Abstract: 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 disease such as ccRCC.

Abstract: 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.

Abstract: 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.

Abstract: 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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.