25 results about "Predictive biomarker" patented technology

The present disclosure provides, among other things, methods for treating cancer comprising administering an effective dose of azenosertib, or a pharmaceutically acceptable salt thereof, to subjects selected to have a homologous recombination repair deficiency (HRD).

This disclosure provides a method for treating lymphoma, the method comprising administering a bispecific CD20xCD3 antibody to a patient in need, wherein the patient is selected based on an improvement in the level of circulating tumor (ct) DNA. In some embodiments, this disclosure provides a method for identifying lymphoma patients who may respond well to a therapy comprising a bispecific CD20xCD3 antibody.

The invention relates to the technical field of biology, and provides a predictive biomarker for treating adverse reactions of tumor patients by using CCL5 as an immune checkpoint inhibitor and application of the predictive biomarker, and the predictive biomarker is the dynamic change of the CCL5 relative to a baseline proportion. The detection kit is used for detecting the proportion of CCL5. The dynamic change refers to the change of the CCL5 proportion before and after the immune checkpoint inhibitor is used; if the dynamic change is greater than or equal to 50% or continuously increased, the immune checkpoint inhibitor causes adverse reaction when treating the tumor patient. According to the method, high-risk patients can be identified through peripheral blood detection before irAEs have obvious clinical manifestations, so that a basis for advanced intervention is provided for clinicians, and the occurrence probability of serious adverse events is reduced.

The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

The invention relates to the technical field of biology, and provides a predictive biomarker for treating adverse reactions of tumor patients through an immune checkpoint inhibitor and application of the predictive biomarker, and the predictive biomarker is the dynamic change of the proportion of CX3CR1 + CD8 + T cells. The detection kit is used for detecting the proportion of CX3CR1 + CD8 + T cells. The dynamic change refers to the change of the CX3CR1 + CD8 + T cell proportion before and after the immune checkpoint inhibitor is used; if the dynamic change is greater than or equal to 50% or continuously increased, the immune checkpoint inhibitor causes adverse reaction when treating the tumor patient. According to the method, high-risk patients can be identified through peripheral blood detection before irAEs have obvious clinical manifestations, so that a basis for advanced intervention is provided for clinicians, and the occurrence probability of serious adverse events is reduced.

Disclosed are kits and methods for detecting whether a patient with metastatic colorectal cancer should receive AVASTIN® (bevacizumab) in addition to chemotherapy.

The invention provides methods of treating patients with Parkinson's disease (PD) associated with wild-type LRRK2. The invention recognizes that analysis of biomarkers in such patients allows identification of those patients who will respond to LRRK2 inhibitors. Thus, the invention provides methods of identifying PD patients who will respond to LRRK2 inhibitors and methods of treating such patients.

Filgotinib or a pharmaceutically acceptable salt thereof for use in a method of treating ulcerative colitis is provided, along with methods of deciding whether to continue treating ulcerative colitis in a patient with filgotinib or a pharmaceutically acceptable salt. The treatments are based on the assessment of the levels of certain predictive biomarkers in the patient having ulcerative colitis.

Methods are described for identifying and / or treating patients who may benefit from PARP inhibitors based on an HRDsig status and a RAD51 gene status identified using genomic profiling data.

The present disclosure provides, inter alia, methods for treating cancer comprising administering to a subject selected to suffer from homologous recombination repair deficiency (HRD) an effective dose of Asensertib, or a pharmaceutically acceptable salt thereof.

This invention discloses the application of TBC1D8, a member of the TBC1 domain family, in the preparation of predictive biomarkers for prognosis and immunotherapy responsiveness in human colorectal cancer patients. This invention evaluated the correlation between TBC1D8 levels and clinical characteristics and its biological functions using next-generation sequencing results from the TCGA database and in vitro and in vivo experiments. The results showed that elevated TBC1D8 levels were associated with poor prognosis in colorectal cancer patients. Experimental studies also observed that TBC1D8 is associated with a hypoxic microenvironment and may participate in regulating the malignant phenotype of colorectal cancer cells and tumor cell stemness. Furthermore, TBC1D8 is closely related to the infiltration of immunosuppressive macrophages; experiments showed that knocking down TBC1D8 significantly inhibited the differentiation and infiltration of immunosuppressive macrophages. Therefore, TBC1D8 can serve as a prognostic biomarker for colorectal cancer patients and may also be a novel therapeutic target for human colorectal cancer.

The present application belongs to the technical field of medical examination, and relates to a drug sensitivity prediction method based on blood NMR editing spectrum, such as cancer immunotherapy drugs, immunological checkpoint inhibitors (ICIs), which are commonly resistant to drugs. The method first acquires pathological indexes and NMR editing spectrum fingerprint data of plasma or serum before and after drug treatment, then adopts a machine learning algorithm to construct a drug sensitivity prediction model and screen predictive biomarkers, and finally uses the biomarkers to predict the drug sensitivity of blood NMR data of patients in need of treatment. The NMR editing spectrum used in the present application is non-destructive to the sample, can comprehensively observe large and small molecules in the blood, retains the interaction information between the drug and the body, and the blood metabolism phenotype analysis can avoid the uncertainty caused by the prediction based on the upstream data set such as genomics, improve the prediction accuracy, and is expected to be applied to the clinical drug guidance of the drug-resistant drugs.

An exemplary system and method for predicting treatment-related outcomes of patients after a cancer therapy and / or treatment (e.g., PDA treatment) using DNA (e.g., cell-free DNA (cfDNA)) or RNA methylation signatures and / or an RNA sequencing signature as predictive biomarkers for treatment response and overall survival in the patients.

This invention provides a system and method for discovering relevant biomarkers using data collected as part of standard of care (SoC). [Solution] A method for predicting the activity of a patient's molecular analytes includes training a first module of a machine learning model based on multiple medical images from a first cohort, training a second module of the machine learning model based on one or more molecular analyte datasets obtained from a second cohort, receiving medical images from the patient, and using the trained first and second modules of the machine learning model to predict the activity of a molecular analyte from the patient's medical images.

The invention belongs to the technical field of biological medicine, and particularly relates to a group of markers for predicting non-small cell lung cancer NSCLC new auxiliary curative effect and application thereof. The invention finds that the baseline carcinoembryonic antigen expression level is related to the NSCLC new auxiliary curative effect, and the baseline CEA is combined with tissue PD-L1 to promote and predict the NSCLC new auxiliary curative effect. The baseline CEA and tissue PD-L1 sampling is simple, the operation is simple and convenient, the cost is low, the result obtaining speed is high, and the patient compliance is high. As a biomarker, the biomarker has good accuracy, and the prediction efficiency reaches 0.8 or above. The method can assist doctors to predict and judge more accurately, so that a more appropriate treatment scheme is formulated, more references are provided for realizing NSCLC individualized treatment, a new thought is provided for clinical application of CEA, a reliable and convenient predictive marker is provided for NSCLC new adjuvant treatment, and the method has good social benefits.

The invention provides an application of 5, 6-EET as a predictive marker of triple negative breast cancer pulmonary metastasis, and 5, 6-EET promotes triple negative breast cancer pulmonary metastasis. And 5, 6-EET promotes lung metastasis of triple-negative breast cancer by enhancing the permeability of pulmonary vessels. The 5, 6-EET promotes TNBC cells to generate pulmonary parenchyma metastasis by enhancing the permeability of mouse pulmonary vessels, and the discovery helps people to understand the scientific significance of arachidonic acid metabolism reprogramming in the TNBC pulmonary metastasis process, clarifies the molecular mechanism of 5, 6-EET metabolism in the TNBC pulmonary metastasis process, and provides a potential reference index for predicting the pulmonary metastasis risk.

The invention relates to a hormone-induced femoral head necrosis (SONFH) high-risk prediction marker, a kit and a detection system. The marker is ANK1 and EPB41. It is found that ANK1 and EPB41 mutations are closely associated with SONFH, early warning, prediction and screening of high-risk individuals can be conducted through sequencing, a foundation is laid for follow-up development of drugs for preventing (intervening) SONFH, and good application prospects are achieved.

It was discovered that ICIs elevated extracellular NAD levels and conferred cancer cells resistance to ICIs. It was also discovered that NAMPT was induced by IFN-y and ICIs resulting in an increase of extracellular NAD level, which could be reduced by NAMPT knockdown. NAMPT deficiency in HCC sensitized tumors to anti-PD-1 treatment. It was further discovered that extracellular NAD promoted T cell apoptosis, exhaustion, and T cell differentiation to regulatory T cells. The blockade of P2X7R reversed the immunosuppressive effect of NAD and worked effectively in combination with immunotherapies, indicating that it can be a combination therapeutic approach for ICI-resistant tumors. It was shown that serum NAD level can be a predictive biomarker for ICI responses in HCC. Overall, extracellular NAD was shown to act as an immunosuppressive metabolite that could serve as a biomarker for ICI responses and the P2X7R can be a therapeutic target to improve ICI efficacy.

A method and system are provided for using at least one hardware processor to train a machine learning algorithm for identifying one or more predictive drug molecule features and / or predictive genetic biomarkers for cancer treatment. In some embodiments, the method trains at least one learning algorithm in a predictive model using at least one cancer drug discovery dataset, validates the predictive model using a xenograft mouse model, and the biological response is fed back into the predictive model to further train the learning algorithm.

Methods and systems of using at least one hardware processor to train a machine learning algorithm to identify one or more predictive drug molecular features and / or predictive gene biomarkers for cancer treatment is provided. In some embodiments, the method uses at least one cancer drug discovery data set to train at least one learning algorithm in a prediction model, and uses a xenograft mouse model to validate the prediction model, with biological response fed back to the prediction model to further train the learning algorithm.

Filgotinib or a pharmaceutically acceptable salt thereof for use in a method of treating ulcerative colitis is provided, along with methods of deciding whether to continue treating ulcerative colitis in a patient with filgotinib or a pharmaceutically acceptable salt. The treatments are based on the assessment of the levels of certain predictive biomarkers in the patient having ulcerative colitis.

The invention discloses application of a NOD1 gene and an MYBBP1A gene as targets in screening candidate drugs for inhibiting or relieving papillary thyroid carcinoma (PTC) lymph node metastasis, application of the NOD1 gene and the MYBBP1A gene as markers in preparing products for evaluating the risk of PTC lymph node metastasis, a method for screening the candidate drugs and a kit for screening. Belongs to the technical field of biological medicine. Aiming at the problem that at present, the PTC lymph node metastasis is lack of effective predictive biomarkers and therapeutic targets, the invention reveals for the first time that the NOD1 gene can regulate and control the BCL2 / BAX pathway by up-regulating the MYBBP1A gene expression so as to enhance the anoikal apoptosis sensitivity of PTC cells, thereby inhibiting the lymph node metastasis. On the basis, the gene can be used as a target for drug screening and a marker for risk assessment, the screening method and the kit can be used for discovering potential treatment drugs, and a new tool is provided for accurate diagnosis and treatment of PTC.

A predictive biomarker, being a programmed death cell 1 (PD-1) single nucleotide polymorphisms (SNPs), is used in an in vitro method for predicting development of immune-related adverse events (irAEs) in cancer patients, in particular patients undergoing to treatment with immune checkpoint inhibitors (ICIs) and having a higher likelihood of developing said adverse events, is disclosed.

The present application relates to a kind of plasma markers GPNMB for predicting the curative effect of esophageal squamous cell carcinoma immunotherapy and its application, for screening predictive biomarker, we carry out plasma proteomics analysis, identify soluble glycoprotein non-metastatic melanoma protein B (sGPNMB) as the most significant circulating protein in treatment non-response patient is elevated, and through experiment determines, in humanized PDX model, the level of circulating GPNMB can predict PD-1 blocking curative effect, and inhibition GPNMB can enhance treatment effect.The method of the present application can realize the stratified management of esophageal squamous cell carcinoma patient, before carrying out immunotherapy, the plasma GPNMB level of patient is detected, can predict the subsequent treatment effect, and then screen out the patient suitable for immunotherapy and carry out treatment, ensure treatment effect, for the patient who is not suitable for immunotherapy can select other treatment scheme, realize the targeted effective treatment of different patients.

This disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of standard care (SoC), which can be used to identify relevant patient populations for therapeutics having a known mechanism of action (MoA). An exemplary method for predicting the activity of a patient's molecular analytes includes: training a first module of a machine learning model based on multiple medical images from a first cohort; training a second module of the machine learning model based on one or more molecular analyte datasets obtained from a second cohort; receiving medical images from the patient; and using the trained first and second modules of the machine learning model to predict the activity of the molecular analytes from the patient's medical images.