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.
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
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.
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
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
7.
SYSTEMS AND METHODS FOR GLYCOPEPTIDE CONCENTRATION DETERMINATION, NORMALIZED ABUNDANCE DETERMINATION, AND LC/MS RUN SAMPLE PREPARATION
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.
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.
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
10.
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.
A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
C07K 9/00 - Peptides having up to 20 amino acids, containing saccharide radicals and having a fully defined sequenceDerivatives thereof
C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
C12Q 1/37 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving hydrolase involving peptidase or proteinase
C40B 30/10 - Methods of screening libraries by measuring physical properties, e.g. mass
C40B 40/10 - Libraries containing peptides or polypeptides, or derivatives thereof
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 15/30 - Drug targeting using structural dataDocking or binding prediction
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
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 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
11.
BIOMARKERS FOR DIAGNOSING NON-ALCOHOLIC STEATOHEPATITIS (NASH) OR HEPATOCELLULAR CARCINOMA (HCC)
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.
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
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.
C07K 9/00 - Peptides having up to 20 amino acids, containing saccharide radicals and having a fully defined sequenceDerivatives thereof
C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
G01N 27/62 - Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosolsInvestigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electric discharges, e.g. emission of cathode
G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
G01N 33/483 - Physical analysis of biological material
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
13.
AUTOMATED DETECTION OF BOUNDARIES IN MASS SPECTROMETRY DATA
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.
C40B 20/08 - Direct analysis of the library members per se by physical methods, e.g. spectroscopy
G01N 27/00 - Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
G01N 37/00 - Details not covered by any other group of this subclass
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
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 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.
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