173 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.

Provided herein are methods of generating a homologous recombination repair deficiency (HRD) score, determining a reference HRD score, determining a HRD status of a test subject having one or more cancer types, and / or treating a disease based on HRD status. Additional methods as well as related systems, apparatuses, and computer readable media are also provided.

The invention relates to the field of medical auxiliary diagnosis, in particular to a state space duality multi-instance pathological image classification method and system, and the method comprises the following steps: obtaining a lung cancer pathological full-slice image to be detected based on a pathological database; obtaining a tissue area of the lung cancer pathological full-slice image, and cutting the tissue area to obtain an image block set; obtaining a feature vector set of the image block set by using a feature extractor, constructing a fusion feature set based on the feature vector set, and obtaining a weighted feature set according to the fusion feature set; performing dichotomy mapping on the weighted feature set according to a linear classifier to obtain a dichotomy prediction probability vector; and obtaining a cancer prediction category of the lung cancer pathological full-slice image according to the dichotomy prediction probability vector so as to classify the cancer type of the lung cancer pathological full-slice image. The method is used for automatic diagnosis of cancer pathological images and provides efficient and reliable technical support for medical auxiliary diagnosis.

The invention discloses a cancer category detection method based on multi-omics data clustering remarking, and relates to the technical field of machine learning. The learning process of the category detection model is as follows: n multi-omics data samples are acquired, each sample comprises m different categories of omics data, sample labels are cancer categories corresponding to the samples, and K cancer categories are provided; preprocessing the sample, and generating sample features for the preprocessed sample by using the model; according to sample features, clustering the preprocessed samples into K clusters; performing impurity measurement on each cluster, and re-marking sample labels in the clusters; performing comparative learning based on a re-marking result to enable the model to generate accurate sample features; and according to accurate sample characteristics generated by the model, predicting cancer types. According to the invention, cancer category detection is carried out by using the category detection model, and the accuracy of cancer typing is improved through reconstruction of the sample labels and application of a contrast learning method.

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 present invention relates to vaccines based on viral vectors for delivery of antigens against infectious diseases. In particular, the present invention relates to a recombinantly modified vaccinia virus Ankara (MVA) encoding EBV antigens that elicit infectious mononuclear hyperplasia (IM) and different cancer types. The invention also relates to medical application of the recombinant MVA in prevention of diseases caused by EBV (Epstein-Barr Virus).

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 present invention relates to a method of establishing a prediction regarding the development of a type of solid cancer in an individual, comprising: (i) collecting a biological sample that includes lymphocytes of said individual, (ii) sequencing, using NGS techniques, the mRNA associated with the V(D)J regions of the TCRs of the lymphocytes collected in the previous step, (iii) removing from the TOR repertoire obtained in the previous step the TOR sequences that are specific to other diseases than said solid cancer, (iv) using artificial intelligence algorithms to compute a predictive score for the probability that said TOR repertoire is specific for said type of cancer and (v) based on said predictive score determining the probability of said individual of developing said type of solid cancer.

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.

To provide systems and methods for performing quality control analysis.SOLUTION: This method obtains a batch dataset comprising, for each respective sample in a batch of samples, a corresponding plurality of sequence reads derived from the respective sample by targeted or whole transcriptome RNA sequencing, and corresponding metadata for the respective sample. The method determines for the batch dataset a cohort-matched reference batch, where the cohort-matched reference batch is balanced for tissue site, tumor purity, cancer type, sequencer identity, or date sequenced. The method performs global batch quality control tests on the batch dataset using at least the cohort-matched reference batch. The method removes respective samples from the batch dataset that fail any one of the one or more global batch quality control tests or flagging for manual inspection respective samples that fail any one of the one or more global batch quality control tests.SELECTED DRAWING: Figure 14

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.

Provided herein are methods of generating a homologous recombination repair deficiency (HRD) score, determining a reference HRD score, determining a HRD status of a test subject having one or more cancer types, and / or treating a disease based on HRD status. Additional methods as well as related systems, apparatuses, and computer readable media are also provided.