A method of classifying a biological sample from a subject with respect to responsiveness to immune checkpoint inhibitor therapy comprising analyzing an amount or absence of a fucosylated PD-1 variant from the biological sample from the subject and generating a diagnosis output based on the amount or absence of the fucosylated PD-1 variant which can be used to classify whether the subject is likely to benefit or not likely to benefit from the immune checkpoint inhibitor therapy.
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
G01N 27/623 - Ion mobility spectrometry combined with mass spectrometry
G01N 33/543 - ImmunoassayBiospecific binding assayMaterials therefor with an insoluble carrier for immobilising immunochemicals
G01N 33/52 - Use of compounds or compositions for colorimetric, spectrophotometric or fluorometric investigation, e.g. use of reagent paper
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
2.
PEPTIDE BIOMARKERS FOR DIAGNOSING PRIMARY SCLEROSING CHOLANGITIS OR PRIMARY BILIARY CHOLANGITIS
Provided herein are methods of diagnosing and determining a risk of an individual for developing Primary Sclerosing Cholangitis (PSC) or Primary Biliary Cholangitis (PBC) based upon the presence, absence, or amount of biomarkers, such as peptides. Also provided herein are methods of treating PSC or PBC based upon the presence, absence, or amount of such biomarkers and compositions comprising one or more peptides.
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
C07K 7/08 - Linear peptides containing only normal peptide links having 12 to 20 amino acids
G01N 33/533 - Production of labelled immunochemicals with fluorescent label
A61P 1/16 - Drugs for disorders of the alimentary tract or the digestive system for liver or gallbladder disorders, e.g. hepatoprotective agents, cholagogues, litholytics
C07K 9/00 - Peptides having up to 20 amino acids, containing saccharide radicals and having a fully defined sequenceDerivatives thereof
3.
METHODS AND SYSTEMS FOR ANALYZING SITE-SPECIFIC MONOMER COMPOSITION
Embodiments disclosed herein generally relate to technologies for analyzing peptide structures from a biological sample obtained from a subject. Certain methods disclosed herein can include receiving peptide structure data corresponding to the biological sample obtained from the subject and calculating a site occupancy score and monomer weight score from the peptide structure data. An additional step in the disclosed methods can include generating a diagnosis output for an indication or disease state. The diagnosis output can, in some aspects, indicate whether the subject has or does not have a disease and/or whether a subject has a disease that is or is not responsive to a particular therapy.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
4.
DIAGNOSIS OF COLORECTAL CANCER USING TARGETED QUANTIFICATION OF PEPTIDES
The present disclosure encompasses systems, methods, and compositions for diagnosing a subject for an AA or colorectal cancer (CRC) disease state by ascertaining the presence of certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from the subject. Specific embodiments encompass methods of measuring certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from subjects known to have or suspected of having an AA or CRC disease state or subjects undergoing routine health care maintenance for possible presence of an AA or CRC disease state. The disclosure provides systems, methods, and compositions to identify subjects at-risk for CRC or AA and increases subject colonoscopy compliance, in specific embodiments.
Provided herein are methods of diagnosing NSCLC based upon the presence, absence, or amount of biomarkers, such as glycopeptides. Also provided herein are methods of treating NSCLC based upon the presence, absence, or amount of such biomarkers and compositions comprising one or more glycopeptide.
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06F 17/18 - Complex mathematical operations for evaluating statistical data
6.
DIAGNOSIS OF OVARIAN CANCER USING TARGETED QUANTIFICATION OF SITE-SPECIFIC PROTEIN GLYCOSYLATION
A method and system for diagnosing a subject with respect to an ovarian cancer disease state. Peptide structure data corresponding to a biological sample obtained from the subject is received. The peptide structure data is analyzed using a supervised machine learning model to generate a disease indicator that indicates whether biological sample evidences the ovarian cancer disease state based on at least 1 peptide structures selected from a group of peptide structures identified in Table 3B, 3C, or 3D. The group of peptide structures in Table 3B, 3C, or 3D comprises a group of peptide structures associated with the ovarian cancer disease state. A diagnosis output is generated based on the disease indicator.
BIOMARKERS FOR DETERMINING A CANCER DISEASE STATE, RESPONSE TO IMMUNO-ONCOLOGY, STAGES OF FIBROSIS IN NON-ALCOHOLIC STEATOHEPATITIS, OR APPLICATION OF AGE OR SEX RELATED BIOMARKER PANEL FOR QUALITY CONTROL
Provided herein are methods, devices, and kits for identifying glycosylated polypeptide biomarkers and signatures for progression of a disease or a condition, such as cancer or NASH, or and response of the disease or condition to a treatment. Also provided herein are: i) methods of generating and analyzing glycosylated polypeptide biomarkers, ii) methods of validating a model using glycosylated polypeptides for predicting the disease or condition or for making treatment recommendation, iii) systems and methods for implementing QC of a cohort of samples by analyzing peptide structure data for each sample using a machine learning model to generate a predicted age and/or sex associated for each sample. The quality control issue may include an error of mislabeled samples or an error from sample preparation, or a systemic measurement or an instrument error.
A method, system, and composition related to the preparation of samples for glycoproteomic analysis is described. The sample preparation process can include a proteolytic digestion step followed by a measurement step of the glycopeptide and peptide amounts in the proteolytic digest using a liquid chromatography-mass spectrometry system. Optionally, the sample preparation process can also include the collection of the sample on an absorbent or bibulous member where the proteins and glycoproteins are later extracted and then digested for glycoproteomic analysis. Glycopeptide and peptide measurements of biological samples were analyzed to provide a diagnosis of a disease such as, for example, ovarian cancer or to assess whether a patient with melanoma is likely or not likely to benefit from checkpoint inhibitor therapy.
The present disclosure encompasses systems, methods, and compositions for diagnosing a subject for a high-grade advanced pre-malignant lesions or colorectal cancer (CRC) disease state by ascertaining the presence of certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from the subject. Specific embodiments encompass methods of measuring certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from subjects known to have or suspected of having a high-grade advanced pre-malignant lesions or CRC disease state or subjects undergoing routine health care maintenance for possible presence of a high-grade advanced pre-malignant lesions or CRC disease state. The disclosure provides systems, methods, and compositions to identify subjects at-risk for CRC or high-grade advanced pre-malignant lesions and increases subject colonoscopy compliance, in specific embodiments.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
A system and method of training a machine learning model to predict glycopeptide fragmentation patterns and retention times. Spectral data for a plurality of fragments of a glycopeptide structure is received. Glycan fragment composition data is generated using the spectral data. The glycan fragment composition data identifies a plurality of composition codes and a plurality of total intensities for a plurality of glycan fragments identified from the plurality of fragments using the spectral data. A linear glycan sequence is created using the glycan fragment composition data. A training input is formed for a machine learning model using the linear glycan sequence. The machine learning model is trained using the training input to predict a fragmentation pattern and a retention time for the glycopeptide structure.
Set forth herein is a system and method for converting glycan representations between different platform-specific glycan formats via a universal glycan format. The use of a universal glycan format for conversion between different platform-specific glycan formats, such as different glycomolecule search engine-specific formats, can reduce the number of conversion rule sets required to convert glycan data formats among the different platform-specific glycan formats. The universal glycan format can also improve readability, and simplify analysis of glycomolecules. Also provided is a system and method for comparing or analyzing glycomolecule search results from different glycomolecule search engines and a user interface, system and method for curating a consensus list of glycomolecules (e.g., glycopeptides, glycoDNA, glycoRNA, glycolipids) identified in a biological sample on a user interface from glycomolecule search results sets from multiple glycomolecule search engines, where the glycomolecule search results sets may include conflicting identifications between the glycomolecule search engines.
Provided herein are methods of diagnosing preeclampsia based upon the presence, absence, or amount of biomarkers, such as glycopeptides. Also provided herein are methods of treating preeclampsia based upon the presence, absence, or amount of such biomarkers and compositions comprising one or more glycopeptide. Specifically the diagnosis and treatment of preeclampsia utilizes computer-generated analyses of quantitative data to classify correlations between marker profiles and disease states.
A method and system for diagnosing a subject with respect to a pancreatic cancer disease state. Peptide structure data corresponding to a biological sample obtained from the subject is received. The peptide structure data is analyzed using a supervised machine learning model to generate a disease indicator that indicates whether biological sample evidences the PC disease state based on at least 3 peptide structures selected from a group of peptide structures of Group I identified in Table 1 or of Group II of Table 8. The group of peptide structures in Table 1 or Table 8 comprises a group of peptide structures associated with the PC disease state. The group of peptide structures is listed in Table 1 with respect to relative significance to the disease indicator. A diagnosis output is generated based on the disease indicator
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
14.
PREDICTING SARCOMA TREATMENT RESPONSE USING TARGETED QUANTIFICATION OF SITE-SPECIFIC PROTEIN GLYCOSYLATION
A method and system for managing a treatment for a subject diagnosed with a sarcoma disease state. Peptide structure data corresponding to a biological sample obtained from the subject is received. A response score that predicts a likelihood of responsiveness to the treatment is computed using quantification data identified from the peptide structure data for a set of peptide structures. The set of peptide structures includes at least one peptide structure identified from a plurality of peptide structures listed in Table 1. The plurality of peptide structures is listed in Table 1 with respect to relative significance to a survival for the sarcoma disease state. A treatment response output is generated based on the response score.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
G16B 40/10 - Signal processing, e.g. from mass spectrometry [MS] or from PCR
15.
AI-DRIVEN GLYCOPROTEOMICS LIQUID BIOPSY IN NASOPHARYNGEAL CARCINOMA
A method and system for diagnosing a subject with respect to a nasopharyngeal carcinoma (NPC) disease state. Peptide structure data corresponding to a biological sample obtained from the subject is received. The peptide structure data is analyzed using a supervised machine learning model to generate a disease indicator that indicates whether biological sample evidences the NPC disease state based on at least 3 peptide structures selected from a group of peptide structures identified in Table 1A and/or 1B. The group of peptide structures in Table 1A and/or 1B comprises a group of peptide structures associated with the NPC disease state. The group of peptide structures is listed in Table 1A and/or 1B with respect to relative significance to the disease indicator. A diagnosis output is generated based on the disease indicator.
Embodiments described herein generally relate to systems and methods for processing mass spectrometry samples. Aspects of the disclosure include systems and methods for processing samples. Additionally, embodiments of the disclosure can also include systems and methods for sample analysis. Various embodiments include data analysis systems and methods for comparing data across samples and sample runs. Data analysis systems can run normalization methods for normalizing raw abundance mass spectrometry data. In some aspects, the normalized data can be used as input for predictive models.
Embodiments disclosed herein generally relate to technologies for evaluating a biological sample obtained from a subject with respect to a sepsis state or coronavirus disease (COVID). Some methods relating to the technologies can include receiving peptide structure data corresponding to the biological sample obtained from the subject, identifying a peptide structure profile for the biological sample using the peptide structure data, and computing a disease indicator using the peptide structure profile and a model. The disease indicator can indicate whether the biological sample is positive for the sepsis state. The disease indicator can indicate whether the biological sample is positive for COVID. The peptide structure profile can comprise quantification data for a set of peptide structures associated with the sepsis state. The peptide structure profile can include peptides that are glycosylated, aglycosylated, or both. An additional step in the method can comprise generating at least one of a diagnosis output or a treatment output based on the disease indicator.
A61K 31/53 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with three nitrogens as the only ring hetero atoms, e.g. chlorazanil, melamine
18.
BIOMARKERS FOR DIAGNOSING COLORECTAL CANCER OR ADVANCED ADENOMA
Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as colorectal cancer or advanced adenoma. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning algorithms.
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 40/10 - Signal processing, e.g. from mass spectrometry [MS] or from PCR
Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as ovarian cancer. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning systems.
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
20.
BIOMARKERS FOR DETERMINING AN IMMUNO-ONCOLOGY RESPONSE
Provided herein are methods, devices, and kits for identifying glycosylated polypeptide biomarkers and signatures for progression of a disease or a condition, such as cancer, or and response of the disease or condition to a treatment, such as treatment with immune checkpoint blockade for cancer. Provided herein are methods of generating glycosylated polypeptide biomarkers and methods of analyzing glycosylated polypeptides using mass spectrometry. Provided herein are methods of validating a model using glycosylated polypeptides for predicting the disease or condition or for making treatment recommendation.
Embodiments described herein generally relate to technologies for analyzing peptide structures for diagnosing and/or treating a disease state advancing through a disease progression. A non-limiting example of a method relating to the technologies described in the subject application may include receiving peptide structure data corresponding to the biological sample obtained from the subject, identifying a peptide structure profile, and diagnosing a disease state within a disease progression. The example may further include generating a diagnosis output relating to the disease state. In at least some cases, the peptide structure profile may include glycosylated peptides, aglycosylated peptides, or both.
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 50/00 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
Set forth herein are methods useful for identifying disease biomarkers, particularly for diseases such as clear cell renal cell carcinoma (ccRCC). In some examples, the methods set forth herein are useful for monitoring the prognosis of patients having a disease such as ccRCC.
C12Q 1/34 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving hydrolase
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as but not limited to, cancer (e.g., ovarian), an autoimmune disease, fibrosis and aging conditions. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning algorithms.
A system and method for automated detection of the presence or absence of a quantity based on intensities expressed in terms of, or derived from frequency or time dependent data. According to one example intensities from mass spectrometry are identified using a non-linear mathematical model, such as an artificial neural network trained to find start and stop peaks of an intensity, from which an abundance may be determined.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as but not limited to, cancer (e.g., ovarian), an autoimmune disease, fibrosis and aging conditions. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning algorithms.
Systems and methods of quantifying a glycomic parameter, a genomic parameter, a proteomic parameter, a metabolic parameter, and/or a lipidomic parameter of a biological sample; obtaining a clinical parameter associated with a subject from which the one or more biological samples originated; determining one or more relationships between one or more of: (i) one or more of the quantified glycomic parameters, genomic parameters, proteomic parameters, metabolic parameters, and lipidomic parameters, (ii) a predetermined range associated with one or more of the quantified glycomic parameters, genomic parameters, proteomic parameters, metabolic parameters, and lipidomic parameters, and (iii) an obtained clinical parameter; identifying one or more biomarkers based on one or more of the determined relationships satisfying a predetermined significance criteria; and/or determining a wellness classification state of a wellness classification, the determination of the wellness classification state determined based on the one or more identified biomarkers.
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G06F 19/10 - Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology (in silico methods of screening virtual chemical libraries C40B 30/02;in silico or mathematical methods of creating virtual chemical libraries C40B 50/02)
27.
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.
C12Q 1/37 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving hydrolase involving peptidase or proteinase
patents
