Identification and use of glycopeptides as biomarkers for diagnosis and treatment monitoring
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. A method for identifying glycosylated peptide biomarkers, comprising:
fragmenting glycosylated proteins using multiple reaction monitoring mass spectrometry (MRM-MS) in each of a plurality of biological samples isolated from subjects to produce glycosylated peptide fragments;
wherein the MRM-MS is targeted on at least 50 or more glycosylated peptide fragments;
quantitating the glycosylated peptide fragments to provide quantitation results;
analyzing the quantitation results along with classification of the subjects with a machine learning method to select glycosylated peptide fragments useful for predicting the classification; and
determining the identity of glycosylated peptide fragments;
wherein the machine learning method is selected from deep learning, neural network, linear discriminant analysis, quadratic discriminant analysis, support vector machine, random forest, nearest neighbor, or a combination thereof; and
wherein the machine learning method comprises combined discriminant analysis.
2. The method of claim 1 , wherein the subjects comprise subjects having a disease or a condition and subjects not having the disease or the condition.
3. The method of claim 2 , wherein the subjects comprise subjects receiving a treatment for a disease and subjects having the disease but not receiving a treatment.
4. The method of claim 3 , wherein the disease is cancer or an autoimmune disease.
5. The method of claim 4 , wherein the disease is cancer selected from breast cancer, cervical cancer and ovarian cancer.
6. The method of claim 5 , wherein the disease is an autoimmune disease selected from HIV, primary sclerosing cholangitis, primary biliary cirrhosis and psoriasis.
7. The method of claim 1 , wherein the glycosylated peptide fragment is N-glycosylated.
8. The method of claim 1 , wherein the glycosylated peptide fragment is O-glycosylated.
9. The method of claim 1 , wherein the glycosylated proteins are 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.
10. The method of claim 9 , wherein the glycosylated proteins are one or more of alpha-1-acid glycoprotein, immunoglobulin (Ig) A, IgG and IgM.
11. The method of claim 1 , wherein the glycosylated peptide fragments have an average length of from 5 to 50 amino acid residues.
12. The method of claim 1 , wherein the biological sample is body tissue, saliva, tears, sputum, spinal fluid, urine, synovial fluid, whole blood, serum or plasma.
13. The method of claim 12 , wherein the biological samples are whole blood, serum or plasma.
14. The method of claim 1 , wherein the subjects are mammals.
15. The method of claim 14 , wherein the subject are humans.
16. The method of claim 1 , wherein the machine learning method comprises deep learning, neural network or a combination thereof.
17. The method of claim 1 , wherein the analysis further comprises genomic data, proteomics, metabolics, lipidomics data, or a combination thereof.