10898 results about "Oncology" patented technology

Disclosed is a system for detecting whether the breast cancer is benign or malignant based on multiple modalities, which comprises: an ultrasonomics model configured to extract breast tumor position information and breast tumor morphological features on the basis of an acquired grayscale breast ultrasound image, perform global interactive fusion on the breast tumor position information and breast tumor morphological feature information, and convert a Transformer encoder-based feature output into an output vector of the corresponding classification dimensions by means of a fully connected layer; a metabonomics model configured to obtain a metabolic fingerprint spectrogram and analyze the metabolic fingerprint spectrogram on the basis of a metabolic database and a predetermined significantly differential metabolite to obtain abnormal metabolic pathways, signal transduction pathways, and related biochemical reactions associated with breast cancer malignancy; and a detection module configured to perform joint modeling, based on the breast tumor position information, the breast tumor morphological features, and a peak value of the significantly differential metabolite of the breast cancer malignancy, to output a prediction result about whether the breast cancer is benign or malignant.

Provided are a compound represented by formula (I) and a pharmaceutical composition comprising the compound, and a use of a compound of formula (A) for preventing and / or treating cancer, tumor, inflammatory disease, autoimmune disease, or immune-mediated disease.

Compounds of Formula (I) or their pharmaceutically acceptable salts can inhibit the G12C, G12D, G12V, and / or G13D mutants of Kirsten rat sarcoma (KRAS) protein and are expected to have utility as therapeutic agents, for example, for treating cancer. The disclosure also provides pharmaceutical compositions which comprise compounds of Formula (I) or pharmaceutically acceptable salts thereof. The disclosure also relates to methods for use of the compounds or their pharmaceutically acceptable salts in the therapy and prophylaxis of cancer and for preparing pharmaceuticals for this purpose.

Compounds of Formula (I) or their pharmaceutically acceptable salts can inhibit the G12C, G12D, G12V, and / or G13D mutants of Kirsten rat sarcoma (KRAS) protein and are expected to have utility as therapeutic agents, for example, for treating cancer. The disclosure also provides pharmaceutical compositions which comprise compounds of Formula (I) or pharmaceutically acceptable salts thereof. The disclosure also relates to methods for use of the compounds or their pharmaceutically acceptable salts in the therapy and prophylaxis of cancer and for preparing pharmaceuticals for this purpose.

The disclosure provides a method for treating a subject afflicted with a tumor, e.g., derived from a non-small cell lung cancer (NSCLC), comprising administering to the subject a combination of a (a) chemotherapy, (b) an anti-PD-1 antibody or an anti-PD-L1 antibody, and (c) an anti-CTLA-4 antibody, wherein the chemotherapy is administered for a period of time that is less than the standard.

This disclosure provides a method for treating a subject afflicted with a lung cancer, which method comprises administering to the subject therapeutically effective amounts of: (a) an antibody or an antigen-binding portion thereof that specifically binds to a Programmed Death-1 (PD-1) receptor and inhibits PD-1 activity; and (b) an antibody or an antigen-binding portion thereof that specifically binds to a Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4) and inhibits CTLA-4 activity.

The invention relates to the field of natural products, in particular to a dimer compound of guaiane type sesquiterpenes and uric acid as well as an extraction and separation method and application of the dimer compound. According to the invention, a new dimer compound of guaiane type sesquiterpene and uric acid is extracted and separated from carpesium abrotanoides in Guizhou, and it is found that the dimer compound has relatively strong anti-tumor activity. The compound has strong inhibitory activity on breast cancer, prostate cancer, nasopharynx cancer, liver cancer, lung cancer, leukemia, pancreatic cancer, glioma, osteosarcoma, skin cancer, cervical cancer, ovarian cancer, kidney cancer and esophageal cancer, and provides an optional scheme for broad-spectrum anti-cancer drugs. Moreover, in human prostate cancer cells PC3, human cervical cancer cells Hela, human liver cancer cells HepG2 and human glioma cells U87 which are resistant to paclitaxel, the compound also shows strong anti-cancer activity, which indicates that the compound has the potential of treating patients resistant to paclitaxel.

The invention relates to the technical field of prognosis analysis, in particular to a pancreatic cancer prognosis analysis system based on big data mining, which comprises a jump trend extraction module, an index grade mapping module, a sample stability screening module, a path node offset identification module and a prognosis stage classification module. According to the method, a change rate difference value and a third-order response sequence are constructed for continuous three-stage pancreatic cancer biological index sequences, jump trend feature points are extracted, and a risk grade transition sequence is formed in combination with grade span and direction judgment; multi-dimensional normalized parameters of a variable coefficient, a survival score difference value and an organ function score standard deviation are introduced to perform stable sample screening, interference of data disturbance on a path judgment result is reduced, and path mutation node positions are screened in combination with a path stability threshold value; precise classification and marking of high-risk variation stages in prognosis paths of pancreatic cancer patients are realized, stage reference is provided for individualized intervention strategies, and risk identification sensitivity and layered intervention scientificity are improved.

The disclosure provides compounds, e.g., compounds of Formula I, and their use in treating medical diseases or disorders, such as cancer. The compounds are contemplated to be modulators of Ras (e.g., K-Ras).

The present invention relates to compounds of the formulae (I) or (XX) that are panRAS inhibitors for use in a method of treating cancer.

The disclosure provides a method of treating a human subject afflicted with lung cancer (e.g., non-small cell lung cancer (NSCLC)) with a programmed death-1 (PD-1) pathway inhibitor (e.g., an anti-PD-1 antibody) and a concurrent chemoradiotherapy (CCRT, e.g., a platinum doublet chemotherapy (PDCT) and a radiation therapy) followed by a combination of a PD-1 pathway inhibitor (e.g., an anti-PD-1 antibody) and a lymphocyte activation gene-3 (LAG-3) antagonist (e.g., an anti-LAG-3 antibody). In some aspects, the method comprises a recovery period that begins upon completion of the treatment with the PD-1 pathway inhibitor and the CCRT and ends at the start of the treatment with the combination of the PD-1 pathway inhibitor and the LAG-3 antagonist.

The present disclosure provides compounds having activity as inhibitors of the G12C mutant KRAS protein, pharmaceutical compositions comprising the compounds, and methods of treating certain disorders, such as cancer, including but not limited to lung cancer, pancreatic cancer, colorectal cancer, and solid tumors. In particular, the disclosure provides compounds of Formula (I):, and pharmaceutically acceptable salts thereof, wherein the substituents are as described.

The invention relates to application of a targeted inhibition circPDK1 reagent in preparation of an anti-esophageal cancer drug, and belongs to the field of biological medicines. The reagent for targeted inhibition of circPDK1 expression provided by the invention is shRNA or siRNA, and in-vivo and in-vitro experiments prove that the reagent can significantly inhibit circPDK1 expression and inhibit growth and migration of esophageal cancer tumor cells; after siRNA of targeted annular circPDK1 is packaged into efficient and low-toxicity LNP-siRNA, circPDK1 expression is specifically silenced, proliferation and migration of esophageal cancer tumors can be remarkably inhibited, and a basis is provided for clinical treatment and scientific research of esophageal cancer related circRNA.

The invention discloses a breast cancer prognosis real-time evaluation system and method fusing a multi-modal image and a co-disease network, and relates to the technical field of breast cancer prognosis evaluation. According to the system, a comprehensive feature matrix fusing images, genes and clinical features is constructed by acquiring a mammary gland medical image, extracting focus features and combining gene expression information of a focus area and co-disease data of a patient, feature weighting is carried out based on an attention mechanism, and finally a prognosis risk score is output by utilizing a multi-layer perceptron model. And risk grading and intervention suggestion generation are realized. According to the system, a co-disease network and a gene interaction network are introduced for modeling, relevance expression among multi-source information is enhanced, and pathological features and systematic health status of patients can be reflected comprehensively. The intelligent level of prognosis evaluation can be improved, and good clinical popularization value is achieved.

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

Provided is a method for treating and / or preventing of cancer using Compound A or a pharmaceutically acceptable salt thereof.

The invention discloses a gastric cancer multi-omics marker detection method, system and device, and the method comprises the following steps: S1, collecting a public database open-source space transcriptome, a single cell sequencing sample and bulk-RNAseq data for pre-processing, and collecting a primary tissue sample of a gastric cancer patient in the center for data processing; s2, integrating different modal data samples to obtain a patient label of an input end, and constructing a marker detection model and training the marker detection model; and S3, extracting a target feature value from the input external pathological section by using the trained model, and generating a diagnosis prediction result. Through multi-modal data chimerism, algorithm optimization and AI system development, subpopulation cell marker proportion prediction and prognosis diagnosis and marker evaluation with population prognosis information are realized, and an integrated diagnosis scheme for breaking through molecule-space-prognosis information is constructed.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention discloses a lung squamous cell carcinoma survival prediction method and system based on SHAP interpretability, and relates to the technical field of medical data analysis. Comprising the following steps: constructing a dynamically updated physiological feature information table based on a lung squamous cell carcinoma clinical data set of a target under multiple time nodes; and associating different survival analysis models with the physiological feature information table to predict the survival probability of the lung squamous cell carcinoma in the target body, the marginal contribution of each input feature to the output of each model is obtained by using the SHAP interpretation algorithm, and the feature uniqueness score is transparently displayed in the fusion process, so that the survival probability of the lung squamous cell carcinoma in the target body can be predicted in the actual use process. The method can visually understand how each feature affects the final prediction, and achieves automatic arbitration or manual recheck through providing a model to explain conflict measurement, invalid variable elimination and pseudo high risk verification and weighted scoring, thereby guaranteeing the reasonability and safety of prediction and intervention suggestions, reducing the decision risk, and enhancing the clinical trust.

The invention belongs to the cross technical field of nanomedicine, tumor treatment and immunotherapy, and particularly provides tumor mitochondria-targeted copper death selective induction nanoparticles, which are prepared by the following preparation method: S1, specifically coupling TPY and MHI through condensation reaction to obtain MTPY; s2, MTPY and copper ions are subjected to complexation, and an MTPY-Cu complex is obtained; and S3, the MTPY-Cu complex and albumin are subjected to self-assembly, and the MTPY-Cu-coated Alb nanoparticles are formed. The dosage of copper is only 1 / 1000 of that of free Cu < 2 + >, and equivalent immune regulation and killing effects can be achieved; primary tumors and distant tumor focuses can be inhibited, and lung metastasis is reduced; immune memory can be induced, and relapse can be prevented; the traditional Chinese medicine composition is combined with cis-platinum to remarkably enhance the curative effect and reverse the induced immunosuppression; the invention has the advantages of high biological safety and no obvious liver and kidney toxicity or hemolytic reaction.

The invention relates to the technical field of biological medicines, discloses application of circPDK1 protection, and particularly relates to application of a reagent for specific detection of circPDK1 in preparation of products for detection of esophageal cancer or prognosis evaluation of esophageal cancer. According to the application of the protected circular RNA in preparation of the esophageal cancer detection product, the circular RNA is circPDK1, and the nucleotide sequence of the circular RNA is shown as SEQ ID NO: 1. Experiments prove that circPDK1 is highly expressed in esophageal cancer tissues, growth, proliferation and migration of esophageal cancer cells are promoted, occurrence of esophageal cancer is promoted, knock-down of the circular RNA can significantly inhibit growth, proliferation and migration of esophageal cancer tumors, and the average survival time of esophageal cancer patients with low circPDK1 expression is longer than that of esophageal cancer patients with high circPDK1 expression. And a new research direction is provided for esophageal cancer detection, treatment and prognosis.

The invention provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention relates to the technical field of tumor biomedical engineering and organ chips, in particular to a vascularized gastric cancer organ chip and a preparation method thereof.The chip is composed of an integrated micro-fluidic main body, an annular micro-column array and an optical sealing film, and a central culture cavity is divided into a tumor area and a blood vessel area by micro-columns; the preparation method comprises the following steps: injecting a fibrous protein solution containing human umbilical vein endothelial cells and cancer-related fibroblasts into a vascular region, and adding thrombin for in-situ gelation to form a pre-vascularized network; the method comprises the following steps: mixing a patient-derived gastric cancer organ with a methacrylic acid esterified gelatin pre-polymerized solution, injecting the mixture into a tumor area, and carrying out photo-crosslinking immobilization; culturing for 7-14 days under the dynamic perfusion condition of a mixed culture medium to obtain a three-dimensional gastric cancer organ model containing a capillary network; the method is expected to be used for rapid, low-cost and high-throughput screening of chemotherapy or anti-angiogenesis drugs and accurate prediction of individual curative effects of patients.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

To provide a pharmaceutical composition for treating a cancer patient having a tumor having a total passenger gene mutation amount larger than the background mutation amount of the tumor.SOLUTION: A pharmaceutical composition for treating a subject having a tumor with a total passenger gene mutation load that is greater than the background mutation load of the tumor, wherein the background mutation load has been determined based on randomly selected genes of the tumor, comprising antibodies that bind to PD1 as an active ingredient. Antibodies that bind PD1 comprise a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 21 and / or comprise a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 22.SELECTED DRAWING: Figure 1

Disclosed herein are methods and systems of determining a cancer status or an organ health status of a subject, the method comprising: obtaining a biological sample obtained or derived from the subject; enriching a population of cells in the biological sample, wherein the population of cells comprises stem cells and / or progenitor cells; extracting nucleic acids from the enriched population of cells; assaying the extracted nucleic acids to generate at least one of a transcriptomic profile of the subject, a genomic profile of the subject, and / or an exomic profile of the subject; computer processing the at least one of the transcriptomic profile of the subject, the genomic profile of the subject, and / or the exomic profile of the subject; and determining, based at least in part on the computer processing, the cancer status or the organ health status of the subject, wherein the cancer status or the organ health status comprises a presence or an absence of an organ and / or a tissue impacted by the cancer or impacted by a disease, disorder, or condition.

The application describes the first-in-human study on using a anti-KLK2xCD3 bispecific antibody and another anti-cancer drug, in particular, in particular a taxane chemotherapy or Androgen Receptor Pathway Inhibitor (ARPI), to provide enhanced antitumor efficacy with a deeper and more durable clinical response.

The application provides a novel DNA methylation marker TAGMe-3 for tumor identification and use thereof. The TAGMe-3 gene sequence region has significant methylation difference between cancer tissues and paracancer tissues. As long as abnormal high methylation state of the TAGMe-3 gene sequence region is detected, the subject is determined to belong to a tumor high-risk population. Moreover, the significant difference of the TAGMe-3 presented between tumor tissues and non-tumor tissues exists in different types of tumors (Pan-cancer) in a wide range.

The invention discloses an anti-TP53 mutation T cell receptor and application thereof, the T cell receptor comprises specific alpha chain and beta chain variable domains, and the complementary determining region (CDR) sequence is shown as SEQ ID NO: 9-14. The TCR has the core advantage that the TCR has excellent broad-spectrum recognition capability, can target six different amino acid substitutions (A, G, I, N, S and T) at the R249 site, and effectively deals with tumor heterogeneity and mutation difference between patients. Aiming at high-frequency HLA-B * 07: 02 alleles in people, the TCR lays a foundation for developing TCR-T cell therapy covering a wide range of people, and has great clinical application value and market potential in treatment of various solid tumors carrying TP53 R249 hotspot mutation, such as liver cancer.

The invention relates to the technical field of breast cancer detection based on multi-modal input, in particular to a breast cancer detection method and system based on multi-modal input. The method comprises the following steps: acquiring breast tumor original clinical data, including breast X-ray image data, medical history text data and clinical index data; preprocessing the obtained original clinical data of the breast tumor; performing feature extraction on the preprocessed breast tumor original clinical data; performing feature fusion on the extracted features to form a fusion feature vector; and performing breast cancer category classification by using the fusion feature vector. According to the application, the related information of the breast cancer is more comprehensively captured by fusing three types of multi-modal input data of the past medical history, the mammary gland X-ray and the clinical data of the patient. According to the multi-modal fusion method, the advantages of different types of data can be fully utilized, richer information is provided, and therefore the accuracy and reliability of detection are improved.