37 results about "Multiple cancer" patented technology

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.

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.

A nanoparticle for delivery of mRNA to cell, comprising a core comprising a polyethylenimine polymer having fluorinated groups covalently coupled thereto and with mRNA reversibly associated therewith, and a shell surrounding the core comprising heparin. Ij some embodiments, the nanoparticle comprises a targeting agent associated with the shell, wherein the targeting agent is selected from the group consisting of agents that bind to receptors overexpressed on tumor cells, agents that bind to cell surf ace antigens that are expressed on pluripotent stem cells, agents that bind to cell surface antigens on T cells, and agents that bind to antigens presented by MHO molecules. Pharmaceutical compositions that include the nanoparticle and methods for using the nanoparticle for transfecting cells are provided

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 application provides a cancer primary focus tracing method, device, system and storage medium, and relates to the technical field of cancer tracing. The cancer primary focus tracing method comprises the following steps: acquiring sample tissue data, giving each cancer sample a primary focus label through a methylation chip; determining candidate CpG sites; and constructing a cancer tracing classifier. The application realizes the technology of tracing multiple cancers by using DNA methylation biological characteristics and machine learning methods, and has the following characteristics: due to the high stability of DNA molecules, cancer information is relatively completely retained, so that sample storage and transportation are more convenient; the cancers targeted by the application have a wide coverage range and include general common tumors; cancer-specific CpG sites have a wide coverage; through analysis of the cancer-specific sites, the interference of normal tissues is excluded, so that the accuracy and application range of the classifier are improved; and the high-precision classifier makes the cancer diagnosis process simpler, more accurate and more efficient.

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.

The invention provides target sections S6-1, S6-2 and S6-3 for identifying multiple cancer species and application of the target sections S6-1, S6-2 and S6-3. A group of unprecedented gene methylation sites are successfully identified through an innovative screening strategy and large-scale cross-cancer omics data analysis. The invention also comprises a detection reagent (such as a primer) and a kit for detecting the target segment of the genome. The methylation degrees of the target segment between a plurality of cancer tissues and para-carcinoma tissues have significant differences, so that the subject can be judged to belong to tumor high-risk groups. The cancer tissue and para-carcinoma tissue differential methylation state presented by the target segment can be widely applied to various different types of cancers.

The application discloses a kind of gene markers in esophagus, stomach, intestine multiple cancer early screening application, early screening model construction method and detection device, belong to the early non-invasive detection technical field of digestive tract tumor.It establishes a new type of multiple cancer screening system by analyzing the whole genome characteristics of circulating free DNA in peripheral blood.Based on low-depth whole genome sequencing data, three dimensions of molecular markers are detected: genome copy number variation pattern, DNA fragment distribution characteristics of specific length and epigenetic signals of transcription initiation region.Advanced converter neural network architecture is used, and the model can efficiently capture the complex feature correlation in the whole genome range through its unique self-attention mechanism.The model design specially considers the particularity of genomic data, and introduces an adaptive position coding system to accurately reflect the spatial distribution relationship of DNA fragments on the chromosome.The system can still maintain excellent detection performance at very low sequencing depth.

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 relates to a multi-cancer-species pathological feature analysis method and system based on a unified framework, and belongs to the field of data processing. In the method, a unified multi-cancer-species analysis framework is constructed, a graph network is introduced to formally characterize the cross-cancer-species (such as 16 cancers) pathological feature similarity, and a NOTEARS algorithm is applied to aggregated pathological features to carry out causal discovery, so that the cross-cancer-species pathological feature similarity is found. Therefore, integrated analysis of association and causality of pathological characteristics of multiple cancer species in a unified space can be realized, isolation of a traditional single cancer species model is overcome, and migration and fusion of knowledge among different cancers are promoted. Meanwhile, the group features similar to the target patient are obtained from the graph network for causal analysis, the biological reasonability and stability of causal inference are remarkably improved, the pathological features with the real driving effect can be more reliably identified, and deep analysis of the pathological features is achieved.

This invention discloses the application of P4HB as a target in the preparation of therapeutic drugs for metastatic pleural and peritoneal effusions in multiple cancer types. Through combined single-cell transcriptomics and proteomics analysis, this invention revealed that P4HB is significantly overexpressed in metastatic pleural and peritoneal effusion lesions. Multiplex immunofluorescence and immunohistochemistry were used to perform in situ tissue validation in clinical samples of pleural and peritoneal effusions from breast cancer, lung cancer, gastric cancer, colorectal cancer, and ovarian cancer, clarifying the differential expression characteristics of P4HB in metastatic tumor cells and its correlation with poor prognosis. In patient-derived organoid models, targeting and inhibiting P4HB activity with small molecule inhibitors significantly reduced organoid growth capacity and survival rate, and showed a synergistic sensitizing effect on chemotherapeutic drugs. This invention reveals the use of P4HB as a therapeutic target for metastatic pleural and peritoneal effusions in multiple cancer types, providing a new target and direction for drug development in precision medicine.

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.

The invention discloses a tumor mutation load prediction method and system based on digital pathology and clinical data. The method comprises the following steps: acquiring a full-slice image and clinical data of a multi-cancer patient; performing preprocessing, segmentation and clustering-based screening on the image to construct a training set; the method comprises the following steps: preprocessing clinical data, and performing feature screening and word embedding coding based on a random forest; extracting an image feature and a clinical feature vector by using a double-branch network; performing cross-modal feature fusion through a one-dimensional dual-enhancement fusion module; and finally predicting the TMB state through the classifier. The system correspondingly comprises six function modules which are connected in sequence. According to the method, the TMB can be noninvasively predicted at low cost, the problem of training label noise is effectively solved, and the method has excellent cross-cancer generalization ability.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

This invention provides a novel target nucleic acid molecule, P7, for the identification of multiple cancer types and its applications. A group of methylated target nucleic acid segments for the diagnosis of multiple pan-cancer diseases were screened and validated. These segments exhibited significant differences in methylation status within tumor samples, which can be used to identify individuals at high risk for cancer. When these target nucleic acid segments are used in combination for detection, they exhibit unexpected improvements in sensitivity and specificity. The tumor target nucleic acid segments of this invention can serve as novel molecules for auxiliary clinical diagnosis or prognosis of cancer.

The invention discloses a multi-cancer-species early screening method based on fragment omics and microbiological omics characteristics and application thereof, and belongs to the technical field of biomedical detection. The method comprises the following steps: extracting fragment omics characteristics of human cfDNA and microbiome characteristics of plasma microorganism DNA based on sequencing data; fusing the two types of features, inputting the fused features into a first-stage classification model, and judging whether the subject is cancer positive or not; and further predicting the cancer type of the positive result through a second-stage classification model. The fragment omics and microbiome characteristics are combined to be used for early screening of multiple cancer species, early screening and tissue tracing of eight high-incidence cancers such as lung cancer, liver cancer and colorectal cancer can be achieved through single-time non-invasive blood sampling, and the method still has high sensitivity and high specificity under the low sequencing depth, is low in cost and wide in coverage and is suitable for large-scale population screening.

The present disclosure provides monoclonal antibodies against protein programmed cell death 1 ligand (PD-L1), which can block the binding of PD-L1 to PD-1, and therefore block the inhibitory function of PD-Li on PD-1 expressing T cells. The antibodies of disclosure provide very potent agents for the treatment of multiple cancers via modulating human immune function.

This disclosure provides a combination of methylation biomarkers for multiple cancer types, a screening method, and their applications. The screening method for methylation biomarkers, and the multi-cancer methylation biomarkers obtained by this method, and their applications, can be used to simultaneously detect eight cancers, including liver cancer, lung cancer, colorectal cancer, gastric cancer, esophageal cancer, pancreatic cancer, breast cancer, and ovarian cancer, as well as combinations of methylation biomarkers for quasi-traceable tumor sites. Specifically, it provides a method or system for assessing the cancer risk of a sample and predicting the source tissue of cancer signals in patients. This disclosure provides a high-performance, simple, and economical technical means for simultaneous early screening and tracing of eight cancers, helping to further improve the early screening rate for patients.

Compounds were identified from SHP2 Protein Conformational Array (PCA) ELISA. These compounds can disrupt SHP2 Higher Order Structure (HOS) in vitro and induce SHP2 degradation in multiple cancer cell lines. The SHP2-KRAS-ERK pathway analysis indicated that these identified compounds could induce inactivation of the active form of ERK, P-ERK. In addition, some of the compounds can induce SHP2 and / or KRAS degradation in multiple cancer cell lines. Cancer cell line testing demonstrated that these compounds identified through SHP2 HOS disruption showed dose-dependent growth inhibition. Since the SHP2-KRAS-ERK pathway plays an important role in cancer development, these compounds can be used as potential cancer treatments.

Embodiments relate to novel vaccines against human papilloma virus (HPV) and HPV-related diseases, including various cancer types. The HPV vaccine is composed of anti-human dendritic cell (DC) surface receptor antibodies, including E6 / 7 proteins of CD40 and HPV 16 and 18. The described technology is not limited to the preparation of vaccines against HPV 16-and HPV 18-related diseases, and can be suitable for the preparation of vaccines carrying E6 / 7 from any type of HPV. The described HPV vaccines can target DC, major and professional antigen presenting cells (APCs), and are capable of inducing and activating potent HPV E6 / 7-specificity and potent CD4 + and CD8 + T cell responses. The HPV vaccines may be used for the prevention of HPV infections and HPV-related diseases, as well as for the treatment of HPV-related diseases, including cancer.

This invention relates to a cancer signal identification method based on a cfDNA sequence language model and its application in cancer screening and MRD monitoring. The deep learning-based sequence language model analyzes whole-genome sequencing data of cfDNA to automatically extract and identify cancer-related signals, making it suitable for clinical scenarios such as early cancer screening and minimal residual disease (MRD) monitoring. This technology overcomes the limitations of targeting specific known mutations or manually labeled biomarkers, enabling end-to-end deep modeling of high-throughput whole-genome sequences of cfDNA. This significantly improves the sensitivity, specificity, and generalization ability of early cancer screening and MRD monitoring, providing an innovative solution for non-invasive screening of multiple cancer types and longitudinal disease monitoring.

The present invention relates to: a biomarker composition for diagnosing multiple cancers, the biomarker composition comprising metabolites; and an artificial intelligence-based method for providing information for diagnosing multiple cancers and, more specifically, to a biomarker composition for diagnosing multiple cancers and an artificial intelligence-based method for providing information for diagnosing multiple cancers, the biomarker composition comprising at least five biomarkers selected from the group consisting of valine (Val), glutamate (Glu), N-methyl-2-pyridone-5-carboxamide (2PY), tryptophan (Trp), kynurenine (KN), acetylcarnitine (AC), hexanoylcarnitine (HC), octanoylcarnitine (OC), decanoylcarnitine (DC), lauroylcarnitine (LAC), myristoylcarnitine (MC), palmitoylcarnitine (PC), 16:0 Lyso PC (LPC16), 18:0 Lyso PC (LPC18), 15:0-18:1 PC (15PC), 16:0 SM (16SM), 18:1 SM (18SM), and 24:1 SM (24SM).

Provided herein are, inter alia, novel antibodies that bind to fms-like tyrosine kinase 3 (FLT3) thereby effectively targeting cells expressing FLT3. 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 acute myeloid leukemia (AML), lymphoblastic leukemia (ALL), lung cancer, breast cancer, pancreatic cancer, ovarian cancer, colorectal cancer, renal cancer, or glioblastoma.

Provided herein are, inter alia, novel antibodies that bind to cadherin-17 (CDH17) thereby effectively targeting cells expressing CHD17. 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 gastrointestinal cancer, colorectal cancer, stomach cancer, pancreas cancer, neuroendocrine cancer, or ovarian cancer.

The invention relates to the technical field of plasma free DNA methylation markers, and provides a plasma free DNA methylation marker and application thereof. The methylation marker is composed of 50 methylation regions on a human genome hg38, and the cancers comprise lung cancer, colorectal cancer, liver cancer, stomach cancer, breast cancer, esophageal cancer and pancreatic cancer. The universal DNA methylation biomarkers capable of being used for combined diagnosis of multiple cancer species are screened out, and the markers have high specificity and sensitivity to cancer samples of different clinical stages and are of great significance to early diagnosis of cancers.

This invention discloses a combination of protein biomarkers for screening multiple cancer types and its applications, belonging to the field of cancer diagnostic technology. The combination includes 164 candidate proteins such as IGLV4-60 and TPM3. This invention also provides an in vitro detection method, which involves extracting proteins from plasma or serum samples of subjects, enzymatically digesting them, and then using liquid chromatography-mass spectrometry to obtain the relative expression levels of the proteins; subsequently, the expression levels are input into a classification model constructed based on a machine learning algorithm. This invention employs a stratified screening strategy of first binary classification and then multi-class classification, which can not only distinguish between healthy individuals and cancer patients with high sensitivity and specificity, but also further accurately classify colorectal cancer, gastric cancer, liver cancer, lung cancer, ovarian cancer, and pancreatic cancer. This invention achieves non-invasive, high-throughput parallel screening and tissue tracing of multiple cancer types, possessing extremely high clinical application value.

The method may include receiving a positron emission tomography (PET) scan depicting multiple cancer cells. One or more lesions depicted in the PET scan may be identified. An origin cell classification model may be applied to determine the origin cells of each lesion depicted in the PET scan. A molecular subtype profile of multiple cancer cells depicted in the PET scan may be determined based on the origin cells of at least the individual lesions depicted in the PET scan. The molecular subtype profile may include the overall origin cells of the multiple cancer cells and / or the proportion of lesions having each possible origin cell. Related systems and computer program products are also provided.

Systems and methods are directed towards a web application for diagnosing cancer using multimodal artificial intelligence analysis. A web-based artificial intelligence system for multi-modal cancer diagnosis comprises a web application interface that receives uploads of patient data including biopsy images, electronic health records, and omics data; an ensemble of deep learning models including at least a first convolutional neural network and a second convolutional neural network that process the biopsy images at multiple magnification levels to generate cancer grade predictions; a multimodal data integration module that combines the cancer grade predictions with patient demographic information and clinical data from the electronic health records and omics data; and a visualization component that displays the cancer grade predictions as interactive charts with color-coded probability distributions for different cancer severity levels.

The application belongs to the technical field of biological medicine, and particularly relates to a biomarker for early screening, diagnosis or monitoring of multiple cancers and application. The application realizes early high-precision screening and diagnosis of multiple cancers by screening and adopting a brand-new biomarker combination, namely, exosome surface phosphatidylserine (PS) and / or prolyl 4-hydroxylase beta subunit (P4HB), and the performance is significantly better than that of existing biomarkers. The technical scheme has the advantages of non-invasiveness, convenience, rapidness and the like, and simultaneously has high sensitivity and high specificity, and can realize accurate identification in the super-early or early stage of cancer, and is expected to develop into a new generation of non-invasive tumor diagnosis tool.

Provided is a qPCR-based multi-gene methylation detection system for peripheral blood free DNA. Under the conditions of satisfying the nucleic acid extraction efficiency of peripheral blood free DNA and reducing the occurrence of large fragment contamination during extraction, the co-immunoprecipitation technology is further used to improve the antibody enrichment and purification effects of methylated target nucleic acids, and an appropriate amount of a blocking agent is added to effectively reduce non-methylation enrichment. Methylated target signals are amplified indiscriminately by means of pre-amplification, so that the fluorescence quantitative PCR technology can also realize synchronous detection of a large number of target genes. The system has good openness, can expand the number of detection genes according to actual needs, and can meet the requirements of simultaneously carrying out combined detection of a plurality of target genes to obtain indicators of multiple cancer types and multiple types of diseases.