Identification and use of glycopeptides as biomarkers for diagnosis and treatment monitoring

Abstract

Provided herein are methods for identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glvcomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.

1 .- 20 . (canceled) 
     
     
         21 . A method for determining a prediction model for a classification of an unclassified human subject, the method comprising:
 (a) subjecting each of a plurality of biological samples to one or more proteases to produce a set of respective protease-processed samples,   wherein each of the plurality of biological samples is from a human subject having a classification assigned based on having a disease or not having the disease, and   wherein the plurality of biological samples comprises samples from human subjects having the disease and human subjects not having the disease;   (b) subjecting each protease-processed sample to a liquid chromatography multiple reaction monitoring mass spectrometry (LC-MRM-MS) technique configured to selectively interrogate target species of interest,   wherein the target species of interest comprise a plurality of glycopeptides;   (c) analyzing the information obtained from the LC-MRM-MS technique to produce quantitation results for each protease-processed sample; and   (d) subjecting the quantitation results of each protease-processed sample along with the associated classification to a machine learning method to determine the prediction model.   
     
     
         22 . The method of  claim 21 , wherein the plurality of glycopeptides is associated with at least more than 50 glycoproteins. 
     
     
         23 . The method of  claim 21 , wherein the disease is cancer. 
     
     
         24 . The method of  claim 23 , wherein the cancer is breast cancer. 
     
     
         25 . The method of  claim 23 , wherein the cancer is breast cancer, cervical cancer, or ovarian cancer. 
     
     
         26 . The method of  claim 21 , wherein the disease is an autoimmune disease. 
     
     
         27 . The method of  claim 26 , wherein the autoimmune disease is HIV infection-associated autoimmune disease, primary sclerosing cholangitis, primary biliary cirrhosis, or psoriasis. 
     
     
         28 . The method of  claim 27 , wherein the autoimmune disease is primary biliary cholangitis or primary biliary cirrhosis. 
     
     
         29 . The method of  claim 21 , wherein the target species of interest comprise glycopeptides from one or more of alpha-1-acid glycoprotein, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein, alpha-2-macroglobulin, antithrombin-III, apolipoprotein B-100, apolipoprotein D, apolipoprotein F, beta-2-glycoprotein 1, ceruloplasmin, fetuin, fibrinogen, immunoglobulin (Ig) A, IgG, IgM, haptoglobin, hemopexin, histidine-rich glycoprotein, kininogen-1, serotransferrin, transferrin, and vitronectin zinc-alpha-2-glycoprotein. 
     
     
         30 . The method of  claim 29 , wherein the target species of interest comprise glycopeptides from one or more of immunoglobulin (Ig) A, IgG, and IgM. 
     
     
         31 . The method of  claim 30 , wherein the quantitation results comprising information from IgG, IgA, and IgM glycopeptides of each protease-processed sample along with the associated classification are subjected to a machine learning method comprising a combined discriminant analysis. 
     
     
         32 . The method of  claim 21 , wherein the one or more proteases comprise a serine protease. 
     
     
         33 . The method of  claim 32 , wherein the serine protease is selected from the group consisting of trypsin, chymotrypsin, endoproteinase, Arg-C, Glu-C, Lys-C, and proteinase K. 
     
     
         34 . The method of  claim 21 , wherein the plurality of biological samples are each selected from the group consisting of a whole blood sample, serum sample, and plasma sample. 
     
     
         35 . The method of  claim 34 , wherein the plurality of biological samples are whole blood samples. 
     
     
         36 . The method of  claim 34 , wherein the plurality of biological samples are serum samples. 
     
     
         37 . The method of  claim 34 , wherein the plurality of biological samples are plasma samples. 
     
     
         38 . The method of  claim 21 , wherein the machine learning method comprises a deep learning, neural network, discriminant analysis, support vector machine, random forest, nearest neighbor algorithm, or a combination thereof. 
     
     
         39 . The method of  claim 38 , wherein the machine learning method comprises the deep learning, neural network algorithm, or a combination thereof. 
     
     
         40 . The method of  claim 21 , further comprising identifying one or more glycopeptides indicative of the classification of an unclassified human subject. 
     
     
         41 . The method of  claim 21 , wherein the human subjects not having the disease are healthy donors. 
     
     
         42 . The method of  claim 21 , wherein the LC-MRM-MS technique is performed on a triple quadrupole mass spectrometer. 
     
     
         43 . The method of  claim 42 , wherein the triple quadrupole mass spectrometer has a mass accuracy of 10 ppm or better. 
     
     
         44 . The method of  claim 21 , wherein the plurality of biological samples comprises samples from at least 20 human subjects having the disease and at least 20 human subjects not having the disease. 
     
     
         45 . The method of  claim 21 , wherein the plurality of biological samples comprises samples from at least 40 human subjects having the disease and at least 40 human subjects not having the disease.