44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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.
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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.
3.
SENSITIVE AND ACCURATE FEATURE VALUES FROM DEEP MALDI SPECTRA
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.
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.
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.
C07K 16/28 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des récepteurs, des antigènes de surface cellulaire ou des déterminants de surface cellulaire
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
6.
Predictive test for prognosis of myelodysplastic syndrome patients using mass spectrometry of blood-based sample
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.
G01N 33/50 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
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.
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p.ex. pour la gestion du personnel hospitalier ou de salles d’opération
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
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.
C07K 16/28 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains contre des récepteurs, des antigènes de surface cellulaire ou des déterminants de surface cellulaire
C07K 16/18 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux contre du matériel provenant d'animaux ou d'humains
9.
PREDICTIVE TEST FOR IDENTIFICATION OF EARLY STAGE NSCLC STAGE PATIENTS AT HIGH RISK OF RECURRENCE AFTER SURGERY
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.
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
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.
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.
C12Q 1/6888 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour la détection ou l’identification d’organismes
12.
METHOD FOR PREDICTING RISK OF UNFAVORABLE OUTCOMES SUCH AS HOSPITALIZATION, FROM CLINICAL CHARACTERISTICS AND BASIC LABORATORY FINDINGS
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.
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
13.
Method for predicting risk of unfavorable outcomes, e.g., in COVID-19 hospitalization, from clinical characteristics and basic laboratory findings
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.
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p.ex. pour la gestion du personnel hospitalier ou de salles d’opération
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
14.
INTERPRETATION OF MACHINE LEARNING CLASSIFICATIONS IN CLINICAL DIAGNOSTICS USING SHAPELY VALUES AND USES THEREOF
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.
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p.ex. leur création ou leur transmission
15.
COMPOSITIONS, METHODS AND KITS FOR DIAGNOSIS OF LUNG CANCER
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).
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
16.
Classifier generation methods and predictive test for ovarian cancer patient prognosis under platinum chemotherapy
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.
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p.ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
17.
METHOD FOR IDENTIFICATION OF CANCER PATIENTS WITH DURABLE BENEFIT FROM IMMUNOTEHRAPY IN OVERALL POOR PROGNOSIS SUBGROUPS
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.
G01N 33/50 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p.ex. leurs effets secondaires ou leur usage prévu
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/564 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour complexes immunologiques préexistants ou maladies auto-immunes
G01N 33/531 - Production de matériaux de tests immunochimiques
G01N 33/58 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des substances marquées
G01N 33/96 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir un étalon de contrôle du sang ou du sérum
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
25.
Apparatus and method for identification of primary immune resistance in cancer patients
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.
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
PREDICTIVE TEST FOR PATIENT BENEFIT FROM ANTIBODY DRUG BLOCKING LIGAND ACTIVATION OF THE T-CELL PROGRAMMED CELL DEATH 1 (PD-1) CHECKPOINT PROTEIN AND CLASSIFIER DEVELOPMENT METHODS
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.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G16B 40/10 - Traitement du signal, p.ex. de spectrométrie de masse ou de réaction en chaîne par polymérase
G16H 40/63 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement local
G16H 20/30 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies ou des activités physiques, p.ex. la physiothérapie, l’acupression ou les exercices
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
27.
SRM methods in Alzheimer's disease and neurological disease assays
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
28.
PREDICTIVE TEST FOR IDENTIFICATION OF EARLY STAGE NSCLC PATIENTS AT HIGH RISK OF RECURRENCE AFTER SURGERY
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.
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
H01J 49/26 - Spectromètres de masse ou tubes séparateurs de masse
G01N 33/49 - Analyse physique de matériau biologique de matériau biologique liquide de sang
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
31.
Method for identification of cancer patients with durable benefit from immunotherapy in overall poor prognosis subgroups
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.
G01N 33/50 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p.ex. leurs effets secondaires ou leur usage prévu
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
32.
APPARATUS AND METHOD FOR IDENTIFICATION OF PRIMARY IMMUNE RESISTANCE IN CANCER PATIENTS
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.
A61K 39/00 - Préparations médicinales contenant des antigènes ou des anticorps
C12N 5/00 - Cellules non différenciées humaines, animales ou végétales, p.ex. lignées cellulaires; Tissus; Leur culture ou conservation; Milieux de culture à cet effet
C12N 5/02 - Propagation de cellules individuelles ou de cellules en suspension; Leur conservation; Milieux de culture à cet effet
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.
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et services
Medical services; Medical testing; Information and advisory services relating to blood collection kits for medical testing relating to immunological detection of antibodies in the serum of patients
Medical apparatus, namely, blood collection kits for medical testing relating to immunological detection of anti-bodies in the serum of patients, comprising a serum transfer tube, biohazard return bag with pocket, absorbent sheet, bubble pocket, integrity seal for the transfer tube, and a sterile disposable pipette; Blood collection kits comprising a contact-activated lancet and collection tube
36.
COMPOSITIONS, METHODS AND KITS FOR DIAGNOSIS OF LUNG CANCER
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).
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
39.
Predictive test for melanoma patient benefit from interleukin-2 (IL2) therapy
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.
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
C12Q 1/68 - Procédés de mesure ou de test faisant intervenir des enzymes, des acides nucléiques ou des micro-organismes; Compositions à cet effet; Procédés pour préparer ces compositions faisant intervenir des acides nucléiques
A61K 39/00 - Préparations médicinales contenant des antigènes ou des anticorps
G16H 20/17 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients administrés par perfusion ou injection
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
40.
Early detection of hepatocellular carcinoma in high risk populations using MALDI-TOF mass spectrometry
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.
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
41.
Predictive test for patient benefit from antibody drug blocking ligand activation of the T-cell programmed cell death 1 (PD-1) checkpoint protein and classifier development methods
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.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G16B 40/10 - Traitement du signal, p.ex. de spectrométrie de masse ou de réaction en chaîne par polymérase
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
G16H 20/30 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies ou des activités physiques, p.ex. la physiothérapie, l’acupression ou les exercices
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
42.
Diagnostic test system for specific, sensitive and reproducible detection of circulating nucleic acids in whole blood
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.
C12Q 1/68 - Procédés de mesure ou de test faisant intervenir des enzymes, des acides nucléiques ou des micro-organismes; Compositions à cet effet; Procédés pour préparer ces compositions faisant intervenir des acides nucléiques
C12Q 1/6886 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/50 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
C07K 16/00 - Immunoglobulines, p.ex. anticorps monoclonaux ou polyclonaux
44.
Predictive test for aggressiveness or indolence of prostate cancer from mass spectrometry of blood-based sample
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.
G01N 24/00 - Recherche ou analyse des matériaux par l'utilisation de la résonance magnétique nucléaire, de la résonance paramagnétique électronique ou d'autres effets de spin
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
45.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
46.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p.ex. basé sur des systèmes experts médicaux
G16H 40/63 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement local
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
47.
Compositions, methods and kits for diagnosis of lung cancer
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).
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).
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.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06F 19/10 - Bio-informatique, c. à d. procédés ou systèmes pour le traitement de données génétiques ou se rapportant aux protéines en biologie moléculaire informatique (procédés in silico de criblage de bibliothèques chimiques virtuelles C40B 30/02;procédés mathématiques ou in silicio de création de bibliothèques chimiques virtuelles C40B 50/02)
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
C07K 7/06 - Peptides linéaires ne contenant que des liaisons peptidiques normales ayant de 5 à 11 amino-acides
C07K 7/08 - Peptides linéaires ne contenant que des liaisons peptidiques normales ayant de 12 à 20 amino-acides
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 27/62 - Recherche ou analyse des matériaux par l'emploi de moyens électriques, électrochimiques ou magnétiques en recherchant les décharges électriques, p.ex. l'émission cathodique
53.
Method of predicting development and severity of graft-versus-host disease
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.
A61K 38/17 - Peptides ayant plus de 20 amino-acides; Gastrines; Somatostatines; Mélanotropines; Leurs dérivés provenant d'humains
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
G01N 33/49 - Analyse physique de matériau biologique de matériau biologique liquide de sang
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
H01J 49/26 - Spectromètres de masse ou tubes séparateurs de masse
55.
BAGGED FILTERING METHOD FOR SELECTION AND DESELECTION OF FEATURES FOR CLASSIFICATION
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.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
56.
Bagged filtering method for selection and deselection of features for classification
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.
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
42 - Services scientifiques, technologiques et industriels, recherche et conception
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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.
58.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
59.
Early detection of hepatocellular carcinoma in high risk populations using MALDI-TOF mass spectrometry
The Board of Regents of the University of Texas System (USA)
Inventeur(s)
Röder, Joanna
Oliveira, Carlos
Grigorieva, Julia
Röder, Heinrich
Mahalingam, Devalingam
Abrégé
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.
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
G16H 10/40 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données relatives aux analyses de laboratoire, p.ex. pour des analyses d’échantillon de patient
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
60.
EARLY DETECTION OF HEPATOCELLULAR CARCINOMA IN HIGH RISK POPULATIONS USING MALDI-TOF MASS SPECTROMETRY
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 24/00 - Recherche ou analyse des matériaux par l'utilisation de la résonance magnétique nucléaire, de la résonance paramagnétique électronique ou d'autres effets de spin
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
61.
Predictive test for aggressiveness or indolence of prostate cancer from mass spectrometry of blood-based sample
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.
G01N 24/00 - Recherche ou analyse des matériaux par l'utilisation de la résonance magnétique nucléaire, de la résonance paramagnétique électronique ou d'autres effets de spin
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
62.
PREDICTIVE TEST FOR AGGRESSIVENESS OR INDOLENCE OF PROSTATE CANCER FROM MASS SPECTROMETRY OF BLOOD-BASED SAMPLE
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.
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
63.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 27/62 - Recherche ou analyse des matériaux par l'emploi de moyens électriques, électrochimiques ou magnétiques en recherchant les décharges électriques, p.ex. l'émission cathodique
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
64.
Deep MALDI TOF mass spectrometry of complex biological samples, e.g., serum, and uses thereof
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.
G01N 33/487 - Analyse physique de matériau biologique de matériau biologique liquide
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
65.
TREATMENT SELECTION FOR LUNG CANCER PATIENTS USING MASS SPECTRUM OF BLOOD-BASED SAMPLE
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.
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
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.
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
G06F 19/10 - Bio-informatique, c. à d. procédés ou systèmes pour le traitement de données génétiques ou se rapportant aux protéines en biologie moléculaire informatique (procédés in silico de criblage de bibliothèques chimiques virtuelles C40B 30/02;procédés mathématiques ou in silicio de création de bibliothèques chimiques virtuelles C40B 50/02)
H01J 49/26 - Spectromètres de masse ou tubes séparateurs de masse
67.
Treatment selection for lung cancer patients using mass spectrum of blood-based sample
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.
A61K 38/17 - Peptides ayant plus de 20 amino-acides; Gastrines; Somatostatines; Mélanotropines; Leurs dérivés provenant d'humains
H01J 49/26 - Spectromètres de masse ou tubes séparateurs de masse
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
G01N 33/49 - Analyse physique de matériau biologique de matériau biologique liquide de sang
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
68.
INTEGRATED QUANTIFICATION METHOD FOR PROTEIN MEASUREMENTS IN CLINICAL PROTEOMICS
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.
G01N 27/62 - Recherche ou analyse des matériaux par l'emploi de moyens électriques, électrochimiques ou magnétiques en recherchant les décharges électriques, p.ex. l'émission cathodique
69.
Integrated quantification method for protein measurements in clinical proteomics
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.
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06F 19/20 - pour l'hybridation ou l'expression génique, p.ex. microréseaux, séquençage par hybridation, normalisation, profilage, modèles de correction de bruit, estimation du ratio d'expression, conception ou optimisation de sonde
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et 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
71.
Classification generation method using combination of mini-classifiers with regularization and uses thereof
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.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
H01J 49/26 - Spectromètres de masse ou tubes séparateurs de masse
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et services
Diagnostic medical testing to determine which cancer patients may have better outcomes on or benefit from treatments, including, for example, immunotherapies
73.
COMPOSITIONS, METHODS AND KITS FOR DIAGNOSIS OF LUNG CANCER
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).
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.
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Scientific research and technological services in the field of scientific discovery, namely, scientific research and testing to understand patients and their diseases
76.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
77.
COMPOSITIONS, METHODS AND KITS FOR DIAGNOSIS OF LUNG CANCER
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).
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).
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
79.
METHOD FOR PREDICTING WHETHER A CANCER PATIENT WILL NOT BENEFIT FROM PLATINUM-BASED CHEMOTHERAPY AGENTS
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.
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
80.
MASS-SPECTRAL METHOD FOR SELECTION, AND DE-SELECTION, OF CANCER PATIENTS FOR TREATMENT WITH IMMUNE RESPONSE GENERATING THERAPIES
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.
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
81.
MASS-SPECTRAL METHOD FOR SELECTION, AND DE-SELECTION, OF CANCER PATIENTS FOR TREATMENT WITH IMMUNE RESPONSE GENERATING THERAPIES
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.
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
82.
Deep-MALDI TOF mass spectrometry of complex biological samples, e.g., serum, and uses thereof
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.
G01N 33/487 - Analyse physique de matériau biologique de matériau biologique liquide
H01J 49/16 - Sources d'ions; Canons à ions utilisant une ionisation de surface, p.ex. émission thermo-ionique ou photo-électrique
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
H01J 27/24 - Sources d'ions; Canons à ions utilisant l'ionisation photo-électrique, p.ex. utilisant un faisceau laser
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.
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
85.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
86.
Compositions, methods and kits for diagnosis of lung cancer
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).
G01N 33/53 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet
G01N 27/62 - Recherche ou analyse des matériaux par l'emploi de moyens électriques, électrochimiques ou magnétiques en recherchant les décharges électriques, p.ex. l'émission cathodique
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G06F 19/18 - pour la génomique ou la protéomique fonctionnelle, p.ex. associations génotype-phénotype, déséquilibre de liaison, mutagénèse, génotypage ou annotation génomique, interactions protéines-protéines ou interactions protéines-acides nucléiques
C12Q 1/37 - Procédés de mesure ou de test faisant intervenir des enzymes, des acides nucléiques ou des micro-organismes; Compositions à cet effet; Procédés pour préparer ces compositions faisant intervenir une hydrolase faisant intervenir une peptidase ou une protéinase
G01N 27/62 - Recherche ou analyse des matériaux par l'emploi de moyens électriques, électrochimiques ou magnétiques en recherchant les décharges électriques, p.ex. l'émission cathodique
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
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).
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
90.
Method and system for validation of mass spectrometer machine performance
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.
G01C 25/00 - Fabrication, étalonnage, nettoyage ou réparation des instruments ou des dispositifs mentionnés dans les autres groupes de la présente sous-classe
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.
G01N 31/00 - Recherche ou analyse des matériaux non biologiques par l'emploi des procédés chimiques spécifiés dans les sous-groupes; Appareils spécialement adaptés à de tels procédés
A61B 10/00 - Autres méthodes ou instruments pour le diagnostic, p.ex. pour le diagnostic de vaccination; Détermination du sexe; Détermination de la période d'ovulation; Instruments pour gratter la gorge
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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
01 - Produits chimiques destinés à l'industrie, aux sciences ainsi qu'à l'agriculture
05 - Produits pharmaceutiques, vétérinaires et hygièniques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
95.
PSA CAPTURE AGENTS, COMPOSITIONS, METHODS AND PREPARATION THEREOF
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.
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.
A61B 10/00 - Autres méthodes ou instruments pour le diagnostic, p.ex. pour le diagnostic de vaccination; Détermination du sexe; Détermination de la période d'ovulation; Instruments pour gratter la gorge
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G06F 19/24 - pour l'apprentissage automatique, l'exploration de données ou les bio statistiques, p.ex. détection de motifs, extraction de connaissances, extraction de règles, corrélation, agrégation ou classification
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
H01J 49/00 - Spectromètres pour particules ou tubes séparateurs de particules
97.
PREDICTIVE TEST FOR SELECTION OF METASTATIC BREAST CANCER PATIENTS FOR HORMONAL AND COMBINATION THERAPY
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.
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.
C07K 14/47 - Peptides ayant plus de 20 amino-acides; Gastrines; Somatostatines; Mélanotropines; Leurs dérivés provenant d'humains provenant de vertébrés provenant de mammifères
G01N 27/62 - Recherche ou analyse des matériaux par l'emploi de moyens électriques, électrochimiques ou magnétiques en recherchant les décharges électriques, p.ex. l'émission cathodique
G01N 33/68 - Analyse chimique de matériau biologique, p.ex. de sang ou d'urine; Test par des méthodes faisant intervenir la formation de liaisons biospécifiques par ligands; Test immunologique faisant intervenir des protéines, peptides ou amino-acides
G01N 33/574 - Tests immunologiques; Tests faisant intervenir la formation de liaisons biospécifiques; Matériaux à cet effet pour le cancer
99.
DIAGNOSTIC LUNG CANCER PANEL AND METHODS FOR ITS USE
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.
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.
