347 results about "Tumor Sample" patented technology

The invention discloses a somatic cell structure variation detection method based on a monomolecular long-read length sequence chart hybrid model, which comprises the following steps of: extracting a read length local sequence of a target structure variation interval and a flank region thereof, constructing a local chart genome by utilizing a partial sequence alignment chart algorithm, and encoding a multi-sequence alignment result. And then, a multi-category hybrid model is combined with an expectation maximization algorithm, and clustering analysis is performed on the comparison data, so that the somatic cell structure variation category only from the tumor sample is identified. And finally, generating a consensus sequence of a somatic cell structure variation region in the tumor sample, and taking the consensus sequence as an optimization result of a local genome. Compared with an existing short-read-length sequencing method, the method has the advantages that the problems of comparison errors, low-complexity sequences and genome heterogeneity can be effectively solved, and the detection precision of somatic cell structure variation is remarkably improved. Through the application of a single-molecule long-read-length sequencing technology, more comprehensive and accurate genome information can be provided.

The invention relates to an in vitro method for classifying a subject afflicted with a cancer as suffering from a cancer with (at risk of) a metabolic reprograming towards fatty acids oxidation including a step of assaying the activation of RelB in a tumor sample form said cancer. The inventor indeed identified the pivotal role of RelB in energy metabolism and more particularly mitochondrial respiration and fatty acid oxidation. Accordingly, the invention also relates to inhibitors of RelB activity or expression, as well as of lipid metabolism for use in the treatment of cancers showing an activated RelB.

The invention relates to the technical field of tumor neoantigen detection, and particularly discloses a neoantigen prediction method and system for a malignant solid tumor, and the method comprises the following steps: obtaining a tumor sample and a peripheral blood sample, and carrying out DNA and RNA sequencing; analyzing and processing to obtain DNA (Deoxyribonucleic Acid) sequencing data and RNA (Ribonucleic Acid) sequencing data; analyzing to obtain an HLA typing result and a sequence of the mutant protein; calculating to obtain related characteristic values of the new antigens; calculating a new antigen score; and intercepting the first N pieces with the highest scores to obtain a neoantigen prediction result of the malignant solid tumor. Starting from the action mechanism of the new antigens, according to key steps of the prediction process, a plurality of characteristic values are selected, a hard filtering index is not adopted, a product mode is adopted, and the score of the new antigens is calculated according to the score of the relevant characteristic values of the new antigens.

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 relates to the technical field of tumor data processing, in particular to a methylation-based tumor data processing system. The system comprises the following modules: a methylated sample sequencing module, a sequencing difference site recognition module, a tumor gene sequence analysis module and a tumor subtype classification module, performing high-throughput sequencing on the to-be-detected clinical tumor DNA sample to obtain methylated tumor sequencing data; performing methylation level quantification on the methylated tumor sequencing data, and performing tumor difference site analysis to generate tumor difference site data; performing tumor generation key gene sequence identification according to the tumor difference site data to generate tumor methylation characteristic data; and performing tumor subtype tag identification according to the tumor methylation characteristic data to generate tumor type tag data. According to the method, accurate tumor typing is realized on the basis of tumor DNA methylation characteristic analysis, and the false negative rate of low-concentration sample detection is effectively reduced.

The invention relates to a target tumor DNA fragment screening method based on deep learning, and belongs to the technical field of target tumor DNA fragment screening. A tumor DNA fragment screening model is constructed through a deep neural network, a training set is used for model establishment, and a test set is used for model performance testing. And obtaining to-be-identified positive tumor sample DNA sequencing data, carrying out positioning analysis on the to-be-identified positive tumor sample DNA sequencing data according to the trained tumor DNA fragment screening model, obtaining the position of a target tumor DNA fragment, and displaying the position according to a preset mode. According to the method, the training sample data in which a plurality of different types of tumor DNA fragments exist is processed, so that the interference of the plurality of different types of tumor DNA fragments in the same tumor training sample data on model training can be reduced to the greatest extent, the prediction precision of the model can be improved, and the screening precision of the tumor DNA fragments can be improved.

The invention relates to the technical field of medical equipment, in particular to a lung cancer tumor sampling device and method.The lung cancer tumor sampling device comprises multiple needle cores and multiple sleeves in coaxial sliding fit with the needle cores, the multiple needle cores and the multiple sleeves are in one-to-one correspondence, and the multiple needle cores are parallel; the ends, back to the sampling groove, of the sleeves are jointly connected with a shell of a hollow structure, a push plate is slidably connected into the shell in the axial direction of the needle cores, and the ends, back to the sampling groove, of the needle cores penetrate through the shell and are inserted into the shell to be connected with the push plate; and an adjusting part for driving the push plate to slide in the shell is arranged between the shell and the push plate. The problem that in the prior art, single-point sampling may miss a key mutation area, and false negative is caused is solved.

The invention discloses a tumor phenotype analysis method and system based on a disease source database and machine learning assistance, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining clinical, gene and image multi-modal feature vectors of a tumor sample, inputting a multi-modal feature fusion network, and extracting each modal path vector; performing long-distance dependency modeling on clinical and gene features to obtain a first dependency feature map, and generating a second dependency feature map in combination with the feature association strength feature map; and determining a target tumor phenotype feature vector based on the second dependency feature map and the image feature path vector, finally calculating a matching confidence degree with a to-be-selected phenotype feature vector in a tumor phenotype database, and selecting a phenotype corresponding to the highest confidence degree as a target result. Through cross-modal feature dependence modeling and disease source database matching, the accuracy and efficiency of tumor phenotype analysis are improved, and support is provided for clinical precise diagnosis and treatment.

A method of analyzing a tumor sample comprising:(a) acquiring a library comprising a plurality of tumor members from a tumor sample;(b) contacting the library with a bait set to provide selected members;(c) acquiring a read for a subgenomic interval from a tumor member from said library;(d) aligning said read; and(e) assigning a nucleotide value (e.g., calling a mutation) from said read for the preselected nucleotide position, thereby analyzing said tumor sample.

Described herein are methods of preparing an enriched library of nucleic acids, comprising: (a) identifying a patient-specific panel of somatic variants present in a tumor sample from a patient, (b) generating a subset panel of somatic variants from the patient-specific panel, wherein the subset of somatic variants comprises one or more of: multi -nucleotide variants, insertions and deletions, and genomic rearrangements; (c) preparing a sample of cell-free DNA (cfDNA) fragments from the patient for sequencing; and (d) selectively enriching the cfDNA for the subset of somatic variants to generate an enriched library.

Disclosed herein are systems, methods, and compositions for treating a subject diagnosed with, or suffering from cancer. In some embodiments, the method comprises determining whether a tumor sample from the subject includes a cytotoxic gene signature, and treating the subject based on the determination. In some embodiments, the subject has or is suspected of having a loss of heterozygosity in human leukocyte antigen (HLA) class I genes. In some embodiments, the therapy comprises one or more checkpoint inhibitors. In some embodiments, the cancer is colorectal, uterine, stomach, lung, skin, head or neck, or non-small cell lung carcinoma.

This disclosure is directed to two cancer-associated fibroblast (CAP) subtypes, namely permissive (permCAF) and restraining (restCAF), in pancreatic ductal adenocarcinoma (PDAC), mesothelioma, urothelial carcinoma, or renal cell carcinoma. Methods are disclosed that describe how to identify a tumor sample as a perCAF or a restCAF subtypes. Methods are also disclosed that use the subtypes for diagnosis, prognosis, differential treatment, and treatment.

The invention relates to a molecular typing model construction and identification method of BRCA mutation breast cancer. The method comprises the following steps: performing unsupervised clustering analysis on a gene with most significant change in a common transcriptome expression profile of BRCA1 / 2 mutant breast cancer to obtain an optimal clustering number, and dividing the optimal clustering number into subtypes with different clinical prognosis and molecular characteristics of the optimal clustering number; based on the clustering result, a random forest algorithm is utilized to construct a molecular typing model, the model comprises a plurality of key classification genes, and the molecular typing model is verified through a verification set and an external data set so as to ensure the accuracy, the stability and the clinical applicability of the molecular typing model. Molecular typing can be rapidly and accurately carried out according to common transcriptome sequencing data of a BRCA1 / 2 mutant breast cancer patient tumor sample, and a scientific basis is provided for selection of an individualized treatment scheme.

The invention discloses a somatic mutation recognition method and system based on a residual network in combination with a channel attention mechanism, and the method comprises the steps: scanning a sequence comparison result of a tumor sample and a paired normal sample, and constructing a multi-channel feature matrix representation; a somatic mutation recognition model combining a residual network (ResNeXt) and a channel attention mechanism (SE module) is constructed, verification and fine tuning are performed by using a test set, and then the somatic mutation recognition model which is excellent in performance and suitable for resource-constrained environment deployment is obtained. The invention provides a new hybrid neural network, the network fully utilizes sequence comparison result information of a tumor sample and a paired normal sample, somatic mutation information can be extracted and learned more effectively, and mutation recognition accuracy is improved.

The invention discloses a trophoblast-free one-step culture method and application of tumor infiltrating lymphocytes, and particularly discloses a one-step amplification method of tumor infiltrating lymphocytes, which comprises the following steps: treating a tumor sample obtained from a subject into tumor segments; the tumor segments are cultured in a culture medium containing a CD3 agonist, a 4-1BB agonist and IL-2, so that an amplified tumor infiltration lymphocyte population is obtained, and the culture medium does not contain trophoblastic cells. Compared with the prior art, the method has the advantages that the required period for amplifying the tumor infiltrating lymphocytes is shorter, the cost is lower, the cell effect function is stronger, and the clinical application range of the tumor infiltrating lymphocyte therapy is widened.

The invention belongs to the technical field of bioinformatics, and particularly relates to a method for detecting abnormal DNA methylation fragments from cell free DNA (cell free DNA, cfDNA) and classifying the abnormal DNA methylation fragments from the cell free DNA, and the method comprises the following steps: based on a healthy cfDNA sample and a tumor cfDNA sample, selecting methylation haplotype regions (Methylation Haplotype Block, MHB) having significant difference between the two samples by adopting seven DNA methylation measurement indexes; on the basis of the selected methylated haplotype regions with significant differences, constructing total distribution of seven DNA methylation measurement indexes in the healthy cfDNA sample and the tumor cfDNA sample, and carrying out differential analysis; seven DNA methylation measurement indexes are adopted to construct classification models for abnormal DNA methylation fragments, and the classification models of the seven abnormal DNA methylation fragments are integrated on the basis of differential analysis results of overall distribution of the seven DNA methylation measurement indexes. According to the application, the abnormal DNA methylation fragments in the cell free DNA are detected by adopting seven DNA methylation measurement indexes, and the abnormal DNA methylation fragment classification model based on the seven DNA methylation measurement indexes is integrated, so that the improvement of the sensitivity of tumor early screening is facilitated.

The invention relates to a PDC (Polycrystalline Diamond Compact) cell carrying a bar code DNA (Deoxyribose Nucleic Acid) tag convenient to track as well as a preparation method and application thereof, and belongs to the technical field of The invention provides an isolated culture method of PDC primary cells, and provides a method for constructing recombinant PDC cells containing bar code DNA tags. According to the PDC primary cell culture system, the cell caking rate is reduced, the cell viability and the adherence rate are improved, and continuous passage to 20 generations or above can be achieved. The PDC primary cells faithfully retain the heterogeneity of clinical tumor samples in the aspects of mutation types, tumor generation and development mechanisms and the like. The bar code DNA tag introduced into the PDC cell can be detected by carrying out targeted sequencing and single cell RNA sequencing on a bar code DNA tag sequence in cell genome DNA, so that different clone groups reserved from PDX of the PDC cell line are conveniently distinguished and tracked in a high-throughput manner.

The present invention relates to the technical field of tumor immune microenvironment detection and analysis, and discloses an apparatus and method for detecting and analyzing the immune microenvironment of solid tumors. The apparatus for detecting and analyzing the immune microenvironment of solid tumors includes: a detection mechanism: used to contact with a tumor sample or puncture a part of the tumor sample and upload data for operators to analyze; a transportation and cleaning mechanism integrating transportation, cleaning and adjustment functions: cleaning both sides of the sample synchronously during transportation and adapting to different sample sizes; a clamping and turning mechanism: clamping the sample and turning it to position to expose the cleaning surface; a collection component: detachably recovering the shed impurities; a wiping component: used to clean the detection area of the detection mechanism after detection to prevent residual substances from affecting subsequent detection results. The transportation and cleaning mechanism of the present invention can automatically clean the impurities outside the sample while transporting the tumor sample, and can also adjust according to samples of different sizes, improving the accuracy of subsequent detection.

The present invention discloses a tumor sampling device that is convenient for separating tumors, and relates to the technical field of tumor sampling equipment, including: a sampling tube, the sampling tube is arranged in a needle tube and can move along the length direction of the needle tube between a first position and a second position, a separation mechanism is embedded in the sampling tube and arranged near the bottom of the sampling tube, the separation mechanism can separate the tumor sample from the tumor, an adsorption sampling mechanism penetrates the needle tube and is connected to the sampling tube, and the adsorption sampling mechanism can suck the tumor sample out of the sampling tube. During the tumor sampling process, the sampling tube is first inserted into the tumor, and then the tumor sample is separated from the tumor by the separation mechanism. Although there is a sequential operation sequence, the needle does not need to be pulled out during the operation, and the position of the sampling tube after being inserted into the tumor does not need to be changed. As a whole, tumor sampling can be achieved in one operation, avoiding tumor debris from falling into non-tumor areas and reducing the risk of tumor metastasis.

The disclosure provides a method for treating a subject afflicted with a tumor comprising administering to the subject a therapeutically effective amount of an anti-PD-1 antibody or antigen-binding portion thereof or an anti-PD-L1 antibody or anti-gen-binding portion thereof, wherein the subject is identified as having a high inflammatory gene signature score. In some embodiments, the high inflammatory gene signature score is determined by measuring the expression of a panel of inflammatory genes in a tumor sample obtained from the subject, wherein the inflammatory gene panel comprises CD274 (PD-LI), CD8A, LAG3, and STAT1.

The present disclosure relates to methods for characterizing cancer testis antigen burden (“CTAB”), for predicting cancer survival outcomes using CTAB analysis, and for recommending and / or treating cancer using CTAB analysis. Particularly, aspects are directed to measuring expression of a panel of cancer testis antigen (CTA) gene markers in a tumor sample from a subject, determining a CTAB based on the measured expression of the CTA gene markers, classifying, by comparing to a reference CTAB score from the same cancer or tumor type, the CTAB score as a high-CTAB score or a low-CTAB score, and identifying the subject as responsive to a treatment wherein (i) the high-CTAB score is indicative of responsiveness to the treatment, or (ii) the low-CTAB score is indicative of responsiveness to the treatment.

The invention discloses a microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite site micro-offset, and relates to the technical field of bioinformatics, and the microsatellite instability detection method comprises the following steps: processing a sequencing sequence of a to-be-detected sample obtained by a high-throughput sequencing technology to obtain a comparison file; according to the method, selection and quality control of microsatellite sites are strictly controlled, a microsatellite instability detection method based on single-tumor sample high-throughput sequencing can be used for analyzing the repetition times of candidate microsatellite site repetition units without depending on a control sample, and meanwhile, the method has very good sensitivity to a sample with relatively large offset, so that the accuracy of the microsatellite instability detection is greatly improved. The method can also be applied to other cancer species with MSI characteristics, and can adapt to detection requirements of different cancer species by adjusting candidate microsatellite loci no matter whether a micro-migration condition exists or not, provide visualization, assist in seeing the degree of microsatellite migration, facilitate manual recheck and reduce misjudgment.

The present invention belongs to the technical field of sampling devices, in particular a tumor sampling device for oncology. The tumor sampling device comprises a clamping device, and the clamping device comprises a handle section, a bent section integrally formed with the handle section, and a clamping section also integrally formed with the bent section, wherein a disposable sheath is sleeved on the clamping section; a protective pad is mounted on the handle section; and a connecting block is fixedly connected to one end of the handle section. In the present invention, in terms of accurate sampling, the bent section of the clamping device fits the anatomical structure of a human body, enabling the accurate positioning of a tumor, and the disposable sheath can protect a sample, thus greatly increasing the sampling accuracy; and in terms of operation performance, the protective pad for a handle reduces the hand fatigue of medical staff, a lifting ring facilitates storage and retrieval, and a limiting and rebounding device makes a clamping operation convenient and efficient, thus effectively improving the convenience of operation. Compared with traditional tools, the device effectively addresses the shortcomings of traditional sampling tools, and strongly guarantees the accuracy, safety and convenience of tumor sampling.

The invention discloses a bioelectrical impedance tumor identification method based on multi-modal fusion. The method comprises the following steps: S1, acquiring electrical impedance signal data and tissue slice image data of a tumor sample; the tissue slice image data are pathological slice images corresponding to tumor tissues; s2, inputting the electrical impedance signal data and the tissue slice image data into a multi-modal feature coding module for asymmetric feature coding; s3, performing splicing and weighted fusion on the global semantic feature vector and the image semantic feature vector to form a multi-modal feature vector; and S4, inputting the multi-modal feature vector into a decoding network for classification prediction, and outputting a tumor identification result. According to the method, an asymmetric coding structure and a weighted fusion mechanism are constructed by fusing the electric signal modality and the image modality, so that the utilization efficiency and the feature discrimination capability of multi-modal information can be effectively improved, and the accuracy and the reliability of tumor recognition are remarkably improved.

Described herein are techniques for determining tumor mutational burden (TMB) of a tumor sample previously obtained from a subject. In some embodiments, the techniques include obtaining sequence reads, the sequence reads having been previously obtained by sequencing the tumor sample; aligning the sequence reads to a population- specific genomic reference graph representing a linear reference sequence and population- specific variants relative to the linear reference sequence, wherein the population- specific variants are variants associated with at least one population to which the subject belongs; identifying, based on a result of aligning the sequence reads to the population- specific genomic reference graph, a plurality of somatic variants; and determining the TMB of the tumor sample using the identified plurality of somatic variants.

Strategies for blocking anti-apoptotic proteins or activating proapoptotic proteins in combination with immune checkpoint blockade therapy to prevent or reverse acquired resistance are described, providing methods to detect, prevent, and / or reverse acquired resistance to ICB therapy. Anti-melanoma therapy can be enhanced by administering an effective amount of an activator of a pro-apoptotic protein, such as a BAX activator, and / or a downregulator of an anti-apoptotic protein, such as a BH3 mimetic. Also described is a method of detecting acquired resistance to anti-melanoma therapy by assaying a sample of tumor DNA for deletion and / or amplification of genes indicative of acquired resistance.

Methods for treating a patient having a solid tumor are disclosed, comprising: (a) obtaining a tumor sample from the patient; (b) assessing the sample for levels of biomarkers for at least one of innate immune cells and adaptive immune cells; and (c) administering an effective amount of an immunotherapy to the patient if the sample comprises elevated levels of CD68+ PD-L1+ macrophages and CD8+ T cells compared to control and / or elevated levels of CD20+ B cells compared to control and / or low levels of CD163 expression compared to control and PD-L1 expression of greater or equal to (≥) 50%.

Provided herein is a method of detecting circulating tumor DNA (ctDNA) in a sample. The method utilizes a preliminary panel that is cross-referenced with a second non-tumor sample to detect the presence or absence of any of the plurality of preliminary tumor-specific somatic mutations and removing said plurality of preliminary tumor-specific somatic mutations to obtain a patient-specific panel that can be used to detect ctDNA in further samples.

A targeted panel with low sample input requirements from a tumor only sample may be processed to estimate mutation load in a tumor sample. The method may include detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering rule set retains the somatic variants and removes germ-line variants; counting the identified somatic variants to give a number of somatic variants; determining a number of bases in covered regions of the targeted locations in the tumor sample genome; and calculating a number of somatic variants per megabase, provides an estimate of the mutation load per megabase in the tumor sample genome.

Disclosed herein is a method of analyzing flow cytometry data for cells derived from homogenized whole tumor samples.