51 results about "Response to therapy" patented technology

Provided herein is the use of certain biomarkers, such as tryptophan and metabolites thereof for use in certain treatment methods. Suitable tryptophan metabolites that may serve as useful biomarkers include, for example, kynurenine (Kyn), kynurenic acid (KynA), and quinolinic acid. Such biomarkers may be used in identifying a subject who may benefit from treatment, predicting the responsiveness or monitoring the response of the subject to the treatment, or determining or adjusting the dosing or dosing regimen of the treatment to the subject.

Systems and methods are provided for creating report using the integrated prognostic signature indicating at least one of a diagnosis of the disease or a response of a subject to a treatment. The method may include receiving, by a system comprising a processor, a plurality of datasets from different modalities associated with a disease of a subject and determining, by the processor, a matrix of features for each of the plurality of data modalities. The method may also include integrating, by the processor, the matrix of features through a sequential, hierarchical structure to create an integrated prognostic signature for the subject, creating, by the processor, a report using the integrated prognostic signature indicating at least one of a diagnosis of the disease or a response of the subject to a treatment and displaying, by the system, the report.

The invention concerns methods for predicting changes in a tissue, comprising obtaining snap shot information on individual cells of at least one cell type in a section of a tissue biopsy, wherein the information comprises: (i) spatial localization, (ii) cell type and (iii) cell division state at a specific point in time; calculating cell division probabilities for each of said individual cells; determining cell population dynamics based on the cell division probabilities and predicting future changes in the tissue based thereon. Particularly, the method concerns predicting tissue dynamics in cancer biopsies relevant for prognosis, and assessment of response to therapy.

The present invention relates to a method for assessing whether a subject has Polycystic Ovarian Syndrome (PCOS) or is at risk of developing PCOS, to a method of selecting a patient for therapy of PCOS, to a method for monitoring PCOS progression or for monitoring response to treatment and to a computer-implemented method for assessing a subject with suspected PCOS, by determining the amount or concentration of Fibroblast Growth Factor-Binding Protein 1 (FGFBP1) in a sample of the subject.

The present disclosure provides methods and systems for screening or detecting a colorectal cancer or following colorectal disease progression that may be applied to cell-free nucleic acids such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, and detecting colon proliferative cell disorder-associated signals in the sequencing information and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and / or monitoring response to treatment, tumor load, relapse, or colorectal cancer development.

Methods and systems for predicting a subject's response to a therapy by obtaining a first plurality of nucleic acid sequences for genomic DNA from a sample from the gut of a subject. Determine, from the nucleic acid sequences, a plurality of genomic abundance values for a plurality of gut bacteria. Apply a model to the plurality of genomic abundance values, thereby obtaining the prediction of a subject's response to a therapy as an output of the model.

A method for identifying a patient who will or is at risk of liver function decline in response to therapy with an antibiotic and / or antifungal agent, the method comprising measuring ethanol, one or more sugar fermentation products (such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13 the concentration of ethanol, one or more sugar fermentation products, such as 2, 3-butanediol, and / or a direct ethanol biomarker, wherein the concentration of ethanol, one or more sugar fermentation products, such as 2, 3-butanediol, and / or a direct ethanol biomarker indicates that the patient will respond to therapy with an antibiotic and / or antifungal agent.

The present invention is directed to methods for detecting a colon cancer, methods for determining whether a colon cancer is stable or progressive, methods for determining a risk for disease relapse, and methods for determining a response by a subject having a colon cancer to a therapy.

Disclosed are methods and agents for predicting response to therapy, immune status and / or disease progression. More particularly, disclosed are methods, agents and kits for analyzing cellular distribution of PD-L2, including its nuclear localization, for stratifying a patient as a likely responder or non-responder to a therapy, for managing treatment of a patient with a therapy, for monitoring a disease in a patient following treatment with a therapy, for determining the status of a disease and / or for determining the immune status of a patient.

The present invention relates to methods of determining the presence or absence of certain cancers in a human individual, as well as to related methods to determine the response to therapy against certain cancers in a human individual, in particular ovarian cancer in a woman. Such methods are based on the detection—from (eg cell-free) DNA of said human individual—of one or more methylated (or un-methylated) CpGs being associated with differentially methylated regions (DMRs) of the present invention; such as methylation (or un-methylation) at one or more or all of certain CpGs being associated with such DMRs. Accordingly, such methods have diagnostic, prognostic and / or predictive utility for detecting or managing certain cancers in women or men, in particular ovarian cancer in women. The present invention further relates to nucleic acids comprising certain sequences that may be detected during the method, or nucleic acids (such as probes and / or primers) that are useful to detect such sequences, as wells as compositions, kits, computer program products and other aspects that are useful for or related to the practice or application of such methods.

To provide use of molecular profiling for deriving recommendation of individualized treatment for a patient.SOLUTION: Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that correlate with response of cancer patients to platinum-based chemotherapy. Described herein are data structures, data processing, and machine learning models to predict a probability of benefit of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising platinum therapy.SELECTED DRAWING: Figure 1C

In certain example embodiments, an air delivery system includes a controllable flow generator operable to generate a supply of pressurized breathable gas to be provided to a patient for treatment and a pulse oximeter. In certain example embodiments, the pulse oximeter is configured to determine, for example, a measure of patient effort during a treatment period and provide a patient effort signal for input to control operation of the flow generator. Oximeter plethysmogram data may be used, for example, to determine estimated breath phase; sleep structure information; autonomic improvement in response to therapy; information relating to relative breathing effort, breathing frequency, and / or breathing phase; vasoconstrictive response, etc. Such data may be useful in diagnostic systems.

tFUS delivery and tracking systems and methods assess target location accuracy and therapy efficacy by measuring the specific and predicted downstream effects in individualistic responses to tFUS waveforms. The downstream effects include physiological, stress, mood, movement, attention measurements, subjective reports, task-based performance, etc. The measurements are performed before, during, and between tFUS sessions intermixed with optional control periods or sessions. In an embodiment, when a target brain region does not offer any immediate readouts but is surrounded by regions that may, these latter regions can be used instead for triangulation or waveform optimization. Individual and group tracking methods help identify useful measurement modalities and the expected direction and magnitude of change in response to therapy. A method that enables individualized functional targeting in a non-clinical setting by optimizing cost and complexity is described.

Provided are methods and systems for detecting a polymorphism in one or more glucagon-like protein-1 receptor (GLP-1R) related markers, and optionally detecting one or more metabolic markers or neural markers, in a subject. Also provided are methods and systems for predicting a risk for developing GLP-1R associated condition or a response to a GLP-1R agonist therapy in a subject by detecting a polymorphism in one or more GLP-1R related markers, and optionally detecting one or more metabolic markers or neural markers. The methods and systems can be used for selecting a treatment regimen, monitoring a response to the treatment, or adjusting the treatment regimen in the subject.

Methods, devices and systems are provided to analyze the immunologic response of patients at various times before, during and / or after immunomodulating therapy, particularly treatment with specialized pulsed electric field energy, in some embodiments, such methods, devices and systems include a laboratory developed test that utilizes a patient tumor biopsy sample and a series of blood draws to determine whether the locoregional therapy (with or without additional systemic therapies, or systemic therapies-alone) was sufficient to facilitate an increase in tumor-specific antibody production. In some embodiments, the focus is on evaluation of baseline antibody levels, prior to any treatment, and changes in levels of antibodies to given antigens, especially in response to a given therapy treatment. The treatment, such as delivery of specialized FEF energy to a target tissue, is then performed and antibody levels are checked at one or more later timepoints to detect changes in the presence of antibodies and therefore response to therapy.

The present invention is directed to methods for detecting a plasma cell dyscrasia like myeloma or MGUS, methods for determining whether a plasma cell dyscrasiais stable or progressive, methods for determining a risk for disease relapse, and methods for determining a response by a subject having a plasma cell dyscrasia to a therapy.

The present invention is directed to methods for detecting a plasma cell dyscrasia like myeloma or MGUS, methods for determining whether a plasma cell dyscrasiais stable or progressive, methods for determining a risk for disease relapse, and methods for determining a response by a subject having a plasma cell dyscrasia to a therapy.

This invention discloses a method and system for predicting a subject's response to a therapy by: obtaining a first plurality of nucleic acid sequences targeting genomic DNA from a sample of the subject's gut; determining multiple genomic abundance values ​​for various gut bacteria from the nucleic acid sequences; and applying a model to the plurality of genomic abundance values ​​to obtain a prediction of the subject's response to the therapy as the output of the model.

Disclosed are method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS or infection-positive SIRS.

The present invention relates to a method for predicting the survival time of a patient suffering from a solid cancer comprising i) determining in a tumor sample obtained from the patient the gene expression level of at least 7 genes selected from the group consisting of CCR2, CD3D, CD3E, CD3G, CD8A, CXCL10, CXCL11, GZMA, GZMB, GZMK, GZMM, IL15, IRF1, PRF1, STAT1, CD69, ICOS, CXCR3, STAT4, CCL2, and TBX21, ii) comparing every expression level determined at step i) with their predetermined reference value and iii) providing a good prognosis when all expression levels determined at step i) are higher than their predetermined reference values, or providing a bad prognosis when all expression levels determined at step i) are lower than their predetermined reference values or providing an intermediate prognosis when at least one expression level determined value is higher than its predetermined value. The method is also particularly suitable for predicting the responsiveness of the patient to a treatment.

Embodiments described herein provide methods and systems for predicting a subject response to a therapeutic. The methods and systems generally operate by using a machine learning (ML) component trained using time-series responses and image features to generate time-series responses of a subject to a therapeutic that has been administered to the subject.

A method to evaluate individuals with certain neurodegenerative diseases (e.g., Parkinson's Disease) in relation to etiologic diagnosis, prognosis and response to therapy involving the noninvasive collection of a biologic sample (e.g., venous blood), isolation of small, neuronally-derived, extracellular vesicles (e.g., exosomes), assay of their external and / or internal contents for quantities of informative biomarkers (e.g., signaling kinases, catalytic proteins and miRNA species) for the construction of a diagnostic / prognostic / response algorithms of clinical utility.

The present disclosure relates to methods of using biomarkers in cancer patients to predict responsiveness of the cancer patients to treatment.

The invention relates to the field of biological medicine, and discloses a lung cancer marker and application thereof. The invention aims to solve the problem that more new biomarkers are needed due to the fact that different lung adenocarcinoma patients have different responses to treatment due to the specificity of the patients at present, and particularly provides a lung cancer marker and application thereof.

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. Systems and methods described herein relate to DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states on cellular subsystems which are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to design of synergistic drug combinations, which can be validate systematically. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

In an aspect, there is provided a method for diagnosing or prognosing a subject with cancer, the method comprising: providing cancer DNA sequencing data from a cancer sample comprising cancer DNA from the subject; comparing the cancer DNA sequencing data with control DNA sequencing data to determine genetic aberrations; determining, from the genetic aberrations, the clonal and subclonal populations present in the sample; constructing a phylogenetic map of the clonal and subclonal populations; assigning to the subject a risk level associated with a better or worse patient outcome or response to therapy; wherein a relatively higher risk level is associated with a higher level of evolution and number of subclonal populations and a relatively lower risk level is associated with a lower level of evolution and number of subclonal populations.

The invention discloses a prostate cancer treatment target spot and application thereof, and belongs to the technical field of biological medicine, and the prostate cancer treatment target spot comprises that the treatment target spot is tRF-1: T16-Glu-TTC-2-2. The invention relates to an application of a prostate cancer treatment target, in particular to an effect of tRF-1: T16-Glu-TTC-2-2 in proliferation, migration and invasion of prostate cancer cells. According to the prostate cancer early screening kit based on serum tRF-1: T16-Glu-TTC-2-2 detection, prostate cancer is diagnosed in an early stage, and the response of a patient to treatment is tracked by monitoring the dynamic change of the serum tRF-1: T16-Glu-TTC-2-2 level, so that a treatment scheme is adjusted in time. Specific drugs are researched and developed according to a signal channel and a target gene related to tRF-1: T16-Glu-TTC-2-2, and the prostatic cancer treatment pertinence is favorably provided.

Provided herein are methods and kits that use specific biomarkers to identify subtypes of follicular lymphoma, selectively treat subtypes of follicular lymphoma (FL), identify subjects likely to be responsive to FL treatment, and predict the responsiveness of subjects to FL treatment. TIFF2026529040000042.tif122150

Disclosed herein are methods and systems for classifying a cancer from a subject. The methods and systems classify the cancer based on similar characteristics, e.g., molecular profiles. The methods and systems may be predictive of the subject's response to treatments based on the classification of the cancer. The methods and systems may be used to define improved therapies for subjects with cancers with limited treatment options, e.g., rare cancers.