42 results about "MRD Negative" patented technology

The invention discloses a tiny residual focus detection method and system based on ultra-high depth sequencing, and relates to the technical field of tiny residual focus intelligent detection.The tiny residual focus detection method comprises the following steps that on the basis of a sequencing library, splitting is conducted according to a sample index to obtain a to-be-detected sample, and a consensus sequence is obtained according to a molecular identifier of the to-be-detected sample; based on a consensus sequence, filtering out the consensus sequence of which the mass value is less than 25 or the family size is less than 3, and combining a variation type and a distance from a fragment edge as noise introduced into an original nucleic acid molecular chain; a context sequence (context) and a chain direction are used as noise for introducing the capture level of PCR amplification; on the basis of the noise level, the circulating tumor DNA level is estimated in combination with tumor priori knowledge, and the MRD state is determined by detecting the significance of molecular signal sources. According to the invention, the sensitivity and specificity of MRD detection are improved.

The invention discloses a high-specificity minimal residual disease (MRD) monitoring method and system based on circulating tumor DNA (ctDNA). The method comprises the following steps: receiving tumor tissue sequencing data of an UTUC patient, and generating a double-Panel target list containing personalized and fixed Panel; respectively extracting plasma cfDNA and leukocyte gDNA; performing vacuum concentration, hybrid capture and sequencing on the cfDNA library by using the double Panel lists, and performing deep sequencing on the leukocyte gDNA; constructing an individualized clonal hematopoietic mutation filtering database; actively filtering and rejecting clonal hematopoietic background mutation by utilizing a filtering database; and calculating an MRD load score based on the filtered tumor-derived mutation and outputting a report. The system comprises corresponding modules which are used for automatically executing the process. According to the invention, through cooperation of four major technologies of double-Panel design, process optimization, UMI error correction and active clonal hematopoietic filtration, MRD monitoring with extremely high sensitivity and specificity on UTUC is realized, false positive is significantly reduced, and the kit has drug resistance early warning potential.

The invention belongs to the technical field of tumor molecular diagnosis and biological information analysis, and relates to a high-throughput sequencing method and system for monitoring acute lymphocytic leukemia (MRD). Through targeted sequencing with a unique molecular identifier and / or a double-chain tag, error modeling based on a background noise spectrum and statistics / machine learning pseudo variation filtering, ultra-deep accurate detection of IG / TCR cloning and related gene low-frequency variation is realized. And an artificial intelligence recurrence risk prediction model is established by combining a time sequence MRD index, cloning diversity and clinical information, and a structured clinical report is output and docked with LIS / HIS. According to the method, the sensitivity and the specificity of ALL minimal residual disease detection can be remarkably improved, dynamic evaluation on leukemia cloning evolution and recurrence risks is realized, and a reliable basis is provided for individualized treatment decision and long-term follow-up visit.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.

The invention provides a reagent composition for detecting minimal residual diseases of acute megakaryocytic leukemia and application of the reagent composition. The reagent composition comprises two groups of antibodies and is a flow cytometry detection composition for follow-up visit of minimal residual diseases after acute megakaryocytic leukemia treatment, a two-tube parallel scheme is used during application, and the first group of antibodies and the second group of antibodies are respectively used for different tube samples. The reagent composition can be applied to flow cytometry to detect minimal residual diseases after acute megakaryocytic leukemia treatment.

Provided herein are systems and methods for determining a level of disease, methylation, minimal residual diseases (MRDs) in an individual. Provided herein are systems and methods for retaining double-stranded context of a double-stranded nucleic acid molecule during sequencing.

This disclosure relates to a method for detecting minimal residual disease using tumor information. [Solution] A minimal residual disease detection method utilizing tumor information includes the steps of: acquiring first sequencing data associated with a first sample of the patient; acquiring second sequencing data associated with a second sample of the patient; acquiring third sequencing data associated with a third sample of the patient; and performing minimal residual disease detection for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data.

Described herein are methods of treating a hematological malignancy, including minimal residual disease in hematological malignancies, and methods of preventing relapse of hematological malignancies, the methods comprising administering an inflammasome modulator. In some embodiments the method of treatment comprises administering a compound of formula (I) or a pharmaceutically acceptable salt or solvate thereof, wherein the variables are as defined herein.

The present disclosure provides a probabilistic model for accurate and sensitive somatic single nucleotide variant (SNV) detection in cell-free nucleic acid samples comprising a set of sequence data. A joint genotype may be determined for each locus in the set of sequence data, and germline mutations may be intrinsically removed. A set of filtrations can be applied to eliminate low quality somatic variant calls. Further, a global tumor cell-free deoxyribonucleic acid (cfDNA) fraction and overlapping read mates can be considered, thereby enabling accurate SNV detection and variant allele frequency estimation from samples with low tumor cfDNA fraction. A sensitive early detection of minimal residual disease (MRD) is designed by using the probabilistic model and the machine learning model for distinguishing true variants from sequencing errors.

The application discloses a data processing device computer program product and application for ctDNA variation detection in the field of bioinformatics. The technical problem to be solved by the application is how to detect ctDNA variation in early cancer or postoperative minimal residual disease under the conditions of no UMI library construction and medium sequencing depth. The application constructs a support sequence set of a candidate variation site, generates a sequence feature tensor and a fragment physical feature vector (including a normalized fragment length); the former is input into a first neural network branch to extract sequence features, and the latter is input into a second neural network branch to extract physical features; through a fusion module, the sequence features and the physical features are combined, a gating weight is calculated according to the physical features by using a gating unit, and the contribution degree of the sequence features is dynamically adjusted, and a real ctDNA variation probability is output. The application utilizes physical laws to suppress sequencing noise and can be applied to monitoring of tumor minimal residual disease under the condition of no molecular bar code.

The present application provides a kind of acute megakaryocyte leukemia micro residual disease detection reagent composition and its application.The reagent composition includes two groups of antibodies, and is used for the composition of flow cytometry detection for following-up micro residual disease after the treatment of acute megakaryocyte leukemia, and when used, 2 tubes are used in parallel, wherein the first group of antibodies and the second group of antibodies are used for different tube samples respectively.The reagent composition of the present application can be applied to flow cytometry detection of micro residual disease after the treatment of acute megakaryocyte leukemia.

A lymphoma recurrence risk prediction method, device and equipment based on MRD are disclosed, comprising: acquiring clinical feature information related to diffuse large B-cell lymphoma; in response to the clinical feature information, calculating a plurality of baseline clinical features through a structured constraint target Bayesian network model to obtain an initial recurrence risk probability of diffuse large B-cell lymphoma of a target object; the structure constraint is used to prohibit direct connection between each baseline clinical feature; according to the initial recurrence risk probability, the state of minimal residual disease related to diffuse large B-cell lymphoma and high-risk pathological factors, the multidimensional calculation of recurrence risk is carried out through the target random forest model to obtain the final recurrence risk probability of the target object; according to the final recurrence risk probability, the recurrence risk information of diffuse large B-cell lymphoma of the target object is generated. To improve the interpretability and accuracy of lymphoma recurrence risk prediction.

The application discloses a system and a kit for early auxiliary diagnosis of breast cancer and detection of micro residual lesions at various stages, and relates to the technical field of medical diagnosis.The system provided by the application uses the read value ratio of miRNA mature bodies and isomers to perform early auxiliary diagnosis of breast cancer and detection of micro residual lesions at various stages, and has high diagnostic sensitivity and specificity; and the system provides more accurate diagnostic basis for early adoption of treatment or intervention means by breast cancer patients.

This invention discloses primer compositions, kits, and their applications for monitoring minimal residual disease in human leukemia. The primer compositions include upstream-specific primer compositions for IGH, IGK, and IGL, downstream-specific primer compositions for IGH, IGL, and IGK, as well as upstream and downstream adapter primers. The primer compositions are designed with careful consideration of primer TM value, GC content, fragment size, specificity, and coverage to ensure that the amplification products are uniform, specific, and comprehensively cover potential clonal forms.

Provided herein are methods and systems for cancer detection and monitoring. The methods may include minimal residual disease analysis. The methods may include using nucleic acids from a urine sample or a urine cell pellet sample. The methods may include assaying nucleic acids in urine to detect a set of biomarkers from the sample. The methods may include monitoring a subject after transurethral bladder tumor resection.

Methods and systems for determining recurrence of a tumor in a subject using mutational profiles derived from cell-free DNA obtained concurrently with or prior to tumor resection and integrating mutational profiles from the resected tumor to enhance classification models. Low-depth whole-genome sequencing data can be used to obtain a variety of molecular profiles, including genetic and epigenetic profiles, from tumor tissue and blood samples obtained after the tumor is resected from the subject. The methods may also comprise using a classifier trained to recognize mutational profiles from subjects exhibiting minimal residual disease (MRD) to determine whether a tumor recurred in a subject.

Methods for treating leukemia are disclosed based on detecting specific cell states and transcriptional programs within leukemic cells. This disclosure presents a novel therapeutic method for treating acute lymphoblastic leukemia (ALL), including BCR-ABL positive and BCR-ABL1-like ALL subtypes. The method involves detecting specific cell states and transcriptional programs in patient samples and administering targeted therapies based on these characteristics. For a pre-B cell-like state or pre-BCR signaling program, a combination of tyrosine kinase inhibitor (TKI) and SYK inhibitor is used. Conversely, a progenitor-like state or stress-autophagy program is treated with a TKI and a p38 MAPK inhibitor. This approach aims to improve treatment efficacy by tailoring therapy to the leukemia's unique molecular and cellular features, particularly in relapsed cases or when minimal residual disease is present.

Described herein are systems and methods for longitudinal minimal residual disease monitoring. Biomarkers may be detected at various time points to generate data indicative of the presence or absence of minimal residual disease (MRD) in a subject. The biomarkers may be detected at various time points during treatment. The biomarkers may also be detected after treatments and may be used to predict treatment resistance of disease such as cancer.

This disclosure relates to the detection of minimum residual disease (MRD) in surgical drain fluid. The data collected from the drain fluid is also useful in predicting the recurrence of cancer. Comparison is also made between the analysis results of plasma and drain fluid.

This invention discloses a computer program product and its application for data processing devices in the field of bioinformatics for ctDNA variant detection. The technical problem this invention aims to solve is how to detect ctDNA variants in early-stage cancer or postoperative minimal residual disease (MRD) under conditions of no UMI library construction and moderate sequencing depth. This invention constructs a set of supporting sequences for candidate variant sites, generating a sequence feature tensor and a fragment physical feature vector (including normalized fragment length). The former is input into a first neural network branch to extract sequence representation, and the latter into a second neural network branch to extract physical representation. A fusion module combines the sequence representation and physical representation, and a gating unit calculates the gating weight based on the physical representation and dynamically adjusts the contribution of the sequence representation, outputting the probability of the true ctDNA variant. This invention utilizes physical laws to suppress sequencing noise and can be applied to monitor MRD under conditions without molecular barcodes.

The invention provides methods for cancer diagnosis, for predicting recurrence and minimal residual disease, and for informing therapeutic choice / predicting therapeutic efficacy. The invention comprises extracting cfDNA from a bodily fluid sample and performing size selection to extract cfDNA from the sample. The size selection step may be performed any number of times as is suitable to obtain a desired yield.

This disclosure relates to the detection of minimum residual disease (MRD) in surgical drain fluid. The data collected from the drain fluid is also useful in predicting the recurrence of cancer. Comparison is also made between the analysis results of plasma and drain fluid.

The present invention provides methods for detecting and predicting minimal residual disease in a subject who has undergone tumor resection surgery. Method of the invention utilize a personalized cutoff for determining the likelihood for MRD. In some embodiments, the cutoff is determined on a patient-specific basis and may be used in subsequent assays to analyze samples, evaluate MRD, select therapeutics, and / or evaluate therapeutic efficacy.

The application discloses a blood tumor fusion gene micro residual lesion monitoring method, system and equipment, and technical scheme points thereof are as follows: high-throughput sequencing data of fusion gene information and all genetic information are acquired; the high-throughput sequencing data is compared to a reference genome without PCR deduplication, a first alignment result is obtained, real fusion gene sequences are screened from the first alignment result, a first copy number of the fusion gene is calculated according to the real fusion gene sequences; real housekeeping gene sequences are screened from the first alignment result, a second copy number of the housekeeping gene is calculated according to the real housekeeping gene sequences; and the first copy number is divided by the second copy number to obtain a micro residual lesion ratio of the fusion gene. The application can realize high-sensitivity and high-specificity monitoring of MRD.

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.

A method for determining whether a subject has or is likely to develop pediatric cancer, adult cancer, or minimal residual disease (MRD) using a trained machine learning model configured to detect said cancer.

Presented herein are systems and methods of classifying samples based on flow cytometry data. A computing system may receive a dataset comprising a plurality of event sequences for a plurality of flow channels. The flow channels may correspond to white blood cell types in a sample obtained from a subject at risk of or diagnosed with cancer. The computing system may apply the dataset to a first machine learning (ML) model to determine a plurality of first values for the plurality of event sequences. The computing system may sort the dataset by the plurality of first values. The computing system may apply the sorted dataset to a second ML model to determine a second value indicating a probability of tumor associated with cancer in the sample of the subject. The computing system may generate a classification in accordance with the second value.

The present invention relates to a reagent panels for the measurement of minimal residual disease (MRD) associated with pediatric B-cell precursor acute lymphoblastic leukemia (B- ALL) by multiparametric flow cytometry. The reagent panel of the invention comprises a combination of antibodies directed against markers, wherein the combination of antibodies comprises i) antibodies targeting markers CD45, CD20, CD34, CD38, CD10, CD58, CD66c, CD73, CD81, CD123, CD304, CD44, CD86, CD99 and CD371, and ii) antibodies targeting markers CD19 and / or CD22, wherein the antibodies are conjugated with fluorochromes. The invention further relates to the use of said panels for detecting MRD associated with B-ALL and / or for identifying a subject at risk of developing B-ALL relapse. The invention also relates to methods of detecting MRD associated with B-ALL. Key advantages of the invention include achieving high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity.

The invention provides methods for cancer diagnosis, for predicting recurrence and minimal residual disease, and for informing therapeutic choice / predicting therapeutic efficacy. The invention comprises extracting cfDNA from a bodily fluid sample and performing size selection to extract cfDNA from the sample. The size selection step may be performed any number of times as is suitable to obtain a desired yield.

Methods and systems disclosed herein relate to detecting a presence or an absence of a second primary disease in a subject. The methods may comprise performing a workflow comprising a minimal residual disease (MRD) assay on a biological sample and using a result of the workflow to detect a presence or absence of a second primary disease in the subject.