50 results about "Patient stratification" patented technology

Systems and methods of verifying that implantable cardiac devices operate as intended in a particular patient involve one or more of determining proper placement of system components, determining stimulus levels useful for individual patient stratification, and determining stimulus levels that indicate efficacy of devices, implantable within a given patient. A pacing stimulus set at a surface pacing level is delivered to a patient's heart using surface electrodes. The patient is determined to not be a candidate for implantation of a subcutaneous defibrillation system if the surface pacing level needed to capture the heart exceeds a predetermined level. The patient may be determined to be a candidate for implantation of a subcutaneous system if the surface pacing level needed to effect capture is within an acceptance level. Such determinations are preferably based on a proportionality relationship between a subcutaneous defibrillation level and a surface pacing level.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

This disclosure concerns methods for evaluating inflammatory cells and modulators of the inflammatory response in tumor tissue and other relevant tissue types. The methods entail: obtaining a tissue sample and processing said tissue sample to produce histologic slides of tissue sections; staining of the tissue sections to identify inflammatory cells and modulators of the inflammatory response; digitizing slides to produce an image of the stained tissue sections; digitally stratifying the tissue sample into tumor and other relevant tissue compartments; and using digital image analysis to quantify cell-based and cell population-based features. The quantification of cell-based and cell population-based features within a tissue compartment of interest is used to develop a summary score of the immune system-tissue compartment of interest interaction. Patient stratification and selection as candidates for a therapeutic approach is ultimately based on the summary score value.

A system and method for patient stratification include determining a first set of patient groups from patients in a patient similarity graph based on a similarity structure of the patient similarity graph. A second set of patient groups is identified based on expert domain knowledge associated with the patients. Patients in the first set and the second set are aligned using a processor to stratify patients.

Methods for the diagnosis, prognosis, patient stratification and prediction of pharmacological response in patients afflicted by psychiatric disorders based on electroretinography (ERG) parameters are described.

The present invention relates to a method of identification of clinically and genetically distinct sub-groups of patients subject to a medical condition, particularly breast, lung, and colon cancer patients using a composition of respective gene expression values for certain gene pairs. Sense-antisense gene pairs (SAGPs) which are relevant for a medical condition and the disease prognosis are used by the method to generate statistical models based on the expression values of the SAGPs. SAGPs for which the statistical models are found to have high value in prognosis of the variation of medical condition and the diseases are selected and integrated in the prognostic signature including specified parameters (e.g. cut-off values) of the prognostic model. It further relates to using respective gene expression values for these genes to predict patient′ risk groups (in context of patient's survival or / and disease progression) and to using the predicted groups for identification of patient risk, and specific and robust prognostic biomarkers with mechanistic interpretations of biological changes (associated with the gene signatures) appropriating for an implementation of therapeutic targeting.

Objective discrimination of a disease label of a depressive symptom with respect to an active state of a brain is achieved. One means for solving the problems of the present invention is to provide a discriminating device for assisting in determination of whether a subject has a depressive symptom. The discriminating device includes a storage device for storing information for identifying a classifier generated by classifier generation processing based on a signal obtained by using a brain activity detecting apparatus to measure, in advance and time-sequentially, a signal indicating a brain activity of a plurality of predetermined regions of each brain of a plurality of participants in a resting state, the plurality of participants including healthy individuals and patients with depression. The classifier is generated so as to discriminate a disease label of a depressive symptom based on a weighted sum of a plurality of functional connectivities selected by feature selection as being relevant to the disease label of the depressive symptom through machine learning from among functional connectivities of the plurality of predetermined regions. The discriminating device further includes a processor configured to execute discriminating processing of generating a classification result for the depressive symptom of the subject by using the classifier.

The invention provides a method for determining the level of VEGF-A121 isoform in a sample by selectively removing the VEGF-A165 isoform from the sample using a neuropilin-1 pull-down procedure, then determining the total amount of VEGF-A remaining afterward. The invention provides methods of treating a patient suffering from a disease which may benefit from the administration of a VEGF antagonist by determining the level of VEGF-A121 in the patient's circulation. Methods of diagnosis, prognosis, monitoring, and patient stratification are also provided.

Disclosed herein are methods and systems for identifying one or more potential biomarkers for a clinical outcome related to administration of an agent. The method includes processing molecular profile data for a plurality of subjects where the molecular profile data includes data obtained before, during and / or after administration of an agent to the plurality of subjects. The method also includes processing clinical records data for the subjects, where the clinical records data includes clinical outcome data, integrating the processed molecular profile data and the processed clinical records data for the subjects and storing in a database as merged data, selecting two or more subsets of the merged data using one or more criteria based on the clinical records data to generate two or more selected data sets, and analyzing one or more of the selected data sets to identify one or more potential biomarkers for a clinical outcome related to administration of the agent.

Methods and devices for clustering a plurality of sub-networks of a larger interaction network using an enhanced hierarchical clustering algorithm are disclosed. The methods provide expression based sub-network generation using differentially expressed markers. The enhanced hierarchical clustering algorithm clusters the generated sub-networks based on a user defined customizable similarity coefficient. The methods use non-Boolean links to cluster similar sub-networks. This provides consideration of indirect relationships among sub-networks. The customizable similarity coefficient enables the methods to be used for diverse applications such as biomarker detection, patient stratification, personalized therapy, drug efficacy prediction, genetic similarity analysis in genetic diseases. The methods enable patient grouping based on the enhanced hierarchical clustering algorithm.

When evaluating patient cases to determine complexity thereof, a computer-aided stratification technique is applied to analyze historical patient case diagnoses and correctness thereof in order to calculate a stratification score (20) for each of a plurality of abnormality types and / or anatomical locations. When a new patient case is received, the computer-aided stratification technique is applied to evaluate the patient case in view of historical data and assign a stratification score thereto. A ranked list (21) of current patient cases can be generated according to stratification scores, and physician workload can be adjusted as a function thereof so that workload is balanced across physicians and / or according to physician experience level.

In accordance with the embodiment described herein, we describe a method for evaluating muscle fiber nuclei and non-muscle fiber mononuclear cells, and biomarkers expressed within these and muscle fibers, within the context of muscle tissue using digital tissue image analysis. An algorithm process is applied to histologically stained tissue sections to extract the morphometric, staining, and localization features of a plurality of tissue objects. These features can be further analyzed to describe relationships between tissue objects or tissue object image analysis features. One or more of these image analysis features or relationships between objects and features are summarized to derive a patient-specific score. Patient stratification criteria are applied to the patient-specific score and patient strata membership is evaluated to infer presence of disease, natural course of disease, disease severity, treatment efficacy, or response to a therapy and eligibility for said therapy.

Methods and devices for clustering a plurality of sub-networks of a larger interaction network using an enhanced hierarchical clustering algorithm are disclosed. The methods provide expression based sub-network generation using differentially expressed markers. The enhanced hierarchical clustering algorithm clusters the generated sub-networks based on a user defined customizable similarity coefficient. The methods use non-Boolean links to cluster similar sub-networks. This provides consideration of indirect relationships among sub-networks. The customizable similarity coefficient enables the methods to be used for diverse applications such as biomarker detection, patient stratification, personalized therapy, drug efficacy prediction, genetic similarity analysis in genetic diseases. The methods enable patient grouping based on the enhanced hierarchical clustering algorithm.

The disclosure concerns a method for patient stratification and selection of patients who are candidates for a specific therapy is described which is based on quantifying one or more digital image analysis feature distributions from stained tissue. The method extends beyond the abilities of a manual observer and a microscope, and generally comprises: acquiring digital images of stained tissue sections from patients submitted for evaluation, applying an algorithm process to said images with a computer to extract the morphometric and staining features of image pixels and tissue objects, deriving one or more distribution function for one or more image analysis features, calculating a summary statistic of the one or more distribution functions, and using said summary statistic along with an associated predefined patient stratification paradigm to separate a patient cohort into distinct strata which correspond to a decision to include or exclude a patient for a specific therapy.

In a first aspect the present invention is directed to a method of generating a scheme allowing classification of a cancer of an individual patient for estimating a clinical outcome for said patient. It also refers to a method of estimating a clinical outcome of a patient suffering from epithelial ovarian cancer (EOC). The present invention also refers to a method of determining whether the epithelial mesenchymal score of a patient suffering from a cancer can be changed by administering an EMT reversal agent to increase patients susceptibility for an anti-cancer treatment.

The current invention relates to new tools and methods enabling neuroblastoma patient stratification into prognostic favorable or unfavorable groups. The invention is based on the re-analysis of published gene expression data-sets studying neuroblastoma tumors generating different prognostic gene lists. The overlapping gene lists were subsequently tested for their prognostic power on both the published tumor samples and on an unseen large set of unpublished samples, greatly increasing the statistical power of prognostic analyses. In addition, expression analysis of miRNAs in neuroblastoma tumors with different prognosis was performed. By doing this, the inventors could establish a neuroblastoma prognostic classifier with highly improved prognostic power, which is independent from the tumor sample set used to establish it. This classifier and its related prognostic tools and methods are thus perfectly suitable for routine clinical assessment of neuroblastoma prognosis.

The invention provides a combined immuno-infrared assay for the differential diagnosis and sub classification of Alzheimer's disease into different disease stages. The method can be applied for assured disease diagnostics and patient stratification. The assay considers the label-free detection of the change within the Amyloid-beta peptide and Tauprotein secondary structure distribution in bodily fluids. This secondary structure change from native to β-sheet enriched isoforms appears years before clinical disease manifestation. Now, the combined method utilizes this shift for diagnostics based on liquid biopsies.

The present disclosure relates generally to discovery of novel compounds involved in the treatment and prevention of suicidality by bioinformatics drug repurposing using novel genes expression biomarkers involved in suicidality. Disclosed are methods for assessing severity, determining future risk, matching with a drug treatment, and measuring response to treatment, for suicidality. Also disclosed are new methods of use for drugs and natural compounds repurposed for use in preventing and treating suicidality. These methods include computer-assisted methods analyzing the expression of panels of genes, clinical measures, and drug databases. Detailed herein are methods using a universal approach, in everybody, as well as personalized approaches by gender, and by diagnosis. The discovery describes compounds for use in everybody (universal), as well as personalized by gender (males, females), diagnosis (bipolar, depression), gender and diagnosis combined (male bipolar, male depression), male PTSD, male SZ / SZA), and subtypes of suicidality (high anxiety, low mood, combined (affective), and high psychosis (non-affective). Also disclosed are methods for identifying which subjects should be receiving which treatment, using genes expression biomarkers for patient stratification and measuring response to treatment. The disclosure also relates to algorithms, universal and personalized by gender and diagnosis. The algorithms combine biomarkers as well as clinical measures for suicidality and for mental state, in order to identify subjects who are at risk of committing suicide, as well as to track responses to treatments. The disclosure further relates to determining subtypes of suicidality. Such subtypes may delineate groups of individuals that are more homogenous in terms of biology, behavior, and response to treatment.

The present invention features improved Compounds, especially (I) methods of identifying patients having Cancer using biomarkers (e.g., PDE3A, SLFN12 and / or CREB3L1) that correlate with drug sensitivity and consequently treating a stratified patient population with an agent of the invention (e.g., Compounds 1-6 disclosed herein).

Provided herein are compositions and methods related to the production and detection of a histone H1.0 protein dimethylated at lysine residue 180 (K180) (H1.0K180me2 protein) or a histone H1.0 peptide dimethylated at a lysine residue corresponding to K180 (H1.0K180me2 peptides). The H1.0K180me2 protein and H1.0K180me2 peptides are useful for applications including, but not limited to, molecular diagnostics of DNA damage, genotoxic stress, radiation exposure, and Alzheimer's disease, therapeutics, monitoring of therapeutic regimens, patient stratification, and drug screening. Also provided herein are antibodies specific for the H1.0K180me2 protein and H1.0K180me2 peptides.

The disclosure concerns a method for patient stratification and selection of patients who are candidates for a specific therapy is described which is based on quantifying one or more digital image analysis feature distributions from stained tissue. The method extends beyond the abilities of a manual observer and a microscope, and generally comprises: acquiring digital images of stained tissue sections from patients submitted for evaluation, applying an algorithm process to said images with a computer to extract the morphometric and staining features of image pixels and tissue objects, deriving one or more distribution function for one or more image analysis features, calculating a summary statistic of the one or more distribution functions, and using said summary statistic along with an associated predefined patient stratification paradigm to separate a patient cohort into distinct strata which correspond to a decision to include or exclude a patient for a specific therapy.

The invention provides a method for determining the level of VEGF-A121 isoform in a sample by selectively removing the VEGF-A165 isoform from the sample using a neuropilin-1 pull-down procedure, then determining the total amount of VEGF-A remaining afterward. The invention provides methods of treating a patient suffering from a disease which may benefit from the administration of a VEGF antagonist by determining the level of VEGF-A121 in the patient's circulation. Methods of diagnosis, prognosis, monitoring, and patient stratification are also provided.

Provided herein are compositions and methods related to the production and detection of a histone H1.0 protein dimethylated at lysine residue 180 (K180) (H1.0K180me2 protein) or a histone H1.0 peptide dimethylated at a lysine residue corresponding to K180 (H1.0K180me2 peptides). The H1.0K180me2 protein and H1.0K180me2 peptides are useful for applications including, but not limited to, molecular diagnostics of DNA damage, genotoxic stress, radiation exposure, and Alzheimer's disease, therapeutics, monitoring of therapeutic regimens, patient stratification, and drug screening. Also provided herein are antibodies specific for the H1.0K180me2 protein and H1.0K180me2 peptides.

Compositions and methods are provided for diagnosis, prognosis and treatment of AR-related diseases, such as prostate diseases, such as prostate cancer, breast cancer, and many other diseases. In particular, a novel and clinically relevant mutation at position 876 of the androgen receptor (AR) has been identified. Drug sensitivity can be predicted and therapeutic regimens can be planned on the basis of the presence or absence of this mutation. Polypeptides comprising, antibodies to, and polynucleotides encoding the mutant AR can be used to identity novel treatments. A double mutation in AR at positions 741 and 877 is also shown to be useful for patient stratification.

Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

The present invention relates to methods for predicting prognosis and overall survival among tumour / cancer patients, and methods for classifying and stratifying these patients, particularly patients having pancreatic neuroendocrine tumors (PanNETs). The invention also relates to therapeutic methods for treating classified patients. Measuring gene expression levels of at least some of a selected group 198 genes is shown to be useful in the stratification of patients into groups with prognostic significance, and making a prediction of prognosis.

The invention provides a combined immuno-infrared assay for the differential diagnosis and sub classification of Alzheimer's disease into different disease stages. The method can be applied for assured disease diagnostics and patient stratification. The assay considers the label-free detection of the change within the Amyloid-beta peptide and Tauprotein secondary structure distribution in bodily fluids. This secondary structure change from native to -sheet enriched isoforms appears years before clinical disease manifestation. Now, the combined method utilizes this shift for diagnostics basedon liquid biopsies.