138 results about "Cancer type" patented technology

The invention relates to novel trabectedin and rubitedin derivatives, corresponding antibody drug conjugates and a method for preparing the antibody drug conjugates. The present invention also relates to a pharmaceutical dosage form comprising a novel trabectedin or rubitedin derivative or a corresponding antibody drug conjugate. Furthermore, the present invention relates to novel trabectedin and rubitedin derivatives, corresponding antibody drug conjugates and corresponding pharmaceutical dosage forms for use as a medicament or for the treatment of specific cancer types.

The invention relates to a specific driver gene and common driver gene identification method based on federal transfer learning and deep learning algorithms, and the method comprises the steps: constructing a data set based on data of different cancer types; based on a multi-head attention mechanism, preprocessing the data set; based on the preprocessed data set, training a neural network model to obtain a gene recognition model; wherein the neural network model is constructed based on a Chebyshev graph convolutional network and a graph convolutional network, model training adopts a federated transfer learning method, and the training process is divided into a server side and a client side; based on the gene recognition model, cancer specificity and common driver genes across a tumor are identified. Compared with the existing method, the method provided by the invention can more accurately and efficiently identify the specific and common driver genes across tumors.

The invention relates to the technical field of medical care informatics, in particular to a cancer type prediction method and system based on methylation data. The method comprises the following steps: acquiring whole genome methylation data of a sample; performing layering processing based on an epigenome functional region on the whole genome methylation data to obtain functional region layering data; according to the layered data of the functional regions, constructing a local methylation topological mode in each functional region to obtain a local methylation topological representation, the local methylation topological mode comprising a methylation space gradient, a methylation curvature feature and a region methylation entropy; mapping of methylation mode variable coefficients among different cell types is carried out according to local methylation topological representation, and a regional methylation stability index is obtained through calculation. According to the method, functional region layering and local topology construction are performed on methylation data, so that the regulation state of each functional region in the genome can be reflected more finely.

The invention relates to methods for treating a cancer patient comprising administering a Zn(II) agent or a Zn(II) agent / immune-oncology agent combination to provide a therapeutic benefit to the cancer patient. The methods are useful in treating a broad spectrum of human cancers, including solid tumors and blood-based cancerous cells. In particular embodiments, the treatment methods are directed to cancer types characterized by genetic instability mutations.

The present invention relates to a method for diagnosing cancer and predicting cancer types using a methylated cell-free nucleic acid, and more particularly, to a method for diagnosing cancer and predicting cancer types using a method for extracting methylated nucleic acids from a biospecimen, generating vectorized data of nucleic acid fragments based on aligned reads by obtaining sequence information, and then inputting the data into a trained artificial intelligence model so as to analyze a calculated value. The method for diagnosing cancer and predicting cancer types using methylated cell-free nucleic acids according to the present invention is useful because it generates vectorized data and analyzes it using an AI algorithm, compared to methods that use a conventional step of determining the amount of chromosomes based on the read count or detection methods that use the concept of distance between aligned reads to utilize values related to reads as one by one structured values, so that a similar effect can be achieved even if the read coverage is low.

The invention relates to the technical field of bioinformatics, in particular to a method for identifying an abnormally hypermethylated region in cancer methylation data. The method comprises the following steps: acquiring whole genome methylation sequencing data of a sample to be detected and genome conserved sequence data of primate species; performing density analysis based on a progressive hierarchical scanning strategy on the CpG loci according to the whole genome methylation sequencing data to obtain CpG enriched region data; performing distance calculation including short-range, medium-range and long-range on the genome conservative sequence data, and performing weighted correction on a calculation result according to the CpG enrichment region data to obtain region conservative score data. According to the method, through unsupervised clustering, self-adaptive threshold setting and multi-scale feature extraction, high methylation regions with high regulation and control effects in different cancer types can be more accurately captured.

The invention discloses a generic cancer prognosis biomarker and an application thereof in evaluating the poor prognosis of the generic cancer, which can predict the outcome of a generic cancer type cancer, can be used as a reliable biomarker of a treatment target, and helps clinicians to predict the disease progression and survival rate of patients. The VANGL1 gene is a VANGL1 molecule and comprises a VANGL1 gene and a protein coded by the VANGL1 gene.

Described herein are methods, systems, devices and computer program products for characterizing or identifying a type of cancer. Also described are methods of treating a characterized or identified chancer. For example, certain methods may be used to characterize a homologous recombination deficiency status of a cancer.

Provided herein are, inter alia, novel antibodies that bind to V-domain Ig suppressor of T-cell activation (VISTA) thereby effectively targeting cells expressing VISTA. The antibodies provided herein may be used, inter alia, for therapeutic cancer applications, including, in some embodiments, treatment of multiple cancer types, which may include lung cancer, breast cancer, pancreatic cancer, ovarian cancer, colorectal cancer, renal cancer, or glioblastoma.

Provided in the present application are a computer implemented method for non-invasive stepwise prediction of cancer risks, and a system and a storage medium. The aforementioned method comprises: (1) determining a cancer signal score on the basis of the level of a biomarker in a blood sample from a subject; (2) comparing the cancer signal score with a predetermined threshold value to determine a positive subject via preliminary screening; and (3) on the basis of the positive subject from the preliminary screening, further performing cancer risk determination and cancer type prediction by using an NGS method, wherein the biomarker includes at least one selected from AFP, CA125, CA15-3, CA19-9, CA72-4, CEA, CYFRA 21-1, ProGRP, SCCA and PSA.

The invention provides a cancer subtype classification method, system, equipment and program product based on ferroptosis related miRNA, and belongs to the field of intelligent medical treatment. The breast cancer is divided into four subtypes on the basis of ferroptosis related miRNA, the breast cancer FAP + subtype is identified on the basis of ferroptosis activity, the molecular characteristics of the FAP + subtype of an individual breast cancer patient are measured by establishing FAPscore, and the higher the FAPscore is, the more remarkable the FAP + subtype characteristics of the breast cancer patient are. The invention also finds that FAPscore can be used for predicting response, prognosis and drug sensitivity of individual cancer patients to immunotherapy response, and the discovery has wide cross-cancer species applicability and is not limited to breast cancer. The invention provides a new insight for a more effective and individualized treatment strategy of cancer.

The application discloses a method for constructing an MSI overall state prediction model and belongs to the technical field of MSI detection. The method predicts the MSI overall state by establishing a machine learning model. According to the method, the MSI overall state can be predicted without pairing normal samples in different NGS detection platforms and different cancer types through analysis of second-generation sequencing data, and the method is stable, fast, accurate, highly repeatable and low in detection limitation.

A method capable of determining a probability of a subject having one or multiple cancer tissues of origin is disclosed. The method includes inputting the expression profile of a miRNA set obtained from a biopsy sample such as serum sample of the subject into a classifier which is based on a machine learning model such as a support vector machine model. In certain embodiments, the method can be used to simultaneously determine the probability of the subject having each of multiple different cancer types, which can enhance the prediction accuracy.

The invention discloses a lung adenocarcinoma prognosis and immune infiltration related prediction model. By deeply mining data in TCGA and GEO databases, a prognosis model based on eight pyroptosis related genes is constructed, and compared with a traditional method, the five-year survival prognosis of a lung adenocarcinoma patient can be more accurately predicted. A verification result shows that the model has relatively high accuracy (AUC = 0.687) in prediction of the total survival of one year, and through Kaplan-Meier analysis and Log-Rank inspection, survival differences of patients in high and low risk groups in a training set and a verification set are remarkably distinguished. The research method and the model have strong universality, provide important reference for research and application of a pyroptosis related mechanism in other cancer types, promote the progress of the oncology field in the research related to the pyroptosis mechanism, and have wide clinical application value.

The invention provides a cancer diagnosis model development method. The cancer diagnosis model development method comprises the steps of constructing a training set and establishing a diagnosis model on the training set. The training set includes miRNA expression profiles from non-cancer subjects and cancer patients having two or more cancer types, and establishing the diagnostic model includes calculating a diagnostic index based on a selected miRNA biomarker set, the selected miRNA biomarker set being obtained from a miRNA ranking in a differential expression analysis of the miRNA expression profiles in the training set. Methods of detecting a target cancer by means of the diagnostic model so developed are also provided. A 4-miRNA-based diagnostic model shows high performance in a verification set, and can realize the sensitivity greater than or equal to 0.98 while maintaining the specificity of 0.99 when detecting various cancers including lung cancer, gastric cancer, biliary tract cancer, bladder cancer, prostate cancer and glioma.

Disclosed is a method for diagnosing cancer and predicting a cancer type using fragment end motif frequencies and sizes of cell-free nucleic acid, and more preferably, to a method for diagnosing cancer and predicting a cancer type by extracting nucleic acids from a biological sample to obtain sequence information, acquiring fragment end motif frequencies and sizes of nucleic acids based on the aligned reads, converting the fragment end motif frequencies and sizes of nucleic acids into vectorized data, inputting the vectorized data to a trained artificial intelligence model and analyzing a resulting calculated value. The method includes generating vectorized data and analyzing the same using an AI algorithm and thus is useful due to high sensitivity and accuracy thereof even in the case of low read coverage.

The present invention describes a comprehensive system and method for the simultaneous early detection of multiple cancers in a single analysis. The system involves a Liquid Chromatography-Mass Spectrometry (LC-MS) device coupled with processors and AI / ML algorithms. The LC-MS device analyses metabolite ions from dried extracts of biological fluid samples, aligning and normalizing the data while minimizing errors. Quality control processes, including a neural network model and critical ion monitoring, ensure accurate detection. The system employs AI / ML processes to create two models: the Cancer Detection AI (CDAI) Model for identifying cancerous samples, and the Tissue of Origin Identification (TOOAI) Model for distinguishing specific cancer types. The models are applied to test samples, providing scores based on tissue of origin probabilities. The invention aims to revolutionize early cancer detection through advanced analytical and machine learning techniques.

The present invention relates to a novel extracellular vesicle linked to an anti-TfR1 antibody and to a use thereof. An extracellular vesicle, according to one aspect, exhibits excellent delivery capability to tumor cells and activates immune cells, thereby being effectively usable as a targeted anticancer therapeutic not limited to specific cancer types.

Methods and apparatus for selecting genetic variants for a tumour-informed assay are provided. The method includes receiving a sample collected from a patient, the sample being associated with a cancer type, generating a mutational catalogue for the sample, the mutational catalogue indicating a proportion of genetic mutation types observed in the sample, selecting a set of signatures associated with the cancer type, the set including one or more signatures, each signature comprising a mutational profile, determining, based on the set of signatures associated with the cancer type and the mutational catalogue, a set of genetic variants most likely to be genuine somatic variants associated with the sample, and outputting the set of genetic variants for use in creating a tumour-informed assay for the patient.

The application discloses a lung cancer pathological tissue typing system based on a deep semantic segmentation network, and comprises a lung cancer recognition model and a lung cancer typing recognition model; image prediction processing is performed on an input pathological tissue slice image according to the lung cancer recognition model, a first probability of each pixel point in the slice image being cancer is predicted based on deep semantic segmentation network technology, and pixel points with the first probability greater than or equal to a first probability threshold are set as cancerous regions; all cancerous region images are input into the lung cancer typing recognition model, a second probability of each pixel point in the cancerous region image being each cancer type is determined based on deep semantic segmentation network technology, a cancer type corresponding to a pathological tissue slice image and a prediction probability of the cancer type are determined according to a statistical value of the second probability. Through prediction of the cancerous region and the cancer type to which each pixel point in the cancerous region belongs, the prediction probability of the corresponding cancer type is statistically counted, and the accuracy of cancer type classification is improved.

The present invention describes a comprehensive system and method for the simultaneous early detection of multiple cancers in a single analysis. The system involves a liquid chromatography-mass spectrometry (LC-MS) instrument coupled with a processor and AI / ML algorithms. The LC-MS instrument analyzes metabolite ions from dried extracts of biological fluid samples and aligns and normalizes the data while minimizing errors. Quality control processes, including neural network models and critical ion monitoring, ensure accurate detection. The system employs an AI / ML process to create two models: a Cancer Detection AI (CDAI) model for identifying cancer samples and a Tissue of Origin Identification (TOOAI) model for distinguishing specific cancer types. These models are applied to test samples and provide a score based on tissue of origin probability. The present invention aims to revolutionize early cancer detection through advanced analytical and machine learning techniques.

Tumor-specific T cell receptor (TCR) gene transfer enables specific and potent immune targeting of tumor antigens. The canonical cancer-testis antigen, NY-ESO-1, is not expressed in normal tissues but is aberrantly expressed across a broad array of cancer types. It has also been targeted with A2-restricted TCR gene therapy without adverse events or notable side effects. To enable the targeting of NY-ESO-1 in a broader array of HLA haplotypes, we isolated TCRs specific for NY-ESO-1 epitopes presented by four MHC molecules: HLA-A2, -B07, -B18, and -C03. Using these TCRs, we have developed an approach to extend TCR gene therapies targeting NY-ESO-1 to patient populations beyond those expressing HLA-A2.

Methods and devices are provided for selecting genetic variants for tumor awareness assays. A method includes receiving a sample collected from a patient, the sample associated with a cancer type; generating a directory of mutations for the sample, the directory of mutations indicating a proportion of genetic mutation types observed in the sample; selecting a set of features associated with the cancer type, the set comprising one or more features, each feature comprising a profile of mutations; determining a set of genetic variants most likely to be a true cell variant associated with the sample based on the set of features and the directory of mutations associated with the type of cancer; and outputting the set of genetic variants for creating a tumor awareness assay for the patient.

The present application relates to compounds of formula I, which are (3S)- and (3R)-6,7-bis(Hydroxymethyl)-1H,3H-pyrrolo[1,2-c]thiazoles. The present application also relates to pharmaceutical compositions having the compounds and the use of these compounds in the treatment of conditions influenced by wild-type or mutant p53 forms. More specifically, these compounds represent a completely new chemical family of p53-activating agents and show a higher selectivity towards the p53-pathway compared to the reactivators of p53 currently under clinical trials. For some cancer types these compounds revealed to be more potent than the reactivators of p53 currently under clinical trials. In addition to these advantages, the presently disclosed compounds are not genotoxic and have no apparent undesirable toxic side effects.

The present invention provides a genetically modified composition for treating cancer, and a method for preparing and using a genetically modified composition in the treatment of cancer. [Solution] The compositions and methods disclosed herein can be used to identify cancer-specific T cell receptors (TCRs) that recognize unique immunogenic mutations in a patient's cancer and to treat any type of cancer in the patient. Insertion of these transgenes encoding cancer-specific TCRs into T cells, using non-viral methods (e.g., CRISPR, TALEN, transposon-based ZEN, meganuclease, or Mega-TAL), is a novel technique that opens up new opportunities to extend immunotherapy to many cancer types.

Embodiments of the present application disclose a pan-cancer cell recognition model training method, device, equipment and storage medium. The method comprises selecting transcriptome test data of a plurality of preset cancer types according to a preset ratio of normal cells and malignant cells to construct an initial data set; obtaining a preset number of high variable genes in the transcriptome test data of the initial data set as training features to obtain a training set; based on a binary classification label, performing label annotation on the transcriptome test data in the training set to generate a binary classification label system test data set; taking the pre-training weight of a preset base model as an initialization parameter, mounting a binary classification head at the top layer of a transformer layer, and performing parameter fine-tuning on the preset base model based on the binary classification label system test data set to obtain a pan-cancer cell recognition model. The scheme of the embodiments of the present application can realize accurate identification of benign and malignant cells in pan-cancer single-cell transcriptome data.

The invention discloses a cancer-specific chromatin accessibility marker-based tissue origin inference method and device and a storage medium, and belongs to the technical field of gene detection. The method aims to solve the problem that low-cost shallow whole genome sequencing data is difficult to carry out accurate cancer traceability. The invention provides a fragment discreteness index which is combined with terminal dispersity and coverage fluctuation of free DNA fragments so as to more accurately characterize chromatin accessibility. And through a global and local combined statistical test strategy, identifying candidate accessibility regions from the data. The method comprises the core step of screening out a unique marker of a specific cancer species by removing a common accessibility region in various cancers and healthy control. Based on the specific markers, multi-dimensional fragment omics features are extracted, a machine learning multi-classification model is constructed, and the probability that a to-be-detected sample comes from different cancer types is predicted.

The invention discloses a cancer typing intelligent decision-making platform fusing genomics and radiomics, and relates to the technical field of medical data analysis. The multi-modal data acquisition module is used for acquiring genome data and medical image data of a patient in parallel; the intelligent preprocessing module carries out variation annotation on the gene data and extracts quantitative features from the image; a gene-image cross-modal association network is constructed through a similar network fusion technology, the problem of data splitting is solved, and non-invasive dynamic monitoring of tumor molecule evolution is achieved; a dynamic constraint optimization strategy is adopted to solve the defects of a traditional fusion method, and the cross-modal correlation precision is improved by 24.7% while the feature dimension is reduced by 80%; in combination with a hierarchical decision model and interpretability analysis, not only is a subtype classification result output, but also a treatment sensitivity quantitative prediction and decision basis visualization report is generated, so that the decision confidence of a clinician is improved by 40%, and the application bottleneck of the prior art is comprehensively broken through.

Methods for quantifying DNA methylation that may be utilized for screening for diseases (e.g., cancer), diagnosing diseases (e.g., cancer type), monitoring progression of a disease, and monitoring response to a therapeutic treatment.

The application discloses a multi-cancer early screening method based on fragmentomics and microbiomics characteristics and application thereof, and belongs to the technical field of biomedical detection. The method comprises the following steps: extracting fragmentomics characteristics of human cfDNA and microbiomics characteristics of plasma microbial DNA based on sequencing data; inputting the two types of characteristics into a first-stage classification model after fusion, and judging whether a subject is cancer positive; and further predicting the cancer type through a second-stage classification model for the positive result. The application combines fragmentomics and microbiomics characteristics, and is used for multi-cancer early screening. Through single non-invasive blood sampling, early screening and organization tracing of eight high-incidence cancers such as lung cancer, liver cancer and colorectal cancer can be realized. The method has high sensitivity and high specificity under low sequencing depth, is low in cost, wide in coverage, and suitable for large-scale population screening.