246 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 technical field of drug sensitivity prediction, and discloses a platinum drug sensitivity prediction model construction method and application thereof, and the method comprises the steps: constructing a training set; according to the training set, a plurality of candidate models are obtained through training through a plurality of machine learning algorithms; and screening the plurality of candidate models according to a preset model index to obtain at least two platinum drug sensitivity prediction models. Based on the cancer species data of the target group, the applied platinum drug type data, the immune cell infiltration level data of the tumor sample tissue and the PTC data, a plurality of high-precision platinum drug sensitivity prediction models are obtained through training by utilizing a machine learning algorithm, and the method can be used for accurately predicting the platinum drug sensitivity in combination with a hard voting method. The method is high in model prediction accuracy, suitable for people, more targeted, simple and clear in prediction result, short in service cycle and low in cost.

The invention discloses a cancer risk prediction method and system, and relates to the technical field of biological information. A construction method of a cancer risk prediction model involved in the cancer risk prediction method comprises the following steps: S1, acquiring a CpG cluster conforming to expectation on a whole genome based on a methylation probe in a methylation chip; s2, grouping according to the tumor purity of the tumor tissue sample to be analyzed; s3, identifying cancer specific CpG clusters (DPCC) of each group according to a difference mark screening method; s4, generating a reference fragment of the DPCC; s5, simulating the methylation state of the reference fragment, and generating a pseudo fragment database; and S6, training a fragment dichotomy model based on DNA methylation and sequence features of the pseudo fragment data. The method is suitable for analysis of various cancer types, and can process different DNA methylation sequencing data.

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.

[Abstract] Disclosed is a technology for capturing or detecting extracellular vesicles derived from all types of cancer cells regardless of the types of the cancer. A method for capturing extracellular vesicles derived from cancer cells and contained in a biological sample includes a step for bringing the biological sample into contact with (A) a polypeptide that includes an ID1-ID2a domain of a VAR2CSA protein derived from a protozoan parasite that causes tropical malaria, (B) a polypeptide that has 90% or higher sequence identity with an amino acid sequence for the polypeptide mentioned in (A) and is capable of binding to an extracellular vesicle derived from a cancer cell, or (C) a polypeptide that is a portion of the polypeptide mentioned in (A) or the polypeptide mentioned in (B) and is capable of binding to an extracellular vesicle derived from a cancer cell, to cause the binding of the polypeptide to the extracellular vesicles derived from the cancer cells.

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 discloses a pathological image-oriented image analysis and diagnosis method and system based on a large model, and relates to the technical field of image processing. The large-model-based image analysis and diagnosis method comprises the following steps: performing normalization processing on a plurality of pathological images of a plurality of pathological image types and a plurality of cancer species, and performing foreground extraction on each pathological image; after each pathological image foreground is subjected to normalization processing, each processed pathological image foreground is segmented into a plurality of patch image blocks; and carrying out cancer seed prediction training on the patch image blocks of each pathological image. According to the method, the salient region of the pathological image can be quickly positioned, so that more efficient and accurate disease and cancer type prediction of the pathological image is realized.

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.

To provide model-based featurization and classifiers for predicting a disease state from nucleic acid samples.SOLUTION: In various embodiments, an analytics system uses models to determine features and classification of a disease state. The disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding the disease state.SELECTED DRAWING: Figure 1

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 relates to the field of bioinformatics data processing, in particular to a machine learning method, device and equipment for multi-cancer classification and a storage medium, and the machine learning method for multi-cancer classification comprises the following steps: preprocessing a sample; the samples in the training set are divided into a plurality of sub-groups, the samples in each sub-group are classified, the AUC value of each binary classification model is calculated, and sub-optimal models of the sub-groups are obtained through screening; the hyper-parameter of the sub-optimal model of each sub-group is optimized, and the prediction probability of the category to which the cancer species of each sub-group belongs is output; calculating the AUC value of each multi-classification model, and selecting the multi-classification model with the highest AUC value as a total optimal model; the hyper-parameters of the total optimal model are optimized; and calculating a model evaluation quantitative index. The method has the effects of integrating different types of clinical data and carrying out combined diagnosis on a plurality of cancer species.

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 discloses a microsatellite instability detection method independent of contrast and reference baselines, and relates to the technical field of microsatellite instability detection.The microsatellite instability detection method comprises the following steps that the cancer type of a sample to be detected is known, site screening is conducted according to samples of the same cancer type, and the sample to be detected is obtained; obtaining a difference site set suitable for microsatellite instability detection; screening a constant stable site set which is always stable in the cancer type sample and meets a certain detection rate requirement; sequencing the difference site set and the constant stable site set of the to-be-detected sample, and extracting the repetition frequency distribution of each site; collecting microsatellite frequency distribution of the constant stable site set to construct a reference dictionary, and extracting data in the reference dictionary from the difference site set according to repetitive units of the difference site set to perform Pearson correlation analysis; and obtaining the instability score of the current sample to realize sample instability detection. According to the invention, the problems of high detection cost and low accuracy caused by limitation of negative sample collection and inaccurate site screening are solved.

Provided herein are methods of making negative predictions. In some aspects, methods of determining, including via epigenomic detection, that a first target nucleic acid variant is absent at a first genetic locus in a cell-free nucleic acid (cfNA) sample obtained from a subject having a given cancer type at least partially using a computer are provided. Certain of these methods include determining that the first target nucleic acid variant is not detected in the cfNA sample obtained from the subject, generating, by the computer, at least one tumor fraction based value including methylation based estimation; generating, by the computer, at least one mutual exclusivity value; and determining that the first target nucleic acid variant is absent at the first genetic locus in the cfNA sample using the tumor fraction based value and / or the mutual exclusivity value. Additional methods and related systems and computer readable media are also provided.

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.

The present disclosure relates to the field of cancer. More particularly, the invention relates to methods of diagnosing and treating cancer, including determining a cancer type thereof. These methods involve the detection of markers in an exosome sample of the subject.

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.