111 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 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.

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 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 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.

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.

The invention belongs to the technical field of cancer diagnosis, and particularly discloses a cancer diagnosis model based on a transposon fragmentation mode in cfDNA and application. The first aspect of the invention provides application of a fragmentation mode of transposon in cfDNA as a marker in preparation of a product. The application of the product is at least one of the following applications: (1) cancer diagnosis; and (2) predicting cancer types. Compared with an existing marker, the cancer diagnosis marker provided by the invention has a better diagnosis effect, has good specificity and sensitivity, can be used for diagnosis of various cancer types, and also has certain tumor tissue traceability. The invention further provides a cancer diagnosis device, a cancer diagnosis model construction method, a cancer diagnosis model and a sample classification method.

Techniques for identifying a cancer type of a subject are described. An example method includes identifying sequence read data indicating sequences of DNA fragments of a sample obtained from a subject. Based on the sequence read data, endpoint positions of the DNA fragments with respect to a reference genome are identified. Further, input features are determined based on the endpoint positions of the DNA fragments with respect to the reference genome. The example method further includes predicting, using a classifier and based on the input features, a primary type of cancer of the subject.

The invention relates to an application of osimertinib combined with chidamide in treatment of osimertinib drug-resistant lung cancer, and particularly relates to the following aspects: (1) osimertinib is an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), and shows a remarkable curative effect in treatment of non-small cell lung cancer (NSCLC) patients carrying specific EGFR mutation; (2) chidamide is a novel oral HDACi, can induce cell apoptosis, cell cycle arrest and cell growth inhibition, and shows good tolerance and antitumor activity in various cancer types; and (3) chidamide is taken as an epigenetic regulating agent and can be in synergistic effect with osimertinib, so that the capability of killing lung cancer cells is enhanced. The invention belongs to the technical field of medicines, and particularly provides application of osimertinib combined with chidamide in treatment of osimertinib drug-resistant lung cancer.

Disclosed is a new class of conjugated virus-like particles (VLPs). These conjugated VLPs bind a wide variety of tumors and comprise epitopes recognized by a prior T cell immune response already existing in a host. These epitopes are derived from pathogens or previous vaccinations (such as early childhood vaccines). This provokes the body's pre-existing cytotoxic immunity obtained through previous infection or previous childhood vaccination to be redirected to the tumor cells for the elimination of cancer, and form long-term anti-tumor immunity. The described conjugated VLPs are useful for tailoring a broad range of tumors towards a response from existing immunity circumventing the need to identify tumor antigens or generate tumor-specific immune responses. Importantly, the compositions and methods described herein broadens opportunities for treatment for all cancer types in subjects who previously had un-targetable cancers due to various technological and biological limitations of currently available immuno-therapeutic drugs.

The invention belongs to the technical field of biological medicines, and particularly relates to a tumor neoantigen peptide induced by retinoic acid and application of the tumor neoantigen peptide. According to the invention, an endogenous retrovirus expression profile is used as a new antigen source and a biomarker independent of TMB for the first time. Even for'cold tumors' (such as prostatic cancer and glioma) with extremely low TMB (Tetramethylbenzidine), as long as abnormal activation of ERV (such as using all-trans retinoic acid in the invention) exists, abundant targets with immunogenicity can be identified, applicable people and cancer types of new antigen immunotherapy are greatly expanded, and the application prospect is wide. The fundamental bottleneck that the prior art is limited by TMB is solved. Based on the method, the novel tumor antigen peptide TWQRYNK is identified, and the tumor growth can be limited and the sensitivity to ICB treatment can be improved when the antigen peptide is independently used or combined with ICB.

The application discloses a plasma metabolic marker composition and a screening method and application thereof, and relates to the technical field of biomedicine. The plasma metabolic marker composition comprises glycyl-L-glutamic acid, phosphatidylcholine 38:7, pyrrole-2-formic acid and phosphatidylethanolamine 36:2p. The metabolic marker composition is significantly different in different cancer types, can distinguish different cancer types of cancer patients, and realizes the detection of pan-cancer. The metabolic marker composition is non-invasive and minimally invasive when used for the detection of pan-cancer, is more suitable for the screening and detection of a large number of ordinary people, has high sensitivity and specificity, is more suitable for the early screening of cancer, and is helpful for the early screening, early diagnosis and early treatment of cancer, and is helpful for improving the survival rate and cure rate of cancer patients.

The invention belongs to the technical field of tumor molecular biology, and discloses a digestive tract tumor cross-cancer-species molecular typing method and application thereof. According to the method, six-dimensional molecular characteristics are integrated by using a robust rank polymerization algorithm, and high-confidence functional genes are screened through RNAi and CRISPR dual verification, so that the problems of single dimension and insufficient specificity of traditional screening are solved, the functional gene screening is more accurate, and a reliable foundation is laid for typing; the method has the advantages that tissue origin limitation is broken through, unified typing of the generic gastrointestinal tumors is realized, stability is verified in multi-queue and multi-technical platform samples by determining four types of subtypes, stability and unification of cross-cancer type typing are realized, and a new standardized diagnosis and treatment thought is provided. The molecular subtype determined by the method is used for in-vitro drug response prediction for non-diagnosis / treatment purposes, a scientific basis is provided for risk stratification and individualized medication research, precise treatment research is further promoted, and clinical guidance value is outstanding.

The present disclosure pertains to an in vitro method for the diagnostic classification of cancer based on the biological state of specific genomic sites. The disclosure provides a method that allows for a classification of a tumour sample obtained from a patient by analysing a multitude, preferably genome wide, collection of gene sites, combining the biological state of the analysed gene sites into a biological state pattern and comparing with pre-determined biological state patterns pertaining to different cancer types or tumour species. The disclosure is in particular useful for classifying cancer e.g. of the central nervous system, such as brain tumour samples and tumours of the spinal cord, since these are characterized by a large variety of distinct tumour species which have different prognostic values and require a developed treatment regime for each species in the clinical context. However, other cancers could similarly profit from the disclosure, for example sarcomas.

The present disclosure provides gene expression profiles that are associated with cancer, including certain gene expression profiles that differentiate between cancer that is at a high risk of recurrence. The gene expression profiles can be measured at the nucleic acid or protein level. The gene expression profiles can also be used to identify a subject for cancer treatment. Also provided are kits for use in predicting cancer recurrence and / or prognosing cancer and an array comprising probes for detecting the unique gene expression profiles associated with cancer.