BIOMARKERS FOR DIAGNOSING OVARIAN CANCER

FIELD

The instant disclosure is directed to glycoproteomic biomarkers including, but not limited to, glycans, peptides, and glycopeptides, as well as to methods of using these biomarkers with mass spectroscopy and in clinical applications.

BACKGROUND

Changes in glycosylation have been described in relationship to disease states such as cancer. See, e.g., Dube, D. H.; Bertozzi, C. R. Glycans in Cancer and Inflammation—Potential for Therapeutics and Diagnostics. Nature Rev. Drug Disc. 2005, 4, 477-88, the entire contents of which are herein incorporated by reference in its entirety for all purposes. However, clinically relevant, non-invasive assays for diagnosing cancer, such as ovarian cancer, in a patient based on glycosylation changes in a sample from that patient are not yet sufficiently demonstrated.

Conventional clinical assays for diagnosing ovarian cancer, for example, include measuring the amount of the protein CA 125 (cancer antigen 125) in a patient's blood by an enzyme-linked immunosorbent assay (ELISA). However, ELISA has limited sensitivity and precision. ELISA, for example, only measures CA 125 at concentrations in the ng/mL range. This narrow measurement range limits the relevance of this assay by failing to measure biomarkers at concentrations substantially above or below this concentration range. Also, the CA 125 ELISA assay is limited with respect to the types of samples which can be assayed. As a consequence of the lack of more precise and sensitive tests, patients who might otherwise be diagnosed with ovarian cancer are not and thereby fail to receive proper follow-up medical attention.

As an alternative, mass spectroscopy (MS) offers sensitive and precise measurement of cancer-specific biomarkers including glycopeptides. See, for example, Ruhaak, L. R., et al., Protein-Specific Differential Glycosylation of Immunoglobulins in Serum of Ovarian Cancer Patients DOI: 10.1021/acs.jproteome.5b01071; J. Proteone Res., 2016, 15, 1002-1010 (2016); also Miyamoto, S., et al., Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer, DOI: 10.1021/acs.jproteome.7b00541, J. Proteome Res. 2018, 17, 222-233 (2017), the entire contents of which are herein incorporated by reference in its entirety for all purposes. However, using MS to diagnose cancer, generally, or ovarian cancer specifically, has not been demonstrated to date in a clinically relevant manner.

What is needed are new biomarkers and new methods of using MS to diagnose disease states such as cancer using these biomarkers. Set forth herein in the disclosure below are such biomarkers comprising glycans, peptides, and glycopeptides, as well as fragments thereof, and methods of using the biomarkers with MS to diagnose ovarian cancer.

SUMMARY

In one embodiment, set forth herein is a glycopeptide or peptide consisting of an amino acid sequence selected from SEQ ID NOs:1-262, and combinations thereof.

In another embodiment, set forth herein is a glycopeptide or peptide consisting essentially of an amino acid sequence selected from SEQ ID NOs:1-262, and combinations thereof.

In another embodiment, set forth herein is a method for detecting one or more MRM transitions, comprising: obtaining a biological sample from a patient; digesting and/or fragmenting a glycopeptide in the sample; and detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1-150, described herein.

In another embodiment, set forth herein a method for identifying a classification for a sample, the method comprising: quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample wherein the glycopeptides each, individually in each instance, comprises a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof; and inputting the quantification into a trained model to generate a output probability; determining if the output probability is above or below a threshold for a classification; and identifying a classification for the sample based on whether the output probability is above or below a threshold for a classification.

In yet another embodiment, set forth herein is a method for classifying a biological sample, comprising: obtaining a biological sample from a patient; digesting and/or fragmenting a glycopeptide in the sample; detecting a MRM transition selected from the group consisting of transitions 1-150; and quantifying the glycopeptides; inputting the quantification into a trained model to generate a output probability; determining if the output probability is above or below a threshold for a classification; and classifying the biological sample based on whether the output probability is above or below a threshold for a classification.

In another embodiment, set forth herein is a method for treating a patient having ovarian cancer; the method comprising: obtaining a biological sample from the patient; digesting and/or fragmenting one or more glycopeptides in the sample; and detecting and quantifying one or more multiple-reaction-monitoring (MRM) transitions selected from the group consisting of transitions 1-150; inputting the quantification into a trained model to generate an output probability; determining if the output probability is above or below a threshold for a classification; and classifying the patient based on whether the output probability is above or below a threshold for a classification, wherein the classification is selected from the group consisting of: (A) a patient in need of a chemotherapeutic agent; (B) a patient in need of a immunotherapeutic agent; (C) a patient in need of hormone therapy; (D) a patient in need of a targeted therapeutic agent; (E) a patient in need of surgery; (F) a patient in need of neoadjuvant therapy; (G) a patient in need of chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof, before surgery; (H) a patient in need of chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof, after surgery; (I) or a combination thereof; administering a therapeutically effective amount of a therapeutic agent to the patient: wherein the therapeutic agent is selected from chemotherapy if classification A or I is determined; wherein the therapeutic agent is selected from immunotherapy if classification B or I is determined; or wherein the therapeutic agent is selected from hormone therapy if classification C or I is determined; or wherein the therapeutic agent is selected from targeted therapy if classification D or I is determined wherein the therapeutic agent is selected from neoadjuvant therapy if classification F or I is determined; wherein the therapeutic agent is selected from chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof if classification G or I is determined; and wherein the therapeutic agent is selected from chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof if classification H or I is determined.

In another embodiment, set forth herein is a method for training a machine learning algorithm, comprising: providing a first data set of MRM transition signals indicative of a sample comprising a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262; providing a second data set of MRM transition signals indicative of a control sample; and comparing the first data set with the second data set using a machine learning algorithm.

In another embodiment, set forth herein is a method for diagnosing a patient having ovarian cancer; the method comprising: obtaining a biological sample from the patient; performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect and quantify one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262; or to detect and quantify one or more MRM transitions selected from transitions 1-150; inputting the quantification of the detected glycopeptides or the MRM transitions into a trained model to generate an output probability, determining if the output probability is above or below a threshold for a classification; and identifying a diagnostic classification for the patient based on whether the output probability is above or below a threshold for a classification; and diagnosing the patient as having ovarian cancer based on the diagnostic classification. In some examples, the method includes performing mass spectroscopy of the biological sample using MRM-MS with a QQQ.

In another embodiment, set forth herein is a kit comprising a glycopeptide standard, a buffer, and one or more glycopeptides consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In another embodiment, set forth herein is a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIGS. 1 through 14 illustrate glycan chemical structures, using the Symbol Nomenclature for Glycans (SNFG) system. Each glycan structure is associated with a glycan reference code number.

FIGS. 15 and 16 show work flows for detecting transitions 1-150 by mass spectroscopy.

FIGS. 17 through 19 show machine learning peak quantification analysis of mass spectroscopy data obtained by detecting transitions 1-150 by mass spectroscopy.

FIG. 20 is plot of ELISA results for measuring CA 125 protein in benign and malignant ovarian cancer samples, as set forth in Example 3.

FIG. 21 is a plot of probability of having cancer in benign and malignant ovarian cancer samples, as set forth in Example 4.

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

DETAILED DESCRIPTION

The following description is presented to enable one of ordinary skill in the art to make and use the invention and to incorporate it in the context of particular applications. Various modifications, as well as a variety of uses in different applications will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to a wide range of embodiments. Thus, the inventions herein are not intended to be limited to the embodiments presented, but are to be accorded their widest scope consistent with the principles and novel features disclosed herein.

All the features disclosed in this specification, (including any accompanying claims, abstract, and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

Please note, if used, the labels left, right, front, back, top, bottom, forward, reverse, clockwise and counter clockwise have been used for convenience purposes only and are not intended to imply any particular fixed direction. Instead, they are used to reflect relative locations and/or directions between various portions of an object.

I. GENERAL

The instant disclosure provides methods and compositions for the profiling, detecting, and/or quantifying of glycans in a biological sample. In some examples, glycan and glycopeptide panels are described for diagnosing and screening patients having ovarian cancer. In some examples, glycan and glycopeptide panels are described for diagnosing and screening patients having cancer, an autoimmune disease, or fibrosis.

Certain techniques for analyzing biological samples using mass spectroscopy are known. See, for example, International PCT Patent Application Publication No. WO2019079639A1, filed Oct. 18, 2018 as International Patent Application No. PCT/US2018/56574, and titled IDENTIFICATION AND USE OF BIOLOGICAL PARAMETERS FOR DIAGNOSIS AND TREATMENT MONITORING, the entire contents of which are herein incorporated by reference in its entirety for all purposes. See, also, US Patent Application Publication No. US20190101544A1, filed Aug. 31, 2018 as U.S. patent application Ser. No. 16/120,016, and titled IDENTIFICATION AND USE OF GLYCOPEPTIDES AS BIOMARKERS FOR DIAGNOSIS AND TREATMENT MONITORING, the entire contents of which are herein incorporated by reference in its entirety for all purposes.

II. DEFINITIONS

As used herein, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise.

As used herein, the phrase “biological sample,” refers to a sample derived from, obtained by, generated from, provided from, take from, or removed from an organism; or from fluid or tissue from the organism. Biological samples include, but are not limited to synovial fluid, whole blood, blood serum, blood plasma, urine, sputum, tissue, saliva, tears, spinal fluid, tissue section(s) obtained by biopsy; cell(s) that are placed in or adapted to tissue culture; sweat, mucous, fecal material, gastric fluid, abdominal fluid, amniotic fluid, cyst fluid, peritoneal fluid, pancreatic juice, breast milk, lung lavage, marrow, gastric acid, bile, semen, pus, aqueous humor, transudate, and the like including derivatives, portions and combinations of the foregoing. In some examples, biological samples include, but are not limited, to blood and/or plasma. In some examples, biological samples include, but are not limited, to urine or stool. Biological samples include, but are not limited, to saliva. Biological samples include, but are not limited, to tissue dissections and tissue biopsies. Biological samples include, but are not limited, any derivative or fraction of the aforementioned biological samples.

As used herein, the term “glycan” refers to the carbohydrate residue of a glycoconjugate, such as the carbohydrate portion of a glycopeptide, glycoprotein, glycolipid or proteoglycan.

As used herein, the term “glycoform” refers to a unique primary, secondary, tertiary and quatemary structure of a protein with an attached glycan of a specific structure.

As used herein, the term “glycopeptide,” refers to a peptide having at least one glycan residue bonded thereto.

As used herein, the phrase “glycosylated peptides,” refers to a peptide bonded to a glycan residue.

As used herein, the phrase “glycopeptide fragment” or “glycosylated peptide fragment” refers to a glycosylated peptide (or glycopeptide) having an amino acid sequence that is the same as part (but not all) of the amino acid sequence of the glycosylated protein from which the glycosylated peptide is obtained by digestion, e.g., with one or more protease(s) or by fragmentation, e.g., ion fragmentation within a MRM-MS instrument. MRM refers to multiple-reaction-monitoring.

As used herein, the phrase “multiple reaction monitoring mass spectrometry (MRM-MS),” refers to a highly sensitive and selective method for the targeted quantification of glycans and peptides in biological samples. Unlike traditional mass spectrometry, MRM-MS is highly selective (targeted), allowing researchers to fine tune an instrument to specifically look for certain peptides fragments of interest. MRM allows for greater sensitivity, specificity, speed and quantitation of peptides fragments of interest, such as a potential biomarker. MRM-MS involves using one or more of a triple quadrupole (QQQ) mass spectrometer and a quadrupole time-of-flight (qTOF) mass spectrometer.

As used herein, the phrase “digesting a glycopeptide,” refers to a biological process that employs enzymes to break specific amino acid peptide bonds. For example, digesting a glycopeptide includes contacting a glycopeptide with an digesting enzyme, e.g., trypsin to produce fragments of the glycopeptide. In some examples, a protease enzyme is used to digest a glycopeptide. The term “protease” refers to an enzyme that performs proteolysis or breakdown of large peptides into smaller polypeptides or individual amino acids. Examples of a protease include, but are not limited to, one or more of a serine protease, threonine protease, cysteine protease, aspartate protease, glutamic acid protease, metalloprotease, asparagine peptide lyase, and any combinations of the foregoing.

As used herein, the phrase “fragmenting a glycopeptide,” refers to the ion fragmentation process which occurs in a MRM-MS instrument. Fragmenting may produce various fragments having the same mass but varying with respect to their charge.

As used herein, the term “subject,” refers to a mammal. The non-liming examples of a mammal include a human, non-human primate, mouse, rat, dog, cat, horse, or cow, and the like. Mammals other than humans can be advantageously used as subjects that represent animal models of disease, pre-disease, or a pre-disease condition. A subject can be male or female. However, in the context of diagnosing ovarian cancer, the subject is female unless explicitly specified otherwise. A subject can be one who has been previously identified as having a disease or a condition, and optionally has already undergone, or is undergoing, a therapeutic intervention for the disease or condition. Alternatively, a subject can also be one who has not been previously diagnosed as having a disease or a condition. For example, a subject can be one who exhibits one or more risk factors for a disease or a condition, or a subject who does not exhibit disease risk factors, or a subject who is asymptomatic for a disease or a condition. A subject can also be one who is suffering from or at risk of developing a disease or a condition.

As used herein, the term “patient” refers to a mammalian subject. The mammal can be a human, or an animal including, but not limited to an equine, porcine, canine, feline, ungulate, and primate animal. In one embodiment, the individual is a human. The methods and uses described herein are useful for both medical and veterinary uses. A “patient” is a human subject unless specified to the contrary.

As used herein, “peptide,” is meant to include glycopeptides unless stated otherwise.

As used herein, the phrase “multiple-reaction-monitoring (MRM) transition,” refers to the mass to charge (m/z) peaks or signals observed when a glycopeptide, or a fragment thereof, is detected by MRM-MS. The MRM transition is detected as the transition of the precursor and product ion.

As used herein, the phrase “detecting a multiple-reaction-monitoring (MRM) transition,” refers to the process in which a mass spectrometer analyzes a sample using tandem mass spectrometer ion fragmentation methods and identifies the mass to charge ratio for ion fragments in a sample. The absolute value of these identified mass to charge ratios are referred to as transitions. In the context of the methods set forth herein, the mass to charge ratio transitions are the values indicative of glycan, peptide or glycopeptide ion fragments. For some glycopeptides set forth herein, there is a single transition peak or signal. For some other glycopeptides set forth herein, there is more than one transition peak or signal. Background information on MRM mass spectrometry can be found in Introduction to Mass Spectrometry: Instrumentation, Applications, and Strategies for Data Interpretation, 4th Edition, J. Throck Watson, O. David Sparkman, ISBN: 978-0-470-51634-8, November 2007, the entire contents of which are here incorporated by reference in its entirety for all purposes.

As used herein, the phrase “detecting a multiple-reaction-monitoring (MRM) transition indicative of a glycopeptide,” refers to a MS process in which a MRM-MS transition is detected and then compare to a calculated mass to charge ratio (m/z) of a glycopeptide, or fragment thereof, in order to identify the glycopeptide. In some examples, herein, a single transition may be indicative of two more glycopeptides, if those glycopeptides have identical MRM-MS fragmentation patterns. A transition peak or signal includes, but is not limited to, those transitions set forth herein were are associated with a glycopeptide consisting essentially of an amino acid sequence selected from SEQ ID NOs:1-262, and combinations thereof, according to Tables 1-5, e.g., Table 1, Table 2, Table 3, Table 4, Table 5, or a combination thereof. A transition peak or signal includes, but is not limited to, those transitions set forth herein were are associated with a glycopeptide consisting of an amino acid sequence selected from SEQ ID NOs:1-262, and combinations thereof, according to Tables 1-5, e.g., Table 1, Table 2, Table 3, Table 4, Table 5, or a combination thereof.

As used herein, the term “reference value” refers to a value obtained from a population of individual(s) whose disease state is known. The reference value may be in n-dimensional feature space and may be defined by a maximum-margin hyperplane. A reference value can be determined for any particular population, subpopulation, or group of individuals according to standard methods well known to those of skill in the art.

As used herein, the term “population of individuals” means one or more individuals. In one embodiment, the population of individuals consists of one individual. In one embodiment, the population of individuals comprises multiple individuals. As used herein, the term “multiple” means at least 2 (such as at least 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, or 30) individuals. In one embodiment, the population of individuals comprises at least 10 individuals.

As used herein, the term “treatment” or “treating” means any treatment of a disease or condition in a subject, such as a mammal, including: 1) preventing or protecting against the disease or condition, that is, causing the clinical symptoms not to develop; 2) inhibiting the disease or condition, that is, arresting or suppressing the development of clinical symptoms; and/or 3) relieving the disease or condition that is, causing the regression of clinical symptoms. Treating may include administering therapeutic agents to a subject in need thereof.

Herein, glycans are illustrated in FIGS. 1-15 using the Symbol Nomenclature for Glycans (SNFG) for illustrating glycans. An explanation of this illustration system is available on the internet at www.ncbi.nlm.nih.gov/glycans/snfg.html, the entire contents of which are herein incorporated by reference in its entirety for all purposes. Symbol Nomenclature for Graphical Representation of Glycans as published in Glycobiology 25: 1323-1324, 2015, which is available on the internet at doi.org/10.1093/glycob/cwv091. Additional information showing illustrations of the SNFG system are. Within this system, the term, Hex_i: is interpreted as follows: i indicates the number of green circles (mannose) and the number of yellow circles (galactose). The term, HexNAC_j, uses j to indicate the number of blue squares (GlcNAC's). The term Fuc_d, uses d to indicate the number of red triangles (fucose). The term Neu5AC_l, uses l to indicate the number of purple diamonds (sialic acid). The glycan reference codes used herein combine these i, j, d, and l terms to make a composite 4-5 number glycan reference code, e.g., 5300 or 5320. As an example, glycans 3200 and 3210 in FIG. 1 both include 3 green circles (mannose), 2 blue squares (GlcNAC's), and no purple diamonds (sialic acid) but differ in that glycan 3210 also includes 1 red triangle (fucose).

III. BIOMARKERS

Set forth herein are biomarkers. These biomarkers are useful for a variety of applications, including, but not limited to, diagnosing diseases and conditions. For example, certain biomarkers set forth herein, or combinations thereof, are useful for diagnosing ovarian cancer. In some other examples, certain biomarkers set forth herein, or combinations thereof, are useful for diagnosing and screening patients having cancer, an autoimmune disease, or fibrosis. In some examples, the biomarkers set forth herein, or combinations thereof, are useful for classifying a patient so that the patient receives the appropriate medical treatment. In some other examples, the biomarkers set forth herein, or combinations thereof, are useful for treating or ameliorating a disease or condition in patient by, for example, identifying a therapeutic agent with which to treat a patient. In some other examples, the biomarkers set forth herein, or combinations thereof, are useful for determining a prognosis of treatment for a patient or a likelihood of success or survivability for a treatment regimen.

In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of an amino acid sequence selected from SEQ ID NOs:1-262 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting essentially of an amino acid sequence selected from SEQ ID NOs:1-262 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs:1-262 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs:1-262 in the sample. In some examples, as described below, the presence, absolute amount, and/or relative amount of a glycopeptide is determined by analyzing the MS results. In some examples, the MS results are analyzed using machine learning.

In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 in the sample. In some examples, as described below, the presence, absolute amount, and/or relative amount of a glycopeptide is determined by analyzing the MS results. In some examples, the MS results are analyzed using machine learning.

In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194 in the sample. In some examples, as described below, the presence, absolute amount, and/or relative amount of a glycopeptide is determined by analyzing the MS results. In some examples, the MS results are analyzed using machine learning.

In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196 in the sample. In some examples, a sample from a patient is analyzed by MS and the results are used to determine the presence, absolute amount, and/or relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196 in the sample. In some examples, as described below, the presence, absolute amount, and/or relative amount of a glycopeptide is determined by analyzing the MS results. In some examples, the MS results are analyzed using machine learning.

Set forth herein are biomarkers selected from glycans, peptides, glycopeptides, fragments thereof, and combinations thereof. In some examples, the glycopeptide consists of an amino acid sequence selected from SEQ ID NOs:1-262. In some examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NOs:1-262.

Set forth herein are biomarkers selected from glycans, peptides, glycopeptides, fragments thereof, and combinations thereof. In some examples, the glycopeptide consists of an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194. In some examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

Set forth herein are biomarkers selected from glycans, peptides, glycopeptides, fragments thereof, and combinations thereof. In some examples, the glycopeptide consists of an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194. In some examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194.

Set forth herein are biomarkers selected from glycans, peptides, glycopeptides, fragments thereof, and combinations thereof. In some examples, the glycopeptide consists of an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196. In some examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196.

a. O-Glycosylation

In some examples, the glycopeptides set forth herein include 0-glycosylated peptides. These peptides include glycopeptides in which a glycan is bonded to the peptide through an oxygen atom of an amino acid. Typically, the amino acid to which the glycan is bonded is threonine (T) or serine (S). In some examples, the amino acid to which the glycan is bonded is threonine (T). In some examples, the amino acid to which the glycan is bonded is serine (S).

In certain examples, the O-glycosylated peptides include those peptides from the group selected from Apolipoprotein C-III (APOC3), Alpha-2-HS-glycoprotein (FETUA), and combinations thereof. In certain examples, the O-glycosylated peptide, set forth herein, is an Apolipoprotein C-III (APOC3) peptide. In certain examples, the O-glycosylated peptide, set forth herein, is an Alpha-2-HS-glycoprotein (FETUA).

b. N-Glycosylation

In some examples, the glycopeptides set forth herein include N-glycosylated peptides. These peptides include glycopeptides in which a glycan is bonded to the peptide through a nitrogen atom of an amino acid. Typically, the amino acid to which the glycan is bonded is asparagine (N) or arginine (R). In some examples, the amino acid to which the glycan is bonded is asparagine (N). In some examples, the amino acid to which the glycan is bonded is arginine (R).

In certain examples, the N-glycosylated peptides include members selected from the group consisting of Alpha-1-antitrypsin (AIAT), Alpha-1B-glycoprotein (A1BG), Leucine-richAlpha-2-glycoprotein (A2GL), Alpha-2-macroglobulin (A2MG), Alpha-1-antichymotrypsin (AACT), Afamin (AFAM), Alpha-1-acid glycoprotein 1 & 2 (AGP12), Alpha-1-acid glycoprotein 1 (AGP1). Alpha-1-acid glycoprotein 2 (AGP2), Apolipoprotein A-I (APOA1), Apolipoprotein B-100 (APOB), Apolipoprotein D (APOD), Beta-2-glycoprotein-1 (APOH), Apolipoprotein M (APOM), Attractin (ATRN), Calpain-3 (CAN3), Ceruloplasmin (CERU), ComplementFactorH (CFAH), ComplementFactorI (CFAI), Clusterin (CLUS), ComplementC3 (CO3), ComplementC4-A&B (CO4A&CO4B), ComplementcomponentC6 (CO6), ComplementComponentC8AChain (CO8A), Coagulation factor XII (FA12), Haptoglobin (HPT), Histidine-rich Glycoprotein (HRG), Immunoglobulin heavy constant alpha 1&2 (IgA12), Immunoglobulin heavy constant alpha 2 (IgA2), Immunoglobulin heavy constant gamma 2 (IgG2), Immunoglobulin heavy constant mu (IgM), Inter-alpha-trypsin inhibitor heavy chain Hi (ITIH1), Plasma Kallikrein (KLKB1), Kininogen-1 (KNG1), Serum paraoxonase/arylesterase 1 (PON1), Selenoprotein P (SEPP1), Prothrombin (THRB), Serotransferrin (TRFE), Transthyretin (TTR), Protein unc-13HomologA (UN13A), Vitronectin (VTNC), Zinc-alpha-2-glycoprotein (ZA2G), Insulin-like growth factor-II (IGF2), Apolipoprotein C-I (APOC1), and combinations thereof.

c. Peptides and Glycopeptides

In some examples, set forth herein is a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof.

In some examples, set forth herein is a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:1. In some examples, the glycopeptide comprises either glycans 6501 or 6520, or both, wherein the glycan(s) are bonded to residue 107. In some examples, the glycopeptide is A1AT-GP001_107_6501/6520. Herein AlAT refers to Alpha-1-antitrypsin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:2. In some examples, the glycopeptide comprises glycan 6513 at residue 107. In some examples, the glycopeptide is A1AT-GP001_107_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:3. In some examples, the glycopeptide comprises glycan 5401 at residue 271. In some examples, the glycopeptide is A1AT-GP001_271_5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:4. In some examples, the glycopeptide comprises glycan 5402 at residue 271. In some examples, the glycopeptide is A1AT-GP001_271_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:5. In some examples, the glycopeptide comprises glycan 5402 at residue 271. In some examples, the glycopeptide is A1AT-GP001_271MC_5402. Herein, “MC” refers to a missed cleavage of a trypsin digestion. A missed cleavage peptide includes the amino acid sequence selected from SEQ ID NO:5 but also includes additional residues which were not cleaved by way of trypsin digestion.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:6. In some examples, the glycopeptide comprises glycan 5402 at residue 70. In some examples, the glycopeptide is ATAT-GP001_70_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:7. In some examples, the glycopeptide comprises glycan 5412 at residue 70. In some examples, the glycopeptide is A1AT-GP001_70_5412.

In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:8. In some examples, the glycopeptide is QuantPep-A1AT-GP001.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:9. In some examples, the glycopeptide comprises glycans 5401 or 5402, or both, at residue 179. In some examples, the glycopeptide is A1BG-GP002_179_5421/5402. Herein, when two glycans are recited with a forward slash (/) between them, this means, unless specified otherwise explicitly, that the mass spectrometry method is unable to distinguish between these two glycans, e.g., because they share a common mass to charge ratio. Unless specified to the contrary, 5421/5402 means that either glycan 5421 or 5402 is present. The quantification of the amount of glycans 5421/5402 includes a summation of the detected amount of glycan 5421 as well as the detected amount of glycan 5402. Herein A1BG refers to Alpha-1B-glycoprotein.

In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:10. In some examples, the peptide is pep-A2GL-GP003. Herein A2GL refers to Leucine-richAlpha-2-glycoprotein.

In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:11. In some examples, the glycopeptide is QuantPep-A2GL-GP003.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:12. In some examples, the glycopeptide comprises glycan 5402 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5402. Herein A2MG refers to Alpha-2-macroglobulin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:13, In some examples, the glycopeptide comprises glycan 5402 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:14. In some examples, the glycopeptide comprises glycan 5402 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5402_z3. Herein, z3 refers to the charge state (i.e., +3) for the detected glycopeptide fragment.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:15. In some examples, the glycopeptide comprises glycan 5401 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5402_z3.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:16. In some examples, the glycopeptide comprises glycan 5402 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5402_z5.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:17. In some examples, the glycopeptide comprises glycan 5402 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5402_z5. Herein, z5 refers to the charge state (i.e., +5) for the detected glycopeptide fragment.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:18. In some examples, the glycopeptide comprises glycan 5200 at residue 247. In some examples, the glycopeptide is A2MG-GP004_247_5200.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:19. In some examples, the glycopeptide comprises glycan 5200 at residue 247. In some examples, the glycopeptide is A2MG-GP004_247_5200.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:20. In some examples, the glycopeptide comprises glycan 5402 at residue 247. In some examples, the glycopeptide is A2MG-GP004_247_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:21. In some examples, the glycopeptide comprises glycan 5402 at residue 247. In some examples, the glycopeptide is A2MG-GP004_247_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:22. In some examples, the glycopeptide comprises glycan 5402 at residue 55. In some examples, the glycopeptide is A2MG-GP004_55_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:23. In some examples, the glycopeptide comprises glycan 5402 at residue 55. In some examples, the glycopeptide is A2MG-GP004_55_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:24. In some examples, the glycopeptide comprises glycan 5401 at residue 869. In some examples, the glycopeptide is A2MG-GP004_869_5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:25. In some examples, the glycopeptide comprises glycan 5401 at residue 869. In some examples, the glycopeptide is A2MG-GP004_869_5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:26. In some examples, the glycopeptide comprises glycan 5402 at residue 869. In some examples, the glycopeptide is A2MG-GP004_869_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:27. In some examples, the glycopeptide comprises glycan 5402 at residue 869. In some examples, the glycopeptide is A2MG-GP004_869_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:28. In some examples, the glycopeptide comprises glycan 6301 at residue 869. In some examples, the glycopeptide is A2MG-GP004_869_6301.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:29. In some examples, the glycopeptide comprises glycan 6301 at residue 869. In some examples, the glycopeptide is A2MG-GP004_869_6301.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:30. In some examples, the glycopeptide comprises glycan 7602 at residue 271. In some examples, the glycopeptide is AACT-GP005_271_7602. Herein AACT refers to Alpha-1-antichymotrypsin.

In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:31. In some examples, the glycopeptide is QuantPep-AACT-GP005.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:32. In some examples, the glycopeptide comprises glycan 5402 at residue 33. In some examples, the glycopeptide is AFAM-GP006_33_5402. Herein, AFAM refers to Afamin.

In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:33. In some examples, the glycopeptide is QuantPep-AFAM-GP006.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:34. In some examples, the glycopeptide comprises glycan 6503 at residue 72MC. In some examples, the glycopeptide is AGP12-GP007&008_72MC_6503. Herein AGP12 refers to Alpha-1-acid glycoprotein 1&2.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:35. In some examples, the glycopeptide comprises glycan 7601 at residue 72MC. In some examples, the glycopeptide is AGP12-GP007&008_72MC_7601.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:36. In some examples, the glycopeptide comprises glycan 7602 at residue 72MC. In some examples, the glycopeptide is AGP12-GP007&008_72MC_7602.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:37. In some examples, the glycopeptide comprises glycan 7603 at residue 72MC. In some examples, the glycopeptide is AGP12-GP007&008_72MC_7603.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:38. In some examples, the glycopeptide comprises glycan 7613 at residue 72MC. In some examples, the glycopeptide is AGP12-GP007&008_72MC_7613.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:39. In some examples, the glycopeptide comprises glycan 7614 at residue 72MC. In some examples, the glycopeptide is AGP12-GP007&008_72MC_7614.

In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:40. In some examples, the glycopeptide is QuantPep-AGP12-GP007&008.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:41. In some examples, the glycopeptide comprises glycan 6513 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_6513. Herein AGP1 refers to Alpha-1-acid glycoprotein 1.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:42. In some examples, the glycopeptide comprises glycan 6513 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:43. In some examples, the glycopeptide comprises glycan 7602 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_7602.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:44. In some examples, the glycopeptide comprises glycan 7602 at residue 103. In some examples, the glycopeptide is AGPT-GP007_103_7602.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:45. In some examples, the glycopeptide comprises glycan 7614 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_7614.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:46. In some examples, the glycopeptide comprises glycan 7614 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_7614.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:47. In some examples, the glycopeptide comprises glycan 7624 at residue 103. In some examples, the glycopeptide is AGPT-GP007_103_7624.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:48. In some examples, the glycopeptide comprises glycan 7624 at residue 103. In some examples, the glycopeptide is AGPT-GP007_103_7624.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:49. In some examples, the glycopeptide comprises glycan 8704 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_8704.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:50. In some examples, the glycopeptide comprises glycan 8704 at residue 103. In some examples, the glycopeptide is AGPT-GP007_103_8704.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:51. In some examples, the glycopeptide comprises glycan 9804 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_9804.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:52. In some examples, the glycopeptide comprises glycan 9804 at residue 103. In some examples, the glycopeptide is AGP1-GP007_103_9804.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:53. In some examples, the glycopeptide comprises glycan 5402 at residue 33. In some examples, the glycopeptide is AGPT-GP007_33_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:54. In some examples, the glycopeptide comprises glycan 5402 at residue 33. In some examples, the glycopeptide is AGPT-GP007_33_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:55. In some examples, the glycopeptide comprises glycan 6501 at residue 33. In some examples, the glycopeptide is AGP1-GP007_33_6501.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:56. In some examples, the glycopeptide comprises glycan 6501 at residue 33. In some examples, the glycopeptide is AGP1-GP007_33_6501.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:57. In some examples, the glycopeptide comprises glycan 6502 at residue 33. In some examples, the glycopeptide is AGP1-GP007_33_6502.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:58. In some examples, the glycopeptide comprises glycan 6502 at residue 33. In some examples, the glycopeptide is AGPT-GP007_33_6502.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:59. In some examples, the glycopeptide comprises glycan 6500 at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_6500.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:60. In some examples, the glycopeptide comprises glycan 6500 at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_6500.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:61. In some examples, the glycopeptide comprises glycan 6513 at residue 93. In some examples, the glycopeptide is AGPT-GP007_93_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:62. In some examples, the glycopeptide comprises glycan 6513 at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:63. In some examples, the glycopeptide comprises glycans 7602 or 7621, or both, at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7602/7621.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:64. In some examples, the glycopeptide comprises glycans 7602 or 7621, or both, at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7602/7621.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:65. In some examples, the glycopeptide comprises glycans 7603 or 7622, or both, at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7603/7622.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:66. In some examples, the glycopeptide comprises glycans 7603 or 7622, or both, at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7603/7622.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:67. In some examples, the glycopeptide comprises glycan 7611 at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7611.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:68. In some examples, the glycopeptide comprises glycan 7611 at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7611.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:69. In some examples, the glycopeptide comprises glycan 7613 at residue 93. In some examples, the glycopeptide is AGPT-GP007_93_7613.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:70. In some examples, the glycopeptide comprises glycan 7613 at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_7613.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:71. In some examples, the glycopeptide is pep-AGP1-GP007.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:72. In some examples, the glycopeptide is pep-AGP1-GP007.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:73. In some examples, the glycopeptide is QuantPep-AGP1-GP007.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:74. In some examples, the glycopeptide comprises glycan 6503 at residue 103. In some examples, the glycopeptide is AGP2-GP008_103_6503. Herein AGP2 refers to Alpha-1-acid glycoprotein 2.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:75. In some examples, the glycopeptide comprises glycan 6503 at residue 103. In some examples, the glycopeptide is AGP2-GP008_103_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:76. In some examples, the glycopeptide is pep-APOA1-GP011. Herein APOA1 refers to Apolipoprotein A-I.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:77. In some examples, the glycopeptide is pep-APOA1-GP011.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:78. In some examples, the glycopeptide is QuantPep-APOA1-GP011.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:79. In some examples, the glycopeptide is QuantPep-APOA1-GP011.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:80. In some examples, the glycopeptide comprises glycan 0310 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_0310. Herein APOC3 refers to Apolipoprotein C-III.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:81. In some examples, the glycopeptide comprises glycan 0310 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_0310.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:82. In some examples, the glycopeptide comprises glycan 1102 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_1102.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:83. In some examples, the glycopeptide comprises glycan 1102 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_1102.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:84. In some examples, the glycopeptide comprises glycan 1111 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_1111.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:85. In some examples, the glycopeptide comprises glycan 1111 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_1111.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:86. In some examples, the glycopeptide comprises glycan 2110 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_2110.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:87. In some examples, the glycopeptide comprises glycan 2110 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_2110.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:88. In some examples, the glycopeptide comprises glycan 1102 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74Aoff_1102. As used herein, “Aoff” refers to a peptide sequence that differs by the removal of one alanine residue as a result of digestion in serum.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:89. In some examples, the glycopeptide comprises glycan 110 2 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74Aoff_1102.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:90. In some examples, the glycopeptide comprises glycan 1101 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74MC_1101.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:91. In some examples, the glycopeptide comprises glycan 1101 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74MC_1101.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:92. In some examples, the glycopeptide comprises glycan 5401 at residue 3411. In some examples, the glycopeptide is APOB-GP013_3411_5401. Herein APOB refers to Apolipoprotein B-100.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:93. In some examples, the glycopeptide comprises glycans 5402 or 5421, or both, at residue 98. In some examples, the glycopeptide is APOD-GP014_98_5402/5421. Herein APOD refers to Apolipoprotein D.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:94. In some examples, the glycopeptide comprises glycan 5410 at residue 98. In some examples, the glycopeptide is APOD-GP014_98_5410.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:95. In some examples, the glycopeptide comprises glycan 6510 at residue 98. In some examples, the glycopeptide is APOD-GP014_98_6510.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:96. In some examples, the glycopeptide comprises glycan 6530 at residue 98. In some examples, the glycopeptide is APOD-GP014_98_6530.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:97. In some examples, the glycopeptide comprises glycan 9800 at residue 98. In some examples, the glycopeptide is APOD-GP014_98_9800.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:98. In some examples, the glycopeptide is QuantPep-APOD-GP014.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:99. In some examples, the glycopeptide comprises glycan 5401 at residue 253. In some examples, the glycopeptide is APOH-GP015_253_5401. Herein APOH refers to Beta-2-glycoprotein1.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:100. In some examples, the glycopeptide is QuantPep-APOM-GP016. Herein APOM refers to Apolipoprotein M.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:101. In some examples, the glycopeptide is pep-APOM-GP016.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:102. In some examples, the glycopeptide is QuantPep-ATRN-GP018. Herein ATRN refers to Attractin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:103. In some examples, the glycopeptide comprises glycan 6513 at residue 366. In some examples, the glycopeptide is CAN3-GP022_366_6513. Herein CAN3 refers to Calpain-3.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:104. In some examples, the glycopeptide comprises glycan 6503 at residue 138. In some examples, the glycopeptide is CERU-GP023_138_6503, Herein CERU refers to Ceruloplasmin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:105. In some examples, the glycopeptide comprises glycan 5431 at residue 1029. In some examples, the glycopeptide is CFAH-GP024_1029_5431. Herein CFAH refers to ComplementFactorH.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:106. In some examples, the glycopeptide comprises glycan 7500 at residue 1029. In some examples, the glycopeptide is CFAH-GP024_1029_7500.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:107. In some examples, the glycopeptide comprises glycans 5420 or 5401, or both, at residue 882. In some examples, the glycopeptide is CFAH-GP024_882_5420/5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:108. In some examples, the glycopeptide comprises glycans 5402 or 5421, or both, at residue 911. In some examples, the glycopeptide is CFAH-GP024_911_5402/5421.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:109. In some examples, the glycopeptide comprises glycan 5401 at residue 70. In some examples, the glycopeptide is CFAI-GP025_70_5401. Herein CFAI refers to ComplementFactorI.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:110. In some examples, the glycopeptide comprises glycan 5402 at residue 70. In some examples, the glycopeptide is CFAI-GP025_70_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:111. In some examples, the glycopeptide comprises glycan 6503 at residue 291. In some examples, the glycopeptide is CLUS-GP026_291_6503. Herein CLUS refers to Clusterin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:112. In some examples, the glycopeptide comprises glycan 6503 at residue 86. In some examples, the glycopeptide is CLUS-GP026_86_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:113. In some examples, the glycopeptide is QuantPep-CLUS-GP026.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:114. In some examples, the glycopeptide comprises glycan 5200 at residue 85. In some examples, the glycopeptide is CO3-GP028_85_5200. Herein CO3 refers to ComplementC3.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:115. In some examples, the glycopeptide comprises glycan 5402 at residue 1328. In some examples, the glycopeptide is CO4A&CO4B-GP029&030_1328_5402. Herein CO4A&CO4B refers to ComplementC4-A&B.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:116. In some examples, the glycopeptide comprises glycan 5402 at residue 1328. In some examples, the glycopeptide is CO4A&CO4B-GP029&030_1328_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:117. In some examples, the glycopeptide is pep-CO6-GP032. Herein CO6 refers to ComplementcomponentC6.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:118. In some examples, the glycopeptide comprises glycan 5200 at residue 437. In some examples, the glycopeptide is CO8A-GP033_437_5200. Herein, CO8a refers to ComplementComponentC8AChain.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:119. In some examples, the glycopeptide is QuantPep-CO8A-GP033.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:120. In some examples, the glycopeptide comprises glycan 5410 at residue 553. In some examples, the glycopeptide is CO8B-GP034_553_5410. Herein CO8B refers to ComplementComponentC8AChain.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:121. In some examples, the glycopeptide is QuantPep-FA12-GP035. Herein FA12 refers to Coagulation factor XII.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:122. In some examples, the glycopeptide comprises glycan 5401 at residue 156. In some examples, the glycopeptide is FETUA-GP036_156_5400. Herein FETUA refers to Alpha-2-HS-glycoprotein.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:123. In some examples, the glycopeptide comprises glycan 5401 at residue 176. In some examples, the glycopeptide is FETUA-GP036_176_5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:124. In some examples, the glycopeptide comprises glycan 2200 at residue 346. In some examples, the glycopeptide is FETUA-GP036_346_2200.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:125. In some examples, the glycopeptide is QuantPep-FETUA-GP036.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:126. In some examples, the glycopeptide comprises glycan 11904 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_11904. Herein HPT refers to Haptoglobin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:127. In some examples, the glycopeptide comprises glycan 11904 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_11904.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:128. In some examples, the glycopeptide comprises glycan 11915 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_11915.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:129. In some examples, the glycopeptide comprises glycan 11915 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_11915.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:130. In some examples, the glycopeptide comprises glycan 121005 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_121005.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:131. In some examples, the glycopeptide comprises glycan 121005 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_121005.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:132. In some examples, the glycopeptide comprises glycan 6503 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:133. In some examples, the glycopeptide comprises glycan 6503 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:134. In some examples, the glycopeptide comprises glycan 6512 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6512.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:135. In some examples, the glycopeptide comprises glycan 6512 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6512.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:136. In some examples, the glycopeptide comprises glycan 6513 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:137. In some examples, the glycopeptide comprises glycan 6513 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:138. In some examples, the glycopeptide comprises glycan 7613 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_7613.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:139. In some examples, the glycopeptide comprises glycan 7613 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_7613.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:140. In some examples, the glycopeptide is pep-HPT-GP044.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:141. In some examples, the glycopeptide is QuantPep-HPT-GP044.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:142. In some examples, the glycopeptide comprises glycans 5421 or 5402, or both, at residue 271. In some examples, the glycopeptide is HRG-GP045_125_5421/5402. Herein HRG refers to Histidine-rich Glycoprotein.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:143. In some examples, the glycopeptide comprises glycan 5412 at residue 345. In some examples, the glycopeptide is HRG-GP045_345_5412.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:144. In some examples, the glycopeptide comprises glycan 5502 at residue 144. In some examples, the glycopeptide is IgA12-GP046&047_144_5502. Herein IgA12 refers to Immunoglobulin heavy constant alpha 1&2.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:145. In some examples, the glycopeptide comprises glycan 5411 at residue 205. In some examples, the glycopeptide is IgA2-GP047_205_5411. Herein IgA2 refers to Immunoglobulin heavy constant alpha 2.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:146. In some examples, the glycopeptide comprises glycan 5412 at residue 205. In some examples, the glycopeptide is IgA2-GP047_205_5412.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:147. In some examples, the glycopeptide comprises glycan 5510 at residue 205. In some examples, the glycopeptide is IgA2-GP047_205_5510.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:148. In some examples, the glycopeptide comprises glycan 3410 at residue 297. In some examples, the glycopeptide is IgG2-GP049_297_3410. Herein IgG2 refers to Immunoglobulin heavy constant gamma 2.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:149. In some examples, the glycopeptide comprises glycan 3410 at residue 297. In some examples, the glycopeptide is IgG2-GP049_297_3410.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:150. In some examples, the glycopeptide comprises glycan 4411 at residue 297. In some examples, the glycopeptide is IgG2-GP049_297_4411.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:151. In some examples, the glycopeptide comprises glycan 4411 at residue 297. In some examples, the glycopeptide is IgG2-GP049_297_4411.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:152. In some examples, the glycopeptide is QuantPep-IgG2-GP049.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:153. In some examples, the glycopeptide is QuantPep-IgG2-GP049.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:154. In some examples, the glycopeptide comprises glycan 6200 at residue 439. In some examples, the glycopeptide is IgM-GP053_439_6200. Herein IgM refers to Immunoglobulin heavy constant mu.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:155. In some examples, the glycopeptide comprises glycan 6200 at residue 439. In some examples, the glycopeptide is IgM-GP053_439_6200.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:156. In some examples, the glycopeptide comprises glycan 5601 at residue 46. In some examples, the glycopeptide is IgM-GP053_46_5601.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:157. In some examples, the glycopeptide comprises glycan 5601 at residue 46. In some examples, the glycopeptide is IgM-GP053_46_5601.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:158. In some examples, the glycopeptide comprises glycan 5511 at residue 285. In some examples, the glycopeptide is ITIH1-GP054_285_5511. Herein ITIH1 refers to Inter-alpha-trypsin inhibitor heavy chain H1.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:159. In some examples, the glycopeptide is QuantPep-ITIH1-GP054.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:160. In some examples, the glycopeptide comprises glycans 5420 or 5401, or both, at residue 271. In some examples, the glycopeptide is ITIH4-GP055_517_5420/5401. Herein ITIH4 refers to Inter-alpha-trypsin inhibitor heavy chain H4.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:161. In some examples, the glycopeptide comprises glycan 5400 at residue 494. In some examples, the glycopeptide is KLKB1-GP056_494_5400. Herein KLKB1 refers to Plasma Kallikrein.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:162. In some examples, the glycopeptide comprises glycan 5402 at residue 494. In some examples, the glycopeptide is KLKB1-GP056_494_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:163. In some examples, the glycopeptide comprises glycan 6503 at residue 494. In some examples, the glycopeptide is KLKBT-GP056_494_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:164. In some examples, the glycopeptide is QuantPep-KLKB1-GP056.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:165. In some examples, the glycopeptide is QuantPep-KNG1-GP057. Herein KNG1 refers to Kininogen-1.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:166. In some examples, the glycopeptide comprises glycan 4301 at residue 271. In some examples, the glycopeptide is PON1-GP060_253_4301. Herein PON1 refers to Serum paraoxonase/arylesterase 1.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:167. In some examples, the glycopeptide comprises glycan 5420 at residue 324. In some examples, the glycopeptide is PON1-GP060_324_5420.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:168. In some examples, the glycopeptide comprises glycan 6501 at residue 324. In some examples, the glycopeptide is PON1-GP060_324_6501.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:169. In some examples, the glycopeptide comprises glycan 6502 at residue 324. In some examples, the glycopeptide is PON1-GP060_324_6502.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:170. In some examples, the glycopeptide is QuantPep-PON1-GP060.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:171. In some examples, the glycopeptide is QuantPep-SEPP1-GP061. Herein SEPP1 refers to Selenoprotein P.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:172. In some examples, the glycopeptide comprises glycans 5420 or 5401, or both, at residue 121. In some examples, the glycopeptide is THRB-GP063_121_5420/5401. Herein THRM refers to Prothrombin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:173. In some examples, the glycopeptide comprises glycans 5420 or 5401, or both, at residue 121. In some examples, the glycopeptide is THRB-GP063_121_5421/5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:174. In some examples, the glycopeptide is pep-TRFE-GP064. Herein TRFE refers to Serotransferrin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:175. In some examples, the glycopeptide is QuantPep-TRFE-GP064.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:176. In some examples, the glycopeptide comprises glycan 5401 at residue 432. In some examples, the glycopeptide is TRFE-GP064_432_5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:177. In some examples, the glycopeptide comprises glycan 5402 at residue 432. In some examples, the glycopeptide is TRFE-GP064_432_5402.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:178. In some examples, the glycopeptide comprises glycan 5412 at residue 432. In some examples, the glycopeptide is TRFE-GP064_432_5412.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:179. In some examples, the glycopeptide comprises glycan 5400 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_5400.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:180. In some examples, the glycopeptide comprises glycan 6410 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_6410.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:181. In some examples, the glycopeptide comprises glycan 6411 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_6411.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:182. In some examples, the glycopeptide comprises glycan 6502 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_6502.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:183. In some examples, the glycopeptide comprises glycan 6503 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:184. In some examples, the glycopeptide comprises glycan 6513 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_6513.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:185. In some examples, the glycopeptide is QuantPep-TTR-GP065. Herein TTR refers to Transthyretin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:186. In some examples, the glycopeptide is QuantPep-UN13A-GP066. Herein UN13A refers to Protein unc-13HomologA.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:187. In some examples, the glycopeptide comprises glycan 3420 at residue 1005. In some examples, the glycopeptide is UN13A-GP066_1005_3420.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:188. In some examples, the glycopeptide comprises glycan 5431 at residue 1005. In some examples, the glycopeptide is UN13A-GP066_1005_5431.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:189. In some examples, the glycopeptide comprises glycan 7420 at residue 1005. In some examples, the glycopeptide is UN13A-GP066_1005_7420.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:190. In some examples, the glycopeptide comprises glycan 5401 at residue 169. In some examples, the glycopeptide is VTNC-GP067_169_5401. Herein VTNC refers to Vitronectin.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:191. In some examples, the glycopeptide comprises glycan 5401 at residue 169. In some examples, the glycopeptide is VTNC-GP067_169_5401.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:192. In some examples, the glycopeptide comprises glycan 6502 at residue 242. In some examples, the glycopeptide is VTNC-GP067_242_6502.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:193. In some examples, the glycopeptide comprises glycan 6502 at residue 242. In some examples, the glycopeptide is VTNC-GP067_242_6502.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:194. In some examples, the glycopeptide comprises glycan 6503 at residue 242. In some examples, the glycopeptide is VTNC-GP067_242_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:195. In some examples, the glycopeptide comprises glycan 6503 at residue 242. In some examples, the glycopeptide is VTNC-GP067_242_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:196. In some examples, the glycopeptide comprises glycan 6503 at residue 86. In some examples, the glycopeptide is VTNC-GP067_86_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:197. In some examples, the glycopeptide comprises glycan 6503 at residue 86. In some examples, the glycopeptide is VTNC-GP067_86_6503.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:198. In some examples, the glycopeptide comprises glycan 5412 at residue 112. In some examples, the glycopeptide is ZA2G-GP068_112_5412. Herein ZA2G refers to Zinc-alpha-2-glycoprotein.

In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:199. In some examples, the glycopeptide comprises glycan 5412 at residue 112. In some examples, the glycopeptide is ZA2G-GP068_112_5412.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:200. In some examples, the glycopeptide is pep-IGF2. Herein IGF2 refers to Insulin-like growth factor-II.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:201. In some examples, the glycopeptide is pep-APOC1. Herein APOC1 refers to Apolipoprotein C-1.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:202. In some examples, the glycopeptide is pep-RET4.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:203.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:204.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:205.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:206.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:207.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:208.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:209.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:210.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:211.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:212.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:213.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:214.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:215.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:216.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:217.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:218.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:219.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:220.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:221.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:222.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:223.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:224.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:225.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:226.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:227.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:228.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:229.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:230.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:231.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:232.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:233.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:234.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:235.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:236.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:237.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:238.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:239.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:240.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:241.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:242.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:243.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:244.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:245.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:246.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:247.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:248.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:249.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:250.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:251.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:252.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:253.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:254.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:255.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:256.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:257.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:258.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:259.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:260.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:261.

In certain examples, set forth herein is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NO:262.

In some examples, including any of the foregoing, the glycopeptide is a combination of amino acid sequences selected from SEQ ID NOs:1-262.

In some examples, including any of the foregoing, set forth herein is one or more peptides, in which each peptide, individually in each instance, is a peptide consisting of an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, set forth herein is one or more peptides, in which each peptide, individually in each instance, is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, set forth herein is one or more peptides, in which each peptide, individually in each instance, is a peptide consisting of an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194, and combinations thereof.

In some examples, including any of the foregoing, set forth herein is one or more peptides, in which each peptide, individually in each instance, is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, and 194, and combinations thereof.

In some examples, including any of the foregoing, set forth herein is one or more peptides, in which each peptide, individually in each instance, is a peptide consisting of an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196, and combinations thereof.

In some examples, including any of the foregoing, set forth herein is one or more peptides, in which each peptide, individually in each instance, is a peptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190 and 196, and combinations thereof.

IV. METHODS OF USING BIOMARKERS

A. Methods for Detecting Glycopeptides

In some embodiments, set forth herein is a method for detecting one or more a multiple-reaction-monitoring (MRM) transition, comprising: obtaining a biological sample from a patient, wherein the biological sample comprises one or more glycopeptides; digesting and/or fragmenting a glycopeptide in the sample; and detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1-150. These transitions may include, in various examples, any one or more of the transitions in Tables 1-5. These transitions may include, in various examples, any one or more of the transitions in Tables 1-3. These transitions may include, in various examples, any one or more of the transitions in Table 1. These transitions may include, in various examples, any one or more of the transitions in Table 2. These transitions may include, in various examples, any one or more of the transitions in Table 3. These transitions may include, in various examples, any one or more of the transitions in Table 4. These transitions may include, in various examples, any one or more of the transitions in Table 5. These transitions may be indicative of glycopeptides.

In some examples, set forth herein is a method of detecting one or more glycopeptides, wherein each glycopeptide is individually in each instance selected from a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, set forth herein is a method of detecting one or more glycopeptides, wherein each glycopeptide is individually in each instance selected from a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, set forth herein is a method of detecting one or more glycopeptides, wherein each glycopeptide is individually in each instance selected from a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190, 196, and combinations thereof.

In some examples, set forth herein is a method of detecting one or more glycopeptides, wherein each glycopeptide is individually in each instance selected from a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 3, 7, 14, 15, 20, 24, 25, 27, 40, 52, 98, 99, 104, 116, 177, 190, 196, and combinations thereof.

In some examples, set forth herein is a method of detecting one or more glycopeptides, wherein each glycopeptide is individually in each instance selected from a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, 194, and combinations thereof.

In some examples, set forth herein is a method of detecting one or more glycopeptides, wherein each glycopeptide is individually in each instance selected from a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 12, 22, 28, 32, 35, 36, 38, 53, 65, 69, 99, 104, 115, 128, 146, 154, 177, 190, 194, and combinations thereof.

In some examples, set forth herein is a method of detecting one or more glycopeptides. In some examples, set forth herein is a method of detecting one or more glycopeptide fragments. In certain examples, the method includes detecting the glycopeptide group to which the glycopeptide, or fragment thereof, belongs. In some of these examples, the glycopeptide group is selected from Alpha-1-antitrypsin (AIAT), Alpha-1B-glycoprotein (AlBG), Leucine-richAlpha-2-glycoprotein (A2GL), Alpha-2-macroglobulin (A2MG), Alpha-1-antichymotrypsin (AACT), Afamin (AFAM), Alpha-1-acid glycoprotein 1 & 2 (AGP12), Alpha-1-acid glycoprotein 1 (AGP1), Alpha-1-acid glycoprotein 2 (AGP2), Apolipoprotein A-I (APOA1), Apolipoprotein C-III (APOC3), Apolipoprotein B-100 (APOB), Apolipoprotein D (APOD), Beta-2-glycoprotein-1 (APOH), Apolipoprotein M (APOM), Attractin (ATRN), Calpain-3 (CAN3), Ceruloplasmin (CERU), ComplementFactorH (CFAH), ComplementFactorI (CFAI), Clusterin (CLUS), ComplementC3 (CO3), ComplementC4-A&B (CO4A&CO4B), ComplementcomponentC6 (CO6), ComplementComponentC8AChain (CO8A), Coagulation factor XII (FA12), Alpha-2-HS-glycoprotein (FETUA), Haptoglobin (HPT), Histidine-rich Glycoprotein (HRG), Immunoglobulin heavy constant alpha 1&2 (IgA12), Immunoglobulin heavy constant alpha 2 (IgA2), Immunoglobulin heavy constant gamma 2 (IgG2), Immunoglobulin heavy constant mu (IgM), Inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1), Plasma Kallikrein (KLKB1), Kininogen-1 (KNG1), Serum paraoxonase/arylesterase 1 (PON1), Selenoprotein P (SEPP1), Prothrombin (THRB), Serotransferrin (TRFE), Transthyretin (TTR), Protein unc-13HomologA (UN13A), Vitronectin (VTNC), Zinc-alpha-2-glycoprotein (ZA2G), Insulin-like growth factor-II (IGF2), Apolipoprotein C-I (APOC1), and combinations thereof.

In some examples, including any of the foregoing, the method includes detecting a glycopeptide, a glycan on the glycopeptide and the glycosylation site residue where the glycan bonds to the glycopeptide. In certain examples, the method includes detecting a glycan residue. In some examples, the method includes detecting a glycosylation site on a glycopeptide. In some examples, this process is accomplished with mass spectroscopy used in tandem with liquid chromatography.

In some examples, including any of the foregoing, the method includes obtaining a biological sample from a patient. In some examples, the biological sample is synovial fluid, whole blood, blood serum, blood plasma, urine, sputum, tissue, saliva, tears, spinal fluid, tissue section(s) obtained by biopsy; cell(s) that are placed in or adapted to tissue culture; sweat, mucous, fecal material, gastric fluid, abdominal fluid, amniotic fluid, cyst fluid, peritoneal fluid, pancreatic juice, breast milk, lung lavage, marrow, gastric acid, bile, semen, pus, aqueous humour, transudate, or combinations of the foregoing. In certain examples, the biological sample is selected from the group consisting of blood, plasma, saliva, mucus, urine, stool, tissue, sweat, tears, hair, or a combination thereof. In some of these examples, the biological sample is a blood sample. In some of these examples, the biological sample is a plasma sample. In some of these examples, the biological sample is a saliva sample. In some of these examples, the biological sample is a mucus sample. In some of these examples, the biological sample is a urine sample. In some of these examples, the biological sample is a stool sample. In some of these examples, the biological sample is a sweat sample. In some of these examples, the biological sample is a tear sample. In some of these examples, the biological sample is a hair sample.

In some examples, including any of the foregoing, the method also includes digesting and/or fragmenting a glycopeptide in the sample. In certain examples, the method includes digesting a glycopeptide in the sample. In certain examples, the method includes fragmenting a glycopeptide in the sample. In some examples, the digested or fragmented glycopeptide is analyzed using mass spectroscopy. In some examples, the glycopeptide is digested or fragmented in the solution phase using digestive enzymes. In some examples, the glycopeptide is digested or fragmented in the gaseous phase inside a mass spectrometer, or the instrumentation associated with a mass spectrometer. In some examples, the mass spectroscopy results are analyzed using machine learning algorithms. In some examples, the mass spectroscopy results are the quantification of the glycopeptides, glycans, peptides, and fragments thereof. In some examples, this quantification is used as an input in a trained model to generate an output probability. The output probability is a probability of being within a given category or classification, e.g., the classification of having ovarian cancer or the classification of not having ovarian cancer. In some other examples, the output probability is a probability of being within a given category or classification, e.g., the classification of having cancer or the classification of not having cancer. In some other examples, the output probability is a probability of being within a given category or classification, e.g., the classification of having an autoimmune disease or the classification of not having an autoimmune disease. In some other examples, the output probability is a probability of being within a given category or classification, e.g., the classification of having fibrosis or the classification of not having an fibrosis.

In some examples, including any of the foregoing, the method includes introducing the sample, or a portion thereof, into a mass spectrometer.

In some examples, including any of the foregoing, the method includes fragmenting a glycopeptide in the sample after introducing the sample, or a portion thereof, into the mass spectrometer.

In some examples, including any of the foregoing, the mass spectroscopy is performed using multiple reaction monitoring (MRM) mode. In some examples, the mass spectroscopy is performed using QTOF MS in data-dependent acquisition. In some examples, the mass spectroscopy is performed using or MS-only mode. In some examples, an immunoassay is used in combination with mass spectroscopy. In some examples, the immunoassay measures CA-125 and HE4.

In some examples, including any of the foregoing, the method includes digesting a glycopeptide in the sample occurs before introducing the sample, or a portion thereof, into the mass spectrometer.

In some examples, including any of the foregoing, the method includes fragmenting a glycopeptide in the sample to provide a glycopeptide ion, a peptide ion, a glycan ion, a glycan adduct ion, or a glycan fragment ion.

In some examples, including any of the foregoing, the method includes digesting and/or fragmenting a glycopeptide in the sample to provide a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof. In some examples, the methods provides a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes digesting and/or fragmenting a glycopeptide in the sample to provide a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof. In some examples, the methods provides a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes fragmenting a glycopeptide in the sample to provide a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof. In some examples, the methods provides a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes fragmenting a glycopeptide in the sample to provide a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof. In some examples, the methods provides a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1-150. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide or glycan residue, wherein the glycopeptide consists essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262 and combinations thereof. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262 and combinations thereof. In some examples, the method includes detecting more than one MRM transition selected from a combination of members from the group consisting of transitions 1-150. In some examples, the method includes detecting more than one MRM transition indicative of a combination of glycopeptides having amino acid sequences selected from a combination of SEQ ID NOs: 1-262.

In some examples, the method includes detecting a MRM transition indicative of a glycopeptide or glycan residue, wherein the glycopeptide consists essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof. In some examples, the method includes detecting more than one MRM transition indicative of a combination of glycopeptides having amino acid sequences selected from a combination of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194.

In some examples, including any of the foregoing, the method includes performing mass spectroscopy on the biological sample using multiple-reaction-monitoring mass spectroscopy (MRM-MS).

In some examples, including any of the foregoing, the method includes digesting a glycopeptide in the sample to provide a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof. In certain examples, the biological sample is combined with chemical reagents. In certain examples, the biological sample is combined with enzymes. In some examples, the enzymes are lipases. In some examples, the enzymes are proteases. In some examples, the enzymes are serine proteases. In some of these examples, the enzyme is selected from the group consisting of trypsin, chymotrypsin, thrombin, elastase, and subtilisin. In some of these examples, the enzyme is trypsin. In some examples, the methods includes contacting at least two proteases with a glycopeptide in a sample. In some examples, the at least two proteases are selected from the group consisting of serine protease, threonine protease, cysteine protease, aspartate protease. In some examples, the at least two proteases are selected from the group consisting of trypsin, chymotrypsin, endoproteinase, Asp-N, Arg-C, Glu-C, Lys-C, pepsin, thermolysin, elastase, papain, proteinase K, subtilisin, clostripain, and carboxypeptidase protease, glutamic acid protease, metalloprotease, and asparagine peptide lyase.

In some examples, including any of the foregoing, the method includes detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1-150. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide or glycan residue, wherein the glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262 and combinations thereof. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide or glycan residue, wherein the glycopeptide consists essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262 and combinations thereof. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262 and combinations thereof. In some examples, the method includes detecting more than one MRM transition selected from a combination of members from the group consisting of transitions 1-262. In some examples, the method includes detecting more than one MRM transition indicative of a combination of glycopeptides having amino acid sequences selected from a combination of SEQ ID NOs: 1-262.

In some examples, the method includes detecting a MRM transition indicative of a glycopeptide or glycan residue, wherein the glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194 and combinations thereof. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide or glycan residue, wherein the glycopeptide consists essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194 and combinations thereof. In some examples, the method includes detecting a MRM transition indicative of a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof. In some examples, the method includes detecting more than one MRM transition selected from a combination of members from the group consisting of transitions 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194. In some examples, the method includes detecting more than one MRM transition indicative of a combination of glycopeptides having amino acid sequences selected from a combination of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, the method includes performing mass spectroscopy on the biological sample using multiple-reaction-monitoring mass spectroscopy (MRM-MS).

In some examples, including any of the foregoing, the method includes digesting a glycopeptide in the sample to provide a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof. In certain examples, the biological sample is contacted with one or more chemical reagents. In certain examples, the biological sample is contacted with one or more enzymes. In some examples, the enzymes are lipases. In some examples, the enzymes are proteases. In some examples, the enzymes are serine proteases. In some of these examples, the enzyme is selected from the group consisting of trypsin, chymotrypsin, thrombin, elastase, and subtilisin. In some of these examples, the enzyme is trypsin. In some examples, the methods includes contacting at least two proteases with a glycopeptide in a sample. In some examples, the at least two proteases are selected from the group consisting of serine protease, threonine protease, cysteine protease, aspartate protease. In some examples, the at least two proteases are selected from the group consisting of trypsin, chymotrypsin, endoproteinase, Asp-N, Arg-C, Glu-C, Lys-C, pepsin, thermolysin, elastase, papain, proteinase K, subtilisin, clostripain, and carboxypeptidase protease, glutamic acid protease, metalloprotease, and asparagine peptide lyase.

In some examples, including any of the foregoing, the MRM transition is selected from the transitions, or any combinations thereof, in any one of Tables 1, 2 or 3.

In some examples, including any of the foregoing, the method includes conducting tandem liquid chromatography-mass spectroscopy on the biological sample.

In some examples, including any of the foregoing, the method includes multiple-reaction-monitoring mass spectroscopy (MRM-MS) mass spectroscopy on the biological sample.

In some examples, including any of the foregoing, the method includes detecting a MRM transition using a triple quadrupole (QQQ) and/or a quadrupole time-of-flight (qTOF) mass spectrometer. In certain examples, the method includes detecting a MRM transition using a QQQ mass spectrometer. In certain other examples, the method includes detecting using a qTOF mass spectrometer. In some examples, a suitable instrument for use with the instant methods is an Agilent 6495B Triple Quadrupole LC/MS, which can be found at www.agilent.com/en/products/mass-spectrometry/lc-ms-instruments/triple-quadrupole-lc-ms/6495b-triple-quadrupole-lc-ms. In certain other examples, the method includes detecting using a QQQ mass spectrometer. In some examples, a suitable instrument for use with the instant methods is an Agilent 6545 LC/Q-TOF, which can be found at https://www.agilent.com/en/products/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-instruments!quadrupole-time-of-flight-lc-ms/6545-q-tof-lc-ms.

In some examples, including any of the foregoing, the method includes detecting more than one MRM transition using a QQQ and/or qTOF mass spectrometer. In certain examples, the method includes detecting more than one MRM transition using a QQQ mass spectrometer. In certain examples, the method includes detecting more than one MRM transition using a qTOF mass spectrometer. In certain examples, the method includes detecting more than one MRM transition using a QQQ mass spectrometer.

In some examples, including any of the foregoing, the methods herein include quantifying one or more glycomic parameters of the one or more biological samples comprises employing a coupled chromatography procedure. In some examples, these glycomic parameters include the identification of a glycopeptide group, identification of glycans on the glycopeptide, identification of a glycosylation site, identification of part of an amino acid sequence which the glycopeptide includes. In some examples, the coupled chromatography procedure comprises: performing or effectuating a liquid chromatography-mass spectrometry (LC-MS) operation. In some examples, the coupled chromatography procedure comprises: performing or effectuating a multiple reaction monitoring mass spectrometry (MRM-MS) operation. In some examples, the methods herein include a coupled chromatography procedure which comprises: performing or effectuating a liquid chromatography-mass spectrometry (LC-MS) operation; and effectuating a multiple reaction monitoring mass spectrometry (MRM-MS) operation. In some examples, the methods include training a machine learning algorithm using one or more glycomic parameters of the one or more biological samples obtained by one or more of a triple quadrupole (QQQ) mass spectrometry operation and/or a quadrupole time-of-flight (qTOF) mass spectrometry operation. In some examples, the methods include training a machine learning algorithm using one or more glycomic parameters of the one or more biological samples obtained a triple quadrupole (QQQ) mass spectrometry operation. In some examples, the methods include training a machine learning algorithm using one or more glycomic parameters of the one or more biological samples obtained by a quadrupole time-of-flight (qTOF) mass spectrometry operation. In some examples, the methods include quantifying one or more glycomic parameters of the one or more biological samples comprises employing one or more of a triple quadrupole (QQQ) mass spectrometry operation and a quadrupole time-of-flight (qTOF) mass spectrometry operation. In some examples, machine learning algorithms are used to quantify these glycomic parameters. In some examples, including any of the foregoing, the mass spectroscopy is performed using multiple reaction monitoring (MRM) mode. In some examples, the mass spectroscopy is performed using QTOF MS in data-dependent acquisition. In some examples, the mass spectroscopy is performed using or MS-only mode. In some examples, an immunoassay (e.g., ELISA) is used in combination with mass spectroscopy. In some examples, the immunoassay measures CA-125 and HE4 proteins.

In some examples, including any of the foregoing, the glycopeptide or combination thereof consists of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 and combinations thereof.

In some examples, including any of the foregoing, the glycopeptide or combination thereof consists of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the glycopeptide or combination thereof consists essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes digesting and/or fragmenting a glycopeptide in the sample to provide a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes digesting and/or fragmenting a glycopeptide in the sample to provide a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes detecting one or more MRM transitions indicative of glycans selected from the group consisting of glycan 3200, 3210, 3300, 3310, 3320, 3400, 3410, 3420, 3500, 3510, 3520, 3600, 3610, 3620, 3630, 3700, 3710, 3720, 3730, 3740, 4200, 4210, 4300, 4301, 4310, 4311, 4320, 4400, 4401, 4410, 4411, 4420, 4421, 4430, 4431, 4500, 4501, 4510, 4511, 4520, 4521, 4530, 4531, 4540, 4541, 4600, 4601, 4610, 4611, 4620, 4621, 4630, 4631, 4641, 4650, 4700, 4701, 4710, 4711, 4720, 4730, 5200, 5210, 5300, 5301, 5310, 5311, 5320, 5400, 5401, 5402, 5410, 5411, 5412, 5420, 5421, 5430, 5431, 5432, 5500, 5501, 5502, 5510, 5511, 5512, 5520, 5521, 5522, 5530, 5531, 5541, 5600, 5601, 5602, 5610, 5611, 5612, 5620, 5621, 5631, 5650, 5700, 5701, 5702, 5710, 5711, 5712, 5720, 5721, 5730, 5731, 6200, 6210, 6300, 6301, 6310, 6311, 6320, 6400, 6401, 6402, 6410, 6411, 6412, 6420, 6421, 6432, 6500, 6501, 6502, 6503, 6510, 6511, 6512, 6513, 6520, 6521, 6522, 6530, 6531, 6532, 6540, 6541, 6600, 6601, 6602, 6603, 6610, 6611, 6612, 6613, 6620, 6621, 6622, 6623, 6630, 6631, 6632, 6640, 6641, 6642, 6652, 6700, 6701, 6711, 6721, 6703, 6713, 6710, 6711, 6712, 6713, 6720, 6721, 6730, 6731, 6740, 7200, 7210, 7400, 7401, 7410, 7411, 7412, 7420, 7421, 7430, 7431, 7432, 7500, 7501, 7510, 7511, 7512, 7600, 7601, 7602, 7603, 7604, 7610, 7611, 7612, 7613, 7614, 7620, 7621, 7622, 7623, 7632, 7640, 7700, 7701, 7702, 7703, 7710, 7711, 7712, 7713, 7714, 7720, 7721, 7722, 7730, 7731, 7732, 7740, 7741, 7751, 8200, 9200, 9210, 10200, 11200, 12200, and combinations thereof. Herein, these glycans are illustrated in FIGS. 1-14.

In some examples, including any of the foregoing, the method includes quantifying a glycan.

In some examples, including any of the foregoing, the method includes quantifying a first glycan and quantifying a second glycan; and further comprising comparing the quantification of the first glycan with the quantification of the second glycan.

In some examples, including any of the foregoing, the method includes associating the detected glycan with a peptide residue site, whence the glycan was bonded.

In some examples, including any of the foregoing, the method includes generating a glycosylation profile of the sample.

In some examples, including any of the foregoing, the method includes spatially profiling glycans on a tissue section associated with the sample. In some examples, including any of the foregoing, the method includes spatially profiling glycopeptides on a tissue section associated with the sample. In some examples, the method includes matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectroscopy in combination with the methods herein.

In some examples, including any of the foregoing, the method includes quantifying relative abundance of a glycan and/or a peptide.

In some examples, including any of the foregoing, the method includes normalizing the amount of a glycopeptide by quantifying a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof and comparing that quantification to the amount of another chemical species. In some examples, the method includes normalizing the amount of a peptide by quantifying a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof, and comparing that quantification to the amount of another glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262. In some examples, the method includes normalizing the amount of a peptide by quantifying a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof, and comparing that quantification to the amount of another glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

B. Methods for Classifying Samples Comprising Glycopeptides

In another embodiment, set forth herein a method for identifying a classification for a sample, the method comprising: quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample wherein the glycopeptides each, individually in each instance, comprises a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of, or consisting essentially of, SEQ ID NOs:1-262, and combinations thereof, and inputting the quantification into a trained model to generate a output probability; determining if the output probability is above or below a threshold for a classification; and identifying a classification for the sample based on whether the output probability is above or below a threshold for a classification.

In another embodiment, set forth herein a method for identifying a classification for a sample, the method comprising: quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample wherein the glycopeptides each, individually in each instance, comprises a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of, or consisting essentially of, SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof; and inputting the quantification into a trained model to generate a output probability; determining if the output probability is above or below a threshold for a classification; and identifying a classification for the sample based on whether the output probability is above or below a threshold for a classification.

In some examples, set forth herein is a method for classifying glycopeptides, comprising: obtaining a biological sample from a patient; digesting and/or fragmenting a glycopeptide in the sample; detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1-150; and classifying the glycopeptides based on the MRM transitions detected. In some examples, a machine learning algorithm is used to train a model using the analyzed the MRM transitions as inputs. In some examples, a machine learning algorithm is trained using the MRM transitions as a training data set. In some examples, the methods herein include identifying glycopeptides, peptides, and glycans based on their mass spectroscopy relative abundance. In some examples, a machine learning algorithm or algorithms select and/or identify peaks in a mass spectroscopy spectrum.

In some examples, set forth herein is a method for classifying glycopeptides, comprising: obtaining a biological sample from an individual; digesting and/or fragmenting a glycopeptide in the sample; detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1-150; and classifying the glycopeptides based on the MRM transitions detected. In some examples, a machine learning algorithm is used to train a model using the analyzed the MRM transitions as inputs. In some examples, a machine learning algorithm is trained using the MRM transitions as a training data set. In some examples, the methods herein include identifying glycopeptides, peptides, and glycans based on their mass spectroscopy relative abundance. In some examples, a machine learning algorithm or algorithms select and/or identify peaks in a mass spectroscopy spectrum.

In some examples, set forth herein is a method of training a machine learning algorithm using MRM transitions as an input data set. In some examples, set forth herein is a method for identifying a classification for a sample, the method comprising quantifying by mass spectroscopy (MS) a glycopeptide in a sample wherein the glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof; and identifying a classification based on the quantification. In some examples, the quantifying includes determining the presence or absence of a glycopeptide, or combination of glycopeptides, in a sample. In some examples, the quantifying includes determining the relative abundance of a glycopeptide, or combination of glycopeptides, in a sample.

In some examples, set forth herein is a method of training a machine learning algorithm using MRM transitions as an input data set. In some examples, set forth herein is a method for identifying a classification for a sample, the method comprising quantifying by mass spectroscopy (MS) a glycopeptide in a sample wherein the glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262, and combinations thereof; and identifying a classification based on the quantification. In some examples, the quantifying includes determining the presence or absence of a glycopeptide, or combination of glycopeptides, in a sample. In some examples, the quantifying includes determining the relative abundance of a glycopeptide, or combination of glycopeptides, in a sample.

In some examples, including any of the foregoing, the sample is a biological sample from a patient having a disease or condition.

In some examples, including any of the foregoing, the patient has ovarian cancer.

In some examples, including any of the foregoing, the patient has cancer.

In some examples, including any of the foregoing, the patient has fibrosis.

In some examples, including any of the foregoing, the patient has an autoimmune disease.

In some examples, including any of the foregoing, the disease or condition is ovarian cancer.

In some examples, including any of the foregoing, the MS is MRM-MS with a QQQ and/or qTOF mass spectrometer.

In some examples, including any of the foregoing, the machine learning algorithm is selected from the group consisting of a deep learning algorithm, a neural network algorithm, an artificial neural network algorithm, a supervised machine learning algorithm, a linear discriminant analysis algorithm, a quadratic discriminant analysis algorithm, a support vector machine algorithm, a linear basis function kernel support vector algorithm, a radial basis function kernel support vector algorithm, a random forest algorithm, a genetic algorithm, a nearest neighbor algorithm, k-nearest neighbors, a naive Bayes classifier algorithm, a logistic regression algorithm, or a combination thereof. In certain examples, the machine learning algorithm is lasso regression.

In some examples, including any of the foregoing, the method includes classifying a sample as within, or embraced by, a disease classification or a disease severity classification.

In some examples, including any of the foregoing, the classification is identified with 80% confidence, 85% confidence, 90% confidence, 95% confidence, 99% confidence, or 99.9999% confidence.

In some examples, including any of the foregoing, the method includes quantifying by MS the glycopeptide in a sample at a first time point; quantifying by MS the glycopeptide in a sample at a second time point; and comparing the quantification at the first time point with the quantification at the second time point.

In some examples, including any of the foregoing, the method includes quantifying by MS a different glycopeptide in a sample at a third time point; quantifying by MS the different glycopeptide in a sample at a fourth time point; and comparing the quantification at the fourth time point with the quantification at the third time point.

In some examples, including any of the foregoing, the method includes monitoring the health status of a patient.

In some examples, including any of the foregoing, monitoring the health status of a patient includes monitoring the onset and progression of disease in a patient with risk factors such as genetic mutations, as well as detecting cancer recurrence.

In some examples, including any of the foregoing, the method includes quantifying by MS a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In some examples, including any of the foregoing, the method includes quantifying by MS a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In some examples, including any of the foregoing, the method includes quantifying by MS one or more glycans selected from the group consisting of glycan 3200, 3210, 3300, 3310, 3320, 3400, 3410, 3420, 3500, 3510, 3520, 3600, 3610, 3620, 3630, 3700, 3710, 3720, 3730, 3740, 4200, 4210, 4300, 4301, 4310, 4311, 4320, 4400, 4401, 4410, 4411, 4420, 4421, 4430, 4431, 4500, 4501, 4510, 4511, 4520, 4521, 4530, 4531, 4540, 4541, 4600, 4601, 4610, 4611, 4620, 4621, 4630, 4631, 4641, 4650, 4700, 4701, 4710, 4711, 4720, 4730, 5200, 5210, 5300, 5301, 5310, 5311, 5320, 5400, 5401, 5402, 5410, 5411, 5412, 5420, 5421, 5430, 5431, 5432, 5500, 5501, 5502, 5510, 5511, 5512, 5520, 5521, 5522, 5530, 5531, 5541, 5600, 5601, 5602, 5610, 5611, 5612, 5620, 5621, 5631, 5650, 5700, 5701, 5702, 5710, 5711, 5712, 5720, 5721, 5730, 5731, 6200, 6210, 6300, 6301, 6310, 6311, 6320, 6400, 6401, 6402, 6410, 6411, 6412, 6420, 6421, 6432, 6500, 6501, 6502, 6503, 6510, 6511, 6512, 6513, 6520, 6521, 6522, 6530, 6531, 6532, 6540, 6541, 6600, 6601, 6602, 6603, 6610, 6611, 6612, 6613, 6620, 6621, 6622, 6623, 6630, 6631, 6632, 6640, 6641, 6642, 6652, 6700, 6701, 6711, 6721, 6703, 6713, 6710, 6711, 6712, 6713, 6720, 6721, 6730, 6731, 6740, 7200, 7210, 7400, 7401, 7410, 7411, 7412, 7420, 7421, 7430, 7431, 7432, 7500, 7501, 7510, 7511, 7512, 7600, 7601, 7602, 7603, 7604, 7610, 7611, 7612, 7613, 7614, 7620, 7621, 7622, 7623, 7632, 7640, 7700, 7701, 7702, 7703, 7710, 7711, 7712, 7713, 7714, 7720, 7721, 7722, 7730, 7731, 7732, 7740, 7741, 7751, 8200, 9200, 9210, 10200, 11200, 12200, and combinations thereof. Herein, these glycans are illustrated in FIGS. 1-14.

In some examples, including any of the foregoing, the method includes diagnosing a patient with a disease or condition based on the quantification.

In some examples, including any of the foregoing, the method includes diagnosing the patient as having ovarian cancer based on the quantification.

In some examples, including any of the foregoing, the method includes treating the patient with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, a neoadjuvant therapy, surgery, and combinations thereof.

In some examples, including any of the foregoing, the method includes diagnosing an individual with a disease or condition based on the quantification.

In some examples, including any of the foregoing, the method includes diagnosing the individual as having an aging condition.

In some examples, including any of the foregoing, the method includes treating the individual with a therapeutically effective amount of an anti-aging agent. In some examples, the anti-aging agent is selected from hormone therapy. In some examples, the anti-aging agent is testosterone or a testosterone supplement or derivative. In some examples, the anti-aging agent is estrogen or an estrogen supplement or derivative.

C. Methods of Treatment

In some examples, set forth herein is a method for treating a patient having a disease or condition, comprising measuring by mass spectroscopy a glycopeptide in a sample from the patient. In some examples, the patient is a human. In certain examples, the patient is a female. In certain other examples, the patient is a female with ovarian cancer. In certain examples, the patient is a female with ovarian cancer at Stage 1. In certain examples, the patient is a female with ovarian cancer at Stage 2. In certain examples, the patient is a female with ovarian cancer at Stage 3. In certain examples, the patient is a female with ovarian cancer at Stage 4. In some examples, the female has an age equal or between 10-20 years. In some examples, the female has an age equal or between 20-30 years. In some examples, the female has an age equal or between 30-40 years. In some examples, the female has an age equal or between 40-50 years. In some examples, the female has an age equal or between 50-60 years. In some examples, the female has an age equal or between 60-70 years. In some examples, the female has an age equal or between 70-80 years. In some examples, the female has an age equal or between 80-90 years. In some examples, the female has an age equal or between 90-100 years.

In another embodiment, set forth herein is a method for treating a patient having ovarian cancer; the method comprising: obtaining a biological sample from the patient; digesting and/or fragmenting one or more glycopeptides in the sample; and detecting and quantifying one or more multiple-reaction-monitoring (MRM) transitions selected from the group consisting of transitions 1-150; inputting the quantification into a trained model to generate an output probability; determining if the output probability is above or below a threshold for a classification; and classifying the patient based on whether the output probability is above or below a threshold for a classification, wherein the classification is selected from the group consisting of (A) a patient in need of a chemotherapeutic agent; (B) a patient in need of a immunotherapeutic agent; (C) a patient in need of hormone therapy; (D) a patient in need of a targeted therapeutic agent; (E) a patient in need of surgery; (F) a patient in need of neoadjuvant therapy; (G) a patient in need of chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof, before surgery; (H) a patient in need of chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof, after surgery; (I) or a combination thereof; administering a therapeutically effective amount of a therapeutic agent to the patient: wherein the therapeutic agent is selected from chemotherapy if classification A or I is determined; wherein the therapeutic agent is selected from immunotherapy if classification B or I is determined; or wherein the therapeutic agent is selected from hormone therapy if classification C or I is determined; or wherein the therapeutic agent is selected from targeted therapy if classification D or I is determined wherein the therapeutic agent is selected from neoadjuvant therapy if classification F or I is determined; wherein the therapeutic agent is selected from chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof if classification G or I is determined; and wherein the therapeutic agent is selected from chemotherapeutic agent, immunotherapeutic agent, hormone therapy, targeted therapeutic agent, neoadjuvant therapy, or a combination thereof if classification H or I is determined.

In some examples, the machine learning is used to identify MS peaks associated with MRM transitions. In some examples, the MRM transitions are analyzed using machine learning. In some examples, the machine learning is used to train a model based on the quantification of the amount of glycopeptides associated with an MRM transition(s). In some examples, the MRM transitions are analyzed with a trained machine learning algorithm. In some of these examples, the trained machine learning algorithm was trained using MRM transitions observed by analyzing samples from patients known to have ovarian cancer.

In some examples, the patient is treated with a therapeutic agent selected from targeted therapy. In some examples, the methods herein include administering a therapeutically effective amount of a (poly(ADP)-ribose polymerase) (PARP) inhibitor if combination D is detected. In some examples, the therapeutic agent is selected from Olaparib (Lynparza), Rucaparib (Rubraca), and Niraparib (Zejula).

In some examples, the patient is an adult with platinum-sensitive relapsed high-grade epithelial ovarian, fallopian tube, or primary peritoneal cancer.

In some examples, the therapeutic agent is administered at 150 mg, 250 mg, 300 mg, 350 mg, and 600 mg doses. In some examples, the therapeutic agent is administered twice daily.

Chemotherapeutic agents include, but are not limited to, platinum-based drug such as carboplatin (Paraplatin) or cisplatin with a taxane such as paclitaxel (Taxol) or docetaxel (Taxotere). Paraplatin may be administered at 10 mg/mL injectable concentrations (in vials of 50, 150, 450, and 600 mg). For advanced ovarian carcinoma a single agent dose of 360 mg/m²IV for 4 weeks may be administered. Paraplatin may be administered in combination=as 300 mg/m²IV (plus cyclophosphamide 600 mg/m²IV) q4Weeks. Taxol may be administered at 175 mg/m²IV over 3 hours q3Weeks (follow with cisplatin). Taxol may be administered at 135 mg/m²IV over 24 hours q3Weeks (follow with cisplatin). Taxol may be administered at 135-175 mg/m²IV over 3 hours q3Weeks.

Immunotherapeutic agents include, but are not limited to, Zejula (Niraparib). Niraparib may be administered at 300 mg PO qDay.

Hormone therapeutic agents include, but are not limited to, Luteinizing-hormone-releasing hormone (LHRH) agonists, Tamoxifen, and Aromatase inhibitors.

Targeted therapeutic agents include, but are not limited to, PARP inhibitors.

In some examples, including any of the foregoing, the method includes conducting multiple-reaction-monitoring mass spectroscopy (MRM-MS) on the biological sample.

In some examples, including any of the foregoing, the mass spectroscopy is performed using multiple reaction monitoring (MRM) mode. In some examples, the mass spectroscopy is performed using QTOF MS in data-dependent acquisition. In some examples, the mass spectroscopy is performed using or MS-only mode. In some examples, an immunoassay (e.g., ELISA) is used in combination with mass spectroscopy. In some examples, the immunoassay measures CA-125 and HE4.

In some examples, including any of the foregoing, the method includes quantifying one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes quantifying one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method includes training a machine learning algorithm to identify a classification based on the quantifying step.

In some examples, including any of the foregoing, the method includes using a machine learning algorithm to identify a classification based on the quantifying step.

D. Methods for Diagnosing Patients

In some examples, set forth herein is a method for diagnosing a patient having a disease or condition, comprising measuring by mass spectroscopy a glycopeptide in a sample from the patient.

In another embodiment, set forth herein is a method for diagnosing a patient having ovarian cancer; the method comprising: inputting the quantification of detected glycopeptides or MRM transitions into a trained model to generate an output probability, determining if the output probability is above or below a threshold for a classification; and identifying a diagnostic classification for the patient based on whether the output probability is above or below a threshold for a classification; and diagnosing the patient as having ovarian cancer based on the diagnostic classification. In some examples, the method includes obtaining a biological sample from the patient; performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect and quantify one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262; or to detect and quantify one or more MRM transitions selected from transitions 1-150.

In some examples, set forth herein is a method for diagnosing a patient having ovarian cancer; the method comprising: obtaining a biological sample from the patient; performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect one or more glycopeptides consisting or, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262; or to detect one or more MRM transitions selected from transitions 1-150; analyzing the detected glycopeptides or the MRM transitions to identify a diagnostic classification; and diagnosing the patient as having ovarian cancer based on the diagnostic classification. In some examples, the method includes obtaining a biological sample from the patient; and performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect one or more glycopeptides consisting or, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-76; or to detect one or more MRM transitions selected from transitions 1-76.

In some examples, set forth herein is a method for diagnosing, monitoring, or classifying aging in an individual; the method comprising: obtaining a biological sample from the patient; performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect one or more glycopeptides consisting or, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262; or to detect one or more MRM transitions selected from transitions 1-150; analyzing the detected glycopeptides or the MRM transitions to identify a diagnostic classification; and diagnosing, monitoring, or classifying the individual as having an aging classification based on the diagnostic classification.

In another embodiment, set forth herein is a method for diagnosing a patient having ovarian cancer; the method comprising: obtaining a biological sample from the patient; performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect and quantify one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194; inputting the quantification of the detected glycopeptides or the MRM transitions into a trained model to generate an output probability, determining if the output probability is above or below a threshold for a classification; and identifying a diagnostic classification for the patient based on whether the output probability is above or below a threshold for a classification; and diagnosing the patient as having ovarian cancer based on the diagnostic classification.

In some examples, set forth herein is a method for diagnosing, monitoring, or classifying aging in an individual; the method comprising: obtaining a biological sample from the patient; performing mass spectroscopy of the biological sample using MRM-MS with a QQQ and/or qTOF spectrometer to detect one or more glycopeptides consisting or, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194; analyzing the detected glycopeptides or the MRM transitions to identify a diagnostic classification; and diagnosing, monitoring, or classifying the individual as having an aging classification based on the diagnostic classification.

E. Diseases and Conditions

Set forth herein are biomarkers for diagnosing a variety of diseases and conditions.

In some examples, the diseases and conditions include cancer. In some examples, the diseases and conditions are not limited to cancer.

In some examples, the diseases and conditions include fibrosis. In some examples, the diseases and conditions are not limited to fibrosis.

In some examples, the diseases and conditions include an autoimmune disease. In some examples, the diseases and conditions are not limited to an autoimmune disease.

In some examples, the diseases and conditions include ovarian cancer. In some examples, the diseases and conditions are not limited to ovarian cancer.

In some examples, the condition is aging. In some examples, the “patient” described herein is equivalently described as an “individual.” For example, in some methods herein, set forth are biomarkers for monitoring or diagnosing aging or aging conditions in an individual. In some of these examples, the individual is not necessarily a patient who has a medical condition in need of therapy. In some examples, the individual is a male. In some examples, the individual is a female. In some examples, the individual is a male mammal. In some examples, the individual is a female mammal. In some examples, the individual is a male human. In some examples, the individual is a female human.

In some examples, the individual is 1 year old. In some examples, the individual is 2 years old. In some examples, the individual is 3 years old. In some examples, the individual is 4 years old. In some examples, the individual is 5 years old. In some examples, the individual is 6 years old. In some examples, the individual is 7 years old. In some examples, the individual is 8 years old. In some examples, the individual is 9 years old. In some examples, the individual is 10 years old. In some examples, the individual is 11 years old. In some examples, the individual is 12 years old. In some examples, the individual is 13 years old. In some examples, the individual is 14 years old. In some examples, the individual is 15 years old. In some examples, the individual is 16 years old. In some examples, the individual is 17 years old. In some examples, the individual is 18 years old. In some examples, the individual is 19 years old. In some examples, the individual is 20 years old. In some examples, the individual is 21 years old. In some examples, the individual is 22 years old. In some examples, the individual is 23 years old. In some examples, the individual is 24 years old. In some examples, the individual is 25 years old. In some examples, the individual is 26 years old. In some examples, the individual is 27 years old. In some examples, the individual is 28 years old. In some examples, the individual is 29 years old. In some examples, the individual is 30 years old. In some examples, the individual is 31 years old. In some examples, the individual is 32 years old. In some examples, the individual is 33 years old. In some examples, the individual is 34 years old. In some examples, the individual is 35 years old. In some examples, the individual is 36 years old. In some examples, the individual is 37 years old. In some examples, the individual is 38 years old. In some examples, the individual is 39 years old. In some examples, the individual is 40 years old. In some examples, the individual is 41 years old. In some examples, the individual is 42 years old. In some examples, the individual is 43 years old. In some examples, the individual is 44 years old. In some examples, the individual is 45 years old. In some examples, the individual is 46 years old. In some examples, the individual is 47 years old. In some examples, the individual is 48 years old. In some examples, the individual is 49 years old. In some examples, the individual is 50 years old. In some examples, the individual is 51 years old. In some examples, the individual is 52 years old. In some examples, the individual is 53 years old. In some examples, the individual is 54 years old. In some examples, the individual is 55 years old. In some examples, the individual is 56 years old. In some examples, the individual is 57 years old. In some examples, the individual is 58 years old. In some examples, the individual is 59 years old. In some examples, the individual is 60 years old. In some examples, the individual is 61 years old. In some examples, the individual is 62 years old. In some examples, the individual is 63 years old. In some examples, the individual is 64 years old. In some examples, the individual is 65 years old. In some examples, the individual is 66 years old. In some examples, the individual is 67 years old. In some examples, the individual is 68 years old. In some examples, the individual is 69 years old. In some examples, the individual is 70 years old. In some examples, the individual is 71 years old. In some examples, the individual is 72 years old. In some examples, the individual is 73 years old. In some examples, the individual is 74 years old. In some examples, the individual is 75 years old. In some examples, the individual is 76 years old. In some examples, the individual is 77 years old. In some examples, the individual is 78 years old. In some examples, the individual is 79 years old. In some examples, the individual is 80 years old. In some examples, the individual is 81 years old. In some examples, the individual is 82 years old. In some examples, the individual is 83 years old. In some examples, the individual is 84 years old. In some examples, the individual is 85 years old. In some examples, the individual is 86 years old. In some examples, the individual is 87 years old. In some examples, the individual is 88 years old. In some examples, the individual is 89 years old. In some examples, the individual is 90 years old. In some examples, the individual is 91 years old. In some examples, the individual is 92 years old. In some examples, the individual is 93 years old. In some examples, the individual is 94 years old. In some examples, the individual is 95 years old. In some examples, the individual is 96 years old. In some examples, the individual is 97 years old. In some examples, the individual is 98 years old. In some examples, the individual is 99 years old. In some examples, the individual is 100 years old. In some examples, the individual is more than 100 years old.

V. MACHINE LEARNING

In some examples, including any of the foregoing, the methods herein include quantifying one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 using mass spectroscopy and/or liquid chromatography. In some examples, the methods includes quantifying one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof using mass spectroscopy and/or liquid chromatography. In some examples, the quantification results are used as inputs in a trained model. In some examples, the quantification results are classified or categorized with a diagnostic algorithm based on the absolute amount, relative amount, and/or type of each glycan or glycopeptide quantified in the test sample, wherein the diagnostic algorithm is trained on corresponding values for each marker obtained from a population of individuals having known diseases or conditions. In some examples, the disease or condition is ovarian cancer.

In some examples, including any of the foregoing, set forth herein is a method for training a machine learning algorithm, comprising: providing a first data set of MRM transition signals indicative of a sample comprising a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-262; providing a second data set of MRM transition signals indicative of a control sample; and comparing the first data set with the second data set using a machine learning algorithm. In some examples, the methods include providing a first data set of MRM transition signals indicative of a sample comprising a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof.

In some examples, including any of the foregoing, the method herein include using a sample comprising a glycopeptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof is a sample from a patient having ovarian cancer.

In some examples, including any of the foregoing, the method herein include using a sample comprising a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof is a sample from a patient having ovarian cancer.

In some examples, including any of the foregoing, the method herein include using a control sample, wherein the control sample is a sample from a patient not having ovarian cancer.

In some examples, including any of the foregoing, the method herein include using a control sample, which is a pooled sample from one or more patients not having ovarian cancer.

In some examples, including any of the foregoing, the methods include generating machine learning models trained using mass spectrometry data (e.g., MRM-MS transition signals) from patients having a disease or condition and patients not having a disease or condition. In some examples, the disease or condition is ovarian cancer. In some examples, the methods include optimizing the machine learning models by cross-validation with known standards or other samples. In some examples, the methods include qualifying the performance using the mass spectrometry data to form panels of glycans and glycopeptides with individual sensitivities and specificities. In certain examples, the methods include determining a confidence percent in relation to a diagnosis. In some examples, one to ten glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 may be useful for diagnosing a patient with ovarian cancer with a certain confidence percent. In some examples, ten to fifty glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 may be useful for diagnosing a patient with ovarian cancer with a higher confidence percent.

In some examples, including any of the foregoing, the methods include performing MRM-MS and/or LC-MS on a biological sample. In some examples, the methods include constructing, by a computing device, theoretical mass spectra data representing a plurality of mass spectra, wherein each of the plurality of mass spectra corresponds to one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262. In some examples, the methods include comparing, by the computing device, the mass spectra data with the theoretical mass spectra data to generate comparison data indicative of a similarity of each of the plurality of mass spectra to each of the plurality of theoretical target mass spectra associated with a corresponding glycopeptide of the plurality of glycopeptides.

In some examples, including any of the foregoing, the methods include generating machine learning models trained using mass spectrometry data (e.g., MRM-MS transition signals) from patients having a disease or condition and patients not having a disease or condition. In some examples, the disease or condition is ovarian cancer. In some examples, the methods include optimizing the machine learning models by cross-validation with known standards or other samples. In some examples, the methods include qualifying the performance using the mass spectrometry data to form panels of glycans and glycopeptides with individual sensitivities and specificities. In certain examples, the methods include determining a confidence percent in relation to a diagnosis. In some examples, one to ten glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 may be useful for diagnosing a patient with ovarian cancer with a certain confidence percent. In some examples, ten to fifty glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 may be useful for diagnosing a patient with ovarian cancer with a higher confidence percent.

In some examples, including any of the foregoing, the methods include performing MRM-MS and/or LC-MS on a biological sample. In some examples, the methods include constructing, by a computing device, theoretical mass spectra data representing a plurality of mass spectra, wherein each of the plurality of mass spectra corresponds to one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194. In some examples, the methods include comparing, by the computing device, the mass spectra data with the theoretical mass spectra data to generate comparison data indicative of a similarity of each of the plurality of mass spectra to each of the plurality of theoretical target mass spectra associated with a corresponding glycopeptide of the plurality of glycopeptides.

In some examples, machine learning algorithms are used to determine, by the computing device and based on the MRM-MS data, a distribution of a plurality of characteristic ions in the plurality of mass spectra; and determining, by the computing device and based on the distribution, whether one or more of the plurality of characteristic ions is a glycopeptide ion.

In some examples, the methods herein include training a diagnostic algorithm. Herein, training the diagnostic algorithm may refer to supervised learning of a diagnostic algorithm on the basis of values for one or more glycopeptides consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262. Training the diagnostic algorithm may refer to variable selection in a statistical model on the basis of values for one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262. Training a diagnostic algorithm may for example include determining a weighting vector in feature space for each category, or determining a function or function parameters.

In some examples, the methods herein include training a diagnostic algorithm. Herein, training the diagnostic algorithm may refer to supervised learning of a diagnostic algorithm on the basis of values for one or more glycopeptides consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof. Training the diagnostic algorithm may refer to variable selection in a statistical model on the basis of values for one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194, and combinations thereof. Training a diagnostic algorithm may for example include determining a weighting vector in feature space for each category, or determining a function or function parameters.

In certain examples, the machine learning algorithm is LASSO, Ridge Regression, Random Forests, K-nearest Neighbors (KNN), Deep Neural Networks (DNN), and Principal Components Analysis (PCA). In certain examples, DNN's are used to process mass spec data into analysis-ready forms. In some examples, DNN's are used for peak picking from a mass spectra. In some examples, PCA is useful in feature detection.

In some examples, LASSO is used to provide feature selection.

In some examples, machine learning algorithms are used to quantify peptides from each protein that are representative of the protein abundance. In some examples, this quantification includes quantifying proteins for which glycosylation is not measured.

In some examples, glycopeptide sequences are identified by fragmentation in the mass spectrometer and database search using Byonic software.

In some examples, the methods herein include unsupervised learning to detect features of MRMS-MS data that represent known biological quantities, such as protein function or glycan motifs. In certain examples, these features are used as input for classifying by machine. In some examples, the classification is performed using LASSO, Ridge Regression, or Random Forest nature.

In some examples, the methods herein include mapping input data (e.g., MRM transition peaks) to a value (e.g., a scale based on 0-100) before processing the value in an algorithm. For example, after a MRM transition is identified and the peak characterized, the methods herein include assessing the MS scans in an m/z and retention time window around the peak for a given patient. In some examples, the resulting chromatogram is integrated by a machine learning algorithm that determines the peak start and stop points, and calculates the area bounded by those points and the intensity (height). The resulting integrated value is the abundance, which then feeds into machine learning and statistical analyses training and data sets.

In some examples, machine learning output, in one instance, is used as machine learning input in another instance. For example, in addition to the PCA being used for a classification process, the DNN data processing feeds into PCA and other analyses. This results in at least three levels of algorithmic processing. Other hierarchical structures are contemplated within the scope of the instant disclosure.

In some examples, including any of the foregoing, the methods include comparing the amount of each glycan or glycopeptide quantified in the sample to corresponding reference values for each glycan or glycopeptide in a diagnostic algorithm. In some examples, the methods includes a comparative process by which the amount of a glycan or glycopeptide quantified in the sample is compared to a reference value for the same glycan or glycopeptide using a diagnostic algorithm. The comparative process may be part of a classification by a diagnostic algorithm. The comparative process may occur at an abstract level, e.g., in n-dimensional feature space or in a higher dimensional space.

In some examples, the methods herein include classifying a patient's sample based on the amount of each glycan or glycopeptide quantified in the sample with a diagnostic algorithm. In some examples, the methods include using statistical or machine learning classification processes by which the amount of a glycan or glycopeptide quantified in the test sample is used to determine a category of health with a diagnostic algorithm. In some examples, the diagnostic algorithm is a statistical or machine learning classification algorithm.

In some examples, including any of the foregoing, classification by a diagnostic algorithm may include scoring likelihood of a panel of glycan or glycopeptide values belonging to each possible category, and determining the highest-scoring category. Classification by a diagnostic algorithm may include comparing a panel of marker values to previous observations by means of a distance function. Examples of diagnostic algorithms suitable for classification include random forests, support vector machines, logistic regression (e.g. multiclass or multinomial logistic regression, and/or algorithms adapted for sparse logistic regression). A wide variety of other diagnostic algorithms that are suitable for classification may be used, as known to a person skilled in the art.

In some examples, the methods herein include supervised learning of a diagnostic algorithm on the basis of values for each glycan or glycopeptide obtained from a population of individuals having a disease or condition (e.g., ovarian cancer). In some examples, the methods include variable selection in a statistical model on the basis of values for each glycan or glycopeptide obtained from a population of individuals having ovarian cancer. Training a diagnostic algorithm may for example include determining a weighting vector in feature space for each category, or determining a function or function parameters.

In one embodiment, the reference value is the amount of a glycan or glycopeptide in a sample or samples derived from one individual. Alternatively, the reference value may be derived by pooling data obtained from multiple individuals, and calculating an average (for example, mean or median) amount for a glycan or glycopeptide. Thus, the reference value may reflect the average amount of a glycan or glycopeptide in multiple individuals. Said amounts may be expressed in absolute or relative terms, in the same manner as described herein.

In some examples, the reference value may be derived from the same sample as the sample that is being tested, thus allowing for an appropriate comparison between the two. For example, if the sample is derived from urine, the reference value is also derived from urine. In some examples, if the sample is a blood sample (e.g. a plasma or a serum sample), then the reference value will also be a blood sample (e.g. a plasma sample or a serum sample, as appropriate). When comparing between the sample and the reference value, the way in which the amounts are expressed is matched between the sample and the reference value. Thus, an absolute amount can be compared with an absolute amount, and a relative amount can be compared with a relative amount. Similarly, the way in which the amounts are expressed for classification with the diagnostic algorithm is matched to the way in which the amounts are expressed for training the diagnostic algorithm.

When the amounts of the glycan or glycopeptide are determined, the method may comprise comparing the amount of each glycan or glycopeptide to its corresponding reference value. When the cumulative amount of one, some or all the glycan or glycopeptides are determined, the method may comprise comparing the cumulative amount to a corresponding reference value. When the amounts of the glycan or glycopeptides are combined with each other in a formula to form an index value, the index value can be compared to a corresponding reference index value derived in the same manner.

The reference values may be obtained either within (i.e., constituting a step of) or external to the (i.e., not constituting a step of) methods described herein. In some examples, the methods include a step of establishing a reference value for the quantity of the markers. In other examples, the reference values are obtained externally to the method described herein and accessed during the comparison step of the invention.

In some examples, including any of the foregoing, training of a diagnostic algorithm may be obtained either within (i.e., constituting a step of) or external to (i.e., not constituting a step of) the methods set forth herein. In some examples, the methods include a step of training of a diagnostic algorithm. In some examples, the diagnostic algorithm is trained externally to the method herein and accessed during the classification step of the invention. The reference value may be determined by quantifying the amount of a glycan or glycopeptide in a sample obtained from a population of healthy individual(s). The diagnostic algorithm may be trained by quantifying the amount of a glycan or glycopeptide in a sample obtained from a population of healthy individual(s). As used herein, the term “healthy individual” refers to an individual or group of individuals who are in a healthy state, e.g., patients who have not shown any symptoms of the disease, have not been diagnosed with the disease and/or are not likely to develop the disease. Preferably said healthy individual(s) is not on medication affecting the disease and has not been diagnosed with any other disease. The one or more healthy individuals may have a similar sex, age and body mass index (BMI) as compared with the test individual. The reference value may be determined by quantifying the amount of a glycan or glycopeptide in a sample obtained from a population of individual(s) suffering from the disease. The diagnostic algorithm may be trained by quantifying the amount of a marker in a sample obtained from a population of individual(s) suffering from the disease. More preferably such individual(s) may have similar sex, age and body mass index (BMI) as compared with the test individual. The reference value may be obtained from a population of individuals suffering from ovarian cancer. The diagnostic algorithm may be trained by quantifying the amount of a glycan or glycopeptide in a sample obtained from a population of individuals suffering from ovarian cancer. Once the characteristic glycan or glycopeptide profile of ovarian cancer is determined, the profile of markers from a biological sample obtained from an individual may be compared to this reference profile to determine whether the test subject also has ovarian cancer. Once the diagnostic algorithm is trained to classify ovarian cancer, the profile of markers from a biological sample obtained from an individual may be classified by the diagnostic algorithm to determine whether the test subject is also at that particular stage of ovarian cancer.

VI. KITS

In some examples, including any of the foregoing, set forth herein is a kit for diagnosing or monitoring cancer in an individual wherein the glycan or glycopeptide profile of a sample from said individual is determined and the measured profile is compared with a profile of a normal patient or a profile of a patient with a family history of cancer. In some examples, the kit comprises one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262. In some examples, the kit comprises one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In some examples, including any of the foregoing, set forth herein is a kit comprising a glycopeptide standard, a buffer, and one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, set forth herein is a kit comprising a glycopeptide standard, a buffer, and one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, set forth herein is a kit for diagnosing or monitoring cancer in an individual wherein the glycan or glycopeptide profile of a sample from said individual is determined and the measured profile is compared with a profile of a normal patient or a profile of a patient with a family history of cancer. In some examples, the kit comprises one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, 194. In some examples, the kit comprises one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, set forth herein is a kit comprising the reagents for quantification of the oxidised, nitrated, and/or glycated free adducts derived from glycopeptides.

VII. CLINICAL ASSAYS

In a clinical setting, samples may be prepared (e.g., by digestion) to include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In some examples, the amount of a glycan or glycopeptide may be assessed by comparing the amount of one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 to the amount of one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In some examples, the amount of a glycan or glycopeptide may be assessed by comparing the amount of one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 to the amount of one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262.

In some examples, including any of the foregoing, the kit may include software for computing the normalization of a glycopeptide MRM transition signal.

In some examples, including any of the foregoing, a trained model is stored on a server which is accessed by a clinician performing a method, set forth herein. In some examples, the clinician inputs the quantification of the MRM transition signals from a patient's sample into a trained model which are stored on a server. In some examples, the server is accessed by the internet, wireless communication, or other digital or telecommunication methods.

In some examples, including any of the foregoing, a trained model is stored on a server which is accessed by a clinician performing a method, set forth herein. In some examples, the clinician inputs the quantification of the glycopeptide or glycopeptides consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:1-262 from a patient's sample into a trained model which are stored on a server. In some examples, the server is accessed by the internet, wireless communication, or other digital or telecommunication methods.

In some examples, including any of the foregoing, MRM transition signals 1-150 are stored on a server which is accessed by a clinician performing a method, set forth herein. In some examples, the clinician compares the MRM transition signals from a patient's sample to the MRM transition signals 1-150 which are stored on a server. In some examples, the server is accessed by the internet, wireless communication, or other digital or telecommunication methods.

In some examples, including any of the foregoing, a machine learning algorithm, which has been trained using the MRM transition signals 1-150, described herein, is stored on a server which is accessed by a clinician performing a method, set forth herein. In some examples, the machine learning algorithm, accessed remotely on a server, analyzes the MRM transition signals from a patient's sample. In some examples, the server is accessed by the internet, wireless communication, or other digital or telecommunication methods.

In some examples, including any of the foregoing, the biomarkers, methods, and/or kits may be used in a clinical setting for diagnosing patients. In some of these examples, the analysis of samples includes the use of internal standards. These standards may include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194. These standards may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In a clinical setting, samples may be prepared (e.g., by digestion) to include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In a clinical setting, samples may be prepared (e.g., by digestion) to include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, the amount of a glycan or glycopeptide may be assessed by comparing the amount of one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 to the amount of one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, the amount of a glycan or glycopeptide may be assessed by comparing the amount of one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 to the amount of one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, the kit may include software for computing the normalization of a glycopeptide MRM transition signal.

In some examples, including any of the foregoing, the kit may include software for quantifying the amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, the kit may include software for quantifying the relative amount of a glycopeptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.

In some examples, including any of the foregoing, a trained model is stored on a server which is accessed by a clinician performing a method, set forth herein. In some examples, the clinician inputs the quantification of the MRM transition signals from a patient's sample into a trained model which are stored on a server. In some examples, the server is accessed by the internet, wireless communication, or other digital or telecommunication methods.

In some examples, including any of the foregoing, a trained model is stored on a server which is accessed by a clinician performing a method, set forth herein. In some examples, the clinician inputs the quantification of the glycopeptide or glycopeptides consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs:4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194 from a patient's sample into a trained model which are stored on a server. In some examples, the server is accessed by the internet, wireless communication, or other digital or telecommunication methods.

VII. EXAMPLES

Chemicals and Reagents. Glycoprotein standards purified from human serum/plasma were purchased from Sigma-Aldrich (St. Louis, Mo.). Sequencing grade trypsin was purchased from Promega (Madison, Wis.). Dithiothreitol (DTT) and iodoacetamide (IAA) were purchased from Sigma-Aldrich (St. Louis, Mo.). Human serum was purchased from Sigma-Aldrich (St. Louis, Mo.).

Sample Preparation. Serum samples and glycoprotein standards were reduced, alkylated and then digested with trypsin in a water bath at 37° C. for 18 hours.

LC-MS/MS Analysis. For quantitative analysis, tryptic digested serum samples were injected into an high performance liquid chromatography (HPLC) system coupled to triple quadrupole (QqQ) mass spectrometer. The separation was conducted on a reverse phase column. Solvents A and B used in the binary gradient were composed of mixtures of water, acetonitrile and formic acid. Typical positive ionization source parameters were utilized after source tuning with vendor supplied standards. The following ranges were evaluated: source spray voltage between 3-5 kV, temperature 250-350° C., and nitrogen sheath gas flow rate 20-40 psi. The scan mode of instrument used was dMRM.

For the glycoproteomic analysis, enriched serum glycopeptides were analyzed with a Q Exactive™ Hybrid Quadrupole-Orbitrap™ Mass spectrometer or an Agilent 6495B Triple Quadrupole LC/MS.

MRM Mass Spectroscopy settings, sample preparation, and reagents are set forth in Li, et al., Site-Specific Glycosylation Quantification of 50 serum Glycoproteins Enhanced by Predictive Glycopeptidomics for Improved Disease Biomarker Discovery, Anal. Chem. 2019, 91, 5433-5445; DOI: 10.1021/acs.analchem.9b00776, the entire contents of which are herein incorporated by reference in its entirety for all purposes.

Example 1—Identifying Glycopeptide Biomarkers

This Example refers to FIGS. 15 and 17-19.

As shown in FIG. 15, in step 1, samples from patients having ovarian cancer and samples from patients not having ovarian cancer were provided. In step 2, the samples were digested using protease enzymes to form glycopeptide fragments. In step 3, the glycopeptide fragments were introduced into a tandem LC-MS/MS instrument to analyze the retention time and MRM-MS transition signals associated with the aforementioned samples. In step 4, glycopeptides and glycan biomarkers were identified. Machine learning algorithms selected MRM-MS transition signals from a series of MS spectra and associated those signals with the calculated mass of certain glycopeptide fragments. See FIGS. 17-18 for MRM-MS transition signals identified by machine learning algorithms.

In step 5, the glycopeptides identified in samples from patients having ovarian cancer were compared using machine learning algorithms, including lasso regression, with the glycopeptides identified in samples from patients not having ovarian cancer. This comparison included a comparison of the types, absolute amounts, and relative amounts of glycopeptides. From this comparison, normalization of peptides, and relative abundance of glycopeptides was calculated. See FIG. 19 for output results of this comparison.

Example 2—Identifying Glycopeptide Biomarkers

This Example refers to FIG. 16.

As shown in FIG. 1, in step 1, samples from patients are provided. In step 2, the samples were digested using protease enzymes to form glycopeptide fragments. In step 3, the glycopeptide fragments were introduced into a tandem LC-MS/MS instrument to analyze the retention time and MRM-MS transition signals associated with the sample. In step 4, the glycopeptides were identified using machine learning algorithms which select MRM-MS transition signals and associate those signals with the calculated mass of certain glycopeptide fragments. In step 5, the data is normalized. In step 6, machine learning is used to analyzed the normalized data to identify biomarkers indicative of a patient having ovarian cancer.

IX. TABLES

TABLE 2

Transition Numbers with Precursor Ion and Product Ion (m/z)

Transition No.
Precursor Ion
Product Ion

1
1195.4
366.1

2
1341
366.1

3
1223.9
366.1

4
991.2
366.1

5
1149.9
366.1

6
1078.5
274.1

7
1107.7
366.1

8
1209.5
366.1

9
1093.1
366.1

10
1456.7
1183.6

11
874.4
1183.6

12
1239.2
1314.2

13
1189.2
366.1

14
1151.6
366.1

15
1066.7
366.1

16
1124.9
366.1

17
1322.3
366.1

18
1172.7
366.1

19
1134.1
366.1

20
1152.5
366.1

21
1109.1
366.1

22
1167.3
366.1

23
1225.5
366.1

24
1254.7
366.1

25
1313.1
366.1

26
1262
366.1

27
1238
366.1

28
1110.8
366.1

29
1147.3
366.1

30
1165.6
366.1

31
1256.8
366.1

32
1196.5
366.1

33
1215
366.1

34
1287.7
366.1

35
1301.9
366.1

36
1231.8
274.1

37
1213.8
366.1

38
1286.6
366.1

39
1177.2
366.1

40
1323.1
366.1

41
1208.6
366.1

42
1174.2
366.1

43
975.4
204.1

44
1028.8
274.1

45
980.4
274.1

46
937.4
366.1

47
1005.1
274.1

48
989.1
274.1

49
1115.7
366.1

50
1341.6
366.1

51
1098
366.1

52
1171
366.1

53
1335.3
366.1

54
1055.8
366.1

55
1236.2
366.1

56
1189.5
366.1

57
1259.5
366.1

58
908.6
366.1

59
984.7
366.1

60
1256.1
366.1

61
993.1
366.1

62
1090.1
366.1

63
952.1
366.1

64
1270.2
366.1

65
1157.9
204.1

66
1103.8
366.1

67
850.7
366.1

68
1152.4
366.1

69
1132.2
366.1

70
1070.4
366.1

71
916.1
366.1

72
1252.5
366.1

73
1247.7
366.1

74
1335.1
366.1

75
1378.9
366.1

76
1165
366.1

77
1128.8
366.1

78
1201.5
366.1

79
1292.8
366.1

80
1407.9
366.1

81
994.4
366.1

82
1075.1
366.1

83
1006.8
366.1

84
1103.8
366.1

85
977.5
366.1

86
868.1
204.1

87
1019.1
204.1

88
1248.5
204.1

89
901.9
366.1

90
1039.1
366.1

91
1181.5
366.1

92
968.2
366.1

93
1114.7
366.1

94
1277.8
366.1

95
450.8
501.3

96
877
745.9

97
416.2
647.3

98
381.7
305.2

99
445.2
487.3

100
694.3
244.2

101
585.3
771.3

102
1033
742.4

103
437.7
464.3

104
910.4
366.1

105
1057.7
366.1

106
1149.3
366.1

107
1221.5
366.1

108
444.8
605.3

109
590.3
342.2

110
480.8
404.2

111
944.5
502.3

112
580.8
827.4

113
871.9
563.3

114
618.3
736.4

115
436.3
545.3

116
409.2
486.3

117
636.8
499.3

118
697.4
922.4

119
501.7
726.3

120
442.3
685.4

121
387.7
209.1

122
462.3
697.4

123
412.7
486.3

124
545.3
662.4

125
478.8
230.1

126
515.8
720.4

127
660.4
529.3

128
416.7
646.4

129
461.2
491.3

130
819.1
609.3

131
693.9
675.4

132
904.4
366.1

133
1001.4
366.1

134
1131.1
366.1

135
921.4
366.1

136
957.9
366.1

137
1035.6
366.1

138
1112.2
366.1

139
1185
366.1

140
1018.1
366.1

141
1076.4
366.1

142
1105.6
366.1

143
1382.6
366.1

144
1227.5
366.1

145
1199.2
366.1

146
942.4
366.1

147
1336.3
366.1

148
1409.1
366.1

149
1239
366.1

150
1472.6
366.1

MS1 and MS2 resolution was 1 unit.

TABLE 3

Transition Numbers with Retention Time, ΔRetention

Time, Fragmentor and Collision Energy

Transition
Ret Time
Delta Ret

Collision

No.
(min)
Time
Fragmentor
Energy

1
43.1
3
380
30

2
43.4
3
380
34

3
29.4
3
380
30

4
29.8
3
380
24

5
42.5
4
380
28

6
46
4
380
26

7
46
4
380
27

8
24
2
380
36

9
43.7
3
380
22

10
43.7
3
380
22

11
43.7
3
380
20

12
32
2
380
25

13
35.2
3
380
26

14
41.1
3
380
23

15
26
2
380
22

16
27
2
380
25

17
26.2
2
380
24

18
19
1.2
380
35

19
9.3
1
380
30

20
38.2
3
380
28

21
37.3
3
380
30

22
36.5
3
380
29

23
38
3
380
31

24
37.8
3
380
31

25
39.4
3
380
27

26
3.7
1.2
380
32

27
3.4
1
380
31

28
3.8
1.2
380
27

29
3.8
1.2
380
28

30
3.7
1.2
380
29

31
3.5
1
380
31

32
29
2
380
30

33
27.9
2
380
30

34
28.6
2
380
32

35
17
1
380
30

36
17.4
1.2
380
31

37
16.9
1.2
380
30

38
17.3
1.2
380
32

39
16.8
1.2
380
30

40
17.2
1.2
380
33

41
3
1.2
380
30

42
10.9
1.2
380
30

43
28.3
1.6
380
24

44
30.2
2
380
25

45
29.9
1.6
380
24

46
29.1
1.6
380
22

47
30
2
380
24

48
28.2
2
380
24

49
21.6
1.2
380
34

50
21.6
1.2
380
35

51
21.5
1.2
380
34

52
22.2
1.2
380
35

53
21.6
1.2
380
39

54
14.4
2
380
33

55
24.3
2
380
37

56
13.5
2
380
36

57
9.6
1.2
380
38

58
10.2
1.2
380
29

59
11.8
1.2
380
31

60
10.5
1.2
380
38

61
4
1.2
380
30

62
4.4
1.2
380
34

63
3.7
1.2
380
30

64
6.4
1.2
380
38

65
19.5
1.2
380
30

66
13.6
1.2
380
34

67
11.2
1.2
380
28

68
18
2
380
35

69
19.3
1.2
380
30

70
20.6
1.2
380
26

71
16.3
1.2
380
22

72
16.2
1.2
380
37

73
10.7
1.2
380
31

74
10.8
1.4
380
34

75
10.7
1.2
380
35

76
22.2
1.3
380
29

77
21.4
1.2
380
28

78
22.1
1.2
380
30

79
22
1.4
380
32

80
20.6
1.2
380
41

81
3.6
2
380
25

82
40.1
2
380
26

83
10.7
1.2
380
24

84
11.3
1.2
380
27

85
10.2
1.2
380
24

86
10.6
1
380
21

87
11.3
1
380
25

88
22.2
1.2
380
30

89
4.4
1
380
21

90
30
2
380
32

91
23.7
2
380
36

92
20
1.2
380
30

93
19.5
1.2
380
34

94
20.6
1.2
380
38

95
13.45
1
380
11

96
20
1
380
33

97
3.85
1
380
10

98
3.55
1
380
9

99
12.1
1
380
11

100
8.3
1
380
25

101
13.5
1
380
16

102
29.9
1
380
32

103
8.7
1
380
14

104
17
1.2
380
29

105
25.3
2
380
33

106
25.2
2
380
35

107
24.9
2
380
37

108
12
1
380
11

109
22.1
1.2
380
15

110
11.7
1
380
13

111
33.5
1.2
380
29

112
27.9
1
380
16

113
29.4
1
380
27

114
26.2
1
380
17

115
8.8
1
380
11

116
17.4
1
380
10

117
13.1
1
380
18

118
29.5
1
380
20

119
8.1
1
380
13

120
16.8
1
380
11

121
5.6
1
380
12

122
8.9
1
380
12

123
12
1
380
12

124
17.1
1.2
380
15

125
4.1
1
380
16

126
23
1
380
14

127
23.3
1
380
19

128
10.3
1
380
10

129
8.7
1
380
12

130
24.2
1
380
25

131
32
1
380
20

132
3.1
1.2
380
29

133
3.1
1.2
380
31

134
18.9
1.2
380
28

135
19.7
1.2
380
22

136
19.7
1.2
380
23

137
21.7
1.2
380
25

138
21.7
1.2
380
25

139
22.2
1.4
380
25

140
23.8
1.6
380
25

141
24.1
1.2
380
26

142
24.1
1.2
380
27

143
24.2
2
380
41

144
24.3
2
380
37

145
25.9
2
380
36

146
17.6
1.2
380
30

147
29.7
3
380
40

148
29.3
3
380
41

149
14.6
2
380
37

150
19.6
1.2
380
43

Cell accelerator voltage was 5.

TABLE 4

Glycan Residue Compound Numbers, Molecular

Mass, and Glycan Fragment mass-to-charge

(m/z) (+2) & (m/z) (+3) ratios

Composition
mass
m/z (+2)
m/z (+3)

3200
910.327
456.1708
304.449633

3210
1056.386
529.2003
353.135967

3300
1113.407
557.7108
372.142967

3310
1259.465
630.7398
420.828967

3320
1405.523
703.7688
469.514967

3400
1316.487
659.2508
439.8363

3410
1462.544
732.2793
488.521967

3420
1608.602
805.3083
537.207967

3500
1519.566
760.7903
507.5293

3510
1665.624
833.8193
556.2153

3520
1811.682
906.8483
604.9013

3600
1722.645
862.3298
575.2223

3610
1868.703
935.3588
623.9083

3620
2014.761
1008.3878
672.5943

3630
2160.89
1081.4523
721.303967

3700
1925.724642
963.869621
642.915514

3710
2071.782551
1036.898576
691.601484

3720
2217.84046
1109.92753
740.287453

3730
2363.898369
1182.956485
788.973423

3740
2509.956277
1255.985439
837.659392

4200
1072.380603
537.1976015
358.467501

4210
1218.438512
610.226556
407.153471

4300
1275.459976
638.737288
426.160625

4301
1566.555392
784.284996
523.192431

4310
1421.517884
711.766242
474.846595

4311
1712.613301
857.3139505
571.8784

4320
1567.575793
784.7951965
523.532564

4400
1478.539348
740.276974
493.853749

4401
1769.634765
885.8246825
590.885555

4410
1624.597257
813.3059285
542.539719

4411
1915.692673
958.8536365
639.571524

4420
1770.655166
886.334883
591.225689

4421
2061.750582
1031.882591
688.257494

4430
1916.713074
959.363837
639.911658

4431
2207.808491
1104.911546
736.943464

4500
1681.618721
841.8166605
561.546874

4501

1.0073
1.0073

4510
1972.714137
987.3643685
658.578679

4511
2118.772046
1060.393323
707.264649

4520
1973.734538
987.874569
658.918813

4521
2264.829955
1133.422278
755.950618

4530
2119.792447
1060.903524
707.604782

4531
2410.887864
1206.451232
804.636588

4540
2265.850356
1133.932478
756.290752

4541
2556.945772
1279.480186
853.322557

4600
1884.698093
943.3563465
629.239998

4601
2175.79351
1088.904055
726.271803

4610
2030.756002
1016.385301
677.925967

4611
2321.851418
1161.933009
774.957773

4620
2176.813911
1089.414256
726.611937

4621
2467.909327
1234.961964
823.643742

4630
2322.87182
1162.44321
775.297907

4631
2613.967236
1307.990918
872.329712

4641
2760.025145
1381.019873
921.015682

4650
2614.987637
1308.501119
872.669846

4700
2087.777466
1044.896033
696.933122

4701
2378.872882
1190.443741
793.964927

4710
2233.835374
1117.924987
745.619091

4711
2524.930791
1263.472696
842.650897

4720
2379.893283
1190.953942
794.305061

4730
2525.951192
1263.982896
842.991031

5200
1234.433426
618.224013
412.485109

5210
1380.491335
691.2529675
461.171078

5300
1437.512799
719.7636995
480.178233

5301
1728.608215
865.3114075
577.210038

5310
1583.570708
792.792654
528.864203

5311
1874.666124
938.340362
625.896008

5320
1729.628617
865.8216085
577.550172

5400
1640.592171
821.3033855
547.871357

5401
1931.687588
966.851094
644.903163

5402
2222.783005
1112.398803
741.934968

5410
1786.65008
894.33234
596.557327

5411
2077.745497
1039.880049
693.589132

5412
2368.840913
1185.427757
790.620938

5420
1932.707989
967.3612945
645.243296

5421
2223.803406
1112.909003
742.275102

5430
2078.765898
1040.390249
693.929266

5431
2369.861314
1185.937957
790.961071

5432
2660.956731
1331.485666
887.992877

5500
1843.671544
922.843072
615.564481

5501
2134.766961
1068.390781
712.596287

5502
2425.862377
1213.938489
809.628092

5510
1989.729453
995.8720265
664.250451

5511
2280.824869
1141.419735
761.282256

5512
2571.920286
1286.967443
858.314062

5520
2135.787362
1068.900981
712.936421

5521
2426.882778
1214.448689
809.968226

5522
2717.978195
1359.996398
907.000032

5530
2281.84527
1141.929935
761.62239

5531
2572.940687
1287.477644
858.654196

5541
2718.998596
1360.506598
907.340165

5600
2046.750917
1024.382759
683.257606

5601
2337.846333
1169.930467
780.289411

5602
2628.94175
1315.478175
877.321217

5610
2192.808825
1097.411713
731.943575

5611
2483.904242
1242.959421
828.975381

5612
2774.999658
1388.507129
926.007186

5620
2338.866734
1170.440667
780.629545

5621
2629.962151
1315.988376
877.66135

5631
2776.020059
1389.01733
926.34732

5650
2777.040461
1389.527531
926.687454

5700
2249.830289
1125.922445
750.95073

5701
2540.925706
1271.470153
847.982535

5702
2832.021122
1417.017861
945.014341

5710
2395.888198
1198.951399
799.636699

5711
2686.983614
1344.499107
896.668505

5712
2978.079031
1490.046816
993.70031

5720
2541.946107
1271.980354
848.322669

5721
2833.041523
1417.528062
945.354474

5730
2688.004016
1345.009308
897.008639

5731
2979.099432
1490.557016
994.040444

6200
1396.48625
699.250425
466.502717

6210
1542.544159
772.2793795
515.188686

6300
1599.565622
800.790111
534.195841

6301
1890.661039
946.3378195
631.227646

6310
1745.623531
873.8190655
582.88181

6311
2036.718948
1019.366774
679.913616

6320
1891.68144
946.84802
631.56778

6400
1802.644995
902.3297975
601.888965

6401
2093.740411
1047.877506
698.92077

6402
2384.835828
1193.425214
795.952576

6410
1948.702904
975.358752
650.574935

6411
2239.79832
1120.90646
747.60674

6412
2530.893737
1266.454169
844.638546

6420
2094.760813
1048.387707
699.260904

6421
2385.856229
1193.935415
796.29271

6432
2823.009554
1412.512077
942.010485

6500
2005.724367
1003.869484
669.582089

6501
2296.819784
1149.417192
766.613895

6502
2587.9152
1294.9649
863.6457

6503
2879.010617
1440.512609
960.677506

6510
2151.782276
1076.898438
718.268059

6511
2442.877693
1222.446147
815.299864

6512
2733.973109
1367.993855
912.33167

6513
3025.068526
1513.541563
1009.36348

6520
2297.840185
1149.927393
766.954028

6521
2588.935602
1295.475101
863.985834

6522
2880.031018
1441.022809
961.017639

6530
2443.898094
1222.956347
815.639998

6531
2734.99351
1368.504055
912.671803

6532
3026.088927
1514.051764
1009.70361

6540
2589.956003
1295.985302
864.325968

6541
2881.051419
1441.53301
961.357773

6600
2208.80374
1105.40917
737.275213

6601
2499.899157
1250.956879
834.307019

6602
2790.994573
1396.504587
931.338824

6603
3082.08999
1542.052295
1028.37063

6610
2354.861649
1178.438125
785.961183

6611
2645.957065
1323.985833
882.992988

6612
2937.052482
1469.533541
980.024794

6613
3228.147898
1615.081249
1077.0566

6620
2500.919558
1251.467079
834.647153

6621
2792.014974
1397.014787
931.678958

6622
3083.110391
1542.562496
1028.71076

6623
3374.205807
1688.110204
1125.74257

6630
2646.977466
1324.496033
883.333122

6631
2938.072883
1470.043742
980.364928

6632
3229.168299
1615.59145
1077.39673

6640
2793.035375
1397.524988
932.019092

6641
3084.130792
1543.072696
1029.0509

6642
3375.226208
1688.620404
1126.0827

6652
3521.284117
1761.649359
1174.76867

6700
2411.883113
1206.948857
804.968338

6701
2702.978529
1352.496565
902.000143

6703
3285.169362
1643.591981
1096.06375

6710
2557.941021
1279.977811
853.654307

6711
2849.036438
1425.525519
950.686113

6711
2849.036438
1425.525519
950.686113

6712
3140.131854
1571.073227
1047.71792

6713
3431.227271
1716.620936
1144.74972

6713
3431.227271
1716.620936
1144.74972

6720
2703.99893
1353.006765
902.340277

6721
2995.094347
1498.554474
999.372082

6721
2995.094347
1498.554474
999.372082

6730
2850.056839
1426.03572
951.026246

6731
3141.152255
1571.583428
1048.05805

6740
2996.114748
1499.064674
999.712216

7200
1558.539073
780.2768365
520.520324

7210
1704.596982
853.305791
569.206294

7400
1964.697818
983.356209
655.906573

7401
2255.793235
1128.903918
752.938378

7410
2110.755727
1056.385164
704.592542

7411
2401.851144
1201.932872
801.624348

7412
2692.94656
1347.48058
898.656153

7420
2256.813636
1129.414118
753.278512

7421
2547.909052
1274.961826
850.310317

7430
2402.871545
1202.443073
801.964482

7431
2693.966961
1347.990781
898.996287

7432
2985.062378
1493.538489
996.028093

7500
2167.777191
1084.895896
723.599697

7501
2458.872607
1230.443604
820.631502

7510
2313.8351
1157.92485
772.285667

7511
2604.930516
1303.472558
869.317472

7512
2896.025933
1449.020267
966.349278

7600
2370.856563
1186.435582
791.292821

7601
2661.95198
1331.98329
888.324627

7602
2953.047396
1477.530998
985.356432

7603
3244.142813
1623.078707
1082.38824

7604
3535.23823
1768.626415
1179.42004

7610
2516.914472
1259.464536
839.978791

7611
2808.009889
1405.012245
937.010596

7612
3099.105305
1550.559953
1034.0424

7613
3390.200722
1696.107661
1131.07421

7614
3681.296138
1841.655369
1228.10601

7620
2662.972381
1332.493491
888.66476

7621
2954.067798
1478.041199
985.696566

7622
3245.163214
1623.588907
1082.72837

7623
3536.258631
1769.136616
1179.76018

7632
3391.221123
1696.617862
1131.41434

7640
2955.088199
1478.5514
986.0367

7700
2573.935936
1287.975268
858.985945

7701
2865.031352
1433.522976
956.017751

7702
3156.126769
1579.070685
1053.04956

7703
3447.222186
1724.618393
1150.08136

7710
2719.993845
1361.004223
907.671915

7711
3011.089261
1506.551931
1004.70372

7712
3302.184678
1652.099639
1101.73553

7713
3593.280094
1797.647347
1198.76733

7714
3884.375511
1943.195056
1295.79914

7720
2866.051754
1434.033177
956.357885

7721
3157.14717
1579.580885
1053.38969

7722
3448.242587
1725.128594
1150.4215

7730
3012.109662
1507.062131
1005.04385

7731
3303.205079
1652.60984
1102.07566

7732
3594.300495
1798.157548
1199.10747

7740
3158.167571
1580.091086
1053.72982

7741
3449.262988
1725.638794
1150.76163

7751
3595.320897
1798.667749
1199.4476

8200
1720.591897
861.3032485
574.537932

9200
1882.64472
942.32966
628.55554

9210
2028.702629
1015.358615
677.24151

10200
2044.697544
1023.356072
682.573148

11200
2206.750367
1104.382484
736.590756

12200
2368.80319
1185.408895
790.608363

TABLE 5

Glycan Residue Compound Numbers, Molecular

Mass, and Classification

Compound
Glycan Mass
Glycan Composition
Class

3200
910.328
GlcNAc₂Man₃
HM

3200

3210
1056.386
GlcNAc₂Man₃Fuc₁
HM-F

3210

3300
1113.407
Hex₃HexNAc₃
C

3300

3310
1259.465
Hex₃HexNAc₃Fuc₁
C-F

3310

3320
1405.523
Hex₃HexNAc₃Fuc₂
C-F

3400
1316.487
Hex₃HexNAc₄
C

3410
1462.544
Hex₃HexNAc₄Fuc₁
C-F

3410

3420
1608.602
Hex₃HexNAc₄Fuc₂
C-F

3500
1519.566
Hex₃HexNAc₅
C

3510
1665.624
Hex₃HexNAc₅Fuc₁
C-F

3520
1811.682
Hex₃HexNAc₅Fuc₂
C-F

3600
1722.645
Hex₃HexNAc₆
C

3610
1868.703
Hex₃HexNAc₆Fuc₁
C-F

3620
2014.761
Hex₃HexNAc₆Fuc₂
C-F

3630
2160.819
Hex₃HexNAc₆Fuc₃
C-F

3700
1925.725
Hex₃HexNAc₇
C

3710
2071.783
Hex₃HexNAc₇Fuc₁
C-F

3720
2217.841
Hex₃HexNAc₇Fuc₂
C-F

3720
2217.841
Hex₃HexNAc₇Fuc₂
C-F

3730
2363.898
Hex₃HexNAc₇Fuc₃
C-F

3740
2509.956
Hex₃HexNAc₇Fuc₄
C-F

4200
1072.381
GlcNAc₂Man₄
HM

4200

4210
1218.438
GlcNAc₂Man₄Fuc₁
HM-F

4210

4300
1275.460
Hex₄HexNAc₃
CH

4300

4301
1566.555
Hex₄HexNAC₃Neu5Ac₁
C-S

4301
1566.555
Hex₄HexNAC₃Neu5Ac₁
C-S

4301

4310
1421.518
Hex₄HexNAc₃Fuc₁
C/H-F

4310
1566.555
Hex₄HexNAC₃Neu5Ac₁
C-S

4310

4311
1712.613
Hex₄HexNAc₃Fuc₁Neu5Ac₁
C-FS

4311

4320

4400
1478.539
Hex₄HexNAc₄
CH

4400

4401
1769.635
Hex₄HexNAc₄Neu5Ac₁
C-S

4410
1624.597
Hex₄HexNAc₄Fuc₁
C/H-F

4410

4411
1915.693
Hex₄HexNAc₄Fuc₁Neu5Ac₁
C-FS

4411

4420
1770.655
Hex₄HexNAc₄Fuc₂
C/H-F

4420

4421
2061.751
Hex₄HexNAc₄Fuc₂Neu5Ac₁
C-FS

4430
1916.713
Hex₄HexNAc₄Fuc₃
C/H-F

4431
2207.808
Hex₄HexNAc₄Fuc₃Neu5Ac₁
C-FS

4431
2207.808
Hex₄HexNAc₄Fuc₃Neu5Ac₁
C-FS

4531
2410.888
Hex₄HexNAc₅Fuc₃Neu5Ac₁
C-FS

4541
2556.946
Hex₄HexNAc₅Fuc₄Neu5Ac₁
C-FS

4600
1884.698
Hex₄HexNAc₆
C

4601
2175.794
Hex₄HexNAc₆Neu5Ac₁
C-S

4610
2030.756
Hex₄HexNAc₆Fuc₁
C-F

4611
2321.851
Hex₄HexNAc₆Fuc₁Neu5Ac₁
C-FS

4620
2176.814
Hex₄HexNAc₆Fuc₂
C-F

4621
2467.909
Hex₄HexNAc₆Fuc₂Neu5Ac₁
C-FS

4630
2322.872
Hex₄HexNAc₆Fuc₃
C-F

4641
2760.025
Hex₄HexNAc₆Fuc₄Neu5Ac₁
C-FS

4650
2614.988
Hex₄HexNAc₆Fuc₅
C-F

4700
2087.778
Hex₄HexNAc₇
C

4701
2378.873
Hex₄HexNAc₇Neu5Ac₁
C-S

4710
2233.835
Hex₄HexNAc₇Fuc₁
C-F

4711
2524.931
Hex₄HexNAc₇Fuc₁Neu5Ac₁
C-FS

4720
2379.893
Hex₄HexNAc₇Fuc₂
C-F

4730
2525.951
Hex₄HexNAc₇Fuc₃
C-F

5200

5200

5210
1380.491
GlcNAc₂Man₅Fuc₁
HM-F

5300
1437.513
Hex₅HexNAc₃
H

5300

5301
1728.608
Hex₅HexNAc₃Neu5Ac₁
H-S

5301

5310
1583.571
Hex₅HexNAc₃Fuc₁
H-F

5310

5311
1874.666
Hex₅HexNAc₃Fuc₁Neu5Ac₁
H-FS

5311

5320
1729.629
Hex₅HexNAc₃Fuc₂
H-F

5320

5400

5401

5401

5402

5410

5411

Hex₅HexNAc₄Fuc₁Neu5Ac₁
C-FS

5411

5412

5420

5421

5430

5431
2369.861
Hex₅HexNAc₄Fuc₃Neu5Ac₁
C/H-FS

5432
2660.957
Hex₅HexNAc₄Fuc₃Neu5Ac₂
C-FS

5432
2660.957
Hex₅HexNAc₄Fuc₃Neu5Ac₂
C-FS

5531
2572.941
Hex₅HexNAc₅Fuc₃Neu5Ac₁
C/H-FS

5541
2718.999
Hex₅HexNAc₅Fuc₄Neu5Ac₁
C-FS

5631
2776.020
Hex₅HexNAc₆Fuc₃Neu5Ac₁
C-FS

5650
2777.040
Hex₅HexNAc₆Fuc₅
C-F

5700
2249.830
Hex₅HexNAc₇
C

5701
2540.926
Hex₅HexNAc₇Neu5Ac₁
C-S

5702
2832.021
Hex₅HexNAc₇Neu5Ac₂
C-S

5710
2395.888
Hex₅HexNAc₇Fuc₁
C-F

5711
2686.984
Hex₅HexNAc₇Fuc₁Neu5Ac₁
C-FS

5712
2978.079
Hex₅HexNAc₇Fuc₁Neu5Ac₂
C-FS

5720
2541.946
Hex₅HexNAc₇Fuc₂
C-F

5721
2833.042
Hex₅HexNAc₇Fuc₂Neu5Ac₁
C-FS

5730
2688.004
Hex₅HexNAc₇Fuc₃
C-F

5730
2688.004
Hex₅HexNAc₇Fuc₃
C-F

5731
2979.099
Hex₅HexNAc₇Fuc₃Neu5Ac₁
C-FS

6200

6200

6210
1542.544
GlcNAc₂Man₆Fuc₁
HM-F

6300
1599.566
Hex₆HexNAc₃
H

6300

6301
1890.661
Hex₆HexNAc₃Neu5Ac₁
H-S

6301

6310
1745.623
Hex₆HexNAc₃Fuc₁
H-F

6310

6311
2036.719
Hex₆HexNAc₃Fuc₁Neu5Ac₁
H-FS

6311
2036.719
Hex₆HexNAc₃Fuc₁Neu5Ac₁
H-FS

6311

6320
1891.681
Hex₆HexNAc₃Fuc₂
H-F

6400
1802.645
Hex₆HexNAc₄
H

6401
2093.740
Hex₆HexNAc₄Neu5Ac₁
H-S

6401

6402
2384.836
Hex₆HexNAc₄Neu5Ac₂
H-S

6410
1948.703
Hex₆HexNAc₄Fuc₁
H-F

6410

6411
2239.798
Hex₆HexNAc₄Fuc₁Neu5Ac₁
H-FS

6421
2385.856
Hex₆HexNAc₄Fuc₂Neu5Ac₁
H-FS

6432
2823.009
Hex₆HexNAc₄Fuc₃Neu5Ac₂
H-FS

6500
2005.724
Hex₆HexNAc₅
C/H

6500

6501
2296.820
Hex₆HexNAc₅Neu5Ac₁
C/H-S

6501

6502
2587.915
Hex₆HexNAc₅Neu5Ac₂
C/H-S

6503
2879.011
Hex₆HexNAc₅Neu5Ac₃
C-S

6510
2151.782
HexeHexNAc₅Fuc₁
C/H-F

6510

6511
2442.878
Hex₆HexNAc₅Fuc₁Neu5Ac₁
C/H-FS

6511
2442.878
Hex₆HexNAc₅Fuc₁Neu5Ac₁
C/H-FS

6511

6512
2733.973
Hex₆HexNAc₅Fuc₁Neu5Ac₂
C/H-FS

6513
3025.068
Hex₆HexNAc₅Fuc₁Neu5Ac₃
C-FS

6520

6521
2588.936
Hex₆HexNAc₅Fuc₂Neu5Ac₁
C/H-FS

6522
2880.031
Hex₆HexNAc₅Fuc₂Neu5Ac₂
C/H-FS

6530
2443.898
Hex₆HexNAc₅Fuc₃
C/H-F

6530
2879.011
Hex₆HexNAc₅Neu₅Ac₃
C-S

6531
2734.993
Hex₆HexNAc₅Fuc₃Neu5Ac₁
C/H-FS

6532
3026.089
Hex₆HexNAc₅Fuc₃Neu5Ac₂
C/H-FS

6603
3082.090
Hex₆HexNAc₆Neu5Ac₃
C-S

6623
3374.206
Hex₆HexNAc₆Fuc₂Neu5Ac₃
C-FS

6630
3082.090
Hex₆HexNAc₆Neu5Ac₃
C-S

6631
2938.073
Hex₆HexNAceFuc₃Neu5Ac₁
C-FS

6632
3229.168
Hex₆HexNAc₆Fuc₃Neu5Ac₂
C-FS

6641
3084.131
Hex₆HexNAc₆Fuc₄Neu5Ac₁
C-FS

6642
3375.226
Hex₆HexNAc₆Fuc₄Neu5Ac₂
C-FS

6652
3521.284
Hex₆HexNAc₆Fuc₅Neu5Ac₂
C-FS

6713
3431.227
Hex₆HexNAc₇Fuc₁Neu5Ac₃
C-FS

6731
3141.152
Hex₆HexNAc₇Fuc₃Neu5Ac₁
C-FS

6740
2996.115
Hex₆HexNAc₇Fuc₄
C-F

7200
1558.539
GlcNAc₂Man₇
HM

7200

7200

7210
1704.597
GlcNAc₂Man₇Fuc₁
HM-F

7400
1964.698
Hex₇HexNAc₄
H

7400

7401
2255.793
Hex₇HexNAc₄Neu5Ac₁
H-S

7410
2110.756
Hex₇HexNAc₄Fuc₁
H-F

7411
2401.851
Hex₇HexNAc₄Fuc₁Neu5Ac₁
H-FS

7412
2692.946
Hex₇HexNAc₄Fuc₁Neu5Ac₂
H-FS

7420
2256.814
Hex₇HexNAc₄Fuc₂
H-F

7421
2547.909
Hex₇HexNAc₄Fuc₂Neu5Ac₁
H-FS

7430
2402.871
Hex₇HexNAc₄Fuc₃
H-F

7431
2693.967
Hex₇HexNAc₄Fuc₃Neu5Ac₁
H-FS

7432
2985.062
Hex₇HexNAc₄Fuc₃Neu5Ac₂
H-FS

7500
2167.777
Hex₇HexNAc₅
H

7500
2167.777
Hex₇HexNAc₅
H

7511
2604.930
Hex₇HexNAc₅Fuc₁Neu5Ac₁
H-FS

7512
2896.026
Hex₇HexNAc₅Fuc₁Neu5Ac₂
H-FS

7601
2661.952
Hex₇HexNAc₆Neu5Ac₁
C-S

7602
2953.047
Hex₇HexNAc₆Neu5Ac₂
C-S

7610
2516.914
Hex₇HexNAc₆Fuc₁
C-F

7610

7611
2808.010
Hex₇HexNAc₆Fuc₁Neu5Ac₁
C-FS

7611

7612
3099.105
Hex₇HexNAc₆Fuc₁Neu5Ac₂
C-FS

7613
3390.201
Hex₇HexNAc₆Fuc₁Neu5Ac₃
C-FS

7620
2662.972
Hex₇HexNAc₆Fuc₂
C-F

7621
2954.068
Hex₇HexNAc₆Fuc₂Neu5Ac₁
C-FS

7640
2955.088
Hex₇HexNAc₆Fuc₄
C-F

7713
3593.280
Hex₇HexNAc₇Fuc₁Neu5Ac₃
C-FS

7731
3303.205
Hex₇HexNAc₇Fuc₃Neu5Ac₁
C-FS

7740
3158.168
Hex₇HexNAc₇Fuc₄
C-F

7741
3449.263
Hex₇HexNAc₇Fuc₄Neu5Ac₁
C-FS

8200
1720.592
GlcNAc₂Man₈
HM

8200

GlcNAc₂Man₈

8200

9200
1882.645
GlcNAc₂Man₉
HM

9200

GlcNAc₂Man₉

9200

9210
2028.702
GlcNAc₂Man₉Fuc₁
HM-F

9210
2028.702
GlcNAc₂Man₉Fuc₁
HM-F

10200
2044.697
GlcNAc₂Man₁₀
HM

10200

11200

Example 3—CA 125 ELISA

This Example refers to FIG. 20.

An protein CA 125 (cancer antigen 125) enzyme-linked immunosorbent assay (ELISA) was performed on patient samples. The patient pool consisted of n=187 women with malignant ovarian cancer (stages 1-4) and n=198 women with benign breast or pelvic masses, purchased from Indivumed, GmbH in March, 2018.

The results of the ELISA assay are shown in FIG. 20.

At a Cutoff=35; the ELISA assay was observed to diagnose malignant ovarian cancer at the following levels of accuracy, sensitivity and specificity:

Accuracy=85.2% Sensitivity=84.0% Specificity=86.4%

The samples had a positive predictive value at 20% Prevalence=60.7%

The samples had a negative predictive value at 20% Prevalence=95.6%

There are approximately 22,000 new cases of ovarian cancer in the United States every year, which stem from approximately 110,000 pelvic masses (at 20% prevalence). Though the CA-125 ELISA set forth in this Example showed higher than commonly reported values, with comparison to literature (which is more typically around 80% sensitive and 70% specific), as observed the CA-125 ELISA test would correctly identify 18,480 of the malignant cancer and 76,032 of the benign cancers. This results in 11,968 false positives and 3520 false negatives.

Example 4—Glycoproteomic Trained Model Test

This Example refers to FIG. 21.

A model trained using SEQ ID NOs.: 1-150 was to identify the probability that a given patient sample had ovarian cancer.

The patient pool consisted of n=187 women with malignant ovarian cancer (stages 1-4) and n=198 women with benign breast or pelvic masses, purchased from Indivumed, GmbH in March, 2018.

The results are shown in FIG. 21.

At a Cutoff=0.32; the model was observed to diagnose malignant ovarian cancer at the following levels of accuracy, sensitivity and specificity:

Accuracy=91.9% Sensitivity=91.4% Specificity=92.4%

The samples had a positive predictive value at 20% Prevalence=75.0%.

The samples had a negative predictive value at 20% Prevalence=97.7%.

There are approximately 22,000 new cases of ovarian cancer in the United States every year, which stem from approximately 110,000 pelvic masses (at 20% prevalence).

The glycoproteomic test set forth in this Example correctly identified 20,108 of the malignant cancers and 81,312 of the benign cancers. This results in 6,688 false positives and 1,892 false negatives.

Compared with CA-125 ELISA test in Example 3, herein, and in the United States alone, using the glycoproteomic test set forth, herein, in Example 4, results in 5,280 less incorrect cancer diagnoses per year, and 1,628 more correct diagnoses that would otherwise have been missed. These 6,908 additional correctly-diagnosed patients would all be triaged to the appropriate surgery and surgeon, where they would not have been with the CA-125 test. This results in significantly less stress on patients, as well as on the gynecologic oncologists required to perform surgeries on predicted malignancies.

The embodiments and examples described above are intended to be merely illustrative and non-limiting. Those skilled in the art will recognize or will be able to ascertain using no more than routine experimentation, numerous equivalents of specific compounds, materials and procedures. All such equivalents are considered to be within the scope and are encompassed by the appended claims.

BIOMARKERS FOR DIAGNOSING OVARIAN CANCER

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

International Classifications

Abstract

Description

Claims

CROSS-REFERENCE TO RELATED APPLICATIONS

PCT Information

Provisional Applications (1)