11 results about "Mutational status" patented technology

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.

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 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 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.

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 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 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.