Patent Application
20230065917
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.
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 still needed.
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. Proteome 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.
In another embodiment, set forth herein is a glycopeptide or peptide consisting of, or consisting essentially of, an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76.
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 1-76, as 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: 1-76, and combinations thereof; and 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 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 glycopeptides in the sample; detecting a MRM transition selected from the group consisting of transitions 1-76; 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 1-76; 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; wherein the therapeutic agent is selected from hormone therapy if classification C or I is determined; wherein the therapeutic agent is selected from targeted therapy if classification D or I is determined; or 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: 1-76; 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: 1-76; or to detect and quantify one or more MRM transitions selected from transitions 1-76; 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; 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: 1-76.
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.
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.
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.
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. Glycan structures are described by a glycan reference code number, and also illustrated in International PCT Patent Application No. PCT/US2020/016286, filed Jan. 31, 2020, which is herein incorporated by reference in its entirety for all purposes. For example see FIGS. 1 through 14 of PCT Patent Application No. PCT/US2020/016286, filed Jan. 31, 2020, which are herein incorporated by reference in their entirety for all purposes. Glycans are illustrated 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_1, uses 1 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
As used herein, the term “glycopeptide,” refers to a peptide having at least one glycan residue bonded thereto. In each embodiment described herein, the glycopeptide may comprise, consist essentially of, or consist of, the amino acid sequence specified by the indicated SEQ ID NO together with one or more glycans, for instance those described herein associated with that SEQ ID NO. For instance, a glycopeptide according to SEQ ID NO: 1, as used herein, can refer to a glycopeptide according to the amino acid sequence of SEQ ID NO: 1 and glycan 5402, wherein the glycan is bonded to residue 1424 of SEQ ID NO: 1. A glycopeptide comprising SEQ ID NO: 1, as used herein, can refer to a glycopeptide comprising the amino acid sequence of SEQ ID NO: 1 and glycan 5402, wherein the glycan is bonded to residue 1424. A glycopeptide consisting essentially of SEQ ID NO: 1, as used herein, can refer to a glycopeptide consisting essentially of the amino acid sequence of SEQ ID NO: 1 and glycan 5402, wherein the glycan is bonded to residue 1424. A glycopeptide consisting of SEQ ID NO: 1, as used herein, can refer to a glycopeptide consisting of the amino acid sequence of SEQ ID NO: 1 and glycan 5402, wherein the glycan is bonded to residue 1424. Similarly usage applies to SEQ ID NOs: 2-30, with the glycans described in sections below.
As used herein, the phrase “glycosylated peptides,” refers to a peptide bonded to a glycan.
As used herein, the phrase “glycopeptide fragment” or “glycosylated peptide fragment” or “glycopeptide” 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, e.g., ion fragmentation within a MRM-MS instrument. MRM refers to multiple-reaction-monitoring. Unless specified otherwise, within the specification, “glycopeptide fragments” or “fragments of a glycopeptide” refer to the fragments produced directly by using a mass spectrometer optionally after the glycoprotein has been digested enzymatically to produce the glycopeptides.
As used herein, the phrase “glycoprotein” refers to the glycosylated protein from which the glycosylated peptide is obtained.
As used herein, the phrase “peptide,” is meant to include glycopeptides, and not the glycosylated protein from which the glycosylated peptide is obtained, unless stated otherwise.
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, 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 phrase also refers to 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. 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. 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 of, or consisting essentially of, an amino acid sequence selected from SEQ ID NOs: 1-76, and combinations thereof, according to Tables e.g., Table 1, Table 2, Table 3, Table 4, or Table 5, or a combination thereof. 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 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.
As used herein, the term “about” indicates and encompasses an indicated value and a range above and below that value. In certain embodiments, the term “about” indicates the designated value ±10%, ±5%, or ±1%. In certain embodiments, the term “about” indicates the designated value ±one standard deviation of that value.
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: 1-76 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: 1-76 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: 1-76 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: 1-76 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: 1-76. In some examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NOs: 1-76.
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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76. In some examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76.
a. O-Glycosylation
In some examples, the glycopeptides set forth herein include O-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 (A1AT), 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 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), Hemopexin (HEMO), Immunoglobulin heavy constant gamma 1 (IgG1), Immunoglobulin J chain (IgJ), and combinations thereof.
c. Peptides and Glycopeptides
In some examples, set forth herein is a glycopeptide or peptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting of an amino acid sequence selected from SEQ ID NO: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting essentially of an amino acid sequence selected from SEQ ID NO: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting of an amino acid sequence selected from SEQ ID NO: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
In some examples, set forth herein is a glycopeptide or peptide consisting essentially of an amino acid sequence selected from SEQ ID NOs: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, 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 glycan 5402, wherein the glycan is bonded to residue 271. In some examples, the glycopeptide is A1AT-GP001_271_5402. Herein A1AT 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 5412 at residue 271. In some examples, the glycopeptide is A1AT-GP001_271_5412.
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 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:3 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:4. In some examples, the glycopeptide comprises glycans 5421 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 glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:5. 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:6. In some examples, the glycopeptide comprises glycan 5411 at residue 1424. In some examples, the glycopeptide is A2MG-GP004_1424_5411.
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 5401 at residue 55. In some examples, the glycopeptide is A2MG-GP004_55_5401.
In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO: 8. 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:9. In some examples, the glycopeptide comprises glycan 5411 at residue 55. In some examples, the glycopeptide is A2MG-GP004_55_5411.
In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:10. In some examples, the glycopeptide comprises glycan 5200 at residue 869. In some examples, the peptide is A2MG-GP004_869_5200.
In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:11. In some examples, the glycopeptide comprises glycan 869 at residue 5401. 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:12. 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:13. 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 glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:14. In some examples, the glycopeptide comprises glycan 6503 at residue 72. 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:15. In some examples, the glycopeptide comprises glycan 6513 at residue 72. In some examples, the glycopeptide is AGP12-GP007&008_72MC_6513.
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 7601 at residue 72. 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:17. In some examples, the glycopeptide comprises glycan 7602 at residue 72. 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:18. In some examples, the glycopeptide comprises glycan 7603 at residue 72. 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:19. In some examples, the glycopeptide comprises glycan 7613 at residue 72. 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:20. In some examples, the glycopeptide comprises glycan 5402 at residue 33. In some examples, the glycopeptide is AGP1-GP007_33_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 glycans 6503 or 6522, or both, at residue 93. In some examples, the glycopeptide is AGP1-GP007_93_6503/6522. 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:22. 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:23. 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:24. 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:25. In some examples, the glycopeptide comprises glycan 1102 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74_1102. Herein APOC3 refers to Apolipoprotein C-III.
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 1102 at residue 74. In some examples, the glycopeptide is APOC3-GP012_74MC_1102.
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 5401 at residue 253. In some examples, the glycopeptide is APOH-GP015_253_5401. Herein APOH refers to Beta-2-glycoproteinl.
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 5412 at residue 138. In some examples, the glycopeptide is CERU-GP023_138_5412. Herein CERU refers to Ceruloplasmin.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:29. In some examples, the glycopeptide comprises glycans 5421 or 5402, or both, at residue 138. In some examples, the glycopeptide is CERU-GP023_138_5421/5402.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:30. In some examples, the glycopeptide comprises glycans 6503 or 6522, or both, at residue 138. In some examples, the glycopeptide is CERU-GP023_138_6503/6522.
In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:31. In some examples, the glycopeptide comprises glycans 5421 or 5402, or both, at residue 882. In some examples, the glycopeptide is CFAH-GP024_882_5421/5402. Herein CFAH refers to ComplementFactorH.
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 5200 at residue 85. In some examples, the glycopeptide is CO3-GP028_85_5200. Herein CO3 refers to ComplementC3.
In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:33. In some examples, the glycopeptide comprises glycan 6200 at residue 85. In some examples, the glycopeptide is CO3-GP028_85_6200.
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 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:35. In some examples, the glycopeptide comprises glycans 5421 or 5402, or both, at residue 156. In some examples, the glycopeptide is FETUA-GP036_156_5402/5421. 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:36. In some examples, the glycopeptide comprises glycan 6513 at residue 176. In some examples, the glycopeptide is FETUA-GP036_176_6513.
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 1101 at residue 346. In some examples, the glycopeptide is FETUA-GP036_346_1101.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:38. In some examples, the glycopeptide comprises glycans 5412 or 5431, or both, at residue 187. In some examples, the glycopeptide is HEMO-GP042_187_5412/5431. Herein HEMO refers to Hemopexin.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:39. In some examples, the glycopeptide comprises glycans 5420 or 5401, or both, at residue 453. In some examples, the glycopeptide is HEMO-GP042_453_5420/5401.
In certain examples, the peptide comprises an amino acid sequence selected from SEQ ID NO:40. In some examples, the glycopeptide comprises glycan 6502 at residue 184. In some examples, the glycopeptide is HPT-GP044_184_6502. Herein HPT refers to Haptoglobin.
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 10803 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_10803.
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 10804 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_10804.
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 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:44. In some examples, the glycopeptide comprises glycan 11914 at residue 207. In some examples, the glycopeptide is HPT-GP044_207_11914.
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 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:46. In some examples, the glycopeptide comprises glycans 5401 or 5420, or both, at residue 241. In some examples, the glycopeptide is HPT-GP044_241_5401/5420.
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 5412 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_5412.
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 6501 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6501.
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 6502 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6502.
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 6511 at residue 241. In some examples, the glycopeptide is HPT-GP044_241_6511.
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 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:52. In some examples, the glycopeptide comprises glycan 5501 at residue 144. In some examples, the glycopeptide is IgA12-GP046&047_144_5501. 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:53. In some examples, the glycopeptide comprises glycan 4510 at residue 205. In some examples, the glycopeptide is IgA2-GP047_205_4510. 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:54. 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:55. In some examples, the glycopeptide comprises glycan 5501 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:56. In some examples, the glycopeptide comprises glycan 3410 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_3410. Herein IgG1 refers to Immunoglobulin heavy constant gamma 1.
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 4400 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_4400.
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 4510 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_4510.
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 5400 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_5400.
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 5410 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_5410.
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 5411 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_5411.
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 5510 at residue 297. In some examples, the glycopeptide is IgG1-GP048_297_5510.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:63. In some examples, the glycopeptide is IgG1-GP048_297_nonglycosylated.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:64. In some examples, the glycopeptide comprises glycan 3510 at residue 297. In some examples, the glycopeptide is IgG2-GP049_297_3510. 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:65. 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:66. In some examples, the glycopeptide comprises glycan 4510 at residue 297. In some examples, the glycopeptide is IgG2-GP049_297_4510.
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 5401 at residue 71. In some examples, the glycopeptide is IgJ-GP052_71_5401. Herein IgJ refers to Immunoglobulin J chain.
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 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:69. In some examples, the glycopeptide comprises glycan 4311 at residue 46. In some examples, the glycopeptide is IgM-GP053_46_4311.
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 6503 at residue 205. In some examples, the glycopeptide is KNG1-GP057_205_6503. Herein KNG1 refers to Kininogen-1.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:71. In some examples, the glycopeptide comprises glycan 5402 at residue 432. In some examples, the glycopeptide is TRFE-GP064_432_5402. Herein TRFE refers to Serotransferrin.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:72. In some examples, the glycopeptide comprises glycan 5401 at residue 630. In some examples, the glycopeptide is TRFE-GP064_630_5401.
In certain examples, the glycopeptide consists essentially of an amino acid sequence selected from SEQ ID NO:73. 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:74. 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:75. 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:76. In some examples, the glycopeptide comprises glycan 5421 at residue 86. In some examples, the glycopeptide is VTNC-GP067_86_5421.
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 1-76. In certain examples, the method includes detecting MRM transitions selected from the group consisting of 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, 76, and combinations thereof. In certain examples, the method includes detecting MRM transitions selected from the group consisting of 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof. In certain examples, the method includes detecting MRM transitions selected from the group consisting of 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof. 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, including any of the foregoing, the methods include fragmenting a glycopeptide in the sample; and detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof.
In some examples, including any of the foregoing, the methods include fragmenting a glycopeptide in the sample; and detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
In some examples, including any of the foregoing, the methods include fragmenting a glycopeptide in the sample; and detecting a multiple-reaction-monitoring (MRM) transition selected from the group consisting of transitions 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, 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: 1-76, 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: 1-76, 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 (A1AT), 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 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: 1-76, 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: 1-76, and combinations thereof.
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: 1-76, and combinations thereof.
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: 1-76, 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: 1-76, 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: 1-76, 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, 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 1-76. 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: 1-76 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: 1-76 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 1-76. 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: 1-76.
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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75. 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75. 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75.
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 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76. 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76. 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76.
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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76.
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: 1-76, 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 1-76. 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: 1-76 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: 1-76 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: 1-76 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 1-76. 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: 1-76.
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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75. 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75. 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75.
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 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76. 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76. 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76.
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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76.
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: 1-76, 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 by 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: 1-76 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: 1-76 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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, and 75 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, and 76 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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: 1-76 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: 1-76 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76 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
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: 1-76, 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: 1-76, 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: 1-76. 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: 1-76, 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: 1-76.
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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76.
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: 1-76, 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 1-76; 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 1-76; 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: 1-76, 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 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 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: 1-76.
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: 1-76.
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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76.
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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76.
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
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 1-76; 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/m2 IV for 4 weeks may be administered. Paraplatin may be administered in combination=as 300 mg/m2 IV (plus cyclophosphamide 600 mg/m2 IV) q4Weeks. Taxol may be administered at 175 mg/m2 IV over 3 hours q3Weeks (follow with cisplatin). Taxol may be administered at 135 mg/m2 IV over 24 hours q3Weeks (follow with cisplatin). Taxol may be administered at 135-175 mg/m2 IV 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: 1-76 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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof.
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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, 76, and combinations thereof.
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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, 76, 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: 1-76 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 1-76 using a QQQ and/or a qTOF mass spectrometer.
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.
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.
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: 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: 1-76; or to detect and quantify one or more MRM transitions selected from transitions 1-76; 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 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: 1-76; or to detect and quantify one or more MRM transitions selected from transitions 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76; 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 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: 1-76; or to detect and quantify one or more MRM transitions selected from transitions 1-76.
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: 1-76; or to detect one or more MRM transitions selected from transitions 1-76; 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, set forth herein is a method for diagnosing a patient having ovarian cancer; the method comprising: analyzing detected or quantified glycopeptides or 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: 1-76; or to detect one or more MRM transitions selected from transitions 1-76; 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.
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: 1-76 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: 1-76; 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, 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: 1-76 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: 1-76 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 sample comprising a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76, which is a pooled sample from one or more patients 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: 1-76 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: 1-76 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: 1-76. 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: 1-76. 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: 1-76. 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, 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 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.
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: 1-76.
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: 1-76.
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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof.
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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof.
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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof.
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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof.
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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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: 1-76. 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: 1-76. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof. 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, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof. 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75. 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, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75. 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof. 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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.
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: 1-76. These standards may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76. In certain examples, these standards may include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof. In certain examples, these standards may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof. In certain examples, these standards may include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof. In certain examples, these standards may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof. In certain examples, these standards may include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof. In certain examples, these standards may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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: 1-76. In certain examples, these samples may include one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof. In certain examples, these samples may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof. In certain examples, these samples may include one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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: 1-76. In certain examples, the 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: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof. In certain examples, the 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: 1, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof. In certain examples, the 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: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 76, and combinations thereof.
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: 1-76 to the concentration of another biomarker.
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: 1-76 to the concentration of another biomarker.
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: 1-76 to the amount of one or more glycopeptides consisting of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76.
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: 1-76 to the amount of one or more glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1-76.
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: 1-76.
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: 1-76.
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: 1-76 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 1-76 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 1-76 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 1-76, 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.
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.
This Example refers to
As shown in
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
This Example refers to
As shown in
This Example refers to
An protein CA 125 (cancer antigen 125) enzyme-linked immunosorbent assay (ELISA) was performed on patient samples. The patient pool consisted of the first n=41 enrolled women from InterVenn's prospective trial to detect ovarian cancer malignancy (VOCAL). There were n=12 women with borderline or malignant cancer, as well as n=29 women with benign pelvic masses, representing the exact patient population that would be targeted in the clinical setting. Models were trained on previously purchased retrospective samples from commercial biobanks, and applied blind to the VOCAL trial participants.
The results of the ELISA assay are shown in
At a Cutoff=35; the ELISA assay was observed to diagnose malignant ovarian cancer at the following levels of accuracy, sensitivity and specificity:
Accuracy=56% Sensitivity=86% Specificity=48%
The samples had a positive predictive value at 20% Prevalence=29%
The samples had a negative predictive value at 20% Prevalence=93%
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). As observed the CA-125 test would correctly identify 18,920 of the malignant cancers and 42,240 of the benign cancers. There would be 45,760 false positives and 3080 false negatives.
This Example refers to
A model trained using SEQ ID NOs.: 1-76 was to identify the probability that a given patient sample had ovarian cancer.
The patient pool consisted of the first n=41 enrolled women from InterVenn's prospective trial to detect ovarian cancer malignancy (VOCAL). There were n=12 women with borderline or malignant cancer, as well as n=29 women with benign pelvic masses, representing the exact patient population that would be targeted in the clinical setting. Models were trained on previously purchased retrospective samples from commercial biobanks, and applied blind to the VOCAL trial participants.
The results are shown in
At a Cutoff=0.54; the model was observed to diagnose malignant ovarian cancer at the following levels of accuracy, sensitivity and specificity:
Accuracy=92% Sensitivity=86% Specificity=93%
The samples had a positive predictive value at 20% Prevalence=75.0%.
The samples had a negative predictive value at 20% Prevalence=96%.
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 4 would correctly identify 18,920 of the malignant cancers and 81,840 of the benign cancers. There would be 6,160 false positives and 3,080 false negatives.
Compared with CA-125, and in the United States alone, this would amount to 39,600 less incorrect cancer diagnoses per year. These 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.
This application claims priority to, and the benefit of, U.S. Provisional Patent Application No. 62/968,941, filed Jan. 31, 2020, the entire contents of which are herein incorporated by reference in its entirety for all purposes.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/015915 | 1/29/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62968941 | Jan 2020 | US |
