11.3.

[0321] Embodiment 11-47. The method of embodiment 11-46, wherein the multiplex of proteins comprises at least one protein selected from Table 11.4.

[0322] Embodiment 11-48. The method of any one of embodiments 11-34 to 11-38, wherein the NSCLC is the stage 4 NSCLC, and the two or more proteins are selected from Table 12.2.

[0323] Embodiment 11-49. The method of embodiment 11-48, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 12.3.

[0324] Embodiment 11-50. The method of embodiment 11-49, wherein the multiplex of proteins comprises at least one protein selected from Table 12.4.

[0325] Embodiment 11-51. The method of any one of embodiments 11-34 to 11-38, wherein the NSCLC is any one of the stage 1 NSCLC, the stage 2 NSCLC, the stage 3 NSCLC, or the stage 4 NSCLC, and the two or more proteins are selected from Table 13.2.

[0326] Embodiment 11-52. The method of embodiment II-51, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 13.3.Attorney Docket No. NXON-014 / 04WO 346247-2095[00327| Embodiment 11-53. The method of embodiment 11-52, wherein the multiplex of proteins comprises at least one protein selected from Table 13,4.

[0328] Embodiment 11-54. The method of embodiment 11-35 or H-36. wherein the two or more proteins are selected from Table 9.2, 10.2, 11.2, or 12.2.

[0329] Embodiment 11-55. The method of any one of embodiments 11-35 to 11-38, and 11-54, wherein at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC

[0330] Embodiment 11-56, The computer system of embodiment 11-55, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 NSCLC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 2 NSCLC or not having the cancer at an accuracy of at least 98%;a third trained binary classifier configured to, based on the test data, classify the subject as having a stage 3 NSCLC or not having the cancer at an accuracy of at least 98%; anda fourth trained binary classifier configured to, based on the test data, classify the subject as having a stage 4 NSCLC or not having the cancer at an accuracy of at least 98%.

[0331] Embodiment 11-57. The computer system of embodiment 11-55 or 11-56, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

[0332] Embodiment 11-58. The computer system of embodiment 11-55 or 11-55, wherein the trained multiclass classifier utilizes a one-vs-rest strategy.

[0333] Embodiment 11-59. The method of any one of embodiments II-2 to II-5, II-9 to 11-12, 11-16 to II-l 9, 11-29 to 11-33, 11-35 to 11-38, and 11-54 to 11-58, wherein the at least one trained classifier is a model comprising a plurality of coefficients, each of the plurality of the coefficients being associated with one of the two or more proteins, and wherein the model isAttorney Docket No. NXON-014 / 04WO 346247-2095 configured to generate the classification based on the data set comprising the respective quantitative measures of the two or more proteins and the plurality of coefficients.

[0334] Embodiment 11-60. The method of any one of embodiments II- 1 to 11-58, wherein the two or more proteins comprise between 2 and 20 proteins.

[0335] Embodiment 11-61. The method of any one of embodiments II- 1 to 11-58, wherein at least one of the two or more proteins is a fragment thereof, a variant thereof, a homolog thereof, a congener thereof, a phosphorylated modification thereof or a post-translational modification thereof.

[0336] Embodiment 11-62. The method of any one of embodiments II- 1 to 11-58, wherein the providing of the microparticle preparation comprises a use of one or more enrichment processes selected from the group consisting of: centrifugation, ultracentrifugation, density gradients, affinity purification, filtration, electroporation, affinity binding in solution or solid phase, magnetic activated sorting, immunoprecipitation, microfiltration, size-exclusion chromatography, and alternating current (AC) electrokinetic separation.

[0337] Embodiment 11-63. The method of embodiment 11-62, wherein the providing of the microparticle preparation comprises use of size-exclusion chromatography, and the microparticles are eluted from a size exclusion chromatography column comprising a solid phase, using water as a mobile phase.[00338| Embodiment 11-64. The method of embodiment 11-63, wherein the water is distilled water.

[0339] Embodiment 11-65. The method of embodiment 11-64, wherein the distilled water is double distilled water.

[0340] Embodiment 11-66. The method of any one of embodiments II- 1 to 11-65, wherein the biological fluid is or is obtained from: blood or a fraction thereof, interstitial fluid, synovial fluid, bile, breast milk, lacrimal fluid, menstrual fluid, lymph fluid, urine, cerebrospinal fluid, ascites, saliva, lavage, semen, glandular fluid, vaginal fluid, exudate, contents of cysts, or feces.

[0341] Embodiment 11-67. The method of embodiment 11-66, wherein the biological fluid is a fraction of the blood, and the fraction of the blood is serum or plasma.Attorney Docket No. NXON-014 / 04WO 346247-2095

[0342] Embodiment 11-68. The method of any one of embodiments II- 1 to 11-67, wherein the two or more proteins are quantified using an affinity capture assay, mass spectrometry, singlemolecule array assay (SIMOA), a proximity extension assay, and protein identification by short epitope mapping, or combinations thereof.

[0343] Embodiment 11-69. The method of embodiment 11-68, wherein the two or more proteins are quantified using the affinity capture assay, and the affinity capture utilizes a capture agent selected from the group consisting of an antibody, an antibody fragment, a nucleic acidbased protein binding reagent, and a small molecule.[00344| Embodiment 11-70. The method of any one of embodiments 11-1 to 11-67, wherein the two or more proteins are quantified using an immunoassay.

[0345] Embodiment II-71. The method according to embodiment 11-70, wherein the immunoassay is selected from the group consisting of: enzyme-hnked immunosorbent assay (ELISA), enzyme immunoassay (EIA), radioimmunoassay (RIA), antibody detection, immunohistochemistry, western blot, antibody microarray assay, and a proximity ligation assay using a selected antibody with nucleic acid tag that can be amplified by primers for detection of small protein quantities, or a combination thereof.

[0346] Embodiment 11-72 The method of embodiment 11-71, wherein the immunoassay is selected from the group consisting of ELISA, EIA, and RIA.

[0347] Embodiment 11-73. The method of any one of embodiments 11-1 to 11-72, the method further comprising: determining whether the subject is a candidate for receiving a cancer therapy based on the classification.

[0348] Embodiment 11-74. The method of embodiment 11-73, wherein the subject is the candidate, and the method further comprises treating the subject with the cancer therapy.

[0349] Embodiment 11-75. A method of monitoring cancer treatment in a subject, the method comprising:(a) assessing a biological fluid sample from a subject that previously was receiving a cancer therapy, in accordance with any one of embodiments II- 1 to 11-74, to determine the presence or the stage of the cancer; andAttorney Docket No. NXON-014 / 04WO 346247-2095 (b) selecting the subject to be a candidate to receive at least one additional administration of the cancer therapy based on the presence or stage of the cancer.

[0350] Embodiment 11-76. The method according to embodiment 11-75, further comprising administering the at least one additional administration of the cancer therapy to the subject.

[0351] Embodiment 11-77. The method of embodiment 11-75 or 11-76, wherein the at least one additional administration is characterized by an increased dose of the cancer therapy.

[0352] Embodiment 11-78. A method of monitoring cancer treatment in a subject, the method comprising:(a) assessing a biological fluid sample from a subject that previously was administered a therapeutic agent for treating a cancer, in accordance with any one of embodiments II- 1 to 11-74 to determine the presence or the stage of the cancer; and(b) selecting the subject to be a candidate to receive at least one dose of a different therapeutic agent based on the classification.

[0353] Embodiment 11-79. The method according to embodiment 11-78, further comprising administering the different therapeutic agent to the subject in an amount effective to treat the cancer.

[0354] Embodiment 11-80. A computer system comprising:(a) a processor; and(b) a memory, coupled to the processor, the memory storing:(i) test data for a sample from a subject, the test data comprising values indicating a quantitative measure of two or more proteins in a microparticle preparation from a biological fluid sample; and(ii) at least one trained classifier configured to, based on the test data, classify the subject as having a cancer or not having the cancer at an accuracy of at least 98%; andAttorney Docket No. NXON-014 / 04WO 346247-2095 (iii) computer executable instructions for implementing the at least one trained classifier on the test data, wherein:the cancer is a stage 1 uterine cancer, and the two or more proteins are selected from Table 3.2,the cancer is a stage 1 breast cancer, and the two or more proteins are selected from Table 4.2,the cancer is an early stage colorectal cancer (CRC), and the two or more proteins are selected from Table 5.2, 6.2, 7.2, or a combination thereof, orthe cancer is a non-small cell lung cancer (NSCLC), and the two or more proteins are selected from Table 9.2, 10.2, 11.2, 12.2, 13.2 or a combination thereof.

[0355] Embodiment 11-81. The computer system of embodiment 11-80, wherein the cancer is the stage 1 uterine cancer, and the two or more proteins are selected from Table 3.2.

[0356] Embodiment 11-82, The computer system of embodiment 11-81, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 3.3.

[0357] Embodiment 11-83. The computer system of embodiment 11-82, wherein the multiplex of proteins comprises at least one protein selected from Table 3.4.

[0358] Embodiment 11-84. The computer system of embodiment 11-80, wherein the cancer is the stage 1 breast cancer, and the two or more proteins are selected from Table 4.2.

[0359] Embodiment 11-85. The computer system of embodiment 11-84, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 4.3.

[0360] Embodiment 11-86, The computer system of embodiment 11-85, wherein the multiplex of proteins comprises at least one protein selected from Table 4.4.

[0361] Embodiment 11-87. The computer system of embodiment 11-80, wherein the cancer is the early stage CRC, the early stage CRC is a stage 1 CRC, and the two or more proteins are selected from Table 5.2,Attorney Docket No. NXON-014 / 04WO 346247-2095 [00362| Embodiment 11-88. The computer system of embodiment 11-87, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 5.3.

[0363] Embodiment 11-89. The computer system of embodiment 11-88, wherein the multiplex of proteins comprises at least one protein selected from Table 5.4.

[0364] Embodiment 11-90. The computer system of embodiment 11-80, wherein the cancer is the early stage CRC, the early stage CRC is a stage 0 CRC, and the two or more proteins are selected from Table 6.2.

[0365] Embodiment 11-91. The computer system of embodiment 11-90, 'wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 6.3.

[0366] Embodiment 11-92. The computer system of embodiment 11-91, wherein the multiplex of proteins comprises at least one protein selected from Table 6.4.

[0367] Embodiment 11-93. The computer system of embodiment 11-80, wherein the cancer is the early stage CRC, the early stage CRC is an advanced adenoma, and the two or more proteins are selected from Table 7.2.

[0368] Embodiment 11-94. The computer system of embodiment 11-93, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 7.3.

[0369] Embodiment 11-95. The computer system of embodiment 11-94, wherein the multiplex of proteins comprises at least one protein selected from Table 7.4.

[0370] Embodiment 11-96. The computer system of embodiment 11-80, wherein the cancer is the NSCLC, the NSCLC is a stage 1 NSCLC, and the two or more proteins are selected from Table 9.2.

[0371] Embodiment 11-97. The computer system of embodiment 11-96, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 9.3.Attorney Docket No. NXON-014 / 04WO 346247-2095[00372| Embodiment 11-98. The computer system of embodiment 11-97, wherein the multiplex of proteins comprises at least one protein selected from Table 9.4.

[0373] Embodiment 11-99. The computer system of embodiment 11-80, wherein the cancer is the NSCLC, the NSCLC is a stage 2 NSCLC, and the two or more proteins are selected from Table 10.2.

[0374] Embodiment 11-100. The computer system of embodiment 11-99, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 10.3.

[0375] Embodiment 11-101. The computer system of embodiment 11-100, wherein the multiplex of proteins comprises at least one protein selected from Table 10.4.

[0376] Embodiment 11-102. The computer system of embodiment 11-80, wherein the cancer is the NSCLC, the NSCLC is a stage 3 NSCLC, and the two or more proteins are selected from Table 11.2,

[0377] Embodiment 11-103. The computer system of embodiment 11-102, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 11.3.

[0378] Embodiment 11-104. The computer system of embodiment 11-103, wherein the multiplex of proteins comprises at least one protein selected from Table 11.4,

[0379] Embodiment 11-105. The computer system of embodiment 11-80, wherein the cancer is the NSCLC, the NSCLC is a stage 4 NSCLC, and the two or more proteins are selected from Table 12.2.

[0380] Embodiment II- 106. The computer system of embodiment 11-105, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 12.3.

[0381] Embodiment 11-107. The computer system of embodiment 11-106, wherein the multiplex of proteins comprises at least one protein selected from Table 12.4.Attorney Docket No. NXON-014 / 04WO 346247-2095[00382| Embodiment 11-108. The computer system of embodiment 11-80, wherein the cancer is the NSCLC, the NSCLC is a NSCLC at any one of stages 1 to 4, and the two or more proteins are selected from Table 13.2.

[0383] Embodiment 11-109. The computer system of embodiment 11-108, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 13.3.

[0384] Embodiment II- 110. The computer system of embodiment 11-109, wherein the multiplex of proteins comprises at least one protein selected from Table 13.4.

[0385] Embodiment II- 111. The computer system of embodiment 11-80, erein:the cancer is an early stage CRC, andthe at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC.

[0386] Embodiment II- 112. The computer system of embodiment II-l 11, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 CRC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify' the subject as having a stage 0 CRC or not having the cancer at an accuracy of at least 98%; anda third trained binary' classifier configured to, based on the test data, classify' the subject as having an advanced adenoma or not having the cancer at an accuracy of at least 98%.

[0387] Embodiment 11-113. The computer system of embodiment II- 111 or 11-112, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

[0388] Embodiment 11-114. The computer system of embodiment II-l 11 or 11-112, wherein the trained multiclass classifier utilizes a one-vs-rest strategy.Attorney Docket No. NXON-014 / 04WO 346247-2095[00389| Embodiment 11-115. The computer system of embodiment 11-80, wherein:the cancer is a NSCLC, andthe at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the NSCLC.

[0390] Embodiment II- 116, The computer system of embodiment II- 115, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage I NSCLC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 2 NSCLC or not having the cancer at an accuracy of at least 98%;a third trained binary classifier configured to, based on the test data, classify the subject as having a stage 3 NSCLC or not having the cancer at an accuracy of at least 98%; anda fourth trained binary classifier configured to, based on the test data, classify the subject as having a stage 4 NSCLC or not having the cancer at an accuracy of at least 98%.

[0391] Embodiment II- 117, The computer system of embodiment II-l 15 or 11-116, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

[0392] Embodiment II- 118. The computer system of embodiment II- 115 or II- 116, wherein the trained multiclass classifier utilizes a one-v s-rest strategy.

[0393] Embodiment 11-119. A panel of biomarkers for use in determining a presence or stage of stage 1 uterine cancer, the panel comprising two or more biomarkers selected from Table 3.2,

[0394] Embodiment 11-120. The panel of embodiment 11-119, wherein the tw o or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 3.3.Attorney Docket No. NXON-014 / 04WO 346247-2095[00395| Embodiment 11-121. The panel of embodiment 11-120, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 3.4.

[0396] Embodiment 11-122. A panel of biomarkers for use in determining a presence or stage of stage 1 breast cancer, the panel comprising two or more biomarkers selected from Table 4.2.

[0397] Embodiment 11-123. The panel of embodiment 11-122, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 4.3.

[0398] Embodiment 11-124, The panel of embodiment 11-123, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 4.4.

[0399] Embodiment 11-125. A panel of biomarkers for use in determining a presence or stage of a colorectal cancer (CRC), the panel comprising two or more biomarkers selected from Table 5.2, 6.2, 7.2, or a combination thereof.

[0400] Embodiment 11-126. The panel of embodiment 11-125, wherein the CRC is a stage 1 CRC, and the two or more biomarkers are selected from Table 5.2.

[0401] Embodiment 11-127. The panel of embodiment 11-126, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 5.3.

[0402] Embodiment 11-128. The panel of embodiment 11-127, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 5.4,

[0403] Embodiment 11-129. The panel of embodiment 11-125, wherein the CRC is a stage 0 CRC, and the two or more biomarkers are selected from Table 6.2.

[0404] Embodiment 11-130. The panel of embodiment 11-129, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 6,3.

[0405] Embodiment II- 131. The panel of embodiment II- 130, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 6.4.Attorney Docket No. NXON-014 / 04WO 346247-2095[00406| Embodiment 11-132. The panel of embodiment 11-131, wherein the CRC is an advanced adenoma, and the two or more biomarkers are selected from Table 7.2,

[0407] Embodiment II- 133. The panel of embodiment II- 132, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 7.3.

[0408] Embodiment 11-134. The panel of embodiment 11-133, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 7.4.

[0409] Embodiment 11-135, A panel of biomarkers for use in determining a presence or stage of a non-small cell lung cancer (NSCLC), the panel comprising two or more biomarkers selected from Table 9.2, 10.2, 11.2, 12.2, 13.2, or a combination thereof.

[0410] Embodiment 11-136. The panel of embodiment 11-135, wherein the NSCLC is a stage 1 NSCLC, and the two or more biomarkers are selected from Table 9.2.

[0411] Embodiment II- 137. The panel of embodiment II-l 36, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 9.3.

[0412] Embodiment 11-138. The panel of embodiment 11-137, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 9.4.

[0413] Embodiment 11-139. The panel of embodiment 11-135, wherein the NSCLC is a stage 2 NSCLC, and the two or more biomarkers are selected from Table 10,2.

[0414] Embodiment 11-140. The panel of embodiment 11-139, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 10.3.

[0415] Embodiment II- 141. The panel of embodiment II- 140, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 10,4.

[0416] Embodiment 11-142. The panel of embodiment 11-135, wherein the NSCLC is a stage 3 NSCLC, and the two or more biomarkers are selected from Table 11.2.Attorney Docket No. NXON-014 / 04WO 346247-2095[00417| Embodiment 11-143. The panel of embodiment 11-142, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 11.3.

[0418] Embodiment 11-144. The panel of embodiment 11-143, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 11.4.

[0419] Embodiment 11-145. The panel of embodiment 11-135, wherein the NSCLC is a stage 4 NSCLC, and the two or more biomarkers are selected from Table 12.2.

[0420] Embodiment 11-146, The panel of embodiment 11-145, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 12.3.

[0421] Embodiment 11-147. The panel of embodiment 11-146, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 12.4.

[0422] Embodiment 11-148. The panel of embodiment II-l 35, wherein the NSCLC is a NSCLC at any one of stages 1 to 4, and the two or more biomarkers are selected from Table 13.2.

[0423] Embodiment 11-149. The panel of embodiment 11-148, wherein the tw o or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 13.3.

[0424] Embodiment 11-150, The panel of embodiment 11-149, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 13.4.

[0425] Embodiment 11-151. A kit comprising reagents for detecting the biomarkers of the panel of any one of embodiments 11-119 to 11-121, for use in determining the presence or the stage of the stage 1 breast cancer.

[0426] Embodiment II- 152. A kit comprising reagents for detecting the biomarkers of the panel of any one of embodiments 11-122 to 11-124 for use in determining the presence or the stage of the stage 1 uterine cancer.Attorney Docket No. NXON-014 / 04WO 346247-2095[00427| Embodiment 11-153. A kit comprising reagents for detecting the biomarkers of thepanel of any one of embodiments 11-125 to 11-135, for use in determining the presence or the stage of the early stage CRC.

[0428] Embodiment 11-154. A kit comprising reagents for detecting the biomarkers of the panel of any one of claims 11-135 to 11-250, for use in determining the presence or the stage of theNSCLC.EXAMPLESExample 1: Mass spectroscopy-based quantification of MAPsSample collection

[0429] Plasma samples from human subjects (cancer patients and non-cancer controls) were obtained with medical consent and provided to the labs with medical annotation and stored at (-80°C) from a commercial biorepository. Samples were secured with the collaboration of a commercial vendor (Proteogenix (USA)) following informed consent.

[0430] Inclusion criteria required samples to be derived from either non-cancer control subjects or patients with histopathologically defined cancers. The samples were collected via venipuncture in EDTA tubes and centrifuged for 10 minutes at 1,500 ><g to remove large debris. The plasma was de-identified, transferred into clean 1.5mL Eppendorf tubes and stored -80°C. All de-identified plasma samples were stored at --80°C until time of study.

[0431] All samples were received in a frozen state. Plasma samples (1 ml.) were thawed on ice to room temperature, and 500uL of plasma samples loaded onto prewashed and equilibrated agarose SEC columns (bed volume 5 mL; Izon®) and eluted isocratically with double distilled water at a low rate of gravity feed. Once plasma has entered the loading frit, 2,5mL Buffer was loaded. Once the column stopped flowing, an additional 400uL of Buffer was loaded onto the column and effluent collected (Fraction 1) and subsequently repeated until the flow stopped and repeated for Fractions 2-5. Fractions yielded two partially resolved peaks when monitored for particle and protein content as well as presence of canonical proteins associated with the high molecular weight microvesicles. Fractions 1-5 were collected and denoted ‘‘microparticle-enriched fractions’’ (or “microparticle preparations”). Western Blots were run on selectedAttorney Docket No. NXON-014 / 04WO 346247-2095 batches of fractions 1-5 and probed for the tetraspanin proteins CD9 and CD63 to spot-check for quality control to ensure that microparticle enrichment indeed occurred. Tetraspanins are a family of membrane proteins found in all multicellular eukaryotes. As such, an increased concentration of CD9 and CD63 m the fractions provide a positive control confirming the enrichment of microparticles (which as noted above typically comprise cell membrane material). Apool of equivalent amounts of microparticle-enriched fractions 2-5 or 1-5 were then subjected to proteomic analysis using Liquid Chromatography with tandem mass spectrometry (LC-MS / MS) as described below.Protein extraction and digestion for Mass Spectrometry

[0432] After extracting the protein from the microparticle preparations, the protein was desalted on spin columns, and were subjected to trypsin digestion; followed by reduction and alkylation. Following digestion, the solution was centrifuged at 12,000 x g at room temperature for 20 min to collect the digested peptides. The filtrates were collected and lyophilized to obtain the dry powder. The peptide samples were dissolved in buffer and mixed with anhydrous acetonitrile and vortexed and the samples were ready for MS. The peptides were analyzed using LC-MS / MS methods.

[0433] The peptide samples were vortexed and 60pl was combined with an equal volume of lysis buffer resuspended (10% SDS, 100 mM TEAB pH 8.5), vortexed and super-sonicated, and heated to 90°C for 5 minutes. Then the samples were centrifuged at 14,000 rpm and the supernatant were collected for BCA assay and S-Trap (S-Trap™ micro MS sample prep kit, Protifi) procedures as described by the manufacturers. Briefly, 50 pg of each sample was normalized with 5% SDS lOOmM TEAB, then reduced with 10 mM final concentration of DTT at 55 °C for 15 minutes and alkylated with 30mM final concentration of IAM at room temperature for 10 minutes. The protein samples were then acidified with 27.5% phosphoric acid to reach pH < 1. Proteins were trapped into the S-Trap column by centrifuge at 10,000 g for 30 seconds and washed with 100 mM TEAB (final) in 90% methanol repeatedly. Trypsin / LysC (Cat No.: A40007, Thermo Fisher Scientific) was added to protein samples at 1:10 w / w ratio for overnight digestion at 37 °C. Digested samples were quenched with 0.2% formic acid. Samples were then eluted from the S-Trap column with sequential addition and centrifuge of buffer 1 (50 mM TEAB), buffer 2 (0.2% formic acid) and buffer 3 (50% acetonitrile). Hie eluted solutionAttorney Docket No. NXON-014 / 04WO 346247-2095 was pooled and subsequently dried by SpeedVac. (Savant™ SpeedVac™ SPD120, Thermo Fisher Scientific).

[0434] Peptide Fractionation: 50% of each eluted samples were dried by speed-vac and reconstituted in 50 pl of MS injection buffer. Hie remaining 50% of peptides aliquot of each sample was taken and pooled together as library' composite. The library' composite samples were fractionated into 96 fractions with a high pH reverse phase offline HPLC fractionator (Vanquish™, Thermo Fisher Scientific). Mobile phase A was DI H2O with 5.3 mM Formic Acid, 17.3 mM Ammonium Hydroxide, pH 9.3; mobile phase B was Acetonitrile (Optima™, LC / MS grade, Fisher Chemical™) with 5.3 mM Formic Acid, 17.3 mM Ammonium Hydroxide, pH 9.3. Gradient of separation is displayed in Table 1.1. Total 96 fractions were then combined into 12 fractions and ready' for LC-MS / MS analysis.Table 1.1. High pH Reverse Phase HPLC Fractionation Gradient informationTime [min] Flow[ml / min] %B0.00 0.500 2.0LOO 0.500 6.012.00 0.500 20.030.00 0.500 28.050.00 0.500 65.053.00 0.500 98.057.00 0.500 98.059.00 0.500 2.060.00 0.500 2.0L C-MSMS Analysis

[0435] All fractionated samples were analyzed by nano flow HPLC (Ultimate 3000, Thermo Fisher Scientific) followed by Orbitrap Eclipse™ Tribrid™ (Thermo Fisher Scientific).Nanospray Flex™ Ion Source (Thermo Fisher Scientific) was equipped with Column Oven (PRSO-V2, Sonation) to heat up the nano column (Aurora Ultimate. 250 mm x 75 pm ID, 1.7 m Cl 8, lonOpticks) for peptide separation. The nano LC method is water acetonitrile based 120 minutes long with 0.3 pL / min flowrate. For each library' fractions, all peptides were first engaged on a trap column (Cat. No: 164535, Thermo Fisher Scientific) and then were delivered to the separation nano column by the mobile phase. A specific of gradient information was indicated in Table 1.2.Attorney Docket No. NXON-014 / 04WO 346247-2095[00436| For DDA library construction, a DDA library7specific DDA MS2-based mass spectrometry7method on Eclipse™ was used to sequence fractionated peptides that were eluted from the nano column. The ionized peptides were fractionated by FAIMS Pro™ using a 3-CV (-50, -65, -85 V) method. For the full MS, 120,000 resolution was used with the scan range of 350 m / z - 1500 m / z. For the dd-MS(MS2), 30,000 resolution was used, and Isolation window is 1.6 Da, ‘Standard’ AGC target and ‘Auto’ Max Ion Injection Time (Max IT) were selected for both MSI and MS2 acquisition. Collision Energy mode was ‘Fixed’ and total cycle time is 1 sec.[004371 For DIA analytical samples, a high-resolution full MS scan followed by two segment DIA methods was used for the DIA data acquisition. For the full MS scan, 120,000 resolution was used for the range of 400 m / z - 1200 m / z with ‘Standard’ AGC target, 50 ms Max IT and -55 V FAIMS CV. For both DIA segments, details of isolation windows (IW) and precursor mass range are shown m Table 1.3 & Table 1.4. For DIA fragments scan, 30,000 resolution was used for the range of 110 m / z - 1,800 m / z. with ‘Standard’ AGC target and ‘Auto’ Max IT.Table 1.2. nano LC-MS / MS Gradient Information.Timefmin] Flowf pl / minl %B0.00 0.300 2.02.10 0.300 2.03.00 0.300 4.098.00 0.300 35.0108.00 0.300 65.0109.00 0.300 100.0114.00 0.300 100.0115.00 0.300 2.0120.00 0.300 2.0Table 1.3. DIA segment 1 Precursor Scan Range Information.Segment 1(400 - 800 m / z, IW 15 m / z, Overlap 1 m / z)399.5 -415.5 609.5 - 625.5414.5 - 430.5 624.5 - 640.5429.5 -445.5 639.5 - 655.5444.5 -460.5 654.5 - 670.5459.5 -475.5 669.5 - 685.5474.5 -490.5 684.5 - 700.5489.5 - 505.5 699.5 - 715.5504.5 - 520.5 714.5 - 730.5519.5 - 535.5 729.5 - 745.5534.5 - 550.5 744.5 - 760.5Attorney Docket No. NXON-014 / 04WO 346247-2095 Segment 1(400 - 800 m / z, IW 15 m / z, Overlap 1 m / z)549.5 - 565.5 759.5 - 775.5564.5 - 580.5 774.5 - 790.5579.5 - 595.5 789.5 - 800.5594.5 - 610.5Table 1.4. DI A segment 2 Precursor Scan Range InformationSegment 2(800 - 1200 m / z, IW 25 m / z, Overlap 1 m / z)799.5 - 825.5 999.5 - 1025.5824.5 - 850.5 1024.5 - 1050.5849.5 - 875.5 1049.5 - 1075.5874.5 - 900.5 1074.5 - 1100.5899.5 - 925.5 1099.5 - 1125.5924.5 - 950.5 1024.5 - 1150.5949.5 - 975.5 1049.5 - 1175.5974.5 - 1000.5 1074.5 - 1200.5Example 2 - Early stage cancer microparticle preparations and protein quantification

[0438] Serum samples from the following cohorts were obtained from a commercial source: 25 stage 1 colorectal cancer (CRC) patients based on invasion of cancerous cells into the colorectal submucosa; 19 patients diagnosed as having stage 0 CRC (also known as carcinoma in situ) based on presentation of high grade dysplasia in the colon or rectum in which polyps in the colon or rectum have cells that have some cancer-like features; 5 patients diagnosed as being precancerous for CRC based on presentation of colorectal polyps without signs of stage 0 CRC; 30 stage 1 breast cancer patients; 30 stage 1 uterine cancer patients; and 25 healthy, non-cancer control subjects. These cohorts provided samples for the bioinformatic training of models based on MS-based MAP quantification, and may hence be referred to herein as “training” cohorts. The CRC patients had a histological diagnosis of colorectal adenocarcinoma. The breast cancer patients had a histological diagnosis of infiltrating ductal carcinoma or infiltrating lobular carcinoma. The uterine cancer patients had a histological diagnosis of endometrial adenocarcinoma. The serum samples obtained are summarized in Table 2.1:Table 2.1 early stage cancer cohortsCohorts Sample countNon-cancer control 25Attorney Docket No. NXON-014 / 04WO 346247-2095 Stage 1 colorectal cancer 25Stage 1 breast cancer 30Stage 1 uterine cancer 30Stage 0 colorectal cancer ( high grade dysplasia) 19Precancerous colorectal polyps 5[00439| The samples were obtained and processed to prepare microparticle preparations, and MAPs comprised in the microparticle preparations were prepared, then quantified as described above in Example 1. In brief, plasma samples from human subjects (cancer patients and non¬ cancer controls) were obtained with medical consent and provided from a commercial vendor (Proteogenix (USA)), with medical annotation, and the samples were stored at -80°C. The plasma samples were processed to produce microparticle preparations, the MAPs were extracted from the microparticle preparations, then digested and quantified with the Biognosys® True Discovery Mass Spectrometiy Proteomics Platform (“the Biognosys platform”). The quantification data was analyzed in Data Independent Acquisition (DIA) mode. Each plasma sample from each cohort v\ as analyzed separately, and the resulting data was collected to generate the following datasets: Stage 1 CRC dataset; stage 0 CRC dataset; pre-cancer CRC dataset; stage 1 breast cancer dataset; stage 1 uterine cancer dataset; and non-cancer dataset. The newly obtained non-cancer dataset was combined with 50 previously obtained non-cancer datasets to generate an aggregate non-cancer dataset comprising MAP quantification data from 75 non-cancer subjects.[00440| The above-described datasets were then analyzed through a bioinformatics pipeline (see later Examples) to compare MAP levels between different datasets to identify differentially expressed MAP for use, e.g., as cancer biomarkers.Example 3 - Bioinformatic analysis for comparing MAP levels between stage 1 uterine cancer patients and non-cancer subjects

[0441] All data analyses were carried out using Rstudio 2024.09.0 and Python 3.11.8 version. Raw intensities were normalized using Local regression normalization as described by Callister et al. 2006 and was log2 transformed, a constant was added to avoid infinite values for further downstream analysis. The dataset was batch corrected using ‘pycombaf package, using theAttorney Docket No. NXON-014 / 04WO 346247-2095 batch and cancer - normal phenotype information. The dataset was manually curated through a quality control (QC) process to exclude low-quality' data. For example, the QC process involved replacing Missing values by very low values (ranging from le-4 to le-6), as these are likely a result of proteins being at low concentrations below the detection limit, in addition, QC was carried out to remove low intensity proteins, and only proteins with valid intensities (>1000 and not missing) in more than 50% of the samples were kept.

[0442] The stage 1 uterine cancer cohort of 30 subjects was assessed to identify stage 1 uterine cancer biomarkers, as well as generate predictive 3plexes of stage 1 uterine cancer biomarkers that demonstrate a high accuracy metric.

[0443] As shown in Table 3.1, the dataset started with 3332 total proteins. After removing low quality data, the dataset was pruned to 2556 proteins, and this curated dataset of 2556 proteins was used for subsequent machine learning-based analysis, as described below.

[0444] The first round of machine learning involved identifying MAPs that demonstrated differential expression levels between cancer and non-cancer samples. Logistic regression 10- Fold 10 Repeats Stratified Cross-validation using Scikit-leam package and t test with Benjamini Hochberg correction using Scipy stats package was applied on each protein. Cross-validation approach (10-fold) was used to estimate the mean ROC AUC of the model on test dataset. In 10-fold cross-validation all data is randomly split into 10 folds, then the model is trained on the 9 folds, while one fold is used as test dataset. Stratified cross validation is used to preserve the percentage of samples for each class. A two-tailed t test with equal variance was employed in all cases, an exception of Welch t test was used with unequal group variance. Proteins that had Logistic regression average AUC value greater than 0.8 and FDR corrected p-value less than 0.01 and absolute log2 fold change > 0.5 were considered significant. In an imbalanced dataset, one or more phenotypes may have significantly fewer samples compared to others. Machine learning parameter ‘class_weight’ was used to address any phenotype imbalance. ‘clas _ eight’ parameter assign higher weights to the minority' class (i.e., less abundant proteins), allowing the model to pay more attention to its patterns and reducing bias towards the majority class (i.e., more abundant proteins). Proteins thus identified were considered as useful for classifying cancer vs normal (non-cancer) samples with respect to stage 1 uterine cancer. 170 proteins were identified through this method.Attorney Docket No. NXON-014 / 04WO 346247-2095 Table 3.1 Pre-analysis protein curationNumber Validof intensity SignificantIndication samples proteins proteinsUterine Cancer 30 2556 170

[0445] The identified significant proteins are listed in Table 3.2. in Table 3.2, each row represent a protein, each of which is identified by a respective UniprotKB (Uniprot Knowledgebase) unique protein entry name (column 1; “Protein’’), UniprotKB unique accession number (column 2; “Uniprot AN”), and a colloquial protein name (column 3; “Protein Name”). Note that the “ HUMAN” suffix was omitted from each of the protein entry names in column 1, for clarity' of presentation. Column 4 show's a p-value denoting statistical significance. Column 5 shows a q-value, which is an adjusted p-value using Benjamini-Hochberg correction. Column 6 shows log2FC, indicating the scale and direction of differential expression in Log2 units, where a negative value indicates downregulation in the cancer cohorts compared to the non-cancer cohort and a positive value indicates upregulation in the cancer cohorts compared to the noncancer cohort. Column 7 shows the area under the curve (AUC) of the ROC curve generated from the quantification data for each protein.Table 3.2 Significantly differentially expressed stage 1 uterine cancer biomarkers UniprotProtein AN Protein Name p-value q-value log2FC AUC Multiple inositolpolyphosphateMINP1 Q9UNW1 phosphatase 1 1.03E-19 1.32E-16 -0.751 0.959 ADEC1 015204 ADAM DECI 6.44E-17 4.11E-14 -0.924 0.951 ITLN1 Q8WWA0 lntelectin-1 1.20E-18 1.03E-15 -1.183 0.949EndoplasmicreticulumaminopeptidaseERAP2 Q6P179 2 2.71E-12 2.56E-10 -1.674 0.948NucleosidediphosphateNDK3 Q13232 kinase 3 4.10E-13 5.52E-11 -0.776 0.948Leukotriene A-4LKHA4 P09960 hydrolase 1.53E-20 3.91E-17 -1.267 0.941N0E2 095897 Noelin-2 3.58E-15 1.52E-12 -0.953 0.94Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC Peptidyl-g lycinealpha-amidatingAMD P19021 monooxygenase 5.33E-13 6.82E-11 -0.692 0.938T-cell surfaceTACT P40200 protein tactile 5.11E-08 1.07E-06 -6.128 0.935Plasma alpha-L- FUCO2 Q9BTY2 fucosidase 1.37E-08 3.52E-07 -1.353 0.934Tissue alpha-L- FUCO P04066 fucosidase 6.52E-09 1.83E-07 -1.319 0.924Fc receptor-likeFCRL5 Q96RD9 protein 5 2.05E-13 3.95E-11 -1.043 0.924 EGLN P17813 Endoglin 7.67E-12 6.33E-10 -0.763 0.923Pulmonarysurfactant- associatedPSPB P07988 protein B 6.49E-14 1.66E-11 -1.979 0.92PolymericimmunoglobulinPIGR P01833 receptor 2.49E-13 4.24E-11 -0.898 0.915Transforminggrowth factor- beta-inducedBGH3 Q15582 protein ig-h3 3.48E-14 9.90E-12 -0.563 0.911 PLSL P13796 Plastin-2 2.32E-14 8.48E-12 -0.538 0.91 ANXA6 P08133 Annexin A6 5.15E-11 2.99E-09 -1.461 0.907 ANXA3 P12429 Annexin A3 3.20E-13 4.68E-11 -1.805 0.907UDP- glucose:glycoproteinglucosyltransferUGGG1 Q9NYU2 ase 1 5.82E-13 7.08E-11 -0.688 0.905TransferrinreceptorTFR1 P02786 protein 1 3.26E-11 2.19E-09 -1.211 0.903GlutathioneGGT7 Q9UJ14 hydrolase 7 1.62E-10 7.96E-09 -1.683 0.896Haptoglobin- HPTR P00739 related protein 5.38E-10 2.28E-08 -0.574 0.896PhosphoglyceraPGAM1 P18669 te mutase 1 8.03E-11 4.28E-09 -0.953 0.896Immunoglobulin lambdaLV743 P04211 variable 7-43 1.39E-12 1.62E-10 -1.005 0.893Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC Secretoglobinfamily 3 ASG3A2 Q.96PL1 member 2 3.48E-12 3.18E-10 -5.154 0.886Tryptophan-- tRNA ligase,SYWC P23381 cytoplasmic 7.33E-08 1.43E-06 -0.615 0.886LeukocyteelastaseILEU P30740 inhibitor 4.59E-11 2.79E-09 -0.798 0.885 CORIA P31146 Coronin-IA 5.46E-11 3.10E-09 -0.951 0.885 SPON1 Q.9HCB6 Spondin-1 2.77E-11 1.91E-09 -0.791 0.885 TALDO P37837 Transaldolase 2.78E-09 8.84E-08 -0.638 0.883ImmunoglobuliA0A0C4DH n kappaKV108 67 variable 1-8 8.29E-14 1.93E-11 -0.651 0.882Immunoglobulin lambdaLV144 P01699 variable 1-44 5.70E-11 3.10E-09 -0.896 0.88272 kDa type IVMMP2 P08253 collagenase 3.02E-09 9.42E-08 -0.501 0.882GolgimembraneGOLM1 Q8NBJ4 protein 1 5.45E-10 2.28E-08 -0.864 0.88UDP- GlcNAc:betaGalbeta-1, 3-N- acetylglucosamiB3GN8 Q7Z7M8 nyltransferase 8 2.96E-13 4.68E-11 -0.576 0.88Immunoglobulin heavyconstantIGHG3 P01860 gamma 3 2.65E-11 1.91E-09 -0.712 0.879ElongationEF2 P13639 factor 2 1.75E-12 1.93E-10 -0.597 0.879Immunoglobulin kappavariable 2- A0A087W 40;lmmunoglobKV240; KVD W87; P016 ulin kappa40 14 variable 2D-40 3.40E-10 1.58E-08 -0.732 0.879NicotinatephosphoribosyltPNCB Q6XQN6 ransf erase 3.29E-13 4.68E-11 -0.875 0.878Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUG Proproteinconvertasesu btilisin / kexinPCSK9 Q8NBP7 type 9 3.82E-11 2.46E-09 -0.559 0.877Secreted andtransmembraneSCTM1 Q8WVN6 protein 1 2.80E-09 8.84E-08 -1.22 0.877Heat shock 70kDa proteinlA; Heat shockHS71A; HS7 P0DMV8; P 70 kDa proteinIB 0DMV9 IB 1.70E-13 3.62E-11 -0.673 0.877Inactivetyrosine-proteinPTK7 Q13308 kinase 7 1.94E-08 4.78E-07 -0.626 0.876PeroxidasinPXDN 0,92626 homolog 6.85E-10 2.74E-08 -0.551 0.874Chromogranin- CMGA P 10645 A 1.10E-11 8.78E-10 -0.86 0.873Carboxy peptidaCBPQ Q9Y646 se Q 4.61E-10 2.00E-08 -0.801 0.872Glycogenphosphorylase,PYGM P11217 muscle form 7.77E-06 6.65E-05 -0.707 0.871Carcinoembryonic antigen- related celladhesionCEAM1 P13688 molecule 1 9.16E-09 2.46E-07 -1.547 0.868Immunoglobulin heavyIGHD P01880 constant delta 3.85E-11 2.46E-09 -2.863 0.868Haloaciddehalogenase- like hydrolasedomaincontainingHDHD2 Q9H0R4 protein 2 9.65E-04 3.40E-03 -1.846 0.867ArgininosuccinaASSY P00966 te synthase 2.39E-07 3.92E-06 -1.559 0.865ImmunoglobuliIGHE P01854 n heavy 1.87E-10 9.01E-09 -1.53 0.865Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC constantepsilonBPI foldcontainingfamily BBPIB1 Q.8TDL5 member 1 3.71E-10 1.67E-08 -1.001 0.864Carbamoylphosphatesynthase[ammonia],CPSM P31327 mitochondrial 5.60E-09 1.61E-07 -1.377 0.863Protein kinase Cand caseinkinase substratein neuronsPACN1 Q9BY11 protein 1 2.28E-09 7.87E-08 1.062 0.862ComplementCFAD P00746 factor D 2.16E-08 5.11E-07 0.617 0.86 NUCB1 Q02818 Nucleobindin-1 4.92E-08 1.04E-06 -0.777 0.858 HXK3 P52790 Hexokinase-3 2.74E-10 1.30E-08 -1.159 0.858Glycogenphosphorylase,PYGB P11216 brain form 6.42E-08 1.29E-06 -0.698 0.857Actin-relatedprotein 2 / 3complexARC1A Q.92747 subunit 1A 2.25E-08 5.29E-07 -2.721 0.857RibonucleaseRIN! P13489 inhibitor 6.31E-10 2.60E-08 -0.541 0.856 TRY2 P07478 Trypsin-2 4.06E-11 2.53E-09 -0.532 0.855Follistatin- related proteinFSTL1 Q.12841 1 2.64E-08 6.13E-07 -0.643 0.854 FCN2 Q15485 Ficolin-2 2.40E-11 1.80E-09 0.553 0.854Beta- MANBA 000462 mannosidase 2.39E-09 8.05E-08 -0.561 0.852Cell migrationinducing andhyaluronan- CEMIP Q.8WUJ3 binding protein 3.12E-09 9.61E-08 -0.606 0.852ImmunoglobuliA0A075B6 n lambdaLV403 K6 variable 4-3 6.92E-08 1.38E-06 -1.228 0.852Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC IgG receptorFcRn largeFCGRN P55899 subunit p51 1.68E-07 2.92E-06 -1.288 0.852 SAP P07602 Prosa posin 1.39E-09 5.12E-08 -0.546 0.851NucleosidediphosphatephosphataseENTP5 075356 ENTPD5 1.42E-08 3.62E-07 -0.678 0.851 FLNC Q14315 Filamin-C 1.15E-06 1.39E-05 -0.7 0.851Heat shock 70HSP74 P34932 kDa protein 4 2.77E-07 4.46E-06 -0.551 0.851L-xyluloseDCXR Q7Z4W1 reductase 9.46E-07 1.19E-05 -1.458 0.85CoagulationFA12 P00748 factor XII 4.63E-09 1.36E-07 0.655 0.85 CTRC 0,99895 Chymotrypsin-C 7.63E-08 1.48E-06 -4.007 0.848Amino acidtransporterheavy chain4F2 P08195 SLC3A2 6.45E-10 2.62E-08 -0.608 0.846Golgi-residentadenosine 3', 5'- bisphosphateIMPA3 Q9NX62 3’-phosphatase 3.67E-04 1.52E-03 -0.515 0.846Cyclic AMP- dependenttranscriptionfactor ATF-6ATF6A P18850 alpha 2.06E-09 7.20E-08 -0.904 0.8456- phosphogluconatedehydrogenase,6PGD P52209 decarboxylating 1.40E-09 5.12E-08 -0.571 0.844Mannosyl- oligosaccharide1,2-alpha- MA1C1 Q9NR34 mannosidase IC 2.48E-06 2.66E-05 -1.286 0.844Angiopoietin- ANGL8 Q6UXH0 like protein 8 4.06E-09 1.21E-07 -1.261 0.843DCC-interactingprotein 13-DP13A Q9UKG1 alpha 1.20E-06 1.44E-05 -1.099 0.843Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUG HMCN1 Q96RW7 Hemicentin-1 9.70E-10 3.82E-08 -0.625 0.84ElongationEF1A2 Q05639 factor 1-alpha 2 1.01E-10 5.29E-09 -0,808 0.839DipeptidylDPP2 Q9UHL4 peptidase 2 3.67E-07 5.30E-06 -1.751 0.839Acidsphingomyelinase-likephosphodiesterASM3A Q92484 ase 3a 8.46E-08 1.63E-06 -1.233 0.839BetahexosaminidaseHEXB P07686 subunit beta 2.80E-09 8.84E-08 -0.724 0.837GlutathioneGSHB P48637 synthetase 4.84E-07 6.65E-06 -0.561 0.837 ENOA P06733 Alpha-enolase 3.75E-09 1.13E-07 -0.636 0.836Immunoglobulin heavy variableHV169 P01742 1-69 1.60E-08 4.01E-07 -0.987 0.836 PLMN P00747 Plasminogen 1.18E-09 4.49E-08 0.53 0.836 SDC2 P34741 Syndecan-2 8.68E-04 3.13E-03 -3.146 0.8343- hydroxy iso butyratedehydrogenase,3HIDH P31937 mitochondrial 5.39E-06 4.95E-05 -2.091 0.834Protein kinase Cand caseinkinase substratein neuronsPACN2 Q9UNF0 protein 2 1.48E-07 2.68E-06 -0.66 0.833Complementcomponent ClqC1QR1 Q9NPY3 receptor 4.26E-08 9.16E-07 -0.55 0.832ImmunoglobuliA0A075B6I n lambdaLV746 9 variable 7-46 2.40E-03 7.28E-03 -1.19 0.832ThiosulfatesulfurtransferasTHTR Q16762 e 1.02E-05 8.19E-05 -2.73 0.832Triokinase / FMNTKFC Q3LXA3 cyclase 1.65E-04 7.90E-04 -1.48 0.831MYH7 P12883 Myosin-7 1.13E-04 5.74E-04 -1.23 0.831Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC Oxygendependentcoproporphyrinogen-lll oxidase,HEM6 P36551 mitochondrial 1.97E-08 4.78E-07 -1.641 0.8311,4-alpha- glucan- branchingGLGB Q04446 enzyme 3.47E-06 3.46E-05 -0.852 0.83(3R)-3- hydroxyacyl- CoADHB8 Q92506 dehydrogenase 2.83E-07 4.51E-06 -0.864 0.829TransmembranT132C Q8N3T6 e protein 132C 2.98E-07 4.67E-06 -1.069 0.829Glutathionehydrolase 1GGT1 P 19440 proenzyme 1.33E-07 2.42E-06 -1.309 0.829RoundaboutROBO1 Q9Y6N7 homolog 1 2.76E-09 8.84E-08 -0.554 0.827C-l- tetrahydrofolate synthase,C1TC P11586 cytoplasmic 1.70E-06 1.96E-05 -0.982 0.826Procollagen C- endopeptidasePCOC2 Q9UKZ9 enhancer 2 1.83E-07 3.13E-06 -0.675 0.826Protein-argininedeiminase type- PADI2 Q.9Y2J8 2 2.12E-06 2.36E-05 -1.505 0.826Multiple PDZMPDZ 075970 domain protein 7.01E-05 3.86E-04 0.915 0.825ApolipoproteinAPOA4 P06727 A-IV 1.14E-08 3.00E-07 -0.73 0.825Di-N- DIAC Q.01459 acetylchitobiase 3.14E-07 4.81E-06 -0.531 0.824Beta-1, 4- glucuronyltransfB4GA1 043505 erase 1 3.96E-08 8.74E-07 -0.679 0.824UTP-glucose-1- phosphateuridylyltransferUGPA Q16851 ase 4.42E-06 4.20E-05 -0.899 0.824Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC All-trans-retinoldehydrogenaseADH4 P08319 [NAD(+)] ADH4 1.46E-03 4.74E-03 -0.634 0.823Proteinphosphatase 1regulatoryPP1R7 Q15435 subunit 7 4.32E-04 1.74E-03 -2.025 0.823Eukaryoticinitiation factorIF4A1 P60842 4A-I 2.01E-07 3.40E-06 -0.681 0.823MacrophageCAPG P40121 capping protein 3.72E-10 1.67E-08 -1.502 0.822Interleukin-18IL18R Q.13478 receptor 1 3.05E-05 2.02E-04 -0.931 0.822Collagen alpha- CO5A1 P20908 1(V) chain 7.12E-08 1.40E-06 -0.505 0.821Rho GDP- dissociationGDIR2 P52566 inhibitor 2 1.93E-08 4.78E-07 -1.069 0.821N(4)-(beta-N- acetylglucosaminyl)-L- ASPG P20933 asparaginase 5.12E-07 6.99E-06 -1.0 0.82Tripartite motifcontainingTRI75 A6NK02 protein 75 5.14E-04 2.04E-03 2.44 0,818ThymidineTYPH P19971 phosphorylase 1.09E-06 1.33E-05 -0.589 0.818Glutamatedehydrogenase1,DHE3 P00367 mitochondrial 7.52E-06 6.49E-05 -0.765 0.818Arf-GAP withRho-GAPdomain, ANKrepeat and PHdomaincontainingARAP1 Q96P48 protein 1 7.05E-06 6.13E-05 -1.144 0.818Ras-related C3botulinum toxinRAC2 P15153 substrate 2 6.13E-08 1.26E-06 -1.926 0.816Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC Ras-relatedRAB8B Q92930 protein Rab-8B 3.20E-07 4.85E-06 -1.347 0.816ImmunoglobuliA0A075B6J n lambdaLV218 9 variable 2-18 1.91E-07 3.25E-06 -0.601 0.815Immunoglobulin heavyconstant alphaIGHA2 P01877 2 8.62E-09 2.34E-07 -0.555 0.815 STX7 015400 Syntaxin-7 2.19E-07 3.66E-06 -3.609 0.814ChymotrypsinogCTRB2 Q6GPI1 en B2 6.93E-06 6.06E-05 -2.283 0.814V-set andimmunoglobulindomaincontainingVSIG4 Q.9Y279 protein 4 1.80E-04 8.48E-04 -0.706 0.814SphingomyelinphosphodiesterASM P17405 ase 5.60E-07 7.54E-06 -2.594 0.814Hematopoieticlineage cellHCLS1 P14317 specific protein 3.01E-07 4.69E-06 -1.135 0.814SorbitolDHSO Q00796 dehydrogenase 7.59E-06 6.51E-05 -1.082 0.814StaphylococcalnucleasedomaincontainingSND1 Q7KZF4 protein 1 8.41E-06 7.03E-05 -0.744 0.814NeutrophildefensinP59665; P5 l; NeutrophilDEF1; DEF3 9666 defensin 3 9.79E-08 1.85E-06 -0.536 0.813BTB / POZdomaincontainingKCD12 Q96CX2 protein KCTD12 1.41E-06 1.66E-05 -1.08 0.813Isocitratedehydrogenase[NADP]IDHC 075874 cytoplasmic 1.81E-05 1.35E-04 -0.954 0.812Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC Immunoglobulin heavy variableHV434 P06331 4-34 3.37E-07 4.98E-06 -0.538 0,812 OBSCN Q5VST9 Obscurin 8.48E-06 7.04E-05 -1.337 0.812 CATF Q9UBX1 Cathepsin F 6.89E-07 9.08E-06 -0.702 0.812 NRX3A Q9Y4C0 Neurexin-3 3.20E-05 2.10E-04 -1.072 0.812Meteorin-likeMETRL Q641Q3 protein 1.87E-05 1.37E-04 -3.508 0.812ArgininosuccinaARLY P04424 te lyase 1.21E-04 6.05E-04 -0.91 0,812ProteinPPM1A P35813 phosphatase 1A 3.90E-06 3.76E-05 -0.823 0.811Interleukin-27IL27B Q14213 subunit beta 1.19E-08 3.11E-07 -3.778 0.811 LEPR P48357 Leptin receptor 1.08E-10 5.50E-09 -0.688 0.809Alpha-N- acetylgalactosaNAGAB P17050 minidase 1.55E-07 2.78E-06 -2.682 0.809 TFG Q92734 Protein TFG 6.30E-06 5.63E-05 -0.602 0.809lnterleukin-1IL1R2 P27930 receptor type 2 9.73E-07 1.21E-05 -0.701 0.808 MYOM3 Q5VTT5 Myomesin-3 4.02E-04 1.65E-03 -1.836 0.808Glyceraldehyde- 3-phosphateG3P P04406 dehydrogenase 1.98E-08 4.78E-07 -0.665 0.808 SEMG1 P04279 Semenogelin-1 1.58E-05 1.19E-04 -1.668 0.808Keratinocyteproline-richKPRP Q.5T749 protein 3.13E-04 1.34E-03 -0.792 0.808HLA class 1histocompatibility antigen, CHLAC P10321 alpha chain 3.91E-07 5.59E-06 -1.265 0.807ImmunoglobuliA0A075B6J n lambdaLV322 6 variable 3-22 3.74E-05 2.36E-04 -0.812 0.807 GRAN P28676 Grancalcin 5.14E-08 1.07E-06 -1.451 0.807CarbohydratesulfotransferaseCHSTC Q9NRB3 12 3.43E-05 2.21E-04 -0.785 0.806Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC AspartateaminotransferasAATC P17174 e, cytoplasmic 3.24E-07 4.87E-06 -1.105 0.806Glucose-6- phosphate 1- G6PD P11413 dehydrogenase 2.74E-07 4.43E-06 -0.858 0.805Carboxy peptidaCBPD 075976 se D 6.38E-08 1.29E-06 -0.834 0.805CytoplasmicaconitateACOHC P21399 hydratase 1.79E-05 1.34E-04 -0.643 0.804Osteoclaststimulating0STF1 Q.92882 factor 1 9.27E-07 1.17E-05 -2.119 0.804 GRN P28799 Progranulin 3.65E-07 5.30E-06 -0.592 0.804fMet-Leu-PheFPR1 P21462 receptor 8.47E-06 7.04E-05 -3.598 0.801 SPON2 Q.9BUD6 Spondin-2 1.14E-05 9.03E-05 -2.152 0.801UDP- GlcNAc:betaGalbeta-1, 3-N- acetylglucosamiB3GN7 Q8NFL0 nyltransferase 7 3.38E-05 2.20E-04 -1.597 0.801FructosebisphosphateALDOB P05062 aldolase B 4.09E-05 2.56E-04 -0.996 0.8

[0446] The second round of machine learning involved searching for multiplexes of 3 cancer biomarkers C‘3plexes”) from the uterine cancer biomarkers listed in Table 3.2 that would differentiate between cancer and non-cancer MAP samples with a high degree of accuracy. Exhaustive feature selection (EFS) was performed using Linear SVM, in particular 10-fold 10 repeats Stratified Cross Validation. Python-based machine learning extension (MLXTEND) packages were used for this analysis. EFS is a wrapper approach for brute-force evaluation of all possible feature combinations in a specified range. In the present example, the differential expression data obtained from each of the cancer and non-cancer cohorts, for each of the high AUC stage 1 uterine cancer biomarkers provided in Table 3.2, was used as training data. The training data was divided into 10 folds, which one fold being used as a validation set and the remaining 9 folds being used as training sets for training a classifier. The training processAttorney Docket No. NXON-014 / 04WO 346247-2095 included generation of coefficients (e.g., weight coefficients) assigned to each biomarker, with the numerical value of the coefficients (e.g., weight coefficients) becoming optimized through the training process to correctly predict cancer, compared against the known cancer or non¬ cancer statuses provided m the training data. For all possible 3plexes of the stage 1 uterine cancer biomarkers, an accuracy metric was calculated as a ratio of the number of instances correctly predicted by the classifier (based on the respective expression levels of a given set of 3 cancer biomarkers) to the total number of instances in the validation set. Whether the prediction of a given instance was correct was based on whether the prediction matched the known cancer or non-cancer statuses provided for the given instance in the validation set. This process was repeated for each of the 10 folds, and the respective accuracy metrics averaged over the 10 folds was calculated as a ‘T O-fold average accuracy metric”. As shown m Table 3.3, the 3plexes with a 10-fold average accuracy metric of 98% (i.e., average correct prediction ratio of 0.98) or higher were short listed.Table 3.3 - Uterine cancer biomarker 3plexes with Accuracy > 0.9810-Fold AVG3PLEX Accuracy('HPTR', 'PCSK9', ’UGGGT) 1.000('PIGR', 'ITLNT, 'FUCO') 0.990('ITLN1', 'FUCO', 'NOE2') 0.990(‘ERAP21, 'ADECT, 'SPONl') 0.990('ERAP2', 'FUCO2', 'PSPB') 0.989('PIGR', 'ITLNT, 'PNCB') 0.989('ADECT, 'PSPB', 'UGGG1') 0.988('HPTR', 'PSPB', 'UGGG1') 0.988('ERAP2', ‘ADECT, 'HSP74') 0.988(TTLNT, 'ADECT, 'PNCB') 0.987('ERAP2', 'PSPB', 'LV403') 0.987('ERAP2', 'FUCO', 'PSPB') 0.986('IGHD', 'MINPT, 'NOE2') 0.986('ITLNT, 'SAP', 'UGGG1') 0.985('FCN2', 'ADECl', 'PXDN') 0.985('KV240; KVD40', 'ITLNT,'FUCO2') 0.985(’MINPT, 'IGHE' / UGGGl') 0.984('IGHD', 'BGH3', 'ADECl') 0.984('FUCO', 'PSPB1, ’UGGGT) 0.983Attorney Docket No. NXON-014 / 04WO 346247-209510-Fold AVG3PLEX Accuracy('ERAP2', 'MINPT, 'ADECT) 0.982('IGHG3', 'HS71A; HS71B','MINPT) 0.982('FCN2', 'MINPT, 'N0E2') 0.982(TTLNT, 'FUCO', 'PXDN') 0.982('TRY2', 'MINPT, 'N0E2') 0.982('PIGR1, 'LV743', 'IGHD') 0.981(,LV743‘, IGHD', 'PSPB') 0.981('ERAP2', 'PIGR', 'HPTR') 0.981('IGHG3', 'PIGR', 'MINPT) 0.981('PIGR', 'LV743', 'MINPT) 0.981('FCN2', 'MINPT, 'UGGGl') 0.981('ITLNT, 'FUCO', 'CORIA') 0.981('PIGR', 'LV743', 'ITLN1') 0.981('PIGR', 'ITLN1', 'GGT7') 0.981('FCRL5', 'FUCO', 'PSPB') 0.981('CBPQ', 'LV743', 'UGGGl') 0.981('IGHD', 'MINPT, ’ADECT) 0.981('PIGR1, 'HPTR', 'UGGGl') 0.981('ERAP2', 'PIGR', 'ITLN1') 0.980('PIGR', 'ITLNT, 'FUCO2') 0.980('MINPT, 'NDK3', 'N0E2') 0.980('ADECT, 'UGGGl', 'ENTP5') 0.98000447] It was found that a number of cancer biomarkers were unexpectedly overrepresented in the 3plexes, Some of the overrepresented markers include ITLN1 (stage 1 uterine cancer AUC of 0.949) that was included in 12 out of the top 3plexes, MINP1 (individual stage 1 uterine cancer AUC of 0.959) that was included in 11 out of the top 3plexes, and PIGR (individual pan cancer AUC of 0.915) that was included in 11 out of the top 3plexes. It is noted that these overrepresented biomarkers are not necessarily the best performing individual uterine cancer biomarkers, based on AUC metric (see Table 3.2). Among the uterine cancer 3plexes listed in Table 3.3, the most frequently identified proteins from the 3plexes are listed in Table 3.4.Attorney Docket No. NXON-014 / 04WO 346247-2095 Table 3.4 Most common proteins in stage 1 uterine 3plexesProtein 3plexcountITLN1 12MINP1 11PIGR 11UGGG1 10ADEC1 9ERAP2 8PSPB 8FUCO 7NOE2 5LV743 5IGHD 5

[0448] One or more of the top ranked pan cancer 3 pl exes (those listed in Table 3.3) were selected to generate Linear Support Vector Machine equations as classifiers for future prediction of samples of unknown cancer / non-cancer status. Linear SVM using Scikit-leam package with function parameters, kernel = linear, class__weight = balanced, probability = True was used to create a cancer prediction equation as follows:00449 Classifier (target = ‘Cancer’) = bias + weightl *Proteinl + weight2*Protein2 + weight3*Protein3)where probability > 0 is cancer and probability <= 0 is normal (non-cancer)where bias is an intercept, weightl is a weight coefficient for Protein 1, weight2 is a weight coefficient for Protein 2, and weights is a weight coefficient for Protein3.Proteinl, Protein2 and Protein3 are the quantitative measures, respectively of each biomarker.

[0450] The weight coefficients (e.g., weightl, weight2, weights) represent a weight coefficient for each of the proteins. The respective values of the weight for each of the biomarkers were empirically learned through a machine learning training process, and a given weight describes the size and direction of the relationship between a quantitative measure of a given biomarker and the SVM hyperplane (e.g., ‘" Classifier” in the equation above). The intercept in a 3D Linear SVM influences the positioning of the hyperplane that separates the classes.Attorney Docket No. NXON-014 / 04WO 346247-2095 00451 The short listed 3plexes (or a larger multiplex comprising one or more of the 3plexes, and optionally other biomarkers) may thus be used for predicting presence or likelihood of cancer of subject based on quantification of the given combination of proteins.Example 4 - Bioinformatic analysis for comparing MAP levels between stage 1 breast cancer patients and non-cancer subjects

[0452] Following the methods outlined above for stage 1 uterine cancer in Example 3, a stage 1 breast cancer cohort of 30 subjects was similarly assessed to identify stage 1 breast cancer biomarkers, as well as generate predictive 3plexes of stage 1 breast cancer biomarkers that demonstrate a high accuracy metric.

[0453] As shown in Table 4.1, the dataset started with 3332 total proteins. After removing low quality data, the dataset was pruned to 2562 proteins, and this curated dataset of 2562 proteins was used for subsequent machine learning-based analysis, as described below,

[0454] A first round of machine learning was performed, as described in Example 3 (with respect to stage 1 uterine cancer), to identify MAPs that demonstrated differential expression levels between stage 1 breast cancer and non-cancer samples, which resulted in identification of 168 significant proteins (summarized in Table 4.1 below).Table 4.1 Pre-anafysis rotein curationNumber Validof intensity SignificantIndication samples proteins proteinsBreast Cancer 30 2562 168

[0455] The identified significant proteins are listed in Table 4.2. In Table 4.2, each row represent a protein identified by a respective UniprotKB unique protein entry name (column 1; “Protein”), UniprotKB unique accession number (column 2; “Uniprot AN”), and colloquial protein name (column 3; “Protein Name”). Note that the “ HUMAN” suffix was omitted from each of the protein entry names in column 1, for clarity of presentation. Column 4 show's upvalue denoting statistical significance. Column 5 shows a q-value, which is an adjusted p-value using Benjamini-Hochberg correction. Column 6 shows log2FC, indicating the scale and direction of differential expression in Log2 units, where a negative value indicatesAttorney Docket No. NXON-014 / 04WO 346247-2095 downregulation in the cancer cohort compared to the non-cancer cohort and a positive value indicates upregulation in the cancer cohort compared to the non-cancer cohort. Column 7 shows the area under the curve (AUC) of the ROC curve generated from the quantification data for each protein.Table 4.2 Significantly differentially expressed stage 1 breast cancer biomarkers UniprotProtein AN Protein Name p-value q-value log2FC AUC ADEC1 015204 ADAM DECI 4.62E-16 3.95E-13 -0.833 0.947 ITLN1 Q8WWA0 lntelectin-1 2.31E-21 5.92E-18 -1.287 0.946BOS complexsubunitNOMO3; BOScomplexsubunitNOMO1; BOSNOMO3; N P69849; Q1 complexOMO1; NO 5155; Q5JP subunitMO2 E7 NOMO2 1.30E-18 1.66E-15 -0.602 0.945MatrixmetalloproteinMMP19 Q99542 ase-19 5.87E-13 9.04E-11 -0.645 0.942 PLSL P13796 Plastin-2 7.33E-12 7.82E-10 -0.605 0.94MultipleinositolpolyphosphateMINP1 Q9UNW1 phosphatase 1 8.92E-16 5.71E-13 -0.675 0.933Fc receptorFCRL5 Q96RD9 like protein 5 2.61E-14 8.34E-12 -1.094 0.924Elongationfactor 1-alphaEF1A2 Q05639 2 2.59E-13 5.10E-11 -0.875 0.92Peptidyl- glycine alpha- amidatingmonooxygenasAMD P19021 e 8.05E-11 5.46E-09 -0.591 0.918NicotinatephosphoribosylPNCB Q6XQN6 transferase 4.87E-14 1.25E-11 -0.956 0.91Amino acid4F2 P08195 transporter 1.54E-12 1.97E-10 -0.661 0.907Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC heavy chainSLC3A2HMCN1 Q96RW7 Hemicentin-1 1.62E-10 9.04E-09 -0.621 0.902TransferrinreceptorTFR1 P02786 protein 1 4.22E-15 1.80E-12 -1.203 0.902GolgimembraneGOLM1 Q8NBJ4 protein 1 1.99E-13 4.25E-11 -0.836 0.902Immunoglobulin kappavariable 2- A0A087W 40;lmmunogloKV240; KVD W87; P016 bulin kappa40 14 variable 2D-40 6.10E-09 1.86E-07 -0.92 0.901UDP- glucose:glycoprote inglucosyltransfeUGGG1 Q9NYU2 rase 1 3.69E-15 1.80E-12 -0.8 0.901 NECT2 Q92692 Nectin-2 3.95E-08 8.79E-07 -3.366 0.901Sushi, nidogenand EGF-likedomaincontainingSNED1 Q8TER0 protein 1 6.18E-15 2.26E-12 -0.524 0.898von Willebrandfactor C andEGF domaincontainingVWCE Q96DN2 protein 3.83E-11 2.88E-09 -1.821 0.897PolymericimmunoglobuliPIGR P01833 n receptor 6.00E-13 9.04E-11 -0.906 0.892NucleosidediphosphateNDK3 Q13232 kinase 3 1.58E-10 8.97E-09 -0.637 0.892T-cell surfaceTACT P40200 protein tactile 4.92E-06 5.00E-05 -4.456 0.891ChlorideintracellularchannelCLIC1 O00299 protein 1 1.55E-10 8.97E-09 -0.782 0.889Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC 1,4-alpha- glucan- branchingGLGB Q04446 enzyme 5.79E-13 9.04E-11 -1.401 0.887HLA class IIhistocompatibility antigen, DPDPB1 P04440 beta 1 chain 9.37E-10 4.08E-08 -3.073 0.885 ENOA P06733 Alpha-enolase 1.93E-10 1.05E-08 -0.719 0.883Leukotriene A- LKHA4 P09960 4 hydrolase 3.48E-12 3.87E-10 -1.105 0.883 NOE2 095897 Noelin-2 7.12E-13 1.01E-10 -0.959 0.881ADAMTS-likeATL2 Q86TH1 protein 2 8.31E-11 5.46E-09 -0.504 0.881Rho GTPase- activatingRHG01 Q07960 protein 1 2.59E-12 3.02E-10 -1.22 0.88SerineTRY1 P07477 protease 1 1.19E-07 2.22E-06 -0.697 0.88Proproteinconvertasesu btilisin / kexinPCSK9 Q8NBP7 type 9 1.29E-12 1.73E-10 -0.623 0.878C-l- tetrahydrofolate synthase,C1TC P11586 cytoplasmic 1.24E-09 4.96E-08 -1.246 0.878Immunoglobulin lambdaLV743 P04211 variable 7-43 6.54E-11 4.66E-09 -0.963 0.877AldehydedehydrogenaseALDH2 P05091, mitochondrial 1.39E-10 8.68E-09 -1.051 0.877CytoplasmicaconitateACOHC P21399 hydratase 1.32E-09 5.14E-08 -0.869 0.877P0DJD7; P0 Pepsin A- PEPA4; PEP DJD8; P0DJ 4; Pepsin A- A3; PE PAS D9 3; Pepsin A-5 4.43E-09 1.45E-07 -2.804 0.876RoundaboutROBO1 Q9Y6N7 homolog 1 1.82E-09 6.77E-08 -0.556 0.873PulmonaryPSPB P07988 surfactant- 2.78E-09 9.61E-08 -1.395 0.871Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC associatedprotein BAcetyl-CoAacetyltransferaTHIC Q9BWD1 se, cytosolic 1.46E-08 3.74E-07 -1.737 0.87Heat shock 70kDa proteinlA; Heat shockHS71A; HS7 P0DMV8; P 70 kDa proteinIB 0DMV9 IB 1.34E-10 8.55E-09 -0.648 0.868 FGL2 Q14314 Fibroleukin 6.41E-09 1.93E-07 -0.805 0.868GlutathioneGGT7 Q9UJ14 hydrolase 7 1.88E-09 6.89E-08 -1.491 0.867Immunoglobulin lambdaLV144 P01699 variable 1-44 6.54E-11 4.66E-09 -0.876 0.867IntercellularadhesionICAM3 P32942 molecule 3 6.79E-07 9.50E-06 -0.605 0.867 STX7 015400 Syntaxin-7 1.83E-06 2.22E-05 -4.593 0.867CarbohydratesulfotransferasCHSTC Q9NRB3 e 12 3.27E-06 3.62E-05 -0.906 0.865 GRN P28799 Progranulin 1.51E-07 2.76E-06 -0.619 0.861HLA class IIhistocompatibility antigen, DQDQB1 P01920 beta 1 chain 9.40E-10 4.08E-08 -4.937 0.861UTP--glucose- 1-phosphateu ridy lyltra nsferUGPA Q16851 ase 3.52E-09 1.19E-07 -1.066 0.861L-xyluloseDCXR Q7Z4W1 reductase 2.18E-10 1.14E-08 -1.521 0.86ImmunoglobuliA0A0C4DH n kappaKV108 67 variable 1-8 3.02E-11 2.34E-09 -0.585 0.859Immunoglobulin lambdaLV140 P01703 variable 1-40 7.93E-12 8.13E-10 -0.641 0.858N- acetylglucosamGNPTG Q9UJJ9 ine-1- 2.48E-09 8.71E-08 -0.5 0.858Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC phosphotransferase subunitgammaFructosebisphosphateALDOB P05062 aldolase B 2.21E-11 1.77E-09 -1.199 0.858ElongationEF2 P13639 factor 2 1.02E-09 4.29E-08 -0.588 0.857Immunoglobulin heavyHV169 P01742 variable 1-69 2.26E-09 8.04E-08 -1.087 0.857ImmunoglobuliA0A075B6I n lambdaLV746 9 variable 7-46 6.09E-06 5.95E-05 -1.8 0.856 CORIA P31146 Coronin-IA 2.18E-10 1.14E-08 -0.956 0.856Chymotrypsin- CTRC Q99895 C 4.04E-10 1.92E-08 -3.44 0.855ComplementCFAD P00746 factor D 1.07E-11 1.01E-09 0.646 0.855lnterleukin-1IL1R2 P27930 receptor type 2 7.16E-09 2.10E-07 -0.954 0.855Leucine-richrepeatcontainingLRC39 Q96DD0 protein 39 2.59E-07 4.31E-06 -0.505 0.854LivercarboxylesteraESTI P23141 se 1 3.74E-08 8.40E-07 -0.586 0.853Glycogenphosphorylase,PYGL P06737 liver form 5.90E-08 1.25E-06 -0.978 0.853AdeninephosphoribosylAPT P07741 transferase 1.04E-09 4.29E-08 -1.113 0.853(3R)-3- hydroxyacyl- CoADHB8 Q92506 dehydrogenase 2.18E-08 5.33E-07 -0.879 0.852Aminoacylase- ACY1 Q03154 1 2.12E-09 7.66E-08 -1.808 0.851 FCN2 Q15485 Ficolin-2 8.82E-07 1.19E-05 0.549 0.851CarboxypeptidCBPD 075976 ase D 6.00E-08 1.25E-06 -0.825 0.851Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC HemoglobinsubunitHBG1 P69891 gamma-1 2.20E-05 1.69E-04 1.203 0.85Beta- hexosaminidasHEXB P07686 e subunit beta 1.03E-08 2.85E-07 -0.688 0.85N-alpha- acetyltransferase 15, NatAauxiliaryNAA15 Q9BXJ9 subunit 3.70E-08 8.38E-07 -2.441 0.848UrocanateHUTU Q96N76 hydratase 1.64E-07 2.93E-06 -1.202 0.847AldehydedehydrogenaseAL1A1 P00352 1A1 6.78E-07 9.50E-06 -0.748 0.846Mannosyl- oligosaccharidMOGS Q13724 e glucosidase 1.18E-09 4.78E-08 -0.601 0.846Beta- MANBA 000462 mannosidase 6.01E-08 1.25E-06 -0.512 0.845Secreted andtransmembranSCTM1 Q8WVN6 e protein 1 7.91E-09 2.28E-07 -1.164 0.845Rho GDP- dissociationGDIR2 P52566 inhibitor 2 5.04E-10 2.35E-08 -1.25 0.843PeroxidasinPXDN Q92626 homolog 2.61E-10 1.34E-08 -0.631 0.843 LEG3 P17931 Galectin-3 3.05E-08 7.17E-07 0.957 0.843ImmunoglobuliA0A0C4DH n heavyHV158 39 variable 1-58 3.48E-08 7.97E-07 -0.599 0.842All-trans- retinoldehydrogenase[NAD(+)]ADH1B P00325 ADH1B 3.92E-10 1.92E-08 -1.381 0.841Immunoglobulin heavyIGHD P01880 constant delta 6.03E-09 1.86E-07 -2.479 0.84Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC ImmunoglobuliA0A0B4J1X n heavyHV374 5 variable 3-74 1.22E-08 3.25E-07 -0.567 0.839Glutathione S- transferaseGSTO1 P78417 omega-1 9.95E-08 1.96E-06 -0.51 0.839SorbitolDHSO Q00796 dehydrogenase 1.85E-11 1.53E-09 -1.226 0.839IgG receptorFcRn largeFCGRN P55899 subunit p51 1.23E-08 3.25E-07 -1.499 0.839Secretoglobinfamily 3 ASG3A2 Q96PL1 member 2 1.57E-10 8.97E-09 -4.527 0.838Epididymisspecific alpha- MA2B2 Q9Y2E5 mannosidase 6.95E-10 3.12E-08 -2.497 0.837PhosphoglycerPGAM1 P18669 ate mutase 1 5.00E-09 1.62E-07 -0.845 0.837All-trans- retinoldehydrogenaseADH4 P08319 [NAD(+)] ADH4 2.66E-07 4.40E-06 -0.903 0.8363- hydroxyisobutyratedehydrogenase3HIDH P31937, mitochondrial 1.97E-06 2.33E-05 -2.181 0.836EndoplasmicreticulumaminopeptidasERAP1 Q9NZ08 e 1 3.13E-08 7.28E-07 -0.578 0.835N-acetyl-D- glucosamineNAGK Q9UJ70 kinase 1.76E-08 4.42E-07 -0.928 0.835Interleukin-27IL27B Q14213 subunit beta 6.02E-09 1.86E-07 -3.955 0.835 TALDO P37837 Transaldolase 5.50E-10 2.52E-08 -0.732 0.833Isocitratedehydrogenase[NADP]IDHC 075874 cytoplasmic 4.97E-07 7.32E-06 -1.085 0.833Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC EndoplasmicreticulumaminopeptidasERAP2 Q6P179 e 2 3.81E-07 5.85E-06 -1.274 0.832 SAP P07602 Prosaposin 3.29E-09 1.12E-07 -0.555 0.832ImmunoglobuliA0A0C4DH n heavyHV70D 43 variable 2-70D 2.59E-05 1.94E-04 -0.672 0.83Glutathionehydrolase 1GGT1 P19440 proenzyme 2.69E-08 6.43E-07 -1.411 0.83Calpain-1catalyticCAN1 P07384 subunit 4.01E-10 1.92E-08 -0.506 0.83ArgininosuccinARLY P04424 ate lyase 1.40E-09 5.36E-08 -1.154 0.829NeuronalNPTX1 Q15818 pentraxin-1 3.50E-05 2.48E-04 -3.369 0.829ImmunoglobuliAO AO AO MS n heavyHV145 14 variable 1-45 9.04E-09 2.57E-07 -0.948 0.828AlanineaminotransferaALAT1 P24298 se 1 1.05E-06 1.39E-05 -1.101 0.827 NF1 P21359 Neurofibromin 1.62E-09 6.11E-08 -3.939 0.827AdenosineADA2 Q9NZK5 deaminase 2 4.19E-09 1.39E-07 -0.594 0.827Immunoglobulin heavyconstantIGHE P01854 epsilon 9.98E-09 2.81E-07 -1.499 0.826Triokinase / FMTKFC Q3LXA3 N cyclase 2.29E-06 2.66E-05 -1.801 0.824Immunoglobulin kappaKV230 P06310 variable 2-30 1.33E-08 3.43E-07 -0.665 0.824Proteinphosphatase 1regulatoryPP1R7 Q15435 subunit 7 4.00E-05 2.77E-04 -2.942 0.824Procollagen C- endopeptidasePCOC2 Q9UKZ9 enhancer 2 5.87E-07 8.40E-06 -0.589 0.824Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC IntercellularadhesionICAM1 P05362 molecule 1 2.50E-05 1.88E-04 -0.504 0.824 TSK Q.8WUA8 Tsukushi 5.86E-06 5.79E-05 -0.797 0.823GlutathioneGSHB P48637 synthetase 5.13E-08 1.10E-06 -0.627 0.823Oxygen - dependentcoproporphyrinogen-llloxidase,HEM6 P36551 mitochondrial 1.85E-06 2.23E-05 -1.451 0.822Eukaryoticinitiation factorIF4A1 P60842 4A-I 7.22E-09 2.10E-07 -0.793 0.822 TFG Q92734 Protein TFG 2.92E-07 4.77E-06 -0.712 0.822Immunoglobulin heavyHV434 P06331 variable 4-34 3.98E-08 8.79E-07 -0.593 0.822Heat shock 70HSP13 P48723 kDa protein 13 1.74E-07 3.01E-06 -1.452 0.822Tissue alpha-L- FUCO P04066 fucosidase 9.58E-07 1.28E-05 -1.072 0.821V-set andimmunoglobulin domaincontainingVSIG4 Q9Y279 protein 4 1.62E-05 1.31E-04 -0.809 0.82Immunoglobulin lambdaLV151 P01701 variable 1-51 1.28E-08 3.35E-07 -0.618 0.82ThymidineTYPH P19971 phosphorylase 7.14E-09 2.10E-07 -0.682 0.82PoliovirusPVR P15151 receptor 4.93E-07 7.31E-06 -0.527 0.818Glutamatedehydrogenase1,DHE3 P00367 mitochondrial 3.38E-07 5.31E-06 -0.877 0.818Chromogranin-CMGA P10645 A 1.84E-07 3.14E-06 -0.644 0.817Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC Immunoglobulin lambdaLV211 P01706 variable 2-11 3.64E-07 5.65E-06 -0.816 0.815Carcinoembryonic antigen- related celladhesionCEAM1 P13688 molecule 1 2.47E-05 1.87E-04 -1.369 0.815ProteinF234A Q.9H0X4 FAM234A 6.02E-07 8.57E-06 -1.204 0.814Axonemaldynein lightchain domaincontainingAXDN1 Q5T1B0 protein 1 1.96E-03 6.50E-03 -0.638 0.814 RNAS7 Q.9H1E1 Ribonuclease 7 2.63E-05 1.95E-04 -1.289 0.814N(4)-(beta-N- acetylglucosaminy l)-L- ASPG P20933 asparaginase 3.77E-07 5.82E-06 -1.026 0.814TransferrinreceptorTFR2 Q9UP52 protein 2 1.04E-08 2.85E-07 -1.339 0.814YEATS domaincontainingYETS2 Q9ULM3 protein 2 7.12E-07 9.91E-06 -2.369 0.813Immunoglobulin lambdaLV321 P80748 variable 3-21 3.98E-05 2.76E-04 -0.86 0.812CarboxypeptidCBPQ Q.9Y646 ase Q. 5.49E-07 7.90E-06 -0.632 0.812KetohexokinasKHK P50053 e 3.36E-07 5.31E-06 -2.651 0.812Leucine-richrepeatcontainingLRC4B Q9NT99 protein 4B 1.92E-03 6.40E-03 -1.03 0.812Glyoxylatereductase / hydroxy pyruvateGRHPR Q9UBQ.7 reductase 6.02E-08 1.25E-06 -1.341 0.811TripartiteTRI75 A6NK02 motif- 2.37E-03 7.59E-03 2.21 0.81Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC containingprotein 75Immunoglobulin heavyconstantIGHG3 P01860 gamma 3 9.86E-10 4.21E-08 -0.672 0.81HemogiobinHBD P02042 subunit deita 5.95E-05 3.84E-04 0.605 0.81BetaBGLR P08236 glucuronidase 1.72E-07 2.99E-06 -1.269 0.809Collagen triplehelix repeatcontainingCTHR1 Q.96CG8 protein 1 7.32E-07 1.01E-05 -3.082 0.809HemoglobinHBB P68871 subunit beta 1.01E-04 5.96E-04 0.571 0.809MelanocytePMEL P40967 protein PMEL 1.83E-07 3.14E-06 -0.648 0.808 ERLN1 075477 Erlin-1 3.87E-05 2.71E-04 -2.552 0.808Heat shockprotein HSPHS90B P08238 90-beta 1.08E-07 2.10E-06 -0.518 0.808Dual specificitymitogen- activatedprotein kinaseMP2K7 014733 kinase 7 5.30E-07 7.68E-06 -0.628 0.808 NICA Q92542 Nicastrin 9.00E-05 5.44E-04 -0.517 0.808F-actin-cappingprotein subunitCAZA2 P47755 alpha-2 2.30E-08 5.55 E -07 -0.619 0.807DihydropyrimiDPYS Q14117 dinase 2.31E-05 1.77E-04 -1.925 0.807Putativehydroxy pyruvaHYI Q5T013 te isomerase 1.44E-05 1.18E-04 -1.631 0.806Alpha- methylacyl- AMACR Q9UHK6 CoA racemase 2.73E-05 1.99E-04 -1.879 0.806 LEG10 Q05315 Galectin-10 2.69E-06 3.03E-05 -2.577 0.805Immunoglobulin heavyHVD82 P0DP08 variable 4-38-2 1.18E-07 2.22E-06 -0.542 0.805Attorney Docket No. NXON-014 / 04WO 346247-2095 UniprotProtein AN Protein Name p-value q-value log2FC AUC CoagulationFA12 P00748 factor XI i 2.98E-06 3.32E-05 0.697 0.804ProteinphosphatasePPM1A P35813 1A 8.46E-05 5.16E-04 -1.137 0.804Carbonicanhydrase- related proteinCAH11 075493 11 1.18E-07 2.22E-06 -3.691 0.804Ras-relatedRAB8B Q.92930 protein Rab-8B 1.20E-08 3.22E-07 -1.525 0.804AsialoglycoprotASGR2 P07307 ein receptor 2 2.57E-06 2.91E-05 -0.574 0.804Angiopoietin- ANGL8 Q6UXH0 like protein 8 7.02E-06 6.66E-05 -0.899 0.804 CD27 P26842 CD27 antigen 1.46E-07 2.68E-06 -2.585 0.803ChymotrypsinoCTRB2 Q.6GPI1 gen B2 1.72E-05 1.39E-04 -2.008 0.803Procoliagen- lysine,2- oxoglutarate 5-PL0D1 Q.02809 dioxygenase 1 3.33E-08 7.68E-07 -0.782 0.802

[0456] Table 4.3 lists the top-performing 3plexes generated from the stage 1 breast cancer biomarkers provided in Table 4.2. Each 3plex listed in Table 4.3 achieved an accuracy metric (i.e.. a 10-fold average accuracy metric calculated as described above in Example 3) of 0.98 (98%) or higher, representing a correct-prediction ratio of 0.98 or higher.Table 4.3 - Stage J breast Cancer biomarker 3plexes with Accuracy > 0.9810-Foldaverage3PLEX Accuracy('ITLNT, 'GOLMT, 'LV140') 0.995('ITLNT, 'GOLMT, 'ADEC1') 0.993('ITLNT, 'FCRL5', 'ADEC1') 0.991('ITLNT, 'HV169', 'ALDH2') 0.991fCTRC, ’ ITLNT, 'KV108') 0.990('UGGG1', 'LV140', 'PCSK9') 0.990('UGGG1', 'HS71A; HS71B', 'LV140') 0.990('LKHA4', 'UGGGT, 'LV140') 0.990Attorney Docket No. NXON-014 / 04WO 346247-209510-Foldaverage3PLEX Accuracy('ITLNl', 'ADECT, 'SNED1') 0.989('CTRC, 'UGGGT, 'LV140') 0.989('DQ. B1', 'ITLNl', 'ATL2') 0.988('ITLNl', 'ADECT, 'HMCN1') 0.986('STX7', 'ITLNl', 'ATL2') 0.986('THIC, 'ITLNl', 'ATL2') 0.985('UGGGT, '4F2', 'LV140') 0.985('ITLNl', 'ADECT, 'UGGGT) 0.984('ITLNl', 'ADECT, 'KV108') 0.983('STX7', 'ITLNl', 'HBG1') 0.983('ITLNl', 'RHGOl', 'TFR1') 0.982('ITLNl', 'RHGOl',1LV743') 0.982fPSPB', 'ITLNl', 'ADECT) 0.982('PSPB', 'UGGGT, '4F2') 0.982('PSPB', 'ADECT, 'UGGGT) 0.982('ITLNl', 'GOLMT, 'CBPD') 0.982('PSPB', 'ITLNl', 'TFR1') 0.981('UGGGT, 'ENOA', 'LV140') 0.981('ITLNl', 'LV743', 'GOLMT) 0.981('ITLNl', 'GOLMT, 'MMP19') 0.981('ITLNl', 'N0E2', 'LRC39') 0.981('CTRC1, 'ITLNl', 'ALDH2') 0.981('CTRC, 'ITLNl', 'UGGGT) 0.981('ITLNl', 'ACOHC, 'ADECT) 0.981('ITLNl', 'ADECT, 'HS71A; HS71B') 0.981('ITLNl', 'ADECT, 'FCN2') 0.981('KV240; KVD40', 'PCSK9','NOMO3; NOMO1; NOMO2') 0.981('GLGB', 'UGGGT, 'LV140') 0.981('THIC, 'ITLNl', 'LV140') 0.981('STX7', 'ITLNl', 'LV140') 0.981('GOLMT, 'UGGGT, 'LV140') 0.981('ITLNl', 'RHGOl', 'ALDH2') 0.980('ITLNl', 'ADECT, 'CLIC1') 0.980('PNCB', 'UGGGT, 'LV140') 0.980('ITLNl', 'ADECT, 'EF2') 0.98000457 It was found that a number of cancer biomarkers were unexpectedly overrepresented in the top 3plexes listed in Table 4.3, and were deemed as key biomarkers for stage 1 breast cancer.Attorney Docket No. NXON-014 / 04WO 346247-2095 Some of the key biomarkers in ovarian cancer include 1TLN1 that was included in 31 out of the top 3plexes, ADEC1 that was included in 13 out of the top 3plexes, UGGG1 that was included in 13 out of the top 3plexes, and LV140 that was included in 12 out of the top 3plexes. Among the list of stage 1 breast cancer 3plexes provided in Table 4.3, the most frequently identified proteins m this analysis are listed in Table 4.4.Table 4.4 Most common proteins in stage 1 breast cancer biomarker 3plexesProtein PlexcountITLN1 31ADEC1 13UGGG1 13LV140 12GOLM1 6PSPB 4CTRC 4ATL2 3RHG01 3ALDH2 3STX7 3[00458| One or more of the top ranked stage 1 breast 3plexes (those listed in Table 4.3) may then be selected to generate Linear SVM equations as classifiers for future prediction of samples of unknown cancer / non-cancer status, following the methods outlined above for stage 1 uterine cancer in Example 3.Example 5 - Bioinformatic analysis for comparing MAP levels between stage 1 CRC patients and non-cancer subjects

[0459] Following the methods outlined above for stage 1 uterine cancer in Example 3, a stage 1 CRC cohort of 25 subjects was similarly assessed to identify stage 1 CRC biomarkers, as well as generate predictive 3plexes of stage 1 CRC biomarkers that demonstrate a high accuracy metric.

[0460] As shown in Table 5.1, the dataset started with 3332 total proteins. After removing low quality data, the dataset w as pruned to 2545 proteins, and this curated dataset of 2545 proteins was used for subsequent machine learning-based analysis, as described below;Attorney Docket No. NXON-014 / 04WO 346247-2095 [00461 A first round of machine learning was performed, as described in Example 3 (with respect to stage 1 uterine cancer), to identify MAPs that demonstrated differential expression levels between stage 1 CRC and non-cancer samples, which resulted in identification of 448 significant proteins (summarized in Table 5.1 below).Table 5.1 Pre-analysis protein curationNumber Validof intensity SignificantIndication samples proteins proteinsCRC Stagel 25 2545 448[00462| The identified significant proteins are listed in Table 5.2. In Table 5.2, each row represent a protein identified by a respective UniprotKB unique protein entry name (column 1; “Protein”), UniprotKB unique accession number (column 2; “Uniprot AN”), and colloquial protein name (column 3; “Protein Name”). Note that the “ HUMAN” suffix was omitted from each of the protein entry names in column 1, for clarity of presentation. Column 4 shows a p-value denoting statistical significance. Column 5 shows a q-value, which is an adjusted p-value using Benjamini-Hochberg correction. Column 6 shows log2FC, indicating the scale and direction of differential expression in Log2 units, where a negative value indicates downregulation m the cancer cohort compared to the non-cancer cohort and a positive value indicates upregulation in the cancer cohort compared to the non-cancer cohort. Column 7 shows the area under the curve (AUC) of the ROC curve generated from the quantification data for each protein.Table 5.2 Significantly differentially expressed Stage 1 CRC biomarkersProtein Uniprot Protein p-value q- value log2FC AUC AN NameMLN Pl Q9UNW1 Multiple 1.29E-23 3.28E-20 -0.994 0.995inositolpolyphosphatephosphatase1TRFE P02787 Serotransfer 3.15E-20 1.60E-17 -1.021 0.995rinAMD P19021 Peptidyl- 4.3 IE-20 1.83F.-17 -1.177 0.989glycinealpha-Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameamidatingmonooxygenaseCO8G P07360 Complemen 6.33E-16 7.00E-14 -0.914 0.984t componentC8 gammachainFCGBP Q9Y6R7 IgGFc- 1.07E-18 2.27E-16 0.763 0.983bindingproteinLKHA4 P09960 Leukotriene 9.14E-20 2.91E-17 -1.665 0.981A-4hydrolaseLDHB P07195 L -lac tale 8.88E-19 2.08E-16 -0.681 0.979dehydrogenase B chainVTDB P02774 Vitamin D- 6.99E-20 2.54E-17 -0.716 0.979bindingprotein1L1R2 P27930 Interleukin- 6.61E-12 2.37E-10 -1.884 0.9781 receptortype 2PLSL P13796 Plastin-2 1.29E-14 1.02E-12 -0.787 0.976 EGLN P17813 Endoglin 4.25E-13 2.25E-11 -1.224 0.975 TALDO P37837 Transaldola 2.13E-21 1.80E-18 -1.309 0.975sePGAM1 P18669 Phosphogly 2.00E-17 2.99E-15 -1.481 0.973ceratemutase 1B3GN8 Q7Z7M8 UDP- 1.42E-17 2.25E-15 -0.79 0.973GlcNAc:beta Gal beta- 1,3-N- acetylglucosaminyltransferase 8EGFR P00533 Epidermal 3.06E-18 5.99E-16 -0.863 0.973growthfactorreceptorADEC1 015204 ADAM 4.58E-17 6.14E-15 -0.974 0.973DECI GLGB Q04446 1,4-alpha- 8.98E-19 2.08E-16 -1.49 0.97glucan- branchingenzyme4F2 P08195 Amino acid 5.36E-16 6.20E-14 -0.898 0.965transporterheavy chainSLC3A2Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameMCPH1 Q8NEM0 Microcephal 8.31E-05 2.92E-04 1.943 0.962inGSTO1 P78417 Glutathione 1.03E-14 8.72E-13 -0.881 0.962S- transferaseomega- 1PEPD P12955 Xaa-Pro 5.33E-21 3.39E-18 -1.033 0.961dipeptidasePLST P13797 Plastin-3 5.14E-12 1.92E-10 -1.095 0.961 MA1C1 Q9NR34 Mannosyl- 6.82E-18 1.16F.-15 -2.069 0.961oligosaccharide 1,2- alpha- mannosidase lCCIRC Q99895 Chymotryps 2.32E-09 3.78E-08 -5.22 0.961in-CPTK7 QI 3308 Inactive 1.32E-11 4.41E-10 -0.816 0.96tyrosine- proteinkinase 7B3GN2 Q9NY97 N- 2.72E-15 2.56E-13 -0.685 0.953acety llactosaminidebeta- 1,3 -N- acetylglucosaminyltransferase 2OSBL3 Q9H4L5 Oxysterol- 8.71E-13 4.26E-11 -1.612 0.953bindingprotein- relatedprotein 3NDK3 Q13232 Nucleoside 1.18E-08 1.5 IE-07 -1.193 0.949diphosphatekinase 3FCN2 Q15485 Ficolin-2 2.49E-16 3.17E-14 0.742 0.949 CO8B P07358 Complemen 4.10E-16 4.97E-14 -0.794 0.947t componentC8 betachainEF1A2 Q05639 Elongation 1.03E-14 8.72E-13 -0.988 0.943factor 1- alpha 2ATF6A P18850 Cyclic 3.37E-14 2.52E-12 -1.328 0.94AMP- dependenttranscriptionfactor ATF-6 alphaAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameGRN P28799 Progranulin 6.93E-13 3.53E-11 -1.009 0.94 PNCB Q6XQN6 Nicotinate 1.11E-14 9.15E-13 -0.997 0.937phosphoribosyltransferaseSRCRL A1L4H1 Soluble 5.12E-13 2.66E-11 1.194 0.936scavengerreceptorcysteine - richdomaincontainingproteinSSC5DALDH1A1 P00352 Aldehyde 4.23E-14 3.07E-12 -0.955 0.935dehydrogenase 1A1RECK 095980 Reversion1.44E-12 6.52E-11 -0.992 0.934inducingcysteine - rich proteinwith KazalmotifsSYWC P23381 Tryptophan- 2.13E-10 4.64E-09 -0.827 0.932-tRNAligase,cytoplasmicROBO1 Q9Y6N7 Roundabout 7.74E-16 8.20E-14 -0.978 0.931homolog 1B4GA1 043505 Beta- 1,4- 1.74E-12 7.78E-11 -1.014 0.929glucuronyltransferase 1DIAC Q01459 Di-N- 6.91E-14 4.72E-12 -0.985 0.929acetylchitobiaseGSHB P48637 Glutathione 2.67E-13 1.58E-11 -0.917 0.929synthetaseNOE2 095897 Noelin-2 4.39E-07 3.47E-06 -1.22 0.929 F234A Q9H0X4 Protein 1.77E-08 2.12E-07 -1.738 0.926FAM234AFCGRN P55899 IgG 3.11E-07 2.59E-06 -2.943 0.925receptorFcRn largesubunit p51EYS Q5T1H1 Protein eyes 8.92E-06 4.31E-05 -1.222 0.924shuthomologLRC25 Q8N386 Leucine- 1.57E-09 2.71E-08 -1.901 0.923rich repeat-Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Namecontainingprotein 25ACY1 Q03154 Aminoacyla 4.72E-12 1.79E-10 -2.298 0.923se-1FETUA P02765 Alpha-2- 2.73E-13 1.58E-11 -0.729 0.922HS- glycoproteinGNPTG Q9UJJ9 N- 1.79E-11 5.38E-10 -0.609 0.922acetylglucosamine- 1- phosphotransferasesubunitgammaIMPA3 Q9NX62 Golgi- 1.80E-11 5.38E-10 -1.001 0.921residentadenosine3',5'- bisphosphate 3'- phosphataseAMACR Q9UHK6 Alpha- 1.72E-09 2.94E-08 -2.883 0.921methylacyl- CoAracemaseASM3A Q92484 Acid 1.32E-12 6.10E-11 -1.541 0.921sphingomyelinase-likephosphodiesterase 3 aMEG10 Q96KG7 Multiple 1.91E-12 8.36E-11 -0.828 0.92epidermalgrowthfactor-likedomainsprotein 10BGLR P08236 Beta- 7.67E-10 1.40E-08 -1.659 0.919glucuronidaseFSTL1 Q12841 Follistatin- 2.95E-13 1.67E-11 -0.953 0.916relatedprotein 1TACT P40200 T-cell 3.09E-05 1.23E-04 -4.504 0.916surfaceproteintactileLSAMP Q13449 Limbic 2.19E-10 4.72E-09 -0.762 0.915system-associatedAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NamemembraneproteinTFR1 P02786 Transferrin 5.25E-11 1.41E-09 -1.283 0.914receptorprotein 1CTRB2 Q6GPI1 Chymotryps 2.07E-12 8.93E-11 -4.145 0.914inogen B2COCH 043405 Cochlin 3.13E-13 1.73E-11 -5.209 0.914 FUCO P04066 Tissue 1.33E-13 8.71E-12 -1.337 0.912alpha-L- fucosidasePCOC2 Q9UKZ9 Procollagen 2.93E-10 6.02E-09 -1.082 0.912C- endopeptidase enhancer2TIMP3 P35625 Metalloprot 3.09E-12 1.25E-10 -4.815 0.911einaseinhibitor 3RL17 P18621 Large 2.36E-11 6.91E-10 -2.325 0.91ribosomalsubunitproteinuL22TCAM1 P05362 Intercellular 2.18E-09 3.58E-08 -0.824 0.91adhesionmolecule 1CBPQ Q9Y646 Carboxypep 1.27E-12 5.97E-11 -1.058 0.91tidase QB3GN7 Q8NFL0 UDP- 5.47E-09 8.00E-08 -1.898 0.91GlcNAc:betaGal beta- 1.3-N- acetylglucosaminyltransferase 7R4RL2 Q86UN3 Reticulon-4 1.78E-11 5.38E-10 -0.959 0.908receptorlike 2DMKN Q6E0U4 Dennokine 1.18E-11 4.01E-10 -1.069 0.908 TRI75 A6NK02 Tripartite 2.44E-06 1.45E-05 3.356 0.907motifcontainingprotein 75HYI Q5T013 Putative 3.69E-09 5.62E-08 -2.402 0.907hydroxypyruvateisomeraseARC1A Q92747 Actin - 4.57E-14 3.23E-12 -3.322 0.907relatedAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Nameprotein 2 / 3complexsubunit 1ACAH11 O75493 Carbonic 9.01E-12 3.19E-10 -5.555 0.906anhydrase- relatedprotein 11ICAM3 P32942 Intercellular 2.55E-13 1.56E-11 -0.748 0.905adhesionmolecule 3ARLY P04424 Argininosuc 1.66E-06 1.07E-05 -1.201 0.905cinate lyase3HIDH P31937 3- 1.54E-10 3.53E-09 -2.876 0.905hydroxy isobutyratedehydrogenase,mitochondrialANAG P54802 Alpha-N- 1.47E-11 4.73E-10 -0.718 0.904acetylglucosaminidaseFUCO2 Q9BTY2 Plasma 1.77E-15 1.80E-13 -1.239 0.904alpha-L- fucosidaseCILP2 Q8IUL8 Cartilage 3.14E-12 1.25E-10 -1.33 0.904intermediatelayerprotein 2FMOD Q06828 Fibromoduli 2.97E-06 1.70E-05 -2.107 0.904nNECT2 Q92692 Nectin-2 1.54E-06 1.00E-05 -3.757 0.903 IDHC O75874 Isocitrate 6.35E-12 2.31E-10 -1.75 0.902dehydrogenase [NADP]cytoplasmicDPP3 Q9NY33 Dipeptidyl 4.78E-10 9.51E-09 -1.055 0.901peptidase 3H3.1;H3.3; H P68431; P84 Histone 1.41E-14 1.09E-12 -3.184 0.901 3.1T; H3.2 243; Q16695 H3.1; Histon; Q71DI3 e H3.3; Histone H3.1t; Histone H3.2TTHY P02766 Transthyreti 1.86E-06 1.17E-05 -0.735 0.9nANXA5 P08758 Annexin A5 2.05E-09 3.41E-08 -1.18 0.899Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameIL18BP O95998 Interleukin- 7.71E-11 1.98E-09 -2.623 0.89818-bindingproteinAMPE Q07075 Glutamyl 2.06E-08 2.44E-07 -0.513 0.898aminopeptidaseBPIB2 Q8N4F0 BPI fold4.86E-10 9.55E-09 -1.075 0.897containingfamily Bmember 2ACAP2 Q15057 Arf-GAP 7.64E-08 7.76E-07 -2.103 0.897with coiled- coil, ANKrepeat andPH domaincontainingprotein 2LUZP1 Q86V48 Leucine 4.70E-05 1.78E-04 -4.841 0.896zipperprotein 1ERAP1 Q9NZ08 Endoplasmi 3.06E-10 6.23E-09 -0.694 0.896c reticulumaminopeptidase 1SLAF5 Q9UIB8 SLAM 5.49E-11 1.46E-09 -1.231 0.896familymember 5OTUB1 Q96FW1 Ubiquitin 1.10E-09 1.98E-08 -1.513 0.896thioesteraseOTUB1DEFLDEF3 P59665; P59 Neutrophil 3.73E-09 5.62E-08 -0.982 0.896666 defen sin1; Neutrophi1 defensin 3NAGK Q9UJ70 N-acetyl-D- 1.66E-11 5.23E-10 -1.258 0.894glucosaminekinaseUGPA Q16851 UTP- 3.69E-12 1.45E-10 -0.999 0.894glucose-1- phosphateuridylyltransferaseICAM4 Q14773 Intercellular 1.68E-10 3.75E-09 -3.929 0.893adhesionmolecule 4SIA4A Q11201 CMP-N- 6.45E-09 8.88E-08 -3.195 0.893acetylneuraminate-beta- galactosamide-alpha-Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Name2,3- sialyltransferase 1LDHA P00338 L-lactate 2.96E-11 8.27E-10 -0.695 0.893dehydro genase A chainZG16B Q96DA0 Zymogen 2.62E-12 1.08E-10 -1.087 0.891granuleprotein 16homolog BCAPG P40121 Macrophage 3.32E-09 5.18E-08 -1.846 0.891-cappingproteinGDIB P50395 Rab GDP 6.58E-11 1.71E-09 -0.814 0.891dissociationinhibitorbetaASSY P00966 Argininosuc 1.09E-12 5.25E-11 -1.728 0.891cinatesynthaseITLN1 Q8WWA0 Intelectin-1 7.94E-11 2.02E-09 -0.814 0.89 C4BPA P04003 C4b-binding 2.77E-11 7.82E-10 0.98 0.89proteinalpha chainZMAT4 Q9H898 Zinc finger 1.76E-04 5.58E-04 -2.557 0.89matrin-typeprotein 4LRC4B Q9NT99 Leucine- 1.10E-05 5.15E-05 -2.72 0.888rich repeatcontainingprotein 4BSYYC P54577 Tyrosine— 5.22E-09 7.68E-08 -1.373 0.888tRNAligase,cytoplasmicESTD P10768 S- 5.24E-10 9.96E-09 -1.998 0.888formylglutathionehydrolaseAPOA4 P06727 Apolipoprot 7.05E-14 4.72E-12 -1.119 0.887ein A-IVRHOA P61586 Transformin 1.58E-08 1.94E-07 -4.164 0.887g proteinRhoAGSH1 P48506 Glutamate - 3.70E-09 5.62E-08 -1.673 0.886cysteineligasecatalyticsubunitLUM P51884 Lumican 1.42E-11 4.66E-10 -0.728 0.886Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameTRAK2 060296 Trafficking 2.20E-06 1.33E-05 4.364 0.886kinesin- bindingprotein 2PLS1 Q14651 Plastin-1 2.59E-06 1.53E-05 -4.134 0.885 ILEU P30740 Leukocyte 2.63E-12 1.08E-10 -0.934 0.884elastaseinhibitorARBK1 P25098 Beta- 5.21E-08 5.57E-07 -2.029 0.884adrenergicreceptorkinase 1COL18A1 P39060 Collagen 7.34E-10 1.36E-08 0.531 0.884 alpha-1(XVIII) chainFETUB Q9UGM5 Fetuin-B 2.38E-10 5.04E-09 -0.812 0.883 ARK72 O43488 Aflatoxin 1.10E-07 1.05E-06 -3.671 0.883Bl aldehydereductasemember 2CO8A P07357 Complemen 5.51E-12 2.03E-10 -0.637 0.883t componentC8 alphachainPPM1A P35813 Protein 5.68E-09 8.12E-08 -1.218 0.882phosphatase1ANAGAB P17050 Alpha-N- 8.21E-08 8.23E-07 -2.989 0.882 acetylgalact osaminidaseKPLCE Q5T750 Protein 9.20E-08 9.04E-07 -1.417 0.882KPLCE CFAI P05156 Complemen 3.38E-19 9.54E-17 -0.601 0.881t factor IALDOB P05062 Fructose- 1.27E-07 1.20E-06 -1.289 0.881bisphosphate aldolase BGCN1 Q92616 Stalled 3.62E-09 5.62E-08 -3.45 0.88ribosomesensorGCN1IGSF2 Q93033 Immunoglo 5.04E-11 1.36E-09 -1.287 0.88bulinsuperfamilymember 2BPIB1 Q8TDL5 BPI fold- 8.94E-11 2.25E-09 -1.121 0.88containingfamily Bmember 1Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameISK5 Q9NQ38 Serine 4.91E-08 5.32E-07 -2.083 0.88proteaseinhibitorKazal-type5HS71A; HS7 P0DMV8; P Heat shock 3.73E-09 5.62E-08 -0.634 0.88 IB 0DMV9 70 kDaprotein1 A; Heatshock 70kDa proteinIB TKFC Q3LXA3 Triokinase / 4.28E-07 3.40E-06 -2.149 0.88FMNcyclaseERAP2 Q6P179 Endoplasmi 5.20E-07 3.99E-06 -1.627 0.879c reticulumaminopeptidase 2RIN1 P13489 Ribonucleas 3.80E-10 7.67E-09 -0.596 0.879e inhibitorICOSL 075144 ICOS ligand 1.11E-09 1.99E-08 -0.727 0.879 CBPD 075976 Carboxypep 4.39E-09 6.53E-08 -1.09 0.879tidase DLANC1 043813 Glutathione 3.27E-07 2.69E-06 -2.384 0.878S- transferaseLANCE 1ENOA P06733 Alpha4.49E-10 9.00E-09 -0.746 0.878enolaseNECT1 Q15223 Nectin-1 3.37E-08 3.83E-07 -1.515 0.877 RPIA P49247 Ribose-5- 5.80E-09 8.25E-08 -1.692 0.877phosphateisomeraseZMIZ1 Q9ULJ6 Zinc finger 7.13E-09 9.65E-08 -4.387 0.877MIZdomain- containingprotein 1KIT P10721 Mast / stem 2.13E-09 3.52E-08 -0.595 0.877cell growthfactorreceptor KitTFR2 Q9UP52 Transferrin 2.44E-09 3.95E-08 -1.484 0.876receptorprotein 2DHSO Q00796 Sorbitol 1.72E-08 2.09E-07 -1.381 0.876dehydrogenaseAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameDPB1 P04440 HLA class 1.23E-10 2.95E-09 -3.587 0.875IIhistocompatibilityantigen, DPbeta 1 chainNICA Q92542 Nicastrin 5.02E-10 9.75E-09 -0.909 0.875 MYL4 P12829 Myosin 3.56E-06 1.99E-05 -3.732 0.874light chain 4HYAL1 Q12794 Hyaluronida 6.31E-10 1.18E-08 -1.783 0.874se-1FHAD1 B1AJZ9 Forkhead- 2.76E-10 5.71E-09 -2.877 0.874associateddomaincontainingprotein 1KAD2 P54819 Adenylate 1.35E-07 1.27E-06 -2.099 0.874kinase 2,mitochondrialAPOA P08519 Apolipoprot 6.26E-08 6.55E-07 2.134 0.873ein(a)GRAN P28676 Grancalcin 5.22E-10 9.96E-09 -1.746 0.873 KALRN 060229 Kalirin 9.81E-09 1.28E-07 -2.932 0.873 LAMP2 P13473 Lysosome- 2.09E-10 4.59E-09 -0.797 0.871associatedmembraneglycoprotein2SPR1B; SPR P22528; P35 Cornifin- 2.58E-13 1.56E-11 -5.967 0.871 1A 321 B; Cornifin- A PEBP4 Q96S96 Phosphatidy 2.44E-07 2.13E-06 -1.487 0.871lethanolamine-bindingprotein 4SEMG1 P04279 Semenogeli 4.55E-07 3.55E-06 -2.081 0.871n-1PNPH P00491 Purine 2.08E-07 1.85E-06 -0.81 0.871nucleosidephosphorylaseSCF P21583 Kit ligand 7.42E-10 1.37E-08 -3.813 0.87 TYPH Pl 9971 Thymidine 2.52E-10 5.25E-09 -0.802 0.869phosphorylaseSND1 Q7KZF4 Staphylococ 5.92E-09 8.33E-08 -1.152 0.869cal nucleasedomain-Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Namecontainingprotein 1THTR Q16762 Thiosulfate 1.38E-09 2.42E-08 -4.641 0.869sulfurtransferaseCYC P99999 Cytochrome 9.93E-09 1.29E-07 -1.017 0.869cCEL2A P08217 Chymotryps 1.41E-06 9.35E-06 -1.897 0.869in-likeelastasefamilymember 2AOSTF1 Q92882 Osteoclast1.56E-08 1.93E-07 -2.673 0.867stimulatingfactor 1SAHH P23526 Adenosylho 1.07E-06 7.37E-06 -0.87 0.867mocysteinaseTRIPC Q14669 E3 1.54E-10 3.53E-09 3.379 0.867ubiquitin- proteinligaseTRIP 12CSF1R P07333 Macrophage 3.79E-09 5.67E-08 -0.578 0.866colony- stimulatingfactor 1receptorHUTH P42357 Histidine 3.62E-04 1.05E-03 -0.787 0.866ammonialyaseSG3A2 Q96PL1 Secretoglobi 9.65E-10 1.75E-08 -4.53 0.866n family 3 Amember 2VPS4B 075351 Vacuolar 1.20E-08 1.54E-07 -1.459 0.866proteinsorting- associatedprotein 4BCE290 015078 Centrosoma 2.44E-05 1.00E-04 2.074 0.8661 protein of290 kDaAPT P07741 Adenine 1.32E-10 3.12E-09 -1.366 0.866phosphoribosyltransferaseAK1C2 P52895 Aldo-keto 5.97E-07 4.48E-06 -1.151 0.864reductasefamily 1member C2Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameIGHD P01880 Immunoglo 3.31E-09 5.18E-08 -2.756 0.864bulin heavyconstantdeltaMINY1 Q8N5J2 Ubiquitin 5.68E-09 8.12E-08 -3.024 0.864carboxyl - terminalhydrolaseMINDY- 1UBE2N P61088 Ubiquitin- 2.62E-11 7.50E-10 -3.531 0.864conjugatingenzyme E2N ANXA6 P08133 Annexin A6 2.47E-12 1.05E-10 -1.141 0.864 BGH3 Q15582 Transforming 7.59E-13 3.79E-11 -0.594 0.864g growthfactor-beta- inducedprotein ig- h3ASPG P20933 N(4)-(beta- 1.42E-09 2.48E-08 -1.343 0.863 N- acetylglucos aminyl)-L- asparaginaseMA2B2 Q9Y2E5 Epididymis4.28E-12 1.65E-10 -2.329 0.863specificalpha- mannosidaseMRC2 Q9UBG0 C-type 5.02E-11 1.36E-09 -0.574 0.863mannosereceptor 2BST1 Q10588 ADP- 7.50E-05 2.67E-04 -1.209 0.863ribosylcyclase / cyclic ADP- ribosehydrolase 2ACOHC P21399 Cytoplasmic 6.24E-08 6.55E-07 -0.864 0.863aconitatehydratasePA1B2 P68402 Platelet1.27E-07 1.20E-06 -3.337 0.863activatingfactoracetylhydrolase IBsubunitalpha2Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameT132C Q8N3T6 Transniemb 7.77E-08 7.85E-07 -1.047 0.862rane protein132CRHG01 Q07960 Rho 1.38E-09 2.42E-08 -1.125 0.862GTPase- activatingprotein 1FPRP Q9P2B2 Prostaglandi 1.85E-05 8.04E-05 -2.335 0.862n F2receptornegativeregulatorGPX3 P22352 Glutathione 1.61E-10 3.61E-09 -0.666 0.861peroxidase3GDIR2 P52566 Rho GDP- 1.09E-11 3.74E-10 -1.439 0.861dissociationinhibitor 2GGT7 Q9UJ14 Glutathione 4.94E-08 5.33E-07 -1.564 0.86hydrolase 7LCP2 Q13094 Lymphocyte 2.30E-10 4.92E-09 -3.153 0.86cytosolicprotein 2IGDC4 Q8TDY8 Immunoglo 5.75E-08 6.10E-07 -0.614 0.86bulinsuperfamilyDCCsubclassmember 4AMYP P04746 Pancreatic 1.95E-05 8.33E-05 -0.781 0.859alphaamylaseCAN1 P07384 Calpain-1 6.04E-09 8.44E-08 -0.523 0.859catalyticsubunitPROS P07225 Vitamin K- 3.83E-15 3.49E-13 0.616 0.859dependentprotein SLYSM3 Q7Z3D4 LysM and 1.81E-08 2.17E-07 -0.63 0.859putativepeptidoglycan-bindingdomaincontainingprotein 3MANBA 000462 Beta- 3.84E-11 1.06E-09 -0.69 0.858mannosidaseDNAS1 P24855 Deoxyribon 2.02E-05 8.61E-05 -0.736 0.858uclease-1Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameSIA7A Q9NSC7 Alpha-N- 7.66E-09 1.03E-07 -0.997 0.857acetylgalactosaminidealpha-2, 6- sialyltransferase 1MCM5 P33992 DNA 3.14E-03 6.95E-03 1.284 0.857replicationlicensingfactorMCM5NSF1C Q9UNZ2 NSFL1 6.27E-09 8.67E-08 -2.318 0.856cofactor p47HXK3 P52790 Hexokinase- 1.71E-11 5.30E-10 -1.24 0.8563ADH1A P07327 Alcohol 1.82E-07 1.65E-06 -2.429 0.856dehydrogenase 1AP20D1 Q6GTS8 N-fatty- 7.34E-08 7.56E-07 -1.623 0.855acyl-aminoacidsynthase / hydrolasePM20D1PSME1 Q06323 Proteasome 1.80E-09 3.06E-08 -0.703 0.855activatorcomplexsubunit 1SAP P07602 Prosa posin 8.85E-09 1.17E-07 -0.546 0.855 LEG10 Q05315 Galectin-10 6.33E-07 4.71E-06 -2.753 0.855 NDKB P22392 Nucleoside 3.86E-07 3.11E-06 -1.078 0.855diphosphatekinase BVWCE Q96DN2 von 1.75E-06 1.12E-05 -1.3 0.854Willebrandfactor C andEGFdomain- containingproteinIF4A1 P60842 Eukaryotic 4.88E-10 9.55E-09 -0.934 0.854initiationfactor 4A-ICATC P53634 Dipeptidyl 6.71E-08 6.97E-07 -0.823 0.854peptidase 1PYGL P06737 Glycogen 4.47E-07 3.50E-06 -0.925 0.854phosphorylase, liverformAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameS27A4 Q6P1M0 Long-chain 2.43E-06 1.45E-05 -1.894 0.854faty’ acidtransportprotein 4LSR Q86X29 Lipolysis- 1.02E-06 7.10E-06 -1.355 0.853stimulatedlipoproteinreceptorOSMR Q99650 Oncostatin- 5.56E-09 8.04E-08 -1.126 0.853M-specificreceptorsubunit betaG3ST1 Q99999 Galactosylc 8.72E-09 1.16E-07 -2.049 0.853eramidesulfotransferaseFBLN7 Q53RD9 Fibulin-7 2.85E-07 2.40E-06 -0.589 0.852 IL27B QI 4213 Interleukin- 3.71E-09 5.62E-08 -4.099 0.85227 subunitbetaPLXA1 Q9UIW2 Plexin-Al 1.72E-05 7.53E-05 -2.446 0.851 ATPG P36542 ATP 1.45E-06 9.54E-06 -1.491 0.851synthasesubunitgamma,mitochondrialNCAM2 015394 Neural cell 9.50E-07 6.69E-06 -0.588 0.851adhesionmolecule 2NAA15 Q9BXJ9 N-alpha- 1.99E-06 1.23E-05 -3.255 0.851acetyltransferase 15,NatAauxiliary’subunitISLR 014498 Inimunoglo 1.98E-09 3.3 IE-08 -0.642 0.851bulinsuperfamilycontainingleucine-richrepeatproteinITA4 P13612 Integrin 1.83E-04 5.77E-04 -0.889 0.851alpha -4THIC Q9BWD1 Acetyl-CoA 4.69E-08 5.15E-07 -1.546 0.85acetyltransferase,cytosolicAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameHPPD P32754 4- 2.19E-07 1.94E-06 -2.218 0.85hydroxyphenylpyruvatedioxygenaseMK01 P28482 Mitogen- 4.40E-07 3.47E-06 -0.813 0.85activatedproteinkinase 1GGH Q92820 Gamma2.06E-10 4.55E-09 -0.647 0.849glutamylhydrolaseERLN1 075477 Erlin-1 5.85E-09 8.27E-08 -4.389 0.849 PLXD1 Q9Y4D7 Plexin-Dl 1.51E-07 1.40E-06 -0.519 0.849 PP2BB. PP2 P16298; Q08 Serine / threo 5.55E-07 4.21E-06 -2.41 0.849 BA 209 nine -proteinphosphatase2B catalyticsubunit betaisoform; Proteinphosphatase3 catalyticsubunitalphaLIRA2 Q8N149 Leukocyte 1.41E-08 1.75E-07 -1.181 0.849immunoglobulin-likereceptorsubfamily Amember 2ADH1B P00325 All-trans- 1.37E-10 3.20E-09 -1.39 0.849retinoldehydrogenase[NADC+)]ADH1BDHPR P09417 Dihydropter 2.81E-08 3.24E-07 -1.456 0.849idinereductaseBTD P43251 Biotinidase 9.48E-12 3.31E-10 -0.598 0.848 EZRI P15311 Ezrin 2.65E-07 2.28E-06 -1.458 0.848 MGP P08493 Matrix Gia 1.25E-09 2.23E-08 -0.72 0.847proteinPKP3 Q9Y446 Plakophilin- 2.75E-07 2.35E-06 -2.05 0.8473CBPB2 Q96IY4 Carboxypep 2.44E-11 7.06E-10 -0.579 0.846tidase B2PLD4 Q96BZ4 5 '-3' 1.5 IE-06 9.82E-06 -1.137 0.846exonucleasePLD4Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameSH3L3 Q9H299 SH3 1.56E-10 3.54E-09 -2.464 0.846domainbindingglutamicacid-rich- like protein3CBPE Pl 6870 Carboxypep 2.75E-09 4.41E-08 -2.81 0.846tidase EANXA3 P 12429 Annexin A3 7.29E-08 7.54E-07 -1.722 0.845 HSP74 P34932 Heat shock 3.16E-07 2.62E-06 -0.593 0.84570 kDaprotein 4GLNA P15104 Glutamine 1.02E-07 9.83E-07 -1.695 0.844synthetaseSYNE2 Q8WXH0 Nesprin-2 3.09E-05 1.23E-04 -2.001 0.844 TNNC2 P02585 'Troponin C, 2.38E-07 2.08E-06 -4.49 0.844skeletalmuscleDQB1 P01920 HLA class 3.11E-09 4.95E-08 -5.031 0.844IIhistocompatibilityantigen, DQbeta 1 chainIBP7 Q16270 Insulin-like 1.74E-08 2.10E-07 -0.557 0.843growthfactorbindingprotein 7HEXB P07686 Beta- 4.16E-08 4.64E-07 -0.707 0.843hexosaminidase subunitbetaYETS2 Q9ULM3 YEATS 1.29E-07 1.22E-06 -2.604 0.843domaincontainingprotein 2SHH QI 5465 Sonic 9.52E-08 9.28E-07 -0.975 0.843hedgehogproteinCHRD Q9H2X0 Chordin 7.66E-08 7.76E-07 -1.016 0.843 C4BPB P20851 C4b-binding 2.19E-08 2.58E-07 0.976 0.843protein betachainMMP2 P08253 72 kDa type 2.31E-08 2.71E-07 -0.588 0.843IVcollagenaseHCD2 Q99714 3- 2.09E-06 1.28E-05 -1.606 0.842hydroxyacylAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Name-CoAdehydrogenase type-2DR9C7 Q8NEX9 Short-chain 5.50E-09 8.00E-08 -1.041 0.842dehydro genase / reductase family 9Cmember 7AGRE5 P48960 Adhesion G 1.95E-07 1.76E-06 -1.933 0.842protein- coupledreceptor E5PSDE 000487 26S 5.01E-06 2.64E-05 -1.54 0.842proteasomenon-ATPaseregulatorysubunit 14DPP2 Q9UHL4 Dipeptidyl 3.39E-06 1.91E-05 -1.811 0.841peptidase 2CORIA P31146 Coronin- 1 A 3.38E-08 3.83E-07 -0.855 0.841 STX7 015400 Syntaxin-7 3.90E-06 2.15E-05 -3.365 0.841 NIF3L Q9GZT8 NIF3-like 1.05E-06 7.27E-06 -0.695 0.84protein 1KVD30 A0A075B6 Immunoglo 1.96E-06 1.22E-05 -2.455 0.84S6 bulin kappavariable 2D- 30PYGB P11216 Glycogen 5.11E-08 5.48E-07 -0.75 0.839pbosphorylase, brainformDESP Pl 5924 Desmoplaki 1.48E-05 6.64E-05 -0.529 0.839iiMTPN P58.546 Myotrophin 1.05E-07 1.02E-06 -0.982 0.839 GDIR1 P52565 Rho GDP- 9.75E-08 9.47E-07 -1.302 0.839dissociationinhibitor 1APOB P04114 Apolipoprot 4.01E-17 5.67E-15 0.739 0.839ein B-100SPB8 P50452 Serpin B8 3.27E-07 2.69E-06 -0.924 0.838 DYH10 Q8IVF4 Dynein 1.37E-06 9.09E-06 -2.464 0.838axonetnalheavy chain10SEM4B Q9NPR2 Semaphorin 1.21E-05 5.59E-05 -0.562 0.838-4BF74A7; F74 A6NL05; Q4 Protein 2.85E-07 2.40E-06 0.602 0.837 A3; F74A1; VXFRQ5R FAM74A7;FAM74 GS3; Q5TZ PutativeK3 proteinAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameFAM74A3;ProteinFAM74A1;ProteinFAM74A4 / A6HYDIN Q4G0P3 Hydrocepha 3.48E-07 2.84E-06 1.088 0.836lus-inducingproteinhomologBUP1 Q9UBR1 Beta- 1.44E-05 6.49E-05 -1.193 0.836ureidopropionaseEPCR Q9UNN8 Endothelial 2.62E-06 1.54E-05 -0.725 0.836protein CreceptorPSPB P07988 Pulmonary 4.00E-08 4.48E-07 -1.494 0.836surfactant- associatedprotein BHUTU Q96N76 Urocanate 5.09E-06 2.68E-05 -1.1 0.836hydrataseRD23A P54725 UV excision 2.79E-06 1.62E-05 -1.638 0.836repairproteinRAD23homolog AHNRPK P61978 Heterogene 6.29E-06 3.21E-05 -1.207 0.835ous nuclearribonucleoprotein KGSHR P00390 Glutathione 1.00E-08 1.29E-07 -0.511 0.835reductase,mitochondrialPRP1 P04280 Basic 1.06E-05 5.03E-05 -1.058 0.835salivaryproline-richprotein 1CAMP P49913 Cathelicidin 2.01E-07 1.80E-06 0.64 0.835antimicrobia1 peptideSCG3 Q8WXD2 Secretograni 3.37E-07 2.75E-06 -2.956 0.835n-3IHH QI 4623 Indian 4.81E-04 1.37E-03 -1.11 0.834hedgehogproteinCAD 17 Q12864 Cadherin-17 1.19E-04 3.97E-04 -0.7 0.834VIME P08670 Vimentin 1.20E-06 8.11E-06 -0.57 0.834Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameGNL3 Q9BVP2 Guanine 1.50E-07 1.40E-06 -1.015 0.833nucleotide- bindingprotein-like3ZN185 015231 Zinc finger 3.32E-06 1.88E-05 -1.774 0.833protein 185PGCA P16112 Aggrecan 5.27E-07 4.03E-06 1.327 0.833core proteinG6PE 095479 GDH / 6PGL 2.99E-06 1.71E-05 -0.587 0.833endoplasmicbifunctionalproteinEF2 P13639 Elongation 8.04E-08 8.09E-07 -0.562 0.833factor 2GMFB P60983 Glia 6.10E-09 8.49E-08 -4.023 0.833maturationfactor betaCD9 P21926 CD9 4.47E-07 3.50E-06 1.33 0.833antigenIGHE P01854 Immunoglo 4.93E-07 3.82E-06 -1.291 0.833bulin heavyconstantepsilonI.. ACRT Q9GZZ8 Extracellula 1.45E-09 2.51E-08 -3.448 0.833rglycoproteinlacritinGINM1 Q9NU53 Glycoprotei 1.35E-06 9.00E-06 -1.657 0.833n integralmembraneprotein 1DNPH1 043598 2’- 7.14E-05 2.56E-04 -2,365 0.832deoxynucleoside 5'- phosphateN-hydrolase1PAG15 Q8NCC3 Phospholipa 6.46E-07 4.78E-06 -2.731 0.832se A2 groupXV QOR Q08257 Quinone 1.05E-06 7.27E-06 -1.339 0.832oxidoreductaseALBU P02768 Albumin 2.41E-15 2.36E-13 -0.657 0.831 KCD12 Q96CX2 BTB / POZ 2.04E-06 1.25E-05 -0.939 0.831domaincontainingproteinKCTD12Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NamePUR9 P31939 Bifunctional 5.99E-07 4.48E-06 -1.302 0.831purinebiosynthesisproteinATIC TADBP Q13148 TAR DNA- 8.96E-09 1.18E-07 -5.181 0.83bindingprotein 43VATL P27449 V-type 1.91E-09 3.21E-08 -4.396 0.83protonATPase 16kDaproteolipidsubunit cCl 63 A Q86VB7 Scavenger 6.78E-09 9.28E-08 -0.683 0.83receptorcysteine - rich type 1proteinM130IDAS1 Q9NZ38 Uncharacter 2.18E-05 9.16E-05 -1.972 0.83ized proteinID 12 -AS 1PRB4 Pl 0163 Basic 1.96E-08 2.33E-07 -1.726 0.829salivary'proline-richprotein 4QXSR1 095747 Serine / threo 1.14E-06 7.73E-06 -2.17 0.829nine -proteinkinaseOSR1HSP13 P48723 Heat shock 2.46E-08 2.86E-07 -1.685 0.82970 kDaprotein 13DHB8 Q92506 (3R)-3- 9.58E-06 4.58E-05 -0.808 0.829hydroxyacyl-CoA ’dehydrogenaseGPX4 P36969 Phospholipi 8.58E-06 4.16E-05 -2.906 0.828dhydroperoxideglutathioneperoxidaseCPSM P31327 Carbamoyl2.19E-05 9.20E-05 -1.321 0.828phosphatesynthase[ammonia].Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NamemitochondrialANGL8 Q6UXH0 Angiopoieti 6.10E-06 3.13E-05 -0.948 0.828n-likeprotein 8METK1 Q00266 S- 2.25E-05 9.42E-05 -1.738 0.828adenosvlmethioninesynthaseisoformtvpe-1CILP1 075339 Cartilage 1.19E-05 5.53E-05 -0.511 0.827intermediatelayerprotein 1ASPDH A6ND91 Aspartate 9.93E-07 6.94E-06 -1.554 0.827dehydrogenase domaincontainingproteinAOC3 QI 6853 Membrane 5.49E-06 2.84E-05 -0.559 0.827primaryamineoxidaseFA 12 P00748 Coagulation 1.68E-08 2.05E-07 0.714 0.827factor XIIMLEC Q14165 Malectin 7.56E-07 5.50E-06 -1.307 0.827 MA2B1 000754 Lysosomal 3.58E-06 2.00E-05 -1.36 0.827alpha- mannosidaseCBS P35520 Cystathioni 7.28E-07 5.31E-06 -2.219 0.826ne betasynthaseGLYG P46976 Glycogenin- 3.35E-07 2.75E-06 -0.948 0.8261IL 18 Q14116 Interleukin- 9.19E-04 2.40E-03 -1.658 0.82518DX39A 000148 ATP- 5.78E-07 4.36E-06 -1.176 0.825dependentRNAhelicaseDDX39ASF.6L1 Q9BYH1 Seizure 6- 7.66E-06 3.82E-05 -2.382 0.825like proteinNHRF1 O14745 Na(+) / H(-F) 1.64E-07 I.51E-06 -1.563 0.825exchangeregulatorycofactorNHE-RF1Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameAT5F1 P24539 ATP 2.29E-06 1.38E-05 -2.478 0.825synthaseF(0)complexsubunit Bl,mitochondrialTBC19 Q8N5T2 TBC1 7.92E-04 2.12E-03 -2.629 0.825domainfamilymember 19SDK2 Q58EX2 Protein 3.66E-07 2.97E-06 -0.603 0.824sidekick-2ADH4 P08319 A 11 -trans - 1.24E-10 2.96E-09 -0.914 0.824retinoldehydrogenase[NAD(+)1ADH4CPNE1 Q99829 Copine- 1 7.38E-06 3.69E-05 -1.024 0.823 LV861 A0A075B6I Immunoglo 1.11E-03 2.79E-03 1.157 0.8230 bulinlambdavariable 8- 61PP1R7 Q15435 Protein 1.62E-04 5.21E-04 -2.6 0.822phosphatase1 regulatorysubunit 7DPYS Q14117 Dihydropyri 7.04E-06 3.53E-05 -2.244 0.822midinaseXRP2 075695 Protein 6.43E-06 3.27E-05 -1.193 0.822XRP2PROP P27918 Properdin 4.67E-06 2.49E-05 0.84 0.822 MYH7 Pl 2883 Myosin-7 1.61E-04 5.18E-04 -1.255 0.821 PTN12 Q05209 Tyrosine9.08E-08 8.99E-07 -0.523 0.821proteinphosphatasenonreceptorApe 12RET4 P02753 Retinol- 2.13E-07 1.89E-06 -0.664 0.821bindingprotein 4CX3C1 P49238 CX3C 4.06E-04 1.17E-03 -0.793 0.82chemokinereceptor 1BHMT1 Q93088 Betaine- 2.20E-05 9.21E-05 -0.864 0.82homocysteine S-Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Namemethyltransferase 1VWA1 Q6PCB0 von 1.77E-05 7.74E-05 -1.826 0.82Willebrandfactor Adomaincontainingprotein 1DPA1 P20036 HLA class 5.48E-04 1.53E-03 -1.83 0.82IIhistocompatibilityantigen, DPalpha 1chainALDH2 P05091 Aldehyde 8.48E-07 6.03E-06 -0.838 0.819dehydrogenase,mitochondrialHD P42858 Huntingtin 1.17E-10 2.84E-09 -4.698 0.819 C1QC P02747 Complemen 1.96E-06 1.22E-05 1.231 0.819t Clqsubcomponent subunit CENTP5 075356 Nucleoside 1.63E-07 1.51E-06 -0.714 0.819diphosphatephosphataseENTPD5KV240; KV A0A087W Immunoglo 3.97E-05 1.53E-04 -0.675 0.819 D40 W87; P0161 bulin kappa4 variable 2- 40; Immunoglobulinkappavariable 2D- 40RCN1 QI 5293 Reticulocalb 3.15E-09 4.98E-08 -1.788 0.818in-1FA11 P03951 Coagulation 1.80E-07 1.64E-06 -0.647 0.818factor XIIPSP P05154 Plasma 3.54E-08 3.98E-07 -0.684 0.818serineproteaseinhibitorSDC1 Pl 8827 Syndecan-1 2.88E-06 1.66E-05 -3.803 0.818 RPSA2; RSS A0A8I5KQ Small 2.15E-06 1.31E-05 -1.24 0.817 A E6; P08865 ribosomalsubunitproteinAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameuS2B; Smallribosomalsubunitprotein uS2PRAP1 Q96NZ9 Proline -rich 4.20E-07 3.35E-06 -1.676 0.817acidicprotein 1ASGR2 P07307 Asialoglyco 9.14E-06 4.41E-05 -0.627 0.817proteinreceptor 2TTL12 Q14166 Tubulin4.32E-07 3.43E-06 -2.354 0.816tyrosineligase-likeprotein 12ICAL P20810 Calpastatin 6.97E-04 1.89E-03 -0.74 0.816 CATD P07339 Cathepsin D 5.07E-05 1.90E-04 -0.536 0.816 RISC Q9HB40 Retinoid- 1.40E-04 4.62E-04 -1.249 0.815inducibleserinecarboxypeptidaseSIOAG Q96FQ6 Protein 2.88E-06 1.66E-05 -1.58 0.815SI 00-Al 6PKN1 Q16512 Serine / threo 9.43E-07 6.67E-06 -2.646 0.815nine -proteinkinase N 1PTPRM P28827 Receptor8.31E-07 5.94E-06 -0.51 0.814typetyrosineproteinphosphatasemuMUC7 Q8TAX7 Mucin-7 7.71E-06 3.83E-05 -0.67 0.814 QPCT Q16769 Glutaminyl- 7.85E-07 5.69E-06 -0.743 0.814peptidecyclotransferaseAT2A2 P16615 Sarcoplasmi 1.61E-04 5.20E-04 -1.087 0.814c / endoplasmic reticulumcalciumATPase 2SODM P04179 Superoxide 1.78E-06 1.14E-05 -1.295 0.814dismutase[Mn],mitochondrialPGRP1 075594 Peptidoglyc 5.13E-06 2.70E-05 -0.803 0.813anAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Namerecognitionprotein 1CAPRI QI 4444 Caprin-1 2.41E-06 1.44E-05 -2.401 0.813 L1C AM P32004 Neural cell 5.55E-07 4.21E-06 -0.544 0.813adhesionmolecule L 1GNMT QI 4749 Glycine N- 1.33E-06 8.90E-06 -1.951 0.813methyltransferaseK1C25 Q7Z3Z0 Keratin, 4.06E-06 2.23E-05 -3.553 0.813type Icytoskeletal25PCDH8 095206 Protocadher 8.44E-07 6.02E-06 -4.623 0.813in-8LRC47 Q8N1G4 Leucine- 1.12E-06 7.65E-06 -2.272 0.813rich repeatcontainingprotein 47IF4B P23588 Eukaryotic 4.89E-06 2.59E-05 -2.899 0.812translationinitiationfactor 4BC1QB P02746 Complemen 3.85E-06 2.13E-05 1.196 0.812t Clqsubcomponent subunit BRD23B P54727 UV excision 1.66E-06 1.07E-05 -0.814 0.812repairproteinRAD23homolog BCYTS P01036 Cystatin-S 5.61 E-05 2.07E-04 -0.948 0.812 DP13A Q9UKG1 DCC- 2.35E-06 1.41E-05 -1.059 0.812interactingprotein 13- alphaSERC Q9Y617 Phosphoseri 1.34E-06 8.96E-06 -1.65 0.812neaminotransferaseDAG1 Q14118 Dystroglyca 2.83E-06 1.64E-05 -0.522 0.812n 1PCYXL Q8NBM8 Prenylcystei 3.39E-06 I.91E-05 -2.184 0.812ne oxidase- likeK2C80 Q6KB66 Keratin, 5.44E-06 2.82E-05 -0.575 0.812type IIcytoskeletal80Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameRAB1B Q9H0U4 Ras-related 5.07E-05 1.90E-04 -0.881 0.812protein Rab- 1BPTPRK Q15262 Receptor1.65E-06 1.07E-05 -0.5 0.811typetyrosineproteinphosphatasekappaA1BG P04217 Alpha- 1B- 3.99E-13 2.16E-11 -0.561 0.811gly coproteinADA P00813 Adenosine 2.81E-07 2.39E-06 -2.455 0.811deaminaseCRDL2 Q6WN34 Chordin8.31E-09 1.11E-07 -2.705 0.811like protein2NPNT Q6UXI9 Nephronecti 2.26E-05 9.44E-05 -2.348 0.81nHDHD2 Q9H0R4 Haloacid 7.06E-04 1.91E-03 -2.701 0.81dehaiogenase-likehydrolasedomain- containingprotein 2LASP1 Q14847 LIM and 6.54E-07 4.83E-06 -1.522 0.81SH3 domainprotein 1CUL2 Q13617 Cullin-2 1.18E-05 5.51E-05 -1.623 0.81 NF1 P21359 Neurofibro 7.38E-08 7.57E-07 -3.777 0.81minBAS1 P35613 Basigin 3.31E-05 1.30E-04 -1.013 0.81 DCXR Q7Z4W1 L-xylulose 2.77E-06 1.62E-05 -1.481 0.81reductaseMYOM3 Q5VTT5 Mvomesin- 1.92E-05 8.26E-05 -2.338 0.813 'CBPA1 Pl 5085 Carboxy pep 3.29E-06 1.86E-05 -1.594 0.809tidase AlSSRD P51571 Translocon- 2.38E-05 9.84E-05 -3.686 0.809associatedproteinsubunitdeltaIDS P22304 Iduronate 2- 5.70E-04 1.58E-03 -0.904 0.809sulfataseTGM1 P22735 Protein- 2.81E-04 8.44E-04 -0,636 0.809glutaminegamma - glutamyltransferase KAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameKBTB8 Q8NFY9 Kelch 4.62E-06 2.48E-05 0.598 0.809repeat andBTBdomaincontainingprotein 8LECT2 014960 Leukocyte 1.96E-05 8.38E-05 -0.941 0.808cell-derivedchemotaxin- 2CUTA 060888 Protein 2.27E-05 9.46E-05 -4.428 0.808CutAHLAC P10321 HLA class I 1.06E-06 7.29E-06 -1.592 0.808histocompatibilityantigen, Calpha chainDPYD QI 2882 Dihydropyri 9.07E-08 8.99E-07 -2.15 0.808midinedehydrogenase|NADP(+)1BN1P2 QI 2982 BCL2 / aden 1.23E-05 5.68E-05 -2.82 0.808ovirus E1B19 kDaproteininteractingprotein 2MPDZ 075970 Multiple 8.63E-04 2.28E-03 0.866 0.807PDZdomainproteinHFM1 A2PYH4 Probable 1.85E-05 8.04E-05 -0.873 0.807ATP- dependentDNAhelicaseHFM1HCLS1 P14317 Hematopoie 3.78E-06 2.10E-05 -1.033 0.807tic lineagecell-specificproteinPG12B Q9BX93 Group XIIB 4.89E-05 1.84E-04 -0.575 0.807secretoryphospholipase A2-likeproteinGRHPR Q9UBQ7 Glyoxylate 1.35E-08 1.70E-07 -1.073 0.807reductase / hydroxypyruvAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameatereductasePLA1A Q53H76 Phospholipa 4.45E-06 2.39E-05 -0.922 0.807se Almember ASPON2 Q9BUD6 Spondin-2 8.11E-06 3.98E-05 -2.221 0.807 SCTM1 Q8WVN6 Secreted 2.04E-05 8.68E-05 -1.247 0.806andtransmembrane protein1CXCR2 P25025 c-x-c 2.57E-05 1.05E-04 -1.712 0.806chemokinereceptor•\ pe 2LIRA1 075019 Leukocyte 4.39E-08 4.87E-07 -4.271 0.806immunoglobulin-likereceptorsubfamily Amember 1G6PD Pl 1413 Glucose-6- 1.28E-06 8.62E-06 -0.901 0.805pbosphate1- dehydrogenaseALAT1 P24298 Alanine 2.26E-06 1.37E-05 -1.193 0.805aminotransferase 1KYNU QI 6719 Kvnurenina 1.12E-06 7.65E-06 -2.483 0.805seC1QA P02745 Complemen 8.02E-06 3.95E-05 1.102 0.805t Clqsubcomponent subunit ACANT1 Q8WVQ1 Soluble 4.71E-08 5.15E-07 -3.594 0.804calcium- activatednucleotidase1LMNB1 P20700 Lamin-B1 1.47E-05 6.57E-05 -1.585 0.804 ARF6 P62330 ADP- 1.78E-04 5.63E-04 -1.92 0.804ribosylationfactor 6LRP1B Q9NZR2 Low-density 2.34E-05 9.71E-05 -1.244 0.804lipoproteinreceptor- relatedprotein IBTHIO Pl 0599 Thioredoxin 5.76E-05 2.12E-04 -0.608 0.804Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameGLYC P34896 Serine 1.33E-06 8.90E-06 -2.788 0.804hydroxvmethyltransferase, cytosolic1433E P62258 14-3-3 1.92E-06 1.21E-05 -0.58 0.804proteinepsilonFCG2A P12318 Low affinity 1.19E-05 5.52E-05 -0.595 0.804immunoglobulingamma Fcregionreceptor II -aASM P 17405 Sphingomye 2.04E-06 1.25E-05 -2.549 0.804linpbosphodies(eraseLV746 A0A075B6I Immunoglo 2.14E-03 4.94E-03 -2.555 0.8049 bulinlambdavariable 7- 46LAMP1 P11279 Lysosome- 2.23E-07 1.96E-06 -0.572 0.803associatedmembraneglycoprotein1FKBP4 Q02790 Peptidyl- 2.59E-07 2.24F.-06 -2.755 0.803prolyl cistransisomeraseFKBP4MYO5B Q9ULV0 Unconventi 1.46E-05 6.57E-05 -1.587 0.803onalmyosin- VbSDC2 P34741 Syndecan-2 5.73E-05 2.11E-04 -2.672 0.803 NSF P46459 Vesicle6.58E-06 3.33E-05 -1.01 0.803fusingATPaseRAC2 P15153 Ras-related 2.55E-07 2.22E-06 -1.843 0.803C3botulinumtoxinsubstrate 2MXRA8 Q9BRK3 Matrix 2.69E-04 8.13E-04 -2.513 0.802remodeling- associatedprotein 8PFKAM P08237 ATP- 2.34E-05 9.72E-05 -1.082 0.802dependentAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Name6- phosphofructokinase,muscle typeHEM6 P36551 Oxygen5.92E-07 4.46E-06 -1.52 0.802dependentcoproporphyrinogen-IIIoxidase,mitochondrialTXD15 Q96J42 Thioredoxin 8.68E-07 6.16E-06 -1.041 0.801domaincontainingprotein 15ABHEB Q96IU4 Putative 7.25E-05 2.60E-04 -2.375 0.801protein- lysinedeacylaseABHD14BAATC P17174 Aspartate 1.95E-06 1.22E-05 -1.245 0.801aminotransferase,cytoplasmicLCAP Q9UIQ6 Leucyl- 4.84E-05 1.83E-04 -1.218 0.8cvstinylaminopeptidaseMBOA1 Q6ZNC8 Lysophosph 1.23E-03 3.06E-03 -2.084 0.8olipidacyltransferase 1SMRD3; SM Q6STE5; Q9 SWI / SNF- 2.48E-05 1.02E-04 -1.234 0.8 RD1 6GM5 relatedmatrix- associatedactin- dependentregulator ofchromatinsubfamily Dmember3; SWI / SNF- relatedmatrix- associatedactin - dependentregulator ofchromatinAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q-value log2FC AUC AN Namesubfamily Dmember 1

[0463] Table 5.3 lists the top-performing 3plexes generated from the stage 1 CRC biomarkers provided in Table 5.2. Each 3plex listed in Table 5.3 achieved an accuracy metric (i.e., a 10-fold average accuracy metric calculated as described above in Example 3) of 1 (100%) representing a correct-prediction ratio of 1.Table 5.3 Stage 1 CRC biomarker 3plexes with Accuracy =110-Fold average3PLEXAccuracy('TRFE', 'MINPT, 'CO8G') 1('TRFE', 'MINPT, 'EGFR') 1('TRFE', 'MINPT, '0SBL3') 1('TRFE', 'MINPT, 'CO8B') 1('TRFE', 'MINPT, 'ATF6A') 1('TRFE', 'MINPT, 'SRCRL') 1('TRFE', 'MINPT, 'N0E2') 1('TRFE', 'MINPT, 'RL17') 1('TRFE', 'MINPT, 'H31; H33; H31T; H32') 1('TRFE', 'AMD', 'FCGBP') 1('TRFE', 'AMD', 'EGFR') 1('TRFE', 'AMD', 'CO8B') 1('TRFE', 'AMD', 'ATF6A') 1('TRFE', 'CO8G', 'LKHA4') 1('TRFE', 'CO8G', 'GSTO1') 1('TRFE', 'CO8G', 'EF1A2') 1('TRFE', 'CO8G', 'AL1A1') 1('TRFE', 'CO8G', 'FETUA') 1('TRFE', 'CO8G', 'IMPA3') 1('TRFE', 'CO8G', 'ICAM3') 1('TRFE', 'CO8G', 'IDHC') 1('TRFE', 'FCGBP', 'LKHA4') 1('TRFE', 'FCGBP', 'IL1R2') 1('TRFE', 'FCGBP', 'EF1A2') 1('TRFE', 'FCGBP', 'ICAM1') 1('TRFE', 'FCGBP', 'ARC1A') 1('TRFE', 'FCGBP', 'ICAM3') 1Attorney Docket No. NXON-014 / 04WO 346247-209510-Fold average3PLEXAccuracy( 'TRFE', 'FCGBP', 'H31; H33; H31T; H32') 1('TREE', 'LKHA4', 'H31; H33; H31T; H32') 1('TRFE', 'LDHB', 'ATF6A') 1(’TREE', 'IL1R2’, 'EGFR') 1('TRFE', 'IL1R21, 'H31; H33; H31T; H32') 1('TREE', 'PLSL', 'CO8B') 1('TRFE', 'TALDO', 'ATF6A') 1('TRFE', 'B3GN8', 'H31; H33; H31T; H32') 1('TRFE', 'EGFR', 'CO8B') 1('TRFE', 'EGFR', 'ROBOT) 1('TRFE', 'EGFR', 'ARC1A') 1('TRFE', '4F2', 'CO8B') 1('TRFE', 'GSTO1', 'CO8B') 1('TRFE', 'B3GN21, 'H31; H33; H31T; H32') 1('TRFE', 'FCN2', 'ATF6A') 1('TRFE', 'FCN2’, 'GRN') 1('TRFE', 'CO8B', 'EF1A2') 1('TRFE', 'CO8B', 'AL1A1') 1('TRFE', 'CO8B', 'SYWC') 1('TRFE', 'CO8B', 'GNPTG') 1('TRFE', 'CO8B', 'IMPA3') 1(‘TRFE1, 'ATF6A', 'SRCRL') 1('TRFE', 'ATF6A', 'GSHB') 1('TRFE', 'ATF6A', 'EYS') 1('TRFE', 'ATF6A', 'COCH') 1('TRFE', 'GRN', 'SRCRL') 1('TRFE', 'GRN', 'LRC25') 1('TRFE', 'PNCB', 'SRCRL') 1('TRFE', 'SRCRL', 'ARC1A') 1('TRFE', 'SRCRL', 'H31; H33; H31T; H32') 1('TRFE', 'CBPQ', 'H31; H33; H31T; H32') 1('MINP1, 'FCGBP', 'PLST') 1('MINPl', 'FCGBP', 'CTRC') 1('MINPl', 'FCGBP', '0SBL3') 1('MINP1', 'FCGBP', 'EF1A2') 1('MINPl', 'FCGBP', 'IMPA3') 1('MINPl', 'FCGBP', 'TIMP3') 1('MINPl', 'FCGBP', ’CBPQ1) 1Attorney Docket No. NXON-014 / 04WO 346247-209510-Fold average3PLEXAccuracy('MINPT, 'TALDO', 'MEG10') 1('MINPT, 'EGFR', 'FCN2') 1('MINPT, 'GSTO1', 'CO8B') 1(’MINPT,,FCN2’,,RL17') 1('MINPl', 'RL17', 'DPP3') 1('AMD', 'FCGBP', 'LDHB') 1('AMD', 'FCGBP', 'TALDO') 1('AMD', 'FCGBP', 'PGAM1') 1('AMD', 'FCGBP', 'GLGB') 1('AMD', 'FCGBP', 'MCPHl') 1('AMD', 'FCGBP', 'PEPD') 1('AMD', 'FCGBP', 'PLST') 1('AMD', 'FCGBP', 'B3GN2') 1('AMD', 'FCGBP', '0SBL3') 1('AMD', 'FCGBP', 'FCN2') 1('AMD', 'FCGBP', 'CO8B') 1('AMD', 'FCGBP', 'EF1A2') 1('AMD', 'FCGBP', 'ATF6A') 1('AMD', 'FCGBP', 'PNCB') 1('AMD', 'FCGBP', ’SRCRL1) 1('AMD', 'FCGBP', 'AL1A1') 1('AMD', 'FCGBP1, 'SYWC') 1('AMD', 'FCGBP', 'ROBOT) 1('AMD', 'FCGBP', 'N0E2') 1('AMD', 'FCGBP', 'F234A') 1('AMD', 'FCGBP', 'FETUA') 1('AMD', ’FCGBP', 'IMPA3') 1('AMD', ’FCGBP', 'MEG10') 1('AMD', 'FCGBP', 'TACT') 1('AMD', 'FCGBP', 'COCH') 1('AMD', 'FCGBP', 'FUCO') 1('AMD', 'FCGBP', 'TIMP3') 1('AMD', 'FCGBP', 'RL17') 1('AMD', 'FCGBP', 'CBPQ') 1('AMD', 'FCGBP', 'DMKN') 1('AMD', 'FCGBP', 'ARC1A') 1('AMD', 'FCGBP', 'ICAM3') 1('AMD', 'TALDO', 'FCGRN') 1Attorney Docket No. NXON-014 / 04WO 346247-209510-Fold average3PLEXAccuracy('AMD', 'B3GN8', 'CO8B') 1('AMD', 'ADEC1', 'CO8B') 1('AMD', 'GSTO1', 'CO8B') 1('AMD', ’PEPD’, 'CO8B') 1('AMD', 'B3GN21, 'CO8B') 1('AMD', 'CO8B', 'GSHB') 1('AMD', 'CO8B', 'F234A') 1('AMD', 'CO8B', 'FETUA') 1('AMD', 'CO8B', 'GNPTG') 1('AMD', 'CO8B', 'IMPA3') 1('AMD', 'CO8B‘, 'MEG10') 1('AMD', 'CO8B', 'TIMP3') 1('AMD', 'CO8B', 'ICAM1') 1('AMD', 'CO8B', 'ARC1A') 1('AMD', 'CO8B', 'ICAM3') 1('AMD', ’CO8B’, 'ANAG') 1('AMD', 'CO8B', 'NECT2') 1('AMD', 'CO8B', 'IDHC') 1('AMD', 'CO8B', 'DPP3') 1('CO8G', 'FCGBP', 'FETUA') 1('CO8G', 'FCGBP', 'FSTL1') 1('CO8G', 'EGLN', 'GNPTG') 1('CO8G', 'PGAMT, 'PEPD') 1('CO8G', 'PGAM1', 'NDK3') 1('CO8G', 'GLGB', 'NDK3') 1('CO8G', 'MCPH1', 'MA1C1') 1('FCGBP', 'LKHA4', 'F234A') 1('FCGBP', 'LKHA4', ’IDHC) 1('FCGBP', 'LDHB', 'EGLN') 1('FCGBP', 'LDHB’, 'CO8B') 1('FCGBP', 'LDHB', 'EF1A2') 1('FCGBP', 'LDHB', 'ATF6A') 1('FCGBP', 'LDHB', 'AL1A1') 1('FCGBP1, 'LDHB', 'FSTL1') 1('FCGBP', 'LDHB', 'ICAM3') 1('FCGBP', 'LDHB', 'IDHC') 1('FCGBP', 'LDHB', 'H31; H33; H31T; H32') 1('FCGBP', 'PLSL', 'EF1A2') 1Attorney Docket No. NXON-014 / 04WO 346247-209510-Fold average3PLEXAccuracy('FCGBP', 'PLSL', 'AL1A1') 1('FCGBP', 'EGLN', 'CO8B') 1('FCGBP', 'TALDO', 'MCPH1') 1('FCGBP', 'TALDO', 'ROBOT') 1('FCGBP', 'TALDO', 'GN PTG‘) 1('FCGBP', 'TALDO', 'MEG10') 1('FCGBP', 'PGAM1', 'FUCO') 1('FCGBP', 'ADEC1', 'ICAM3') 1('FCGBP', 'EGFR', 'EF1A2') 1('FCGBP', 'EGFR', 'AL1A1') 1('FCGBP', 'GLGB', ‘GSHB') 1('FCGBP', 'GLGB', 'GNPTG') 1('FCGBP', '4F2', TFR1') 1('FCGBP', 'MCPH1', 'SYWC') 1('FCGBP', 'MCPH1', TDHC) 1('FCGBP', 'PEPD', 'EF1A2') 1('FCGBP', ‘PEPD‘, 'ICAM3') 1('FCGBP', 'PEPD', 'H31; H33; H31T; H32') 1('FCGBP', 'PLST', 'ICAMl') 1('FCGBP', '0SBL3', 'CBPQ') 1('FCGBP1, 'FCN2', 'GSHB') 1('FCGBP', ’FCN21, 'ICAMl’) 1(‘FCGBP1, 'CO8B', 'GSHB') 1('FCGBP1, 'CO8B', 'CBPQ') 1('FCGBP', 'EF1A2', 'ATF6A') 1('FCGBP', 'EF1A2', 'ROBO1') 1('FCGBP', 'ATF6A', 'FSTL1') 1('FCGBP', 'ROBOT, 'ARLY') 1('FCGBP1, 'ROBOT,'H31; H33; H31T; H32') 1('FCGBP', 'GSHB', 'RL17') 1('FCGBP', 'NOE2', 'IDHC') 1('FCGBP', 'GNPTG', 'FSTL1') 1('FCGBP', 'FSTL1', 'FUCO') 1('FCGBP', 'TFR1', ’H31; H33; H31T; H32') 1('FCGBP', 'FUCO', 'ICAMl') 1('FCGBP', 'FUCO', 'ICAM3') 1('FCGBP', 'CBPQ', 'NECT2') 1('FCGBP', 'DPP3', 'H31; H33; H31T; H32') 1Attorney Docket No. NXON-014 / 04WO 346247-209510-Fold average3PLEXAccuracy('LKHA4', 'IL1R2', 'IMPA3') 1('LKHA4', 'FCN2', 'F234A') 1('LKHA4', 'F234A', 'MEG10') 1('LDHB1, 'EGFR', ’FCN21) 1('IL1R2', 'TALDO', 'ICAM1') 1('IL1R2', 'MCPH1', 'MA1C1') 1('PLSL', 'EGFR', 'FCN2') 1('EGLN', 'GSTOT, 'CO8B') 1('EGLN', 'CO8B', 'GSHB') 1('EGLN', 'CO8B', 'GNPTG') 1('TALDO', 'ADECl', '0SBL3') 1('TALDO', 'PEPD', 'CO8B') 1('TALDO', 'SRCRL', 'ROBOT) 1('B3GN8', ’GSTOT, 'CO8B') 1('B3GN8', 'CO8B', 'FETUA') 1('B3GN8', 'CO8B', 'CBPQ') 1('B3GN8', 'CO8B', 'H31; H33; H31T; H32') 1('ADECl', 'MCPHT, ’MA1CT) 1('ADECl', 'B3GN2', 'H31; H33; H31T; H32') 1('ADECl1, 'CO8B', 'FETUA') 1('EGFR', 'CO8B', 'FETUA') 1('MCPHT, 'CO8B', 'IMPA3') 1('PEPD', 'NDK31, 'CO8B') 1('PEPD', 'CO8B', 'EF1A2') 1('PEPD', 'CO8B', 'ATF6A') 1('PEPD', 'CO8B', 'GSHB') 1('PEPD', 'CO8B', 'ICAM1') 1('PEPD', 'CO8B', 'CBPQ') 1('PEPD', 'CO8B', 'ICAM3') 1('PEPD', 'CO8B', 'H31; H33; H31T; H32') 1('MA1CT, 'CTRC', 'RL17’) 1('MA1C1', 'SRCRL', 'RL17') 1('FCN2’,,CO8B', 'FETUA') 1('FCN2’,,CO8B', 'ICAM3') 1('FCN2', 'SRCRL', 'DPP3') 1

[0464] It was found that a number of cancer biomarkers were unexpectedly o errepresented in the top 3 pl exes listed in Table 5.3, and were deemed as key biomarkers for stage 1 CRC, SomeAttorney Docket No. NXON-014 / 04WO 346247-2095 of the key biomarkers in stagel CRC include FCGBP that was included in 99 out of the top 3plexes, TRFE that w as included in 58 out of the top 3plexes, C08B that was included in 57 out of the top 3plexes, and AMD that was included in 56 out of the top 3plexes. Among the list of stage 1 CRC 3plexes provided in Table 5.3. the most frequently identified proteins this analysis are listed in Table 5.4.Table 5.4 Most common proteins in Stage 1 CRC biomarker 3plexesProtein Plex countFCGBP 99TRFE 58CO8B 57AMD 56MiNPl 21CO8G 16H31; H33; H31T; H32 16PEPD 15ATF6A 14FCN2 13LDHB 12TALDO 12EGFR 12EF1A2 12SRCRL 10ICAM3 10

[0465] One or more of the top ranked stage 1 CRC 3plexes (those listed in Table 5.3) may then be selected to generate Linear SVM equations as classifiers for future prediction of samples of unknown cancer / non-cancer status, following the methods outlined above for stage 1 uterine cancer in Example 3.Example 6 - Bioinfornuitic analysis for comparing MAP levels between stage 0 CRC patients and non-cancer subjects

[0466] Following the methods outlined above for stage 1 uterine cancer in Example 3, a stage 0 CRC cohort of 19 subjects was similarly assessed to identify stage 0 CRC biomarkers, as well as generate predictive 3plexes of stage 0 CRC biomarkers that demonstrate a high accuracy metric.Attorney Docket No. NXON-014 / 04WO 346247-2095 [00467| As shown in Table 6.1, the dataset started with 3332 total proteins. After removing low quality data, the dataset was pruned to 2542 proteins, and this curated dataset of 2542 proteins was used for subsequent machine learning-based analysis, as described below.

[0468] A first round of machine learning was performed, as described in Example 3 (with respect to stage 1 uterine cancer), to identify MAPs that demonstrated differential expression levels between stage 0 CRC and non-cancer samples, which resulted in identification of 445 significant proteins (summarized in Table 6.1 below).Table 6.1 Pre-analysis protein curationNumber Validof intensity SignificantIndication samples proteins proteinsCRC Stage 0 19 2542 445

[0469] The identified significant proteins are listed in Table 6.2. In Table 6.2, each row represent a protein identified by a respective UniprotKB unique protein entry name (column 1; “Protein”), UniprotKB unique accession number (column 2; “Uniprot AN”), and colloquial protein name (column 3; “Protein Name”), Note that the “_HUMAN” suffix was omitted from each of the protein entry names in column 1, for clarity of presentation. Column 4 shows a p-value denoting statistical significance. Column 5 shows a q-value, which is an adjusted p-value using Benjamini-Hochberg correction. Column 6 shows log2FC, indicating the scale and direction of differential expression in Log2 units, where a negative value indicates downregulation in the cancer cohort compared to the non-cancer cohort and a positive value indicates upregulation in the cancer cohort compared to the non-cancer cohort. Column 7 shows the area under the curve (AUC) of the ROC curve generated from the quantification data for each protein.Table 6.2 Significantly differentially expressed Stage 0 CRC biomarkersProtein Uniprot Protein p-value q-value log2FC AUC AN NameITLN1 Q8WWA0 Intelectin-1 1.21E-19 3.08E-16 -1.391 0.995 ATF6A P18850 Cyclic 8.56E-14 2.39E-11 -1.443 0.974AMP- dependenttranscriptionAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Namefactor ATF- 6 alphaNOE2 095897 Noelin-2 1.00E-16 8.49E-14 -1.264 0.973 M1NP1 Q9UNW1 Multiple 1.17E-17 1.49E-14 -0.859 0.968inositolpolyphosphatephosphatase1CTRC Q99895 Chymotryps 3.16E-16 2.01E-13 -6.087 0.967in-CTALDO P37837 Transaldola 4.95E-13 8.98E-11 -1.023 0.963seNDK3 Q13232 Nucleoside 8.91E-12 1.00E-09 -1.073 0.958diphosphatekinase 3GLGB Q04446 1,4-alpha- 8.49E-11 5.60E-09 -1.399 0.958giucan- branchingenzymeMANBA 000462 Beta- 1.32E-15 6.73E-13 -0.932 0.954mannosidaseAMD P19021 Peptidyl- 3.10E-13 6.58E-11 -0.893 0.954giycinealpha- amidatingmonooxygenasePTPRJ Q12913 Receptor2.18E-13 5.04E-11 -0.52 0.948typetyrosine- proteinphosphataseetaIL1R2 P27930 Interleukin- 1.71E-11 1.74E-09 -1.512 0.9441 receptor•\ pe 2ACY1 Q03154 Aminoacyla 7.92E-15 2.88E-12 -2.185 0.943se-1SNED1 Q8TER0 Sushi, 6.77E-14 2.15E-11 -0.599 0.942nidogen andEGF-likedomaincontainingprotein 1SDK2 Q58EX2 Protein 2.16E-08 4.29E-07 -0.695 0.942sidekick-2NECT1 Q15223 Nectin-1 3.20E-09 9.92E-08 -1.867 0.942GRN P28799 Progranulin 2.30E-11 2.11E-09 -1.008 0.939Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameLKHA4 P09960 Leukotriene 5.85E-11 4.29E-09 -1.265 0.938A-4hydrolaseROBOT Q9Y6N7 Roundabout 1.96E-10 1.05E-08 -0.725 0.938homolog 1ANXA3 P 12429 Annexin A3 5.90E-11 4.29E-09 -1.961 0.934 RPN1 P04843 Dolichyl- 1.00E-08 2.29E-07 -1.773 0.931diphosphooligosaccharide— proteinglycosyltransferasesubunit 1EF1A2 Q05639 Elongation 9.95E-12 1.05E-09 -0.978 0.928factor 1- alpha 2ASM3A Q92484 Acid 2.62E-08 5.01E-07 -1.624 0.928sphingomyelinase-likephosphodiesterase 3 aFUCO P04066 Tissue 6.16E-13 9.2 IE- 11 -1.222 0.927alpha-L- fucosidaseNICA Q92542 Nicastrin 9.14E-08 1.45E-06 -0.782 0.926 PLOD1 Q02809 Procollagen 7.39E-11 5.08E-09 -0.936 0.926-lysine, 2- oxoglutarate5- dioxygenase1OAF Q86UD1 Out at first 5.27E-11 4.29E-09 -0.648 0.924proteinhomologF234A Q9H0X4 Protein 5.92E-13 9.21E-11 -1.662 0.922FAM234AI.. RC4B Q9NT99 Leucine- 5.65E-05 2.84E-04 -3.484 0.921rich repeatcontainingprotein 4BSLAF5 Q9UIB8 SLAM 1.12E-04 4.94E-04 -1.606 0.92familymember 5KHK P50053 Ketohexoki 6.3 IE-09 1.65E-07 -3.704 0.92naseNOMO3; N P69849; Q15 BOS 7.77E-06 5.61E-05 -0.52 0.919 OMOLNO 155; Q5JPE7 complexMO2 subunitNOMO3; BOS complexAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NamesubunitNOMO1; BOS complexsubunitN0M02PCSK9 Q8NBP7 Proprotein 1.19E-10 7.04E-09 -0.699 0.919convertasesubtilisin / kexin type 91CAM3 P32942 Intercellular 4.10F.-08 7.19E-07 -0.828 0.919adhesionmolecule 3HEM6 P36551 Oxygen2.32E-11 2.11E-09 -2.554 0.919dependentcoproporphyrinogen-IIIoxidase,mitochondrialNF. CT2 Q92692 Nectin-2 8.93E-09 2.12F.-07 -3.033 0.919 CBPD 075976 Carboxypep 5.61E-11 4.29E-09 -1.266 0.919tidase DFA20C Q8IXL6 Extracellula 441 E- 12 6.22F.-10 -0.546 0.918rserine / threonine proteinkinaseFAM20CHUTU Q96N76 Urocanate 7.18E-15 2.88E-12 -1.347 0.918hydrataseNCF2 Pl 9878 Neutrophil 9.72E-08 1.53E-06 -2.393 0.918cytosolfactor 2UGDH 060701 UDP- 2.15E-10 1.12E-08 -4.036 0.918glucose 6- dehydrogenaseRECK 095980 Reversion1.85E-09 6.64F.-08 -0.945 0.917inducingcysteine - rich proteinwith KazalmotifsCD 109 Q6YHK3 CD 109 8.99F.-12 1.00E-09 -0.558 0.917antigenC1TC P11586 C-1- 6.14E-13 9.21E-11 -1.282 0.917tetrahydrofolatesynthase,cytoplasmicAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameMRC2 Q9UBG0 C-type 4.82E-10 2.23E-08 -0.549 0.915mannosereceptor 2EGLN P17813 Endoglin 7.94E-10 3.42E-08 -0.906 0.915 LIRA2 Q8N149 Leukocyte 4.01E-09 1.20E-07 -1.3 0.915immunoglobulin-likereceptorsubfamily Amember 2DEF1; DEF3 P59665; P59 Neutrophil 1.07E-06 1.09E-05 -0.863 0.914666 defensin1; Neutrophi1 defensin 3AMPE Q07075 Glutamyl 4.96E-09 1.40E-07 -0.607 0.914aminopeptidaseERAP2 Q6P179 Endoplasmi 1.02E-06 1.04E-05 -1.58 0.914c reticulumaminopeptidase 2IF4A1 P60842 Eukaryotic 2.31E-08 4.55E-07 -0.834 0.913initiationfactor 4A-IALDOB P05062 Fructose- 7.41E-08 1.20E-06 -1.446 0.913bisphosphate aldolase BTFR2 Q9UP52 Transferrin 5.45E-09 1.49E-07 -1.87 0.913receptorprotein 2SERC Q9Y617 Phosphoseri 7.84E-09 1.90E-07 -2.15 0.912neaminotransferaseCBS P35520 Cystathioni 9.64E-11 5.98E-09 -2.553 0.912ne betasynthaseS27A4 Q6P1M0 Long-chain 6.31E-07 7.23E-06 -2.187 0.912fatty acidtransportprotein 4B3GN7 Q8NFL0 UDP- 2.77E-09 9.06E-08 -2.001 0.911GlcNAc:betaGal beta- L3-N- acetylglucosaminyltransferase 7FPRP Q9P2B2 Prostaglandi 4.29E-05 2.26E-04 -2.214 0.911n F2Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NamereceptornegativeregulatorDQB1 P01920 HLA class 5.92E-10 2.69E-08 -5.795 0.91IIhistocompatibilityantigen. DQbeta 1 chainTRI75 A6NK02 Tripartite 2.97E-06 2.57E-05 3.584 0.909motifcontainingprotein 75BGH3 Q15582 Transformin 6.67E-12 8.48E-10 -0.606 0.908g growthfactor-beta - inducedprotein ig- h3PEPD P12955 Xaa-Pro 5.46E-11 4.29E-09 -0.697 0.908dipeptidaseCBPQ Q9Y646 Carboxy pep 9.46E-10 3.88E-08 -0.943 0.907tidase QMEG 10 Q96KG7 Multiple 4.74E-10 2.23E-08 -0.799 0.906epidermalgrowthfactor-likedomainsprotein 10KV240; KV A0A087W Immunoglo 2.13E-09 7.42E-08 -0.822 0.906 D40 W87; P0161 bulin kappa4 variable 2- 40; Immunoglobulinkappavariable 2D- 40HSP74 P34932 Heat shock 4.76E-07 5.57E-06 -0.62 0.90670 kDaprotein 4P1GR P01833 Polymeric 9.45E-10 3.88E-08 -0.921 0.906immunoglobulinreceptorAL7A1 P49419 Alpha7.11E-06 5.24E-05 -1.257 0.905aminoadipicsemialdehydedehydro genaseAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameIDHC 075874 Isocitrate 1.43E-08 3.06E-07 -1.534 0.905dehydrogenase [NADP]cytoplasmicPCOC2 Q9UKZ9 Procollagen 9.17E-09 2.14E-07 -0.881 0.904C- endopeptidase enhancer2PPM1A P35813 Protein 1.41E-08 3.03E-07 -1.227 0.904phosphatase1AESTI P23141 Liver 1.66E-08 3.46E-07 -0.724 0.903carbo xy testerase 1ANXA6 P08133 Annexin A6 4.38E-08 7.63E-07 -1.24 0.903 VWCE Q96DN2 von 4.58E-08 7.91E-07 -1.743 0.902Willebrandfactor C andEGFdomaincontainingproteinEGFR P00533 Epidermal 1.99E-10 1.05E-08 -0.618 0.901growthfactorreceptorERAP1 Q9NZ08 Endoplasmi 2.60E-09 8.83E-08 -0.744 0.901c reticulumaminopeptidase 1CBG P08185 Corticostero 9.09E-09 2.14E-07 -0.503 0.9id-bindingglobulinHS71A; HS7 P0DMV8; P Heat shock 7.66E-09 1.89E-07 -0.691 0.9IB 0DMV9 70 kDaproteinlA; Heatshock 70kDa proteinIB CEAM1 P13688 Carcinoemb 6.72E-06 5.04E-05 -1.278 0.9ryonicantigen- related celladhesionmolecule 1PTPRM P28827 Receptor6.41E-08 1.05E-06 -0.63 0.9typetyrosine-Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameproteinphosphatasemuPLBL2 Q8NHP8 Putative 9.08E-12 1.00E-09 -2.149 0.9phospholipase B-iike 23HIDH P31937 3- 9.16E-08 1.45E-06 -2.935 0.899hydroxyisobutyratedehydrogenase,mitochondrialACOHC P21399 Cytoplasmic 2.08E-11 2.03E-09 -0.925 0.898aconitatehydrataseGPX3 P22352 Glutathione 1.28E-09 4.86E-08 -0.644 0.897peroxidase3CEL3A P09093 Chymotryps 1.35E-08 2.97E-07 -2.195 0.896in-likeelastasefamilymember 3 APP2BB; PP2 P16298; Q08 Serine / threo 4.77E-09 1.36E-07 -3.438 0.895 BA 209 nine-proteinphosphatase2B catalyticsubunit betaisoform; Proteinphosphatase3 catalyticsubunitalphaTRXR1 Q16881 Thioredoxin 6.45E-09 1.67E-07 -1.502 0.895reductase 1,cytoplasmicPLSL P13796 Plastin-2 2.35E-08 4.56E-07 -0.602 0.894 4F2 P08195 Amino acid 1.19E-09 4.58E-08 -0.713 0.894transporterheavy chainSLC3A2SYWC P23381 Tryptophan- 2.87E-07 3.71E-06 -0.72 0.894-tRNAligase,cytoplasmicIGHE P01854 Immunoglo 1.36E-08 2.97E-07 -1.617 0.894bulin heavyAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameconstantepsilonVATL P27449 V-type 2.69E-05 1.55E-04 -5.354 0.894protonATPase 16kDaproteolipidsubunit cASPG P20933 N(4)-(beta- 6.91E-09 1.76E-07 -1.409 0.893N- acetylglucosaminyl)-L- asparaginaseAPOB P04114 Apolipoprot 1.04E-10 6.27E-09 0.53 0.892ein B-100PGAM1 P18669 Phosphogly 1.73E-10 9.55E-09 -1.182 0.892ceratemutase 1MA2B2 Q9Y2E5 Epididymis1.18E-09 4.58E-08 -2.938 0.892specificalpha- mannosidaseSAP P07602 Prosa posin 1.74E-08 3.57E-07 -0.63 0.891 AGAL P06280 Alpha- 3.49E-09 1.06E-07 -2.587 0.89galactosidase APTN1 P18031 Tyrosine- 1.55E-07 2.26E-06 -3.305 0.89proteinphosphatasenonreceptortype 1B3GN2 Q9NY97 N- 7.86E-09 1.90E-07 -0.614 0.89acety llactosaminidebeta- 1,3 -N- acetylglucosaminyltransferase 2MA2B1 000754 Lysosomal 2.10E-06 1.90E-05 -1.582 0.89alpha- mannosidaseLACRT Q9GZZ8 Extracellula 9.75E-10 3.93E-08 -4.007 0.89rglycoproteinlacritinMYH7 P12883 Myosin-7 3.06E-07 3.91E-06 -1.513 0.889Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameS100AG Q96FQ6 Protein 6.86E-10 3.06E-08 -1.955 0.889S100-A16VAMP8 Q9BV40 Vesicle- 3.95E-10 1.92E-08 -3.89 0.889associatedmembraneprotein 8CAH11 075493 Carbonic 2.09E-08 4.18E-07 -4.793 0.888anhydrase- relatedprotein 11GSHB P48637 Glutathione 1.59E-08 3.34E-07 -0.778 0.888synthetaseLAMP2 P13473 Lysosome- 1.23E-07 1.86E-06 -0.678 0.887associatedmembraneglycoprotein2 ’GGT7 Q9UJ14 Glutathione 8.07E-09 1.93E-07 -2.002 0.887hydrolase 7KCD12 Q96CX2 BTB / POZ 3.36E-09 1.03E-07 -1.373 0.885domaincontainingproteinKCTD12MMP2 P08253 72 kDa type 3.30E-10 1.68E-08 -0.726 0.885IVcollagenaseARGL Y P04424 Argininosuc 1.24E-05 8.19E-05 -1.232 0.885cinate lyaseHLAA P04439 HLA class I 3.67E-13 7.18E-11 -2.06 0.885histocompatibilityantigen, Aalpha chainSIA4A Q11201 CMP-N- 3.73E-07 4.51E-06 -2.507 0.885acetylneuraminate-beta- galactosamide-alpha- 2,3- sialyltransferase 1CBPE Pl 6870 Carboxy pep 1.50E-08 3.18E-07 -3.027 0.885tidase ECATC P53634 Dipeptidyl 1.27E-06 1.26E-05 -0.79 0.884peptidase 1AP2A2 094973 AP-2 1.39E-05 8.90E-05 -1.093 0.883complexsubunitalpha-2Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameITA4 P13612 Integrin 2.44E-04 9.34E-04 -0.982 0.882alpha-4TFR1 P02786 Transferrin 2.78E-09 9.06E-08 -1.255 0.882receptorprotein 1TKFC Q3LXA3 Triokinase / 1.34E-06 1.30E-05 -2.248 0.882FMNcyclaseFCG2A P12318 Low affinity 1.96E-07 2.70E-06 -0.758 0.881immunoglobulingamma Fcregionreceptor Il-aT132C Q8N3T6 Transmemb 3.04E-06 2.62E-05 -0.96 0.881rane protein132CNAGAB P17050 Alpha-N- 2.08E-07 2.83E-06 -2.846 0.88acetylgalactosaminidasePP1R7 Q15435 Protein 2.84E-04 1.07E-03 -3.854 0.88phosphatase1 regulatorysubunit 7RHOA P61586 Transform in 1.84E-08 3.75E-07 -2.564 0.88g proteinRhoAGGH Q92820 Gamma1.40E-07 2.09E-06 -0.508 0.879glutamylhydrolaseNHRF1 O14745 Na(+) / H(+) 3.25E-07 4.07E-06 -1.557 0.879exchangeregulatorycofactorNHE-RF1GINM1 Q9NU53 Glycoprotei 4.70E-08 8.08E-07 -2.241 0.879n integralmembraneprotein 1RL30 P62888 Large 2.36E-06 2.11E-05 -2.83 0.879ribosomalsubunitproteineL30ASSY P00966 Argininosuc 3.25E-05 1.80E-04 -1.451 0.878cinatesynthaseIGSF2 Q93033 Immunoglo 1.93E-06 1.78E-05 -1.531 0.878bulinAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN Namesuperfamilymember 2FCGRN P55899 IgG 4.87E-08 8.30E-07 -1.754 0.878receptorFcRn largesubunit p51UGPA Q16851 UTP— 4.01E-10 1.92E-08 -1.072 0.877glucose-1- phosphateuridylyltransferaseTHIC Q9BWD1 Acetyl-CoA 3.27E-08 6.06E-07 -2.048 0.877acetyltransferase,cytosolicMANSI Q9H8J5 MANSC 1.35E-07 2.04E-06 -0.684 0.876domaincontainingprotein 1TYMP P19971 Thymidine 8.98E-07 9.72E-06 -0.722 0.876phosphorylasePAEP P09466 Glycodelin 1.61E-04 6.69E-04 -2.19 0.876 CAD17 Q12864 Cadherin-17 4.11E-09 1.21E-07 -0.923 0.875 COPD P48444 Coatomer 2.68E-07 3.53E-06 -1.264 0.875subunitdeltaLOX 15 Pl 6050 Polyunsatur 1.03E-04 4.61E-04 -2.159 0.875ated fatty7acidlipoxygenase ALOX 15FMOD Q06828 Fibromodulin 7.22E-04 2.27E-03 -1.718 0.874nMOGS Q13724 Mannosy 1- 1.79E-07 2.52E-06 -0.584 0.874oligosaccharideglucosidaseIMPA3 Q9NX62 Golgi- 2.68E-09 8.96E-08 -0.948 0.873residentadenosine3',5'- bisphosphate 3'- phosphatasePYGL P06737 Glycogen 7.47E-07 8.36E-06 -1.01 0.873phosphorylase, liverformAttorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameARK73 095154 Aflatoxin 1.11E-07 1.72E-06 -3.895 0.873Bl aldehydereductasemember 3H4 P62805 Histone H4 1.68E-05 1.04E-04 -0.823 0.872 TPP1 014773 Tripeptidyl- 6.34E-08 1.05E-06 -2.296 0.872peptidase 1DCXR Q7Z4W1 L-xylulose 1.91E-07 2.67E-06 -1.875 0.872reductaseARC1A Q92747 Actin- 2.10E-07 2.83E-06 -2.924 0.871relatedprotein 2 / 3complexsubunit 1ALYSM3 Q7Z3D4 LysM and 6.84E-06 5.12E-05 -0.556 0.871putativepeptidoglycan-bindingdomain - containingprotein 3TNF10 P50591 Tumor 4.58E-06 3.65E-05 -1.709 0.871necrosisfactorligandsuperfamilymember 10OLM2A Q68BL7 Olfactomedi 2.12E-05 1.26E-04 -2.774 0.871n-likeprotein 2ASPR1B; SPR P22528; P35 Cornifin- 1.70E-10 9.55E-09 -5.432 0.871 1A 321 B; Cornifin- A RIN I Pl 3489 Ribonucleas 1.65E-06 1.55E-05 -0.505 0.87e inhibitorAPOA4 P06727 Apolipoprot 3.14E-06 2.66E-05 -1.265 0.87ein A-IVRAB31 Q13636 Ras-related 6.80E-07 7.71E-06 -3.071 0.87protein Rab- 31CILP2 Q8IUL8 Cartilage 9.70E-09 2.24E-07 -1.098 0.869intermediatelayerprotein 2PLST P13797 Plastin-3 2.99E-07 3.84E-06 -0.88 0.869 GDIR1 P52565 Rho GDP- 2.69E-08 5.10E-07 -1.564 0.869dissociationinhibitor 1Attorney Docket No. NXON-014 / 04WO 346247-2095 Protein Uniprot Protein p-value q- value log2FC AUC AN NameDIAC Q01459 Di-N- 3.43E-08 6.28E-07 -0.858 0.868acetylchitobiaseICAL P20810 Calpastatin 2.78E-05 1.60E-04 -1.052 0.868 HXK3 P52790 Hexokinase- 1.66E-10 9.55E-09 -1.323 0.8683ANGL8 ...

Claims

Attorney Docket No. NXON-014 / 04WO 346247-2095CLAIMS1. A method for determining presence or stage of a stage 1 uterine cancer in a subject the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation. wherein the two or more proteins are selected from Table 3.2; and(c) based on the quantification of the two or more proteins, determining the presence or the stage of the stage 1 uterine cancer in the subject.

2. The method of claim 1, wherein the determining of the presence or stage of the stage 1 uterine cancer is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins; andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the stage 1 uterine cancer at an accuracy of at least 98%.

3. A method for determining presence or stage of a stage 1 uterine cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins selected from Table 3.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 uterine cancer at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 uterine cancer, to determine the presence or the stage of the stage 1 uterine cancer.Attorney Docket No. NXON-014 / 04WO 346247-2095 4. The method of claim 2 or claim 3, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known stage 1 uterine cancer patients and known non-cancer subjects.

5. The method of claim 4, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of stage 1 uterine cancer or non-cancer; and (b) a quantitative measure of at least the two or more proteins6. The method of any one of claims 1-5, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 3.3.

7. The method of claim 6, wherein the multiplex of proteins comprises at least one protein selected from Table 3.4.

8. A method for determining presence or stage of a stage 1 breast cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation, wherein the two or more proteins are selected from Table 4.2; and(c) based on the quantification of the two or more proteins, determining the presence of the stage 1 breast cancer in the subject.

9. The method of claim 8, wherein the determining of the presence or the stage of the stage 1 breast cancer is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins: andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the stage 1 breast cancer at an accuracy of at least 98%, to determine the presence or the stage of the stage 1 breast cancer.

10. A method for determining presence or stage of a stage 1 breast cancer in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;Attorney Docket No. NXON-014 / 04WO 346247-2095 (b) assaying the expression level of two or more proteins selected from Table 4.2 from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins:(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for a stage 1 breast cancer at an accuracy of at least 98%; and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the stage 1 breast cancer, to determine the presence or the stage of the stage 1 breast cancer.

11. The method of claim 9 or claim 10, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known stage 1 breast cancer patients and known non-cancer subjects.

12. The method of claim 11, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of stage 1 breast cancer or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

13. The method of any one of claims 8-12, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 4.3.

14. The method of claim 13, wherein the multiplex of proteins comprises at least one protein selected from Table 4.4.

15. A method for determining a presence or a stage of an early stage colorectal cancer (CRC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation; and(c) based on the quantification of the two or more proteins, determining the presence or the stage of the early stage CRC in the subject.

16. The method of claim 15, wherein the determining of the presence of the early stage CRC is based on:Attorney Docket No. NXON-014 / 04WO 346247-2095 yielding a data set comprising respective quantitative measures of each of the two or more proteins; andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the early stage CRC at an accuracy of at least 98%, to determine the presence or the CRC in a subject.

17. A method for determining a presence or a stage of an early stage colorectal cancer (CRC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for an early stage CRC at an accuracy of at least 98%, and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the early stage CRC, to determine the presence or the CRC in a subject.

18. The method of claim 16 or claim 17, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known early stage CRC patients and known non-cancer subjects.

19. The method of claim 18, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of early stage CRC or non-cancer, and (b) a quantitative measure of at least the two or more proteins.

20. The method of any one of claims 15-19, wherein the early stage colorectal cancer is stage 1 CRC, and the two or more proteins are selected from Table 5.2.

21. 'The method of claim 20, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 5.3.

22. The method of claim 21, wherein the multiplex of proteins comprises at least one protein selected from Table 5.4.Attorney Docket No. NXON-014 / 04WO 346247-2095 23. The method of any one of claims 15-19, wherein the early stage colorectal cancer is stage 0 CRC, and the two or more proteins are selected from Table 6.2.

24. The method of claim 23, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 6.3.

25. The method of claim 24, wherein the multiplex of proteins comprises at least one protein selected from Table 6.4.

26. The method of any one of claims 15-19, wherein the early stage colorectal cancer is an advanced adenoma, and the two or more proteins are selected from Table 7.2.

27. The method of claim 26, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 7.3.

28. The method of claim 27, wherein the multiplex of proteins comprises at least one protein selected from Table 7.4.

29. The method of any one of claims 16-19, wherein the two or more proteins are selected from Table 5.2, 6.2, or 7.2.

30. The method of any one of claims 16-19, and 29 wherein at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC.

31. The computer system of claim 30, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 CRC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 0 CRC or not having the cancer at an accuracy of at least 98%; anda third trained binary classifier configured to, based on the test data, classify the subject as having an advanced adenoma or not having the cancer at an accuracy of at least 98%.

32. The computer system of claim 30 or claim 31, wherein the trained multi class classifier utilizes a one-vs-one strategy.Attorney Docket No. NXON-014 / 04WO 346247-2095 33. The computer system of claim 30 or claim 31, wherein the trained multi class classifier utilizes a one-vs-rest strategy,34. A method for determining a presence or a stage of a non-small cell lung cancer (NSCLC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) quantifying two or more proteins in the microparticle preparation, and(c) based on the quantification of the two or more proteins, determining the presence or the stage of the NSCLC in the subject, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC, a stage 3 NSCLC, or a stage 4 NSCLC.

35. The method of claim 34, wherein the determining of the presence of the NSCLC is based on:yielding a data set comprising respective quantitative measures of each of the two or more proteins; andinputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the NSCLC at an accuracy of at least 98%, to determine the presence or the stage of NSCLC in the subject.

36. A method of determining a presence or a stage of a non-small cell lung cancer (NSCLC) in a subject, the method comprising:(a) providing a microparticle preparation prepared from a biological fluid sample from a subject, wherein the biological fluid sample comprises microparticles;(b) assaying the expression level of two or more proteins from the microparticle preparation, to yield a data set comprising respective quantitative measures of each of the two or more proteins;(c) inputting the data set to at least one trained classifier that is configured to generate a classification of said sample as positive or negative for the NSCLC at an accuracy of at least 98%, and(d) electronically outputting a report that identifies said classification of the sample as positive or negative for the NSCLC, wherein the NSCLC is a stage 1 NSCLC, a stage 2 NSCLC,Attorney Docket No. NXON-014 / 04WO 346247-2095 a stage 3 NSCLC, or a stage 4 NSCLC, to determine the presence or the stage of the NSCLC in the subject.

37. The method of claim 35 or claim 36, wherein the at least one trained classifier was trained with training data obtained from a plurality of training samples, and wherein the training samples are microparticle preparations obtained from biological fluid samples from known NSCLC and known non-cancer subjects.

38. The method of claim 37, wherein the training data set comprises, for each of the plurality of training samples: (a) a training classification of NSCLC or non-cancer; and (b) a quantitative measure of at least the two or more proteins.

39. The method of any one of claims 34-38, wherein the NSCLC is the stage 1 NSCLC, and the two or more proteins are selected from Table 9.2,40. The method of claim 39, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 9.

341. The method of claim 40, wherein the multiplex of proteins comprises at least one protein selected from Table 9.4.

42. The method of any one of claims 34-38, wherein the NSCLC is the stage 2 NSCLC, and the two or more proteins are selected from Table 10.2.

43. The method of claim 42, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 10.

344. The method of claim 43, wherein the multiplex of proteins comprises at least one protein selected from Table 10.4.

45. The method of any one of claims 34-38. wherein the NSCLC is the stage 3 NSCLC, and the two or more proteins are selected from Table 11.2.

46. The method of claim 45, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 11.3.

47. The method of claim 46, wherein the multiplex of proteins comprises at least one protein selected from Table 11.4.

48. The method of any one of claims 34-38, wherein the NSCLC is the stage 4 NSCLC, and the two or more proteins are selected from Table 12.2Attorney Docket No. NXON-014 / 04WO 346247-2095 49. The method of claim 48, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 12.

350. The method of claim 49, wherein the multiplex of proteins comprises at least one protein selected from T able 12.4.

51. The method of any one of claims 34-38, wherein the NSCLC is any one of the stage 1 NSCLC, the stage 2 NSCLC, the stage 3 NSCLC, or the stage 4 NSCLC, and the two or more proteins are selected from Table 13.2.

52. The method of claim 51, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 1.

353. The method of claim 52, wherein the multiplex of proteins comprises at least one protein selected from Table 13.4.

54. The method of claim 35 or claim 36, wherein the two or more proteins are selected from Table 9.2, 10.2, 11.2, or 12.2.

55. The method of any one of claims 35-38, and 54. wherein at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC.

56. The computer system of claim 55, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 NSCLC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 2 NSCLC or not having the cancer at an accuracy of at least 98%;a third trained binary classifier configured to, based on the test data, classify the subject as having a stage 3 NSCLC or not having the cancer at an accuracy of at least 98%; andAttorney Docket No. NXON-014 / 04WO 346247-2095 a fourth trained binary classifier configured to, based on the test data, classify the subject as having a stage 4 NSCLC or not having the cancer at an accuracy of at least 98%.

57. The computer system of claim 55 or claim 56, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

58. The computer system of claim 55 or claim 56, wherein the trained multiclass classifier utilizes a one-vs-rest strategy.

59. The method of any one of claims 2-5, 9-12, 16-19, 29-33, 35-38, and 54-58, wherein the at least one trained classifier is a model comprising a plurality of coefficients, each of the plurality of the coefficients being associated with one of the two or more proteins, and wherein the model is configured to generate the classification based on the data set comprising the respective quantitative measures of the two or more proteins and the plurality of coefficients.

60. The method of any one of claims 1-58, wherein the two or more proteins comprise between 2 and 20 proteins.

61. The method of any one of claims 1-58, wherein at least one of the two or more proteins is a fragment thereof, a variant thereof, a homolog thereof, a congener thereof, a phosphorylated modification thereof or a post-translational modification thereof.

62. The method of any one of claims 1-58. wherein the providing of the microparticle preparation comprises a use of one or more enrichment processes selected from the group consisting of: centrifugation, ultracentrifugation, density gradients, affinity purification, filtration, electroporation, affinity binding in solution or solid phase, magnetic activated sorting, immunoprecipitation, microfiltration, size-exclusion chromatography, and alternating current (AC) electrokinetic separation.

63. The method of claim 62, wherein the providing of the microparticle preparation comprises use of size-exclusion chromatography, and the microparticles are eluted from a size exclusion chromatography column comprising a solid phase, using water as a mobile phase, 64. The method of claim 63, wherein the water is distilled water65. The method of claim 64, wherein the distilled water is double distilled water.Attorney Docket No. NXON-014 / 04WO 346247-2095 66. The method of any one of claims 1-65, wherein the biological fluid is or is obtained from:blood or a fraction thereof, interstitial fluid, synovial fluid, bile, breast milk, lacrimal fluid, menstrual fluid, lymph fluid, urine, cerebrospinal fluid, ascites, saliva, lavage, semen, glandular fluid, vaginal fluid, exudate, contents of cysts, or feces.

67. The method of claim 66, wherein the biological fluid is a fraction of the blood, and the fraction of the blood is serum or plasma.

68. The method of any one of claims 1-67, wherein the two or more proteins are quantified using an affinity capture assay, mass spectrometry, single-molecule array assay (SIMOA), a proximity extension assay, and protein identification by short epitope mapping, or combinations thereof.

69. The method of claim 68, wherein the two or more proteins are quantified using the affinity capture assay, and the affinity capture utilizes a capture agent selected from the group consisting of an antibody, an antibody fragment, a nucleic acid-based protein binding reagent, and a small molecule.

70. The method of any one of claims 1-67, wherein the two or more proteins are quantified using an immunoassay.

71. The method according to claim 70, wherein the immunoassay is selected from the group consisting of: enzyme-linked immunosorbent assay (ELISA), enzyme immunoassay (El A), radioimmunoassay (RIA), antibody detection, immunohistochemistry, western blot, antibody microarray assay, and a proximity ligation assay using a selected antibody with nucleic acid tag that can be amplified by primers for detection of small protein quantities, or a combination thereof.

72. The method of claim 71, wherein the immunoassay is selected from the group consisting of ELISA, EIA, and RIA.

73. The method of any one of claims 1-72, the method further comprising: determining whether the subject is a candidate for receiving a cancer therapy based on the classification.

74. The method of claim 73, wherein the subject is the candidate, and the method further comprises treating the subject with the cancer therapy.

75. A method of monitoring cancer treatment in a subject, the method comprising:Attorney Docket No. NXON-014 / 04WO 346247-2095 (a) assessing a biological fluid sample from a subject that previously was receiving a cancer therapy, in accordance with any one of claims 1-74, to determine the presence or the stage of the cancer; and(b) selecting the subject to be a candidate to receive at least one additional administration of the cancer therapy based on the presence or stage of the cancer.

76. The method according to claim 75, further comprising administering the at least one additional administration of the cancer therapy to the subject.

77. The method of claim 75 or claim 76, wherein the at least one additional administration is characterized by an increased dose of the cancer therapy,78. A method of monitoring cancer treatment in a subject, the method comprising:(a) assessing a biological fluid sample from a subject that previously was administered a therapeutic agent for treating a cancer, in accordance with any one of claims 1-74 to determine the presence or the stage of the cancer; and(b) selecting the subject to be a candidate to receive at least one dose of a different therapeutic agent based on the classification.

79. The method according to claim 78, further comprising administering the different therapeutic agent to the subject in an amount effective to treat the cancer.

80. A computer system comprising:(a) a processor; and(b) a memory, coupled to the processor, the memory storing:(i) test data for a sample from a subject, the test data comprising values indicating a quantitative measure of two or more proteins in a microparticle preparation from a biological fluid sample; and(ii) at least one trained classifier configured to, based on the test data, classify the subject as having a cancer or not having the cancer at an accuracy of at least 98%; and (iii) computer executable instructions for implementing the at least one trained classifier on the test data, wherein:the cancer is a stage 1 uterine cancer, and the two or more proteins are selected from Table 3.2,Attorney Docket No. NXON-014 / 04WO 346247-2095 the cancer is a stage 1 breast cancer, and the two or more proteins are selected from Table 4,2,the cancer is an early stage colorectal cancer (CRC), and the two or more proteins are selected from Table 5.2, 6.2, 7.2, or a combination thereof, orthe cancer is a non-small cell lung cancer (NSCLC), and the two or more proteins are selected from Table 9.2, 10.2, 11.2, 12.2, 13.2 or a combination thereof.

81. The computer system of claim 80, wherein the cancer is the stage 1 uterine cancer, and the two or more proteins are selected from Table 3.2.

82. The computer system of claim 81, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 3.

383. The computer system of claim 82, wherein the multiplex of proteins comprises at least one protein selected from Table 3.4.

84. The computer system of claim 80, wherein the cancer is the stage 1 breast cancer, and the two or more proteins are selected from Table 4.2.

85. The computer system of claim 84, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 4.3.

86. The computer system of claim 85, wherein the multiplex of proteins comprises at least one protein selected from Table 4.4.

87. The computer system of claim 80, wherein the cancer is the early stage CRC, the early stage CRC is a stage 1 CRC, and the two or more proteins are selected from Table 5.2.

88. The computer system of claim 87, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 5.3.

89. The computer system of claim 88, wherein the multiplex of proteins comprises at least one protein selected from Table 5.4,90. The computer system of claim 80, wherein the cancer is the early stage CRC, the early stage CRC is a stage 0 CRC, and the two or more proteins are selected from Table 6.2.

91. The computer system of claim 90, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided Table 6.3.Attorney Docket No. NXON-014 / 04WO 346247-2095 92. The computer system of claim 91, wherein the multiplex of proteins comprises at least one protein selected from Table 6.4,93. The computer system of claim 80, wherein the cancer is the early stage CRC, the early stage CRC is an advanced adenoma, and the two or more proteins are selected from Table 7.

2.

94. The computer system of claim 93, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 7.3.

95. The computer system of claim 94, wherein the multiplex of proteins comprises at least one protein selected from Table 7.4.

96. The computer system of claim 80, wherein the cancer is the NSCLC, the NSCLC is a stage 1 NSCLC, and the two or more proteins are selected from Table 9.2.

97. The computer system of claim 96, wherein die two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 9.3.

98. The computer system of claim 97, wherein the multiplex of proteins comprises at least one protein selected from Table 9.4.

99. The computer system of claim 80, wherein the cancer is the NSCLC, the NSCLC is a stage 2 NSCLC, and the two or more proteins are selected from Table 10.2.

100. The computer system of claim 99, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 10.3.

101. The computer system of claim 100, wherein die multiplex of proteins comprises at least one protein selected from Table 10.4.

102. The computer system of claim 80, wherein the cancer is the NSCLC, the NSCLC is a stage 3 NSCLC, and the two or more proteins are selected from Table 11.2.

103. The computer system of claim 102, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 11.3.

104. The computer system of claim 103, wherein die multiplex of proteins comprises at least one protein selected from Table 11.4.

105. The computer system of claim 80, wherein the cancer is the NSCLC, the NSCLC is a stage 4 NSCLC, and the two or more proteins are selected from Table 12.2.Attorney Docket No. NXON-014 / 04WO 346247-2095 106. The computer system of claim 105, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 12.3.

107. The computer system of claim 106, wherein the multiplex of proteins comprises at least one protein selected from Table 12.4.

108. The computer system of claim 80, wherein the cancer is the NSCLC, the NSCLC is a NSCLC at any one of stages 1 to 4, and the two or more proteins are selected from Table 13.2.

109. The computer system of claim 108, wherein the two or more proteins comprise a multiplex of proteins selected from a plurality of multiplexes provided in Table 13.3.

110. The computer system of claim 109, wherein (he multiplex of proteins comprises at least one protein selected from Table 13.4.

111. The computer system of claim 80, wherein:the cancer is an early stage CRC, andthe at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the early stage CRC.

112. The computer system of claim 111, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary classifier configured to, based on the test data, classify the subject as having a stage 1 CRC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 0 CRC or not having the cancer at an accuracy of at least 98%; anda third trained binary classifier configured to, based on the test data, classify the subject as having an advanced adenoma or not having the cancer at an accuracy of at least 98%.

113. The computer system of claim 111 or claim 112, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

114. The computer system of claim 111 or claim 112, wherein the trained multiclass classifier utilizes a one-vs-rest strategy.Attorney Docket No. NXON-014 / 04WO 346247-2095 115. The computer system of claim 80, wherein:the cancer is a NSCLC, andthe at least one trained classifier is a multiclass classifier configured to, based on the test data, determine a stage of the NSCLC116. The computer system of claim 115, wherein the multiclass classifier is based on two or more trained binary classifiers selected from the group consisting of:a first trained binary' classifier configured to, based on the test data, classify the subject as having a stage 1 NSCLC or not having the cancer at an accuracy of at least 98%;a second trained binary classifier configured to, based on the test data, classify the subject as having a stage 2 NSCLC or not having the cancer at an accuracy of at least 98%;a third trained binary' classifier configured to, based on the test data, classify' the subject as having a stage 3 NSCLC or not having the cancer at an accuracy of at least 98%; anda fourth trained binary classifier configured to, based on the test data, classify the subject as having a stage 4 NSCLC or not having the cancer at an accuracy of at least 98%.

117. The computer system of claim 115 or claim 116, wherein the trained multiclass classifier utilizes a one-vs-one strategy.

118. The computer system of claim 115 or claim 116, wherein the trained multiclass classifier utilizes a one-vs-rest strategy.

119. A panel of biomarkers for use in determining a presence or stage of stage 1 uterine cancer, the panel comprising two or more biomarkers selected from Table 3.2.

120. The panel of claim 119, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 3.3.

121. The panel of claim 120, wherein the multiplex of biomarkers comprises at least one biomarkers selected from Table 3.4.Attorney Docket No. NXON-014 / 04WO 346247-2095 122. A panel of biomarkers for use in determining a presence or stage of stage 1 breast cancer, the panel comprising two or more biomarkers selected from Table 4.2.

123. The panel of claim 122, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 4.3.

124. The panel of claim 123, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 4.4.

125. A panel of biomarkers for use in determining a presence or stage of a colorectal cancer (CRC), the panel comprising two or more biomarkers selected from Table 5.2, 6.2, 7.2, or a combination thereof.

126. The panel of claim 125, wherein the CRC is a stage 1 CRC, and the two or more biomarkers are selected from Table 5.2.

127. The panel of claim 126, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 5.3.

128. The panel of claim 127, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 5.4.

129. The panel of claim 125, wherein the CRC is a stage 0 CRC, and the two or more biomarkers are selected from Table 6.2.

130. The panel of claim 129, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 6.3.

131. The panel of claim 130, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 6.4.

132. The panel of claim 131, wherein the CRC is an advanced adenoma, and the two or more biomarkers are selected from Table 7.2,133. The panel of claim 132, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 7.3.

134. The panel of claim 133, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 7.4.Attorney Docket No. NXON-014 / 04WO 346247-2095 135. A panel of biomarkers for use in determining a presence or stage of a non-small cell lung cancer (NSCLC), the panel comprising two or more biomarkers selected from Table 9.2, 10.2, 11.2, 12.2, 13.2, or a combination thereof.

136. The panel of claim 135, wherein the NSCLC is a stage 1 NSCLC, and the two or more biomarkers are selected from Table 9.2.

137. The panel of claim 136, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 9.3.

138. The panel of claim 137, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 9,4,139. The panel of claim 135, wherein the NSCLC is a stage 2 NSCLC, and the two or more biomarkers are selected from Table 10.2.

140. The panel of claim 139, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 10.3.

141. The panel of claim 140, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 10.4.

142. The panel of claim 135, wherein the NSCLC is a stage 3 NSCLC, and the two or more biomarkers are selected from Table 11.2.

143. The panel of claim 142, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 11.3.

144. The panel of claim 143, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 11.4.

145. The panel of claim 135, wherein the NSCLC is a stage 4 NSCLC, and the two or more biomarkers are selected from Table 12.2.

146. The panel of claim 145, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 12.3.

147. The panel of claim 146, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 12.4.

148. The panel of claim 135, wherein the NSCLC is a NSCLC at any one of stages 1 to 4, and the two or more biomarkers are selected from Table 13.2.Attorney Docket No. NXON-014 / 04WO 346247-2095 149. The panel of claim 148, wherein the two or more biomarkers comprise a multiplex of biomarkers selected from a plurality of multiplexes provided in Table 13,3150. The panel of claim 149, wherein the multiplex of biomarkers comprises at least one biomarker selected from Table 13.4.

151. A kit comprising reagents for detecting the biomarkers of the panel of any one of claims 119-121, for use in determining the presence or the stage of the stage 1 breast cancer.

152. A kit comprising reagents for detecting the biomarkers of the panel of any one of claims 122-124, for use in determining the presence or the stage of the stage 1 uterine cancer.

153. A kit comprising reagents for detecting the biomarkers of the panel of any one of claims 125-134, for use in determining the presence or the stage of the early stage CRC.

154. A kit comprising reagents for detecting the biomarkers of the panel of any one of claims 135-150, for use in determining the presence or the stage of the NSCLC.

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