Abstract

Determination of sensitive and accurate feature values from a matrix-assisted laser desorption/ionization (MALDI) spectrum of a sample is provided. A peak shape function of the mass spectrometer is read. A fine structure component is determined for a first range of the mass spectrum by estimating and subtracting a first background from the mass spectrum. A bump structure is determined for the first range by estimating a second background, which is stiffer than the first background, and subtracting it from the first background. A convolution of the fine structure component is computed for the first range of the mass spectrum with the peak shape function. A first plurality of peaks in the first range is determined from the convolution. A feature value indicative of an abundance associated with each of the first plurality of peaks is determined by combining the first plurality of peaks with the bump structure.

IPC Classes  ?

• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments; medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry, enzyme-linked immunosorbent assays and algorithms to test biological specimens for the presence of biomarkers, to assist in assessing the likelihood of malignancy. Diagnostic testing services that may be ordered by health care providers to provide information for medical treatment or medical treatment decisions for patients; medical testing services for testing biological specimens for the presence of biomarkers to assist in assessing the likelihood of malignancy for diagnostic or treatment purposes; Assay development and validation for biomarkers, testing, kitting and sample storage, and diagnostic commercialization.

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments; medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry, enzyme-linked immunosorbent assays and algorithms to test biological specimens for the presence of biomarkers, to assist in assessing the likelihood of malignancy. Diagnostic testing services that may be ordered by health care providers to provide information for medical treatment or medical treatment decisions for patients; medical testing services for testing biological specimens for the presence of biomarkers to assist in assessing the likelihood of malignancy for diagnostic or treatment purposes; Assay development and validation for biomarkers, testing, kitting and sample storage, and diagnostic commercialization.

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments; medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry, enzyme-linked immunosorbent assays and algorithms to test biological specimens for the presence of biomarkers, to assist  in assessing the likelihood of malignancy. Diagnostic testing services that may be ordered by health care providers to provide information for medical treatment or medical treatment decisions for patients; medical testing services for testing biological specimens for the presence of biomarkers to assist in assessing the likelihood of malignancy for diagnostic or treatment purposes;  Assay development and validation for biomarkers, testing, kitting and sample storage, and diagnostic commercialization.

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments; medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry, enzyme-linked immunosorbent assays and algorithms to test biological specimens for the presence of biomarkers, to assist  in assessing the likelihood of malignancy. Diagnostic testing services that may be ordered by health care providers to provide information for medical treatment or medical treatment decisions for patients; medical testing services for testing biological specimens for the presence of biomarkers to assist in assessing the likelihood of malignancy for diagnostic or treatment purposes;  Assay development and validation for biomarkers, testing, kitting and sample storage, and diagnostic commercialization.

Abstract

Determination of sensitive and accurate feature values from a matrix-assisted laser desorption/ionization (MALDI) spectrum of a sample is provided. A peak shape function of the mass spectrometer is read. A fine structure component is determined for a first range of the mass spectrum by estimating and subtracting a first background from the mass spectrum. A bump structure is determined for the first range by estimating a second background, which is stiffer than the first background, and subtracting it from the first background. A convolution of the fine structure component is computed for the first range of the mass spectrum with the peak shape function. A first plurality of peaks in the first range is determined from the convolution. A feature value indicative of an abundance associated with each of the first plurality of peaks is determined by combining the first plurality of peaks with the bump structure.

IPC Classes  ?

• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission

Abstract

A method of predicting whether an MDS patient has a good or poor prognosis uses a general purpose computer configured as a classifier and mass-spectrometry data obtained from a blood-based sample. The classifier assigns a classification label of either Early or Late (or the equivalent) to the patient's sample. Patients classified as Early are predicted to have a poor prognosis or worse survival whereas those patients classified as Late are predicted to have a relatively better prognosis and longer survival time. The groupings demonstrated a large effect size between groups in Kaplan-Meier analysis of survival. Most importantly, while the classifications generated were correlated with other prognostic factors, such as IPSS score and genetic category, multivariate and subgroup analysis showed that they had significant independent prognostic power complementary to the existing prognostic factors.

IPC Classes  ?

• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• H01J 49/40 - Time-of-flight spectrometers
• G01N 33/49 - Physical analysis of biological material of liquid biological material blood

Abstract

Provided are monoclonal antibodies, or antigen-binding fragments thereof, that bind to specific peptides of C163A or LG3BP, compositions comprising such antibodies and/or fragments, as well as methods of use and devices employing such antibodies and/or fragments.

IPC Classes  ?

• 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 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

A method of predicting whether an MDS patient has a good or poor prognosis uses a general purpose computer configured as a classifier and mass-spectrometry data obtained from a blood-based sample. The classifier assigns a classification label of either Early or Late (or the equivalent) to the patient's sample. Patients classified as Early are predicted to have a poor prognosis or worse survival whereas those patients classified as Late are predicted to have a relatively better prognosis and longer survival time. The groupings demonstrated a large effect size between groups in Kaplan-Meier analysis of survival. Most importantly, while the classifications generated were correlated with other prognostic factors, such as IPSS score and genetic category, multivariate and subgroup analysis showed that they had significant independent prognostic power complementary to the existing prognostic factors.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• H01J 49/40 - Time-of-flight spectrometers
• G01N 33/49 - Physical analysis of biological material of liquid biological material blood

Abstract

A method for predicting an unfavorable outcome for a patient admitted to a hospital, e.g., with a COVID-19 infection is described. Attributes from an electronic health record for the patient are obtained including at least findings obtained at admission, basic patient characteristics, and laboratory data. The attributes are supplied to a classifier implemented in a programmed computer which is trained to predict a risk of the unfavorable outcome. The classifier is arranged as a hierarchical combination of (a) an initial binary classifier stratifying the patient into either a high risk group or a low risk group, and (b) child classifiers further classifying the patient in a lowest risk group or a highest risk group depending how the initial binary classifier stratified the patient as either a member of the high risk or low risk group. The initial binary classifier is configured as a combination of a trained classification decision tree and a logistical combination of atomic classifiers with drop-out regularization.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• 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 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G06N 20/00 - Machine learning

Abstract

Provided are monoclonal antibodies, or antigen-binding fragments thereof, that bind to specific peptides of C163A or LG3BP, compositions comprising such antibodies and/or fragments, as well as methods of use and devices employing such antibodies and/or fragments.

IPC Classes  ?

• 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
• C07K 16/18 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans

Abstract

A method for predicting whether an early stage (IA, IB) non-small-cell lung cancer (NSCLC) patient is at a high risk of recurrence of the cancer following surgery involves subjecting a blood-based sample from the patient (obtained prior to, at, or after the surgery) to mass spectrometry and classification with a computer implementing a classifier. If the patients blood sample is classified as “high risk”, highest risk“or the equivalent, the patient can be guided to more aggressive treatment post-surgery. The classifier, or combination of classifiers, can be arranged in a hierarchical manner to make intermediate classifications, such as intermediate/high or intermediate/low, as well as low risk” or “lowest risk” classifications. Such additional classifications may guide clinical decisions as well.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G16B 40/20 - Supervised data analysis

Abstract

Methods and compositions for characterizing a biological sample (e.g., comprising an infectious agent) from a subject are provided. Methods can include detecting linkage of nucleic acids that are linked in a viable cell or organism but that become degraded and thus unlinked in inviable cells or organisms and then characterizing the subject based on the quantity of linked and unlinked sequences.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors

Abstract

Methods and compositions for characterizing a biological sample (e.g., comprising an infectious agent) from a subject are provided. Methods can include detecting linkage of nucleic acids that are linked in a viable cell or organism but that become degraded and thus unlinked in inviable cells or organisms and then characterizing the subject based on the quantity of linked and unlinked sequences.

IPC Classes  ?

• C12Q 1/6888 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms

Abstract

A method for predicting an unfavorable outcome for a patient admitted to a hospital, e.g., with a COVID-19 infection is described. Attributes from an electronic health record for the patient are obtained including, e.g., findings obtained at admission, basic patient characteristics, and laboratory data. The attributes are supplied to a classifier implemented in a programmed computer which is trained to predict a risk of the unfavorable outcome. The classifier is arranged as a hierarchical combination of an initial binary classifier stratifying the patient into a high or low risk group, and child classifiers further classifying the patient in a lowest or a highest risk group depending how the initial binary classifier stratified the patient as either a member of the high or low risk group. The initial binary classifier is configured as a combination of a trained classification decision tree and a logistical combination of atomic classifiers with drop-out regularization.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A method for predicting an unfavorable outcome for a patient admitted to a hospital, e.g., with a COVID-19 infection is described. Attributes from an electronic health record for the patient are obtained including at least findings obtained at admission, basic patient characteristics, and laboratory data. The attributes are supplied to a classifier implemented in a programmed computer which is trained to predict a risk of the unfavorable outcome. The classifier is arranged as a hierarchical combination of (a) an initial binary classifier stratifying the patient into either a high risk group or a low risk group, and (b) child classifiers further classifying the patient in a lowest risk group or a highest risk group depending how the initial binary classifier stratified the patient as either a member of the high risk or low risk group. The initial binary classifier is configured as a combination of a trained classification decision tree and a logistical combination of atomic classifiers with drop-out regularization.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• 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

Abstract

Shapley values (SVs) have become an important tool to further the goal of explainability of machine learning (ML) models. However, the computational load of exact SV calculations increases exponentially with the number of attributes. Hence, the calculation of SVs for models incorporating large numbers of interpretable attributes is problematic. Molecular diagnostic tests typically seek to leverage information from hundreds or thousands of attributes, often using training sets with fewer instances. Methods are described for evaluate SVs using Monte Carlo sampling or exact calculation in polynomial time (i.e., reasonably quickly and efficiently) using the architecture of a ML model designed for robust molecular test generation, and without requiring classifier retraining.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G06N 20/00 - Machine learning
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• 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

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• 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

Abstract

A method of generating a classifier includes a step of classifying each member of a development set of samples with a class label in a binary classification scheme with a first classifier; and generating a second classifier using a classifier development process with an input classifier development set being the members of the development set assigned one of the two class labels in the binary classification scheme by the first classifier. The second classifier stratifies the members of the set with an early label into two further sub-groups. We also describe identifying a plurality of different clinical sub-groups within the development set based on the clinical data and for each of the different clinical sub-groups, conducting a classifier generation process for each of the clinical sub-groups thereby generating clinical subgroup classifiers. We further describe an example of a hierarchical arrangement of such classifiers and their use in predicting, in advance of treatment, ovarian cancer patient outcomes on platinum-based chemotherapy.

IPC Classes  ?

• 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 40/20 - Supervised data 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

Abstract

A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

IPC Classes  ?

• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• 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 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• 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 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
• 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
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Goods & Services

diagnostic kits comprised of blood collecting tubes and bags, holder for medical sample tubes and vials and dessicant for collecting patient blood samples for the purpose of conducting diagnostic testing on the samples medical diagnostic testing and reporting services ordered by health care providers to provide them with information for disease detection, diagnosis, or treatment

Goods & Services

diagnostic kits comprised of blood collecting tubes and bags, holder for medical sample tubes and vials and dessicant for collecting patient blood samples for the purpose of conducting diagnostic testing on the samples medical diagnostic testing and reporting services ordered by health care providers to provide them with information for disease detection, diagnosis, or treatment

Abstract

The invention generally relates to the field of immunoassays. In particular, the invention relates to use of a calibrator material to calibrate immunoassays for autoantibodies.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G01N 33/564 - ImmunoassayBiospecific binding assayMaterials therefor for pre-existing immune complex or autoimmune disease
• G01N 33/531 - Production of immunochemical test materials
• G01N 33/58 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving labelled substances
• G01N 33/96 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving blood or serum control standard

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments in cancer patients Medical testing services relating to the diagnosis and treatment of disease for assessing the likelihood of patient outcomes and medical treatments

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments in cancer patients Medical testing services relating to the diagnosis and treatment of disease for assessing the likelihood of patient outcomes and medical treatments

Goods & Services

Medical testing kit comprised primarily of blood collecting tubes and bags, holder for medical sample tubes, vials and desiccant for testing blood for assessing the likelihood of patient outcomes and medical treatments in cancer patients Medical testing services relating to the diagnosis and treatment of disease for assessing the likelihood of patient outcomes and medical treatments

Abstract

Laboratory test apparatus for conducting a mass spectrometry test on a blood-based sample of a cancer patient includes a classification procedure implemented in a programmed computer that generates a class label. In one form of the test, “Test 1”, if the sample is labelled “Bad” or equivalent the patient is predicted to exhibit primary immune resistance if they are later treated with anti-PD-1 or anti-PD-L1 therapies. In “Test 2” the Bad class label predicts that the patient will have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies or alternative chemotherapies, such as docetaxel or pemetrexed. “Test 3” identifies patients that are likely to have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies but have improved outcomes on alternative chemotherapies. A Good class label by either Test 1 or 2 predicts very good outcome on anti-PD-1 or anti-PD-L1 monotherapy.

IPC Classes  ?

• 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
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G06N 20/00 - Machine learning
• G16B 40/20 - Supervised data 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
• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• H01J 49/44 - Energy spectrometers, e.g. alpha-, beta-spectrometers

Abstract

A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

IPC Classes  ?

• 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 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
• G16B 40/20 - Supervised data analysis
• 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 20/10 - 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
• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation

Abstract

Provided herein are methods for developing selected reaction monitoring mass spectrometry (LC-SRM-MS) assays.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

A method for predicting whether an early stage (IA, IB) non-small-cell lung cancer (NSCLC) patient is at a high risk of recurrence of the cancer following surgery involves subjecting a blood-based sample from the patient (obtained prior to, at, or after the surgery) to mass spectrometry and classification with a computer implementing a classifier. If the patient's blood sample is classified as "high risk", highest risk"or the equivalent, the patient can be guided to more aggressive treatment post-surgery. The classifier, or combination of classifiers, can be arranged in a hierarchical manner to make intermediate classifications, such as intermediate/high or intermediate/low, as well as low risk" or "lowest risk" classifications. Such additional classifications may guide clinical decisions as well.

IPC Classes  ?

• 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
• H01J 49/26 - Mass spectrometers or separator tubes
• G01N 33/49 - Physical analysis of biological material of liquid biological material blood
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)

Goods & Services

Medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, enzyme-linked immunosorbent assays and algorithms to assess the likelihood of malignancy Medical testing services for assessing the likelihood of malignancy for diagnostic or treatment purposes

Goods & Services

Medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry, enzyme-linked immunosorbent assays, and algorithms to assess the likelihood of malignancy Medical testing services for assessing the likelihood of malignancy for diagnostic or treatment purposes

Abstract

A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

IPC Classes  ?

• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• 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 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• 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 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
• 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

Abstract

A laboratory test apparatus for conducting a mass spectrometry test on a blood based sample of a cancer patient includes a classification procedure implemented in a programmed computer that generates a class label for the sample. In one form of the test; "Test 1 " herein, if the sample is labeled "Bad" or equivalent the patient is predicted to exhibit primary immune resistance if they are later treated with anti-PD-1 or anti-PD-L 1 therapies in treatment of the cancer. In another configuration of the test, "Test 2" herein, the Bad class label predicts that the patient will have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L 1 therapies or alternative chemotherapies, such as docetaxel or pemetrexed. 'Test 3" identifies patients that are likely to have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L 1 therapies but have improved outcomes on alternative chemotherapies.

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• C12N 5/00 - Undifferentiated human, animal or plant cells, e.g. cell linesTissuesCultivation or maintenance thereofCulture media therefor
• C12N 5/02 - Propagation of single cells or cells in suspensionMaintenance thereofCulture media therefor
• G01N 30/72 - Mass spectrometers

Abstract

The present disclosure relates to devices, methods and kits for blood sample separation and analysis. More particularly, the disclosure relates to devices, methods and kits that rapidly separate a blood sample into uniform solid and liquid phases in a sealed environment. A specific example includes a device with a door coupled to the housing, a blood sample separation medium, a mesh material and a desiccant. In one example, the blood sample separation medium is disposed between the housing and the mesh material, the desiccant is coupled to the door, the desiccant is distal from the mesh material when the door is in a first open position, and the desiccant is proximal to the mesh material when the door is in a second closed position.

IPC Classes  ?

• G01N 33/49 - Physical analysis of biological material of liquid biological material blood
• B01L 3/00 - Containers or dishes for laboratory use, e.g. laboratory glasswareDroppers
• G01N 1/40 - Concentrating samples
• B01L 3/02 - BurettesPipettes

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Goods & Services

Medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry and algorithms to assess the likelihood of malignancy Medical testing services for assessing the likelihood of malignancy for diagnostic or treatment purposes

Goods & Services

Medical diagnostic apparatus for testing biological specimens, namely, molecular diagnostic tests which use molecular tools, mass spectrometry and algorithms to assess the likelihood of malignancy; medical diagnostic apparatus, namely, molecular diagnostic tests which use molecular tools, mass spectrometry and algorithms to test biological specimens for the presence of biomarkers, to assist in assessing the likelihood of malignancy Medical testing services for assessing the likelihood of malignancy for diagnostic or treatment purposes; medical testing services for testing biological specimens for the presence of biomarkers, to assist in assessing the likelihood of malignancy

Abstract

A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• G16B 40/20 - Supervised data analysis
• 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 50/70 - 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
• C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• G16H 20/17 - 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 delivered via infusion or injection
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

IPC Classes  ?

• H01J 49/00 - Particle spectrometers or separator tubes
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• 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
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• 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 50/70 - 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
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Abstract

A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

IPC Classes  ?

• 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 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• 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 40/10 - Signal processing, e.g. from mass spectrometry [MS] or from PCR
• G16B 40/20 - Supervised data analysis
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• 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
• 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

Abstract

The present disclosure relates to a rapid diagnostic test system that includes the prospective collection of whole blood, preservation of circulating nucleic acids at ambient temperature, and the reproducible detection of nucleic acids including DNA and mRNA (including fusion transcripts and differentially expressed transcripts) by different genomic methodologies.

IPC Classes  ?

• C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
• 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
• C12Q 1/6851 - Quantitative amplification
• C12Q 1/6844 - Nucleic acid amplification reactions

Abstract

A blood-based sample from a cancer patient is subject to mass spectrometsy and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• C07K 16/00 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies

Abstract

A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

IPC Classes  ?

• G01N 24/00 - Investigating or analysing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
• H01J 49/40 - Time-of-flight spectrometers
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• 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
• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• 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

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• 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
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G06N 7/00 - Computing arrangements based on specific mathematical models
• 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
• G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)

Abstract

The present invention provides methods for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. The present invention also provides compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• 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

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

A method of generating a classifier includes a step of classifying each member of a development set of samples with a class label in a binary classification scheme with a first classifier; and generating a second classifier using a classifier development process with an input classifier development set being the members of the development set assigned one of the two class labels in the binary classification scheme by the first classifier. The second classifier stratifies the members of the set with an early label into two further sub -groups. We also describe identifying a plurality of different clinical sub-groups within the development set based on the clinical data and for each of the different clinical sub-groups, conducting a classifier generation process for each of the clinical sub-groups thereby generating clinical subgroup classifiers. We further describe an example of a hierarchical arrangement of such classifiers and their use in predicting, in advance of treatment, ovarian cancer patient outcomes on platinum-based chemotherapy.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• 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)
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission

Abstract

A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

Provided herein are methods for developing selected reaction monitoring mass spectrometry (LC-SRM-MS) assays.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• C07K 7/06 - Linear peptides containing only normal peptide links having 5 to 11 amino acids
• C07K 7/08 - Linear peptides containing only normal peptide links having 12 to 20 amino acids
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• 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

Abstract

A classifier and method for predicting or characterizing graft-versus-host disease in a patient after receiving a transplant of pluripotent hematopoietic stem cells or bone marrow. The classifier operates on mass-spectral data obtained from a blood-based sample of the patient and is configured as a combination of filtered mini-classifiers using a regularized combination method, such as logistic regression with extreme drop-out. The method also uses a “deep-MALDI” mass spectrometry technique in which the blood-based samples are subject to at least 100,000 laser shots in MALDI-TOF mass spectrometry in order to reveal greater spectral content and detect low abundance proteins circulating in serum associated with graft-versus-host disease.

IPC Classes  ?

• H01J 49/26 - Mass spectrometers or separator tubes
• H01J 49/00 - Particle spectrometers or separator tubes
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 38/17 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans
• G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
• G01N 33/49 - Physical analysis of biological material of liquid biological material blood
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• H01J 49/40 - Time-of-flight spectrometers

Abstract

A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label "early" or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label "late" or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

IPC Classes  ?

• 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
• G01N 30/72 - Mass spectrometers
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• H01J 49/26 - Mass spectrometers or separator tubes

Abstract

Classifier generation methods select features used in classification, or deselect using bagged filtering. A development sample set is split into two subsets, one of which is used as a training set the other of which is set aside. We define a classifier using the training subset and at least one of the features. We apply the classifier to a subset of samples. A filter is applied to the performance of the classifier on the sample subset and the at least one feature is added to a "filtered feature list" if the classifier performance passes the filter. We do this for many different realizations of the separation of the development sample set into two subsets, and, for each realization, different features or sets of features in combination. After all the iterations are performed the filtered feature list is used to either select features, or deselect features, for a final classifier.

IPC Classes  ?

• G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)

Abstract

Classifier generation methods are described in which features used in classification (e.g., mass spectral peaks) are selected, or deselected using bagged filtering. A development sample set is split into two subsets, one of which is used as a training set the other of which is set aside. We define a classifier (e.g., K-nearest neighbor, decision tree, margin-based classifier or other) using the training subset and at least one of the features (or subsets of two or more features in combination). We apply the classifier to a subset of samples. A filter is applied to the performance of the classifier on the sample subset and the at least one feature is added to a “filtered feature list” if the classifier performance passes the filter. We do this for many different realizations of the separation of the development sample set into two subsets, and, for each realization, different features or sets of features in combination. After all the iterations are performed the filtered feature list is used to either select features, or deselect features, for a final classifier.

IPC Classes  ?

• G06N 20/00 - Machine learning
• 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 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

Goods & Services

Research services relating to medical diagnosis; Laboratory testing services; Laboratory services relating to medical diagnosis and to medical treatment; Analytical laboratory services; laboratory services for preparation, detection, quantitation, and analysis of biological material, for genotyping, for diagnostic assays, and for carrying out nucleic acid sequencing reactions; Research services related to developing prognostic and predictive tests to determine whether patients or a subgroup of patients are likely to benefit from treatment with therapies or combination of therapies intended to treat a disease or disorder, including anti-cancer drugs for cancer; Design and development of computer hardware and software; Providing information regarding tests via an interactive website. Medical services for the diagnosis of conditions of the human body; Performing diagnosis of diseases; Commercial genomic testing services provided from a central laboratory ordered by oncologists to determine which cancer patients may have better outcomes on or benefit from treatments; information services relating to prediction of likely patient benefit from therapeutic treatment, including anti-cancer drugs for cancer.

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• 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 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification

Abstract

Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

IPC Classes  ?

• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• 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
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• 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
• 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
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Abstract

Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample, A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G01N 24/00 - Investigating or analysing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

IPC Classes  ?

• G01N 24/00 - Investigating or analysing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
• H01J 49/40 - Time-of-flight spectrometers
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• 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
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

IPC Classes  ?

• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• 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
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions

Abstract

A method of analyzing a biological sample, for example serum or other blood-based samples, using a MALDI-TOF mass spectrometer instrument is described. The method includes the steps of applying the sample to a sample spot on a MALDI-TOF sample plate and directing more than 20,000 laser shots to the sample at the sample spot and collecting mass-spectral data from the instrument. In some embodiments at least 100,000 laser shots and even 500,000 shots are directed onto the sample. It has been discovered that this approach, referred to as “deep-MALDI”, leads to a reduction in the noise level in the mass spectra and that a significant amount of additional spectral information can be obtained from the sample. Moreover, peaks visible at lower number of shots become better defined and allow for more reliable comparisons between samples.

IPC Classes  ?

• H01J 49/40 - Time-of-flight spectrometers
• G01N 33/487 - Physical analysis of biological material of liquid biological material
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

A test for predicting whether a non-small-cell lung cancer patient is more likely to benefit from an EGFR-I as compared to chemotherapy uses a computer-implemented classifier operating on a mass spectrum of a blood-based sample obtained from the patient. The classifier makes use of a training set which includes mass spectral data from blood-based samples of other cancer patients who are members of a class of patients predicted to have overall survival benefit on EGFRI-Is, e.g., those patients testing VS Good under the test described in US patent 7,736,905. This class-labeled group is further subdivided into two subsets, i.e., those patients which exhibited early (class label "early") and late (class label "late") progression of disease after administration of the EGFR-I in treatment of cancer.

IPC Classes  ?

• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions

Abstract

A test to identify whether a lung patient is likely to benefit from combination therapy in the form of an epidermal growth factor receptor inhibitor (EGFR-I) and a monoclonal antibody drug targeting hepatocyte growth factor (HGF) as compared to EGFR-I monotherapy. The test makes use of a mass spectrum obtained from a serum or plasma sample and a computer configured as a classifier operating on the mass spectrum and a training set in the form of class-labeled mass spectra from other cancer patients. The computer classifier executes a classification algorithm, such as K-nearest neighbor, and assigns a class label to the serum or plasma sample. Samples classified as "Poor" or the equivalent are associated with patients which are likely to benefit from the combination therapy more than from EGFR-I monotherapy. The invention also includes improved methods of treating patients predicted by the test.

IPC Classes  ?

• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• 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)
• H01J 49/26 - Mass spectrometers or separator tubes

Abstract

A test for predicting whether a non-small-cell lung cancer patient is more likely to benefit from an EGFR-I as compared to chemotherapy uses a computer-implemented classifier operating on a mass spectrum of a blood-based sample obtained from the patient. The classifier makes use of a training set which includes mass spectral data from blood-based samples of other cancer patients who are members of a class of patients predicted to have overall survival benefit on EGFRI-Is, e.g., those patients testing VS Good under the test described in U.S. Pat. No. 7,736,905. This class-labeled group is further subdivided into two subsets, i.e., those patients which exhibited early (class label “early”) and late (class label “late”) progression of disease after administration of the EGFR-I in treatment of cancer.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• A61K 38/17 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans
• H01J 49/26 - Mass spectrometers or separator tubes
• H01J 49/00 - Particle spectrometers or separator tubes
• G01N 33/49 - Physical analysis of biological material of liquid biological material blood
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification

Abstract

Methods are provided for determining the expression level of target proteins in a subject. A plurality of respective peptide transitions are generated from a plurality of proteins obtained from a biological sample from the subject, wherein the plurality of proteins comprises both target and normalizing proteins. A mass spectroscopy (MS) signal intensity is measured from the plurality of respective peptide transitions and a plurality of corresponding stable isotope-labeled internal standard (SIS) peptide transitions. For each of the plurality of proteins, a response ratio is calculated between the MS signal intensity of the respective peptide transition and the corresponding SIS peptide transition. The response ratio for each target protein is normalized by a sample-dependent normalization factor calculated from the response ratio for each normalizing protein, wherein the normalized response ratios provide a determination of the expression level of the target proteins.

IPC Classes  ?

• 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

Abstract

Methods are provided for determining the expression level of target proteins in a subject. A plurality of respective peptide transitions are generated from a plurality of proteins obtained from a biological sample from the subject, wherein the plurality of proteins comprises both target and normalizing proteins. A mass spectroscopy (MS) signal intensity is measured from the plurality of respective peptide transitions and a plurality of corresponding stable isotope-labeled internal standard (SIS) peptide transitions. For each of the plurality of proteins, a response ratio is calculated between the MS signal intensity of the respective peptide transition and the corresponding SIS peptide transition. The response ratio for each target protein is normalized by a sample-dependent normalization factor calculated from the response ratio for each normalizing protein, wherein the normalized response ratios provide a determination of the expression level of the target proteins.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06F 19/20 - for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification

Goods & Services

Commercial genomic testing services provided from a central laboratory ordered by oncologists to determine which cancer patients may have better outcomes on or benefit from treatments

Abstract

A method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label. Individual mini-classifiers are generated using sets of features from the samples. The performance of the mini-classifiers is tested, and those that meet a performance threshold are retained. A master classifier is generated by conducting a regularized ensemble training of the retained/filtered set of mini-classifiers to the classification labels for the samples, e.g., by randomly selecting a small fraction of the filtered mini-classifiers (drop out regularization) and conducting logistical training on such selected mini-classifiers. The set of samples are randomly separated into a test set and a training set. The steps of generating the mini-classifiers, filtering and generating a master classifier are repeated for different realizations of the separation of the set of samples into test and training sets, thereby generating a plurality of master classifiers. A final classifier is defined from one or a combination of more than one of the master classifiers.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 49/26 - Mass spectrometers or separator tubes

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

A method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label. Individual mini-classifiers are generated using sets of features from the samples. The performance of the mini-classifiers is tested, and those that meet a performance threshold are retained. A master classifier is generated by conducting a regularized ensemble training of the retained/filtered set of mini- classifiers to the classification labels for the samples, e.g., by randomly selecting a small fraction of the filtered mini-classifiers (drop out regularization) and conducting logistical training on such selected mini-classifiers. The set of samples are randomly separated into a test set and a training set. The steps of generating the mini-classifiers, filtering and generating a master classifier are repeated for different realizations of the separation of the set of samples into test and training sets, thereby generating a plurality of master classifiers. A final classifier is defined from one or a combination of more than one of the master classifiers.

IPC Classes  ?

• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification

Goods & Services

Scientific research and technological services in the field of scientific discovery, namely, scientific research and testing to understand patients and their diseases

Abstract

The present invention provides methods for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. The present invention also provides compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions

Abstract

The present invention provides methods for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. The present invention also provides compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions

Abstract

A testing method for identification whether a cancer patient is a member of a group or class of cancer patients that are not likely to benefit from administration of a platinum-based chemotherapy agent, e.g., cisplatin, carboplatin or analogs thereof, either alone or in combination with other non-platinum chemotherapy agents, e.g., gemcitabine and paclitaxel. This identification can be made in advance of treatment The method uses a mass spectrometer obtaining a mass spectrum of a blood-based sample from the patient, and a computer operating as a classifier and using a stored training set comprising class-labeled spectra from other cancer patients.

IPC Classes  ?

• 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

Abstract

A method and system for predicting in advance of treatment whether a cancer patient is likely, or not likely, to obtain benefit from administration of a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, alone or in combination with another anti-cancer therapy. The method uses mass spectrometry of a blood-derived patient sample and a computer configured as a classifier using a naming set of class-labeled spectra from other cancer patients that either benefitted or did not benefit from an immune response generating therapy alone or in combination with another anti-cancer therapy. Also disclosed are methods of treatment of a cancer patient, comprising administering a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, to a patient selected by a test in accordance with predictive mass spectral methods disclosed herein, in which the class label for the spectra indicates the patient is likely to benefit from the yeast-based immunotherapy.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

A method and system for predicting in advance of treatment whether a cancer patient is likely, or not likely, to obtain benefit from administration of a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, alone or in combination with another anti-cancer therapy. The method uses mass spectrometry of a blood-derived patient sample and a computer configured as a classifier using a naming set of class-labeled spectra from other cancer patients that either benefitted or did not benefit from an immune response generating therapy alone or in combination with another anti-cancer therapy. Also disclosed are methods of treatment of a cancer patient, comprising administering a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, to a patient selected by a test in accordance with predictive mass spectral methods disclosed herein, in which the class label for the spectra indicates the patient is likely to benefit from the yeast-based immunotherapy.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

A method of analyzing a biological sample, for example serum or other blood-based samples, using a MALDI-TOF mass spectrometer instrument is described. The method includes the steps of applying the sample to a sample spot on a MALDI-TOF sample plate and directing more than 20,000 laser shots to the sample at the sample spot and collecting mass-spectral data from the instrument. In some embodiments at least 100,000 laser shots and even 500,000 shots are directed onto the sample. It has been discovered that this approach, referred to as “deep-MALDI”, leads to a reduction in the noise level in the mass spectra and that a significant amount of additional spectral information can be obtained from the sample. Moreover, peaks visible at lower number of shots become better defined and allow for more reliable comparisons between samples.

IPC Classes  ?

• G01N 33/487 - Physical analysis of biological material of liquid biological material
• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• H01J 49/00 - Particle spectrometers or separator tubes
• H01J 27/24 - Ion sourcesIon guns using photo-ionisation, e.g. using laser beam
• H01J 49/40 - Time-of-flight spectrometers

Abstract

A merhod of analyzing a biological sample, for example seram or other blood-based samples, using a MALDI-TOF mass spectrometer instrument is described. The method includes the steps of applying the sample to a sample spot on a MALDI-TOF sample plate and directing more than 20,000 laser shots to the sample at the sample spot and collecting mass-spectra! data from the instrument. In some embodiments at least 100.000 laser shots and even 500,000 shots are directed onto the sample. It lias been discovered that mis approach, referred to as "deep-MALDF, leads to a reduction in the noise level in the mass spectra and that a significant amount of additional spectral information can be obtained from the sample. Moreover, peaks visible at lower number of shots become better defined and allow for more reliable comparisons between samples.

IPC Classes  ?

• H01J 49/16 - Ion sourcesIon guns using surface ionisation, e.g. field-, thermionic- or photo-emission
• H01J 49/00 - Particle spectrometers or separator tubes
• G01N 33/487 - Physical analysis of biological material of liquid biological material

Abstract

Provided herein are methods for developing selected reaction monitoring mass spectrometry (LC-SRM-MS) assays.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 30/72 - Mass spectrometers
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• 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 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions

Abstract

Provided herein are methods for developing selected reaction monitoring mass spectrometry (LC-SRM-MS) assays.

IPC Classes  ?

• C12Q 1/37 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving hydrolase involving peptidase or proteinase
• 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 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

Provided herein are methods for developing selected reaction monitoring mass spectrometry (LC-SRM-MS) assays.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

A method and system for validating machine performance of a mass spectrometer makes use of a machine qualification set of samples. The mass spectrometer operates on the machine qualification set of samples and obtains a set of performance evaluation mass spectra. The performance evaluation spectra are classified with respect to a classification reference set of spectra with the aid of a programmed computer executing a classification algorithm. The classification algorithm also operates on a set of spectra obtained in a previous standard machine run of the machine qualification set of samples. The results from the classification algorithm are then compared with respect to predefined, objective performance criteria (e.g., class label concordance and others) and a machine validation result, e.g., PASS or FAIL, is generated from the comparison.

IPC Classes  ?

• G01C 25/00 - Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass

Goods & Services

(1) Blood testing apparatus, namely, blood collecting tubes. (1) Medical testing for diagnostic or treatment purposes.

Abstract

A mass-spectral method is disclosed for determining whether breast cancer patient is likely to benefit from a combination treatment in the form of administration of a targeted anti-cancer drug in addition to an endocrine therapy drug. The method obtains a mass spectrum from a blood-based sample from the patient. The spectrum is subject to one or more predefined pre-processing steps. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained. The values are used in a classification algorithm using a training set comprising class-labeled spectra and a class label for the sample is obtained. If the class label is “Poor”, the patient is identified as being likely to benefit from the combination treatment. In a variation, the “Poor” class label predicts whether the patient is unlikely to benefit from endocrine therapy drugs alone, regardless of the patient's HER2 status.

IPC Classes  ?

• G01N 31/00 - Investigating or analysing non-biological materials by the use of the chemical methods specified in the subgroupsApparatus specially adapted for such methods
• A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
• 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 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• H01J 49/00 - Particle spectrometers or separator tubes

Goods & Services

Research services related to developing predictive tests for determining whether a patient is likely to benefit from administration of a particular drug or combination of drugs

Goods & Services

Biochemicals, namely, small molecule chemicals for scientific or research use; laboratory chemicals, namely, small molecule chemicals used for the detection of antigens in cell and tissue analysis for in vitro diagnostic use. Imaging agents for medical diagnostic imaging; small molecule chemicals used as therapeutic agents. Research and development in the fields of imaging agents for medical diagnostic imaging, small molecule chemicals used as therapeutic agents and for the detection of antigens in cell and tissue analysis for in vitro diagnostic use.

Abstract

Disclosed herein are novel synthetic prostate specific antigen (PSA)-targeted capture agents that specifically bind PSA. In certain embodiments, these PSA capture agents are biligand or triligand capture agents containing two or three target-binding moieties, respectively.

IPC Classes  ?

• A61K 38/10 - Peptides having 12 to 20 amino acids
• A61K 38/00 - Medicinal preparations containing peptides
• A61P 35/00 - Antineoplastic agents
• A61K 38/12 - Cyclic peptides

Abstract

A mass-spectral method is disclosed for determining whether a breast cancer patient is likely to benefit from administration of a combination treatment in the form of a targeted anti-cancer drug in addition to an endocrine therapy drug. The method obtains a mass spectrum from a blood-based sample from the patient. The spectrum is subject to one or more predefined pre-processing steps. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained. The values are used in a classification algorithm using a training set comprising class-labeled spectra a class label for the sample is obtained. If the class label is “Poor”, the patient is identified as being likely to benefit from the combination treatment. In a variation, the “Poor” class label predicts whether the patient is unlikely to benefit from endocrine therapy drugs alone, regardless of the patient's HER2 status.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
• 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 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• H01J 49/00 - Particle spectrometers or separator tubes

Abstract

A mass-spectral method is disclosed for determining whether a post-menopausal, HER2-negative breast cancer patient is likely to benefit from administration of a combination treatment in the form of administration of a targeted anti-cancer drug in addition to an endocrine therapy drug. The method obtains a mass spectrum from a blood-based sample from the patient. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained. The values are used in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other cancer patients and a class label for the sample is obtained. If the class label is "Poor," the patient is identified as being likely to benefit from the combination treatment. The "Poor" class label is used to predict whether a breast cancer patient is unlikely to benefit from endocrine therapy drugs alone, regardless of the patient's HER2 status.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

Disclosed herein are novel diagnostic lung cancer panels and the use of these panels to diagnose, predict, and characterize lung cancer and to monitor or predict treatment efficacy.

IPC Classes  ?

• 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 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer

Abstract

Disclosed herein are novel diagnostic lung cancer panels and the use of these panels to diagnose, predict, and characterize lung cancer and to monitor or predict treatment efficacy.

IPC Classes  ?

• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups

Abstract

Novel compositions, methods, assays and kits directed to a diagnostic panel for Alzheimer's disease are provided. In one embodiment, the diagnostic panel includes one or more proteins associated with Alzheimer's disease.

IPC Classes  ?

• G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor

Abstract

A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

IPC Classes  ?

• G01N 24/00 - Investigating or analysing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
• G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions

Abstract

The present invention relates generally to methods for identifying and using organ-specific proteins and transcripts. The present invention further provides compositions comprising organ-specific proteins and transcripts encoding the same, detection reagents for detecting such proteins and transcripts, and diagnostic panels, kits and arrays for measuring organ-specific proteins/transcripts in blood, biological tissue or other biological fluid.

IPC Classes  ?

• A61K 49/00 - Preparations for testing in vivo