32 results about "Mutational status" patented technology

The invention relates to the field of medical data processing prediction, in particular to a lung adenocarcinoma EGFR accompanying mutation state prediction system based on multi-modal data, comprising a database module used for constructing a multi-modal database and a medical knowledge database; the branch prediction module is used for respectively constructing corresponding branch prediction models for the multi-modal data in the multi-modal database, and carrying out analysis and preliminary prediction; and the mixed analysis and prediction module is used for integrating and analyzing the analysis and prediction results of the branch prediction module and predicting the EGFR accompanying mutation state of the patient. Different types of data can be processed and analyzed more accurately, and the accuracy and reliability of a prediction result are improved.

The invention discloses a method for constructing an osimertinib curative effect prediction model based on an elastic score and T790M mutation, and relates to the technical field of biological medicines, and the method is technically characterized in that the osimertinib curative effect prediction model based on the elastic score and the T790M mutation is constructed; the interaction mechanism between the radiomics characteristics and the EGFR mutation state is explored, so that a more accurate decision basis is provided for personalized clinical treatment, and the treatment effect is improved.

The present invention relates to the use of compounds of formula (I) as RAS inhibitors and as a medicament, in particular for use in treating proliferative disorders, inflammatory diseases and / or genetic disorders. The present invention relates further to a pharmaceutical composition comprising the compounds of formula (I). Moreover, the present invention relates to a method of inhibiting growth, proliferation or metastasis of cancer cells in a subject in need thereof, in particular which may encompass subsets of patients defined by their mutational status of the RAS oncogene or patients who might have developed resistance to the standard of care or treatment with RAS mutation specific inhibitors. The present invention also relates to a method of inhibiting RAS molecules in treating genetic disorders like RASopathies or inflammatory disorders like Adenomyosis where KRAS gene is mutationally activated. In addition, the present invention relates to a method of inhibiting proliferation and or secretion of factors from a cell population sensitive towards inhibiting RAS activation in vitro, in particular sensitive towards inhibiting KRAS. HRAS and NRAS activation in vitro. Furthermore, the present invention relates to a kit containing a formulation comprising a pharmaceutical composition comprising a compound of formula (I).

The present invention discloses a method, system, device and medium for predicting the EGFR gene mutation status, relating to the field of artificial intelligence technology, and aiming to solve the technical problem of low accuracy in predicting EGFR gene mutations in the prior art. The cross-section, coronal plane and sagittal plane of the CT image sample data are input into the feature extraction network module of the EGFR gene mutation prediction model, and the features of the three-plane diagrams within each instance are fused into an instance-level latent vector and used as the output; the latent vector together with the corresponding clinical information is used as the input of the encoder, and the encoder outputs a packet-level embedding and a packet-level gating. The packet-level embedding is used as the input of the classification module, and the classification module outputs a prediction result. The packet-level gating selects the prediction result and controls the output. The attention mechanism is used to help the model focus on and highlight the features that are particularly important for predicting EGFR gene mutations, improve the prediction accuracy and generalization ability, and thus effectively improve the accuracy of predicting EGFR gene mutations.

The present disclosure provides a method for detecting the methylation and mutation status of a DNA sample, specifically, a method for simultaneously detecting the methylation and mutation status of DNA molecules in a sample, the method comprising treating the sample with a methylation-sensitive restriction enzyme, performing hybrid capture on amplification products of the restriction enzyme-treated sample and a sample that has not been subjected to enzyme cleavage treatment with a probe set including a mutation capture probe and a methylation capture probe, and sequencing the captured products.

This invention discloses a visual analysis and prediction method for the ZNF469 gene mutation status in colorectal cancer, belonging to the field of intelligent medical image analysis technology. It involves acquiring whole-slice images of H&E-stained tissue from colorectal cancer patients and corresponding ZNF469 gene mutation status data, constructing an image label pairing dataset; preprocessing the whole-slice images, cutting them into image patches, and using a pre-trained three-class classification model to filter out cancerous region image patches; extracting the macroscopic structural features of each image patch, and simultaneously constructing a nuclear perception map (Transformer) to extract microscopic nuclear morphological features and cell nuclear spatial topological features. This invention achieves rapid prediction of the ZNF469 gene mutation status in colorectal cancer based on pathological images, without relying on gene sequencing technology, reducing detection costs, shortening the diagnostic cycle, and enabling simultaneous mutation status prediction during pathological slide reading, thus improving the efficiency of colorectal cancer diagnosis.

Disclosed herein are methods of determining whether a subject suffering from non-small cell lung cancer (NSCLC) is sensitive to treatment with osimertinib. In some embodiments, the method comprises obtaining an experimental matrix comprising a mutation status of at least one gene in an experimental sample associated with the subject, applying the obtained experimental matrix to a model, the model being based on a partitioning matrix comprising, for a plurality of reference samples, a mutation status of at least one gene and a sensitivity label for each of the plurality of reference samples indicating whether a corresponding reference subject is sensitive to osimertinib, wherein the at least one gene of the partitioning matrix corresponds to the at least one gene of the experimental matrix, and determining the subject as sensitive to treatment with osimertinib based on a result of the applied model.

The application discloses a breast cancer HER2 gene mutation prediction method and device based on ABVS, and comprises the following steps: S1, acquiring an ABVS image; S2, performing preliminary segmentation on the ABVS image to obtain a breast tissue part; S3, performing secondary segmentation on the basis of the segmented breast tissue part to obtain a lesion ROI region; S4, obtaining an imageomics feature according to the lesion ROI region, wherein the imageomics feature comprises first-order gray statistical features, shape features, texture features and transformation-based features; and S5, combining segmentation features generated in the segmentation process with the imageomics feature, and using a deep feature classifier to predict a HER2 gene mutation state. The application predicts the HER2 mutation state in breast cancer by using a non-invasive method, is accurate in prediction, and provides help for clinical treatment of breast cancer.

This invention relates to a method for constructing a prognostic assessment model for acute myeloid leukemia (AML) with NPM1 mutations. The method includes the following steps: (1) collecting and detecting gene mutation data required for the prognostic assessment system of AML patients with NPM1 mutations, including the mutation status of FLT3-ITD, IDH1, and SRSF2 genes; (2) assigning values ​​according to the mutation status of FLT3-ITD, IDH1, and SRSF2; (3) substituting the values ​​assigned in step (2) into the formula: mNPM1 AML risk score = 1.077 × FLT3-ITD - 0.800 × IDH1 + 1.512 × SRSF2, and performing grouped and parallel prognostic assessments on the calculated values. This invention provides a prognostic assessment model for AML with NPM1 mutations, providing important guidance for the treatment of such patients and having good clinical application value.

The present invention relates to the use of compounds of formula (I) as RAS inhibitors and as a medicament, in particular for use in treating proliferative disorders, inflammatory diseases and / or genetic disorders. The present invention relates further to a pharmaceutical composition comprising the compounds of formula (I). Moreover, the present invention relates to a method of inhibiting growth, proliferation or metastasis of cancer cells in a subject in need thereof, in particular which may encompass subsets of patients defined by their mutational status of the RAS oncogene or patients who might have developed resistance to the standard of care or treatment with RAS mutation specific inhibitors. The present invention also relates to a method of inhibiting RAS molecules in treating genetic disorders like RASopathies or inflammatory disorders like Adenomyosis where KRAS gene is mutationally activated. In addition, the present invention relates to a method of inhibiting proliferation and / or secretion of factors from a cell population sensitive towards inhibiting RAS activation in vitro, in particular sensitive towards inhibiting KRAS, HRAS and NRAS activation in vitro. Furthermore, the present invention relates to a kit containing a formulation comprising a pharmaceutical composition comprising a compound of formula (I).

This disclosure describes a machine learning (ML) framework, including a graph convolutional neural network (GCN), for identifying gene expression signatures associated with cancer driver events. The model is trained to identify the TP53 mutation status of cancer samples from gene expression, utilizing a comprehensive, curated graph structure of gene interactions. Quantitative scores are generated to rank the severity of driver events in each sample. Very high AUC results for unknown data across several tumor types are achieved in this method. A strong correlation with protein function exists. The Signature in Transcriptome Associated with Mutant Proteins (STAMP) model can also predict driver events in many combinations of key oncogenes / pathways and several tumor types, based on well-established annotations from the literature. Thus, the STAMP model can identify and quantify driver events, which may lead to improved targeted therapy selection and prioritization in cancer patients.

The invention discloses a group of urine metabolites for diagnosing BSG (brain stem glioma) H3K27M gene mutation states, urine of BSG patients is detected through non-targeted LC-MS / MS (liquid chromatography-mass spectrometry / mass spectrometry), metabolites related to the H3K27M gene mutation states are screened, and content differences of Nomilin, Lys-Leu (lysine-leucine) and Hawkinsin in the BSG patients with different H3K27M mutation states are found to be obvious; further verifying the content change of the H3K27M gene mutation related metabolite in the urine of the BSG patient by using targeted LC-MS / MS; the research confirms that the combination of Nomilin, lysine-leucine (Lys-Leu) and Hawkinsin in the urine can be used for predicting the gene mutation state of the brain stem glioma H3K27M, and the gene mutation state of the brain stem glioma H3K27M can be used for predicting the gene mutation state of the brain stem glioma H3K27M.

The present invention provides a bladder cancer gene mutation prediction system based on deep learning, comprising: a histopathological image acquisition module for acquiring original tissue histopathological images to be measured; a preprocessing module for preprocessing the original tissue histopathological images to be measured by using the HE staining method and a whole slide imaging scanner to obtain a WSI of a bladder tissue with HE staining; a cloud server module for calling a deep learning prediction model for bladder cancer to respectively predict the gene expression status, gene methylation status and gene mutation status of bladder cancer for the WSI, and generating corresponding prediction results; and a report generation module for automatically generating an auxiliary diagnosis report for bladder cancer gene mutation prediction according to the corresponding prediction results and feeding it back to a specified terminal.

The invention belongs to the field of biological medicine, and relates to a device for predicting the BRCA1 / 2 mutation state of a Chinese breast cancer patient. The device is used for executing a prediction method of the BRCA1 / 2 mutation carrying probability of the Chinese breast cancer patient, and the prediction method is based on a prediction model of the BRCA1 / 2 mutation carrying probability of the Chinese breast cancer patient: Logit (P) = beta0 + beta1X1 + beta2X2 + beta3X3 + beta4X4 + beta5X5. The BRCA1 / 2 mutation carrying probability prediction model and device established by the invention can accurately calculate the BRCA1 / 2 mutation carrying probability of Chinese breast cancer patients, and have clinical application value.

This invention discloses a gene biomarker panel for detecting PCNSL based on cerebrospinal fluid (cerebrospinal fluid) ctDNA, relating to the fields of gene detection and molecular diagnostics. The gene biomarker panel includes MYD88, PIM1, CD79B, GNA13, IRF4, DTX1, KMT2D, and B2M. By detecting the mutation status of these genes in a subject's cerebrospinal fluid sample, the mutation status is input into a trained random forest classification model to generate auxiliary judgment results, providing molecular evidence for diagnosing PCNSL in the subject. This eight-gene combination panel has shown high diagnostic specificity in multiple cerebrospinal fluid ctDNA-based validation cohorts and has the ability to identify MYD88 wild-type PCNSL.

The present invention relates to the use of compounds of formula (I) as RAS inhibitors and as a medicament, in particular for use in treating proliferative disorders inflammatory diseases and / or genetic disorders. The present invention relates further to a pharmaceutical composition comprising the compounds of formula (I). Moreover, the present invention relates to a method of inhibiting growth, proliferation or metastasis of cancer cells in a subject in need thereof, in particular which may encompass subsets of patients defined by their mutational status of the RAS oncogene or patients who might have developed resistance to the standard of care or treatment with RAS mutation specific inhibitors. The present invention also relates to a method of inhibiting RAS molecules in treating genetic disorders like RASopathies or inflammatory disorders like Adenomyosis where KRAS gene is mutationally activated. In addition, the present invention relates to a method of inhibiting proliferation and or secretion of factors from a cell population sensitive towards inhibiting RAS activation in vitro, in particular sensitive towards inhibiting KRAS, HRAS and NRAS activation in vitro. Furthermore, the present invention relates to a kit containing a formulation comprising a pharmaceutical composition comprising a compound of formula (I).

The present disclosure provides methods for determining a probability that after any of a number of therapeutic interventions, an initial state of a subject, such as somatic cell mutational status of a subject with cancer, will develop a subsequent state. Such probabilities can be used to inform a health care provider as to particular courses of treatment to maximize probability of a desired outcome for the subject.

Constructs for detection of d-2-hydroxyglutarate (d-2-HG), and their use in determining the IDH1 / 2 mutational status of a biological sample obtained from a subject, monitoring a change in D-2-HG levels in a subject, and analysing D-2-HG in a biological sample obtained from a subject, comprising detecting for D-2-HG in the sample or subcellular compartment therein, and methods for the same. Nucleic acid molecules, vectors, cells, and pharmaceutical compositions are also described.

Constructs for detection of d-2-hydroxyglutarate (d-2-HG), and their use in determining the IDH1 / 2 mutational status of a biological sample obtained from a subject, monitoring a change in D-2-HG levels in a subject, and analysing D-2-HG in a biological sample obtained from a subject, comprising detecting for D-2-HG in the sample or subcellular compartment therein, and methods for the same. Nucleic acid molecules, vectors, cells, and pharmaceutical compositions are also described.

The invention relates to the technical field of molecular oncology and drug resistance mechanism research, and discloses an S232F site of acquired drug resistance of a fusion gene STRN3-RARA and application of the S232F site in ATRA. The product is used for detecting the sensitivity of a patient suffering from acute promyelocytic leukemia to all-transretinoic acid (ATRA) treatment or screening whether the patient suffering from acute promyelocytic leukemia is resistant to ATRA treatment, an S232F mutation site is an STRN3-RARA fusion gene coding protein region mutation site, and the nucleotide sequence of the STRN3-RARA fusion gene is shown as SEQ ID NO: 1. The S232F mutation site is located at the 974th nucleotide site of the nucleotide sequence, and the base C is mutated into T. According to the present invention, the S232F mutation site can be adopted as the drug resistance detection marker, the reagent or the kit can be designed according to the STRN3-RARA gene S232F mutation site so as to be used for clinical drug resistance detection, and the gene mutation state of the patient can be monitored so as to guide the personalized drug use.

Methods and products for nucleic acid detection are provided. Specifically, the invention provides a method for detecting a nucleic acid sample, and the method comprises the step of determining a methylation state and a mutation state of a target nucleic acid molecule in the nucleic acid sample by using a nucleic acid mass spectrum.

The present application relates to the field of Raman spectroscopy detection, and discloses a Raman spectroscopy-based glioma IDH mutation detection method and device. The method comprises: performing Raman spectroscopy scanning on a glioma tissue sample under test to obtain Raman spectroscopy data; preprocessing the Raman spectroscopy data to obtain normalized Raman spectroscopy data; extracting intensity values at characteristic shifts from the normalized Raman spectroscopy data; and inputting the intensity values at the characteristic shifts into a classification model to calculate a classification prediction value, wherein the classification model is a multivariate linear equation of the classification prediction value and the intensity values at the characteristic shifts, and the classification prediction value is used for assisting in determining whether the glioma tissue sample under test is of an IDH-mutant type. The present application allows for rapid and accurate detection of the IDH mutation status in gliomas.

The invention relates to a mixture comprising at least one compound (I) and at least one compound (II). The present invention also relates to a method of inhibiting cancer cell growth, proliferation or metastasis in a subject in need thereof, the subject including, inter alia, a patient defined by the mutation status of the RAS oncogene or a subset of patients likely to produce resistance to standard care or treatment of RAS mutation-specific inhibitors. In addition, the invention relates to a pharmaceutical composition and its use in the prevention and / or treatment of proliferative diseases. Furthermore, the present invention relates to a kit comprising a formulation comprising the pharmaceutical composition comprising the mixture.

The application discloses a group of urine metabolites for diagnosing H3K27M gene mutation state of brain stem glioma, and relates to non-target LC-MS / MS detection of urine of brain stem glioma (BSG) patients and screening of H3K27M gene mutation state related metabolites, finding that nomilin, lysine-leucine (Lys-Leu) and Hawkinsin have obvious content differences in BSG patients with different H3K27M mutation states; the content changes of H3K27M gene mutation related metabolites in urine of BSG patients are further verified by using targeted LC-MS / MS; and research confirms that the combination of nomilin, lysine-leucine (Lys-Leu) and Hawkinsin in urine can be used for predicting the H3K27M gene mutation state of brain stem glioma.

The invention relates to a construction method of an MDS / AML prognosis evaluation model, which comprises the following steps: (1) collecting data required by an IPSS-R evaluation system of a patient, and detecting whether TP53, DDX41, WT1, ROBO1 and FLT3-ITD genes in a sample are mutated or not; (2) grouping the IPSS-Rs according to the integral of the IPSS-Rs and assigning the IPSS-Rs; (3) assignment is carried out according to the mutation states of TP53, DDX41, WT1, ROBO1 and FLT3-ITD; (4) substituting the IPSS-R assignment obtained in the step (2) and the assignments corresponding to the various gene mutations obtained in the step (3) into a formula to calculate an MDS / AML-IPSS-M value; and (5) grouping according to the MDS / AML-IPSS-M value calculated in the step (4), and performing prognosis evaluation. The model provided by the invention is used for prognosis evaluation of MDS / AML patients, provides important guidance for subsequent treatment of the patients, and has good clinical application value.

The present disclosure describes a machine learning (ML) framework including a graph convolutional neural network (GCN) for identifying gene expression features associated with cancer driven events. The model is trained to identify the TP53 mutation status of a cancer sample from gene expression using a comprehensive selected gene interaction map structure. A quantitative score is generated to rank the severity of the drive events in each sample. By means of the method, an extremely high AUC result is achieved on unseen data of multiple tumor types. And the gene has strong correlation with protein functions. Based on the annotations established in the literature, a transcriptome feature (STAMP) model associated with the mutant protein can also predict driving events of a variety of important cancer gene / pathway combinations and a variety of tumor types. Therefore, the STAMP model can identify and quantify driving events, which can provide a new way for cancer patients to improve selection and priority ranking of targeted therapy.

The invention discloses a lung cancer CT (Computed Tomography) thin and thick layer image EGFR (Epidermal Growth Factor Receptor) gene mutation prediction device and method, and the device comprises a first processing module which is used for obtaining a lung cancer CT thin and thick layer image of a patient; the second processing module is used for performing data enhancement processing on the lung cancer CT thin and thick layer image; the third processing module is used for performing feature extraction on the lung cancer CT thin and thick layer image after data enhancement processing; the fourth processing module is used for carrying out orthogonal decomposition feature refinement and double loss optimization on the extracted features to obtain an EGFR gene mutation prediction model; and the fifth processing module is used for inputting the tested lung cancer CT thin and thick layer image into the EGFR gene mutation prediction model to carry out EGFR gene mutation detection. By adopting the technical scheme provided by the invention, the EGFR gene mutation state is accurately predicted, and an important decision basis is provided for targeted therapy and personalized diagnosis and treatment of lung cancer.

The invention discloses a noninvasive prediction system and method for thyroid cancer gene mutation. The method comprises the following steps: collecting preoperative enhanced CT (Computed Tomography) image data, clinical data and BRAFV600E gene mutation detection results of papillary thyroid carcinoma patients; preprocessing the CT image, delineating a region of interest and extracting image omics features; optimal radiomics features are obtained through feature screening; constructing a radiomics prediction model by using a machine learning algorithm based on the optimal radiomics characteristics; constructing a clinical prediction model in combination with the clinical risk factors; and fusing the two models to construct a joint prediction model, and analyzing and evaluating the performance of the model through a subject working characteristic curve and a decision curve. The method realizes preoperative noninvasive prediction of papillary thyroid carcinoma BRAFV600E gene mutation state, has high accuracy and clinical application value, and can provide auxiliary support for individualized treatment decision.

The invention relates to the technical field of biomedicine detection, and discloses an accompanying diagnosis system for evaluating the potency of universal metabolic enzyme effect mediated ferroptosis. Comprising a detection module used for detecting the gene expression level, the gene copy number variation and the mutation state of the universal metabolic enzyme in a target sample; the data integration module is used for integrating transcriptome, proteome and metabolome data; the scoring model module is used for constructing a composite scoring model, and the composite scoring model excludes traditional signal interference under the classical effect and focuses on the non-classical effect of the universal type metabolic enzyme based on the gene and protein detection result of the universal type metabolic enzyme, the non-classical effect related marker detection result and multi-omics integrated data; evaluating ferroptosis potency and immunotherapy synergy potential; the output module is used for generating an evaluation report, and the evaluation report comprises the effectiveness evaluation result of the non-classical action mediated ferroptosis of the universal metabolic enzyme.

Provided herein is a method for detecting methylation and mutation states of a DNA sample, and particularly, provided is a method for simultaneously detecting a methylation state and a mutation state of a DNA molecule in a sample. The method includes treating a sample with a methylation-sensitive restriction endonuclease; using a probe group including a mutation capture probe and a methylation capture probe to carry out hybrid capture on amplification products of the sample treated with the restriction endonuclease and a sample untreated with enzyme digestion; and sequencing a capture product.