The present disclosure relates to an improved method for analyzing sequencing data to detect cross-sample contamination in a test sample. Determining cross-contamination in a test sample can be informative for determining that the test sample will be less likely to correctly identify the presence of cancer in the subject. Pre-determined single nucleotide polymorphisms selected from: an allele present in a select database or a genotyping SNP associated with a sample type are used to identify. A sample is determined to be contaminated using the determined contamination probabilities of the one or more pre-determined SNPs.
A system generates a cancer detection panel. The system is configured to generate an assay having a minimized size and number of genomic regions while still detecting the presence of cancer at or above a specific performance threshold. To select the genomic regions for the panel, the system employs a classification model. The classification model receives a set of genomic regions that may be associated with disease presence. The model then determines a sensitivity score for each genomic region and ranks the regions according to their score. The sensitivity score is based on a likelihood that variations in the genomic region are indicative of cancer. The model then selects genomic regions for the panel based on their rank. The model only selects as many genomic indicators as are needed for desired detection performance. The genomic regions can be associated with solid or liquid cancers, viral regions, or cancer hotspots.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
3.
DYNAMICALLY SELECTING SEQUENCING SUBREGIONS FOR CANCER CLASSIFICATION
Methods and systems for segmenting sequencing regions obtained from a sample interval are disclosed. sample contamination detection are disclosed. In particular, an analytics system accesses test sequences from a sample. The test sequences each include a sequencing region which, in aggregate, form an aggregate sequencing region. The analytics system segments sequencing regions from the aggregate sequencing region into sequencing subregions. Several methods of segmenting sequencing regions into sequencing subregions are disclosed: (1) maximizing cancer vs. non-cancer methylation beta differences, (2) minimizing cancer vs. non-cancer methylation beta differences, (3) segmentation based on CpG density in regions, (4) dynamic generation of sequencing subregions based on mutual information scores and cancer classification propensity. The analytics system applies selects sequencing subregions and applies a cancer classifier to those subregions to identify cancer presence in the sample.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
4.
SYSTEMS AND METHODS FOR DEVELOPING AND UTILIZING A HEMATOLOGIC PROGNOSTIC CLASSIFIER
Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
5.
SYSTEMS AND METHODS FOR DEVELOPING AND UTILIZING A HEMATOLOGIC PROGNOSTIC CLASSIFIER
Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
In comparison to conventional sequencing pileup algorithms, the process described herein generates sequencing pileups that contains additional information not typically reported by conventional algorithms while also consuming fewer computational resources (e.g., time, processing power, and memory). First, each of a FASTA reference genome and BAM sequence read files are converted to an internal representation. This enables the rapid iteration across nucleotide bases of the sequence reads to determine support characteristics that summarize information of nucleic acid molecules corresponding to positions across the reference genome. Next, the support characteristics of positions across the reference genome are stored through a memory allocation process that utilizes a first and a second temporary storage. This enables the convenient freeing of one temporary storage while the other temporary storage is being used.
Methods and systems are disclosed for covariate prediction from methylation features. A system trains methylation state models that are configured to regress one or more methylation features at a genomic region based on covariates for a given sample. The system utilizes the methylation state models to determine information gain of genomic regions in predicting covariates of interest. The system may, based on the information gain, identify covariate-informative genomic regions. The system trains a covariate prediction model using non-cancer training samples with reported covariate label(s) and methylation features at a plurality of covariate-informative genomic regions. The system may deploy the covariate prediction model for sample swap detection. Additionally, the system may utilize prediction(s) from covariate prediction model(s) to serve as a feature to cancer classification.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Methods and systems are disclosed for covariate prediction from methylation features. A system trains methylation state models that are configured to regress one or more methylation features at a genomic region based on covariates for a given sample. The system utilizes the methylation state models to determine information gain of genomic regions in predicting covariates of interest. The system may, based on the information gain, identify covariate-informative genomic regions. The system trains a covariate prediction model using non-cancer training samples with reported covariate label(s) and methylation features at a plurality of covariate-informative genomic regions. The system may deploy the covariate prediction model for sample swap detection. Additionally, the system may utilize prediction(s) from covariate prediction model(s) to serve as a feature to cancer classification.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
9.
Base Coverage Normalization and Use Thereof in Detecting Copy Number Variation
Gene copy number variations are identified for genes in a targeted gene panel. For each gene, coverage at each base position across the gene is determined. The coverage at each base position can be influenced by the hybridization probes that are used to determine the base level coverage of the base position. The base level coverage for each base position is normalized to account for the characteristics of the hybridization probes. To determine whether a copy number variation exists for a gene, the base level coverage of base positions across the gene for a subject is analyzed to determine whether it deviates from the base level coverage of base positions across the gene for previously analyzed, healthy individuals. If a significant deviation exists, a copy number variation for the gene is called.
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
An analytics system creates a data structure counting strings of methylation vectors from a healthy control group. The analytics system enumerates possibilities of methylation state vectors given a sample fragment from a subject, and calculates probabilities for all possibilities with a Markov chain probability. The analytics system generates a p-value score for the subject's test methylation state vector by summing the calculated probabilities that are less than or equal to the calculated probability of the possibility matching the test methylation state vector. The analytics system determines the test methylation state vector to be anomalously methylated compared to the healthy control group if the p-value score is below a threshold score. With a number of such sample fragments, the analytics system can filter the sample fragments based on each p-value score. The analytics system can run a classification model on the filtered set to predict whether the subject has cancer.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
11.
CANCER CLASSIFICATION WITH CANCER SIGNAL OF ORIGIN THRESHOLDING
Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of cancer signal origin (CSO) classes. In some embodiments, the plurality of CSO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high prediction score are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to prediction score and applies binary threshold cutoffs determined for each stratum.
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
12.
CANCER CLASSIFICATION WITH CANCER SIGNAL OF ORIGIN THRESHOLDING
Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of cancer signal origin (CSO) classes. In some embodiments, the plurality of CSO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high prediction score are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to prediction score and applies binary threshold cutoffs determined for each stratum.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
Aspects of the invention relate to methods for tracking patient health by longitudinally tracking genetic variants in patients, such that it is possible to provide a tumor, or mutation, classification signature. Longitudinal tracking improves the ability to detect minimal residual disease (MRD; the small number of cells that remain in the patient after treatment and/or during remission) and/or treatment response at an early stage, both of which can help guide treatment decisions and guard against missing different intra-/inter-tumor responses in a patient.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 25/00 - ICT specially adapted for hybridisationICT specially adapted for gene or protein expression
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.
G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
Methods and systems for sample contamination detection are disclosed. In particular, sample barcodes are utilized, wherein each sample barcode is assigned to a sample and ligated to fragments from the sample. The sample barcodes are used in conjunction with indices from sequencing libraries to accurately assign sequence reads to samples during multiplex sequencing. Molecule identifiers may also be utilized to aid in de-duping of sequence reads to precisely identify original NA fragments from a sample. Accordingly, in one or more embodiments, a sequencing method includes isolating DNA fragments in a sample, ligating the DNA fragments with unique molecule identifiers (UMIs), performing an amplification process resulting in amplicons, ligating a sample barcode onto the amplicons, and performing amplicon sequencing. The analytics system looks to whether indices are matched and whether a sample barcode matches to the pair of indices when identifying single-index or double-index hopping events.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
The present disclosure relates to a method for improving sequencing panel assignments for samples from two or more individual. The system is configured to generate a sequencing panel assignment having an optimized set of samples for each panel that reduces sequencing costs but does not compromise Limit of Detection of the assay.
C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
The present invention provides methods for detecting white blood cell (WBC) contamination in a test sample comprising sequence reads corresponding to cell-free DNA (cfDNA) fragments. The computer-implemented methods for WBC contamination detection aim to assess whether a sample is contaminated by the WBC-shed DNA and may further determine a level of contamination.
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Methods and systems for sample contamination detection are disclosed. In particular, sample barcodes are utilized, wherein each sample barcode is assigned to a sample and ligated to fragments from the sample. The sample barcodes are used in conjunction with indices from sequencing libraries to accurately assign sequence reads to samples during multiplex sequencing. Molecule identifiers may also be utilized to aid in de-duping of sequence reads to precisely identify original NA fragments from a sample. Accordingly, in one or more embodiments, a sequencing method includes isolating DNA fragments in a sample, ligating the DNA fragments with unique molecule identifiers (UMIs), performing an amplification process resulting in amplicons, ligating a sample barcode onto the amplicons, and performing amplicon sequencing. The analytics system looks to whether indices are matched and whether a sample barcode matches to the pair of indices when identifying single-index or double-index hopping events.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
Methods for WBC contamination detection are disclosed. The computer-implemented methods for WBC contamination detection aim to assess whether a sample is contaminated by the WBC-shed DNA and may further determine a level of contamination. A first coverage-based approach assesses normalized coverage of sequence reads of a test sample at each genomic locus in a feature set of genomic loci. A contamination metric may be calculated based on a distance of the test sample's normalized coverage to a distribution of purified cfDNA samples. A second methylation-based approach deconvolves tissue type based on methylation features. A distribution is generated based on tissue type fractions of purified cfDNA samples from non-cancer subjects. The contamination metric is calculated based on a distance relative to the distribution of tissue type fractions. A third quantitative coverage-based approach generates distributions of coverage for cfDNA samples and for WBC samples for each genomic locus. A contamination metric is calculated as a fractional contribution of WBC-shed DNA that maximizes a likelihood based on the distributions of coverage.
Methods and systems for redacting non-indicative test sequences from test samples including both indicative and non-indicative test samples are disclosed. Generally, identified non-indicative test sequences originate from WBC cfDNA while indicative test sequences originate from cfDNA associated with the identification of cancer presence in a sample. To identify non-indicative test sequences, the system applies a disambiguation model to test sequences in a test sample. The disambiguation model matches genomic regions from test sequences in a test sample to those in test sequences from a sample cohort. The model then generates a feature set for the matched test sequences and determines a probability that sequences from the test sample represent cfDNA from WBCs. In representing cfDNA from WBCs, the system redacts those test sequences from the sample to form a classification population and applies a cancer classifier to the classification population.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
Methods and systems for redacting non-indicative test sequences from test samples including both indicative and non-indicative test samples are disclosed. Generally, identified non-indicative test sequences originate from WBC cfDNA while indicative test sequences originate from cfDNA associated with the identification of cancer presence in a sample. To identify non-indicative test sequences, the system applies a disambiguation model to test sequences in a test sample. The disambiguation model matches genomic regions from test sequences in a test sample to those in test sequences from a sample cohort. The model then generates a feature set for the matched test sequences and determines a probability that sequences from the test sample represent cfDNA from WBCs. In representing cfDNA from WBCs, the system redacts those test sequences from the sample to form a classification population and applies a cancer classifier to the classification population.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
22.
SYSTEMS AND METHODS FOR ENRICHING FOR CANCER-DERIVED FRAGMENTS USING FRAGMENT SIZE
Systems and methods for determining a cancer class of a subject are provided in which a plurality of sequence reads, in electronic form, are obtained from a biological sample of the subject. The sample comprises a plurality of cell-free DNA molecules including respective DNA molecules longer than a threshold length of less than 160 nucleotides. The plurality of sequence reads excludes sequence reads of cell-free DNA molecules in the plurality of cell-free DNA molecules longer than the threshold length. The plurality of sequence reads is used to identify a relative copy number at each respective genomic location in a plurality of genomic locations in the genome of the subject. The genetic information about the subject obtained from the sample and the genetic information consisting of the identification of the relative copy number at each respective genomic location, is applied to a classifier that determines the cancer class of the subject.
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
23.
Detecting Cross-Contamination in Sequencing Data Using Regression Techniques
Cross-contamination of a test sample used to determine cancer is identified using gene sequencing data. Each test sample includes a number of test sequences that may include a single nucleotide polymorphism (SNP) that can be indicative of cancer. The test sequences are be filtered to remove or negate at least some of the SNPs from the test sequences. Negating the test sequences allows more test sequences to be simultaneously analyzed to determine cross-contamination. Cross-contamination is determined by modeling the variant allele frequency for the test sequences as a function of minor allele frequency, contamination level, and background noise. In some cases, the variant allele frequency is based on a probability function including the minor allele frequency. Cross-contamination of the test sample is determined if the determined contamination level is above a threshold and statistically significant.
In various aspects, the present disclosure provides methods, compositions, reactions mixtures, kits, and systems for analysis of cell-free nucleic acid molecules (e.g., cfRNA and/or cfDNA) from a urine sample. In some embodiments, the analysis is an analysis of methylation patterns in target genomic regions among cfDNA fragments in a urine sample. In some embodiments, compositions include a plurality of different bait oligonucleotides. Methods for the detection of cancer of various cancer types are also provided.
Methods and systems are disclosed for component deconvolution by a mixture model based on methylation information. A mixture model may be trained agnostic of labels or known component contributions. A system generates a methylation signature for each of a plurality of training samples. The methylation signature may be based on a count or a percentage of a methylation variant(s) expressed in the methylation sequence reads of a training sample at each genomic region of a plurality of genomic regions. The system may train the mixture model using maximum likelihood estimation to deconvolve the component contributions. The mixture model may comprise component submodels and a deconvolution submodel. The component submodels predict a component likelihood based on the methylation signature. The deconvolution submodel predicts the component contributions based on the component likelihoods.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
26.
SYSTEMS AND METHODS FOR DETERMINING WHETHER A SUBJECT HAS A CANCER CONDITION USING TRANSFER LEARNING
Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G06F 18/2115 - Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Provided herein are compositions comprising tissue-specific markers for identifying a tissue of origin of a cell-free nucleic acid, e.g., a cell-free DNA molecule. Also provided herein are methods, compositions, and systems for identifying a tissue of origin of a cell-free nucleic acid by determining an absolute amount of cell-free nucleic acids comprising the tissue-specific marker. Also provided herein are methods, compositions, and systems for detecting a cancer in a tissue of an organism by analyzing tissue-specific markers.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
28.
METHYLATION-BASED AGE PREDICTION AS FEATURE FOR CANCER CLASSIFICATION
Methods and systems are disclosed for covariate prediction from methylation features. A system identifies a feature set of genomic regions by training one or more regressions to evaluate a covariance score of a genomic region. The system may select the feature set with the highest indicativeness scores and may consider other selection criteria. The system trains an age prediction model using training samples with reported chronological age label(s). The system can further utilize the chronological age prediction to predict a likelihood of cancer in a test sample. To do so, the system may compare the predicted covariate value and/or label to the reported value and/or label. In one embodiment, the system may utilize an age residual threshold to determine whether there is a strong likelihood of presence of cancer. In other embodiments, the system may utilize the predicted chronological age value as a feature to a cancer classifier.
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
29.
SYSTEMS AND METHODS FOR IDENTIFYING CLONAL EXPANSION OF ABNORMAL LYMPHOCYTES
Systems and methods for determining a disease state of a subject are disclosed. One method may include: determining a disease state of a subject by conducting one or more biological assays analyzing a biological sample of the subject; responsive to determining that the subject has the positive disease state, generating an immune repertoire profile of the subject, the immune repertoire profile generated from an immune repertoire sequencing of the biological sample and comprising a plurality of clonotypes and corresponding clonal frequencies of the clonotypes; identifying one or more clonal expansions of one or more clonotypes in the immune repertoire profile; determining, based on the one or more clonal expansions, that the subject is associated with a heme condition; and determining, based on the determined heme condition and the disease state determined by the one or more biological assays, that the positive disease state is a false positive.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
30.
OPTIMIZATION OF MODEL-BASED FEATURIZATION AND CLASSIFICATION
One or more techniques for optimizing cancer classification based on covariate characteristics is disclosed. In a first approach, an analytics system may determine separate cutoff thresholds for positively detecting disease signal for different labels for a covariate characteristic. The system may subdivide training samples based on their labels for the covariate characteristic, to separately determine the cutoff thresholds. In other approaches, the system may train disparate classifiers for each population. The system separates the training samples based on their labels for the covariate characteristic, and separately trains classifiers to generate a signal vector representing an amount of disease signal detected in a sample. The classifiers may be trained on different feature sets as determined based on mutual information gain, genomic region coverage, and healthy activation fraction.
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
One or more techniques for optimizing cancer classification based on covariate characteristics is disclosed. In a first approach, an analytics system may determine separate cutoff thresholds for positively detecting disease signal for different labels for a covariate characteristic. The system may subdivide training samples based on their labels for the covariate characteristic, to separately determine the cutoff thresholds. In other approaches, the system may train disparate classifiers for each population. The system separates the training samples based on their labels for the covariate characteristic, and separately trains classifiers to generate a signal vector representing an amount of disease signal detected in a sample. The classifiers may be trained on different feature sets as determined based on mutual information gain, genomic region coverage, and healthy activation fraction.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
32.
SYSTEMS AND METHODS FOR MAPPING CANCER CLASSIFICATIONS
Systems and methods for mapping cancer classification labels are provided. In a method for generating a cancer label for a cancer patient, the method entails acquiring, from a data source, a cancer classification for a cancer in the cancer patient; extracting, from the cancer classification, primary anatomic site, primary histology, and invasiveness (or behavior) of the cancer; identifying, in a lookup table, an entry having the extracted anatomic site, histology, and invasiveness (or behavior); and retrieving a corresponding cancer label in the entry for the cancer.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
33.
NEEDLE-BASED DEVICES AND METHODS FOR IN VIVO DIAGNOSTICS OF DISEASE CONDITIONS
Diagnostic devices and methods are provided for screening for a disease condition, including a cancer condition or a mendelian disease. The diagnostic devices allow for in vivo contact of cell-free nucleic acids or circulating tumor cells. The diagnostic device has a needle with a body and a detection reaction module attached to the body.
Methods and systems are disclosed for component deconvolution by a mixture model based on methylation information. A mixture model may be trained agnostic of labels or known component contributions. A system generates a methylation signature for each of a plurality of training samples. The methylation signature may be based on a count or a percentage of a methylation variant(s) expressed in the methylation sequence reads of a training sample at each genomic region of a plurality of genomic regions. The system may train the mixture model using maximum likelihood estimation to deconvolve the component contributions. The mixture model may comprise component submodels and a deconvolution submodel. The component submodels predict a component likelihood based on the methylation signature. The deconvolution submodel predicts the component contributions based on the component likelihoods.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6809 - Methods for determination or identification of nucleic acids involving differential detection
Systems and methods for mapping cancer classification labels are provided. In a method for generating a cancer label for a cancer patient, the method entails acquiring, from a data source, a cancer classification for a cancer in the cancer patient; extracting, from the cancer classification, primary anatomic site, primary histology, and invasiveness (or behavior) of the cancer; identifying, in a lookup table, an entry having the extracted anatomic site, histology, and invasiveness (or behavior); and retrieving a corresponding cancer label in the entry for the cancer.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
36.
SYSTEMS AND METHODS FOR PERFORMING METHYLATION-BASED RISK STRATIFICATION FOR MYELODYSPLASTIC SYNDROMES
Systems and methods for predicting survival outcomes in patients diagnosed with Myelodysplastic Syndrome (MDS) are disclosed. One method may include: receiving DNA sequencing data derived from a methylation assay performed on a biological sample associated with the at least one patient; computing methylation beta-values for one or more CpG-sites identified in the sequencing data; identifying one or more differentially methylated regions (DMRs) based on statistical analysis of the methylation beta-values for the one or more CpG-sites; selecting, via a feature selection process, a subset of the one or more DMRs to utilize as training data; and training, using the training data, the classifier to predict the survival outcome of the at least one patient. Other aspects are described and claimed.
A method of processing an input sample, as well as related kits and compositions, is provided herein. In various instances, the disclosure relates to providing an input sample comprising nucleic acid fragments, wherein in at least a portion of the nucleic acid fragments each fragment comprises one or more methylated cytosines; converting unmethylated cytosines of nucleic acid fragments of the input sample to uracils, yielding converted fragments; copying the converted fragments using a mixture of nucleotides, the mixture comprising a mixture of: binding moiety-modified cytosines and binding moiety-lacking cytosines; binding moiety-modified guanines and binding moiety-lacking guanines; or binding moiety-modified cytosines, binding moiety-lacking cytosines, binding moiety-modified guanines, and binding moiety-lacking guanines; wherein the copying yields a mixture of binding moiety-modified fragments and unmodified fragments which may be separated to provide a set of fragments enriched for hypermethylated fragments.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
38.
ENRICHMENT OF SHORT NUCLEIC ACID FRAGMENTS IN SEQUENCING LIBRARY PREPARATION
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C40B 40/08 - Libraries containing RNA or DNA which encodes proteins, e.g. gene libraries
39.
SYSTEMS AND METHODS FOR AUTOMATED CLASSIFICATION OF A DOCUMENT
A method for extracting information from a dataset, e.g., a document, includes: receiving the dataset at an information handling device, optionally, extracting, via optical character recognition implemented by a processor of the information handling device, textual information associated with the dataset, and classifying the dataset into one of a plurality of classes. Classifying the dataset may include computing a similarity score for each of the plurality of classes for each of a plurality of window regions of the dataset, calculating a subset of highest similarity scores for each of the plurality of classes for each of the plurality of window regions, determining overall similarity scores for each of the plurality of classes, and classifying the dataset as corresponding to a class with a highest overall similarity score.
An analytics system marks duplicate fragments from an initial set of fragments from a subject. The analytics system generates a sample state vector for each fragment. Each sample state vector comprises a sample genomic location within a reference genome and a plurality of methylation states for a plurality of CpG sites in the fragment, the methylation states determined to be one of methylated, unmethylated, variant, and ambiguous. The analytics system identifies two fragments with methylation state vectors as being derived from a matching reference location, e.g., sharing a common plurality of CpG sites. The analytics system calculates a modified Hamming distance based on methylation states in the first sample state vector and methylation states in the second sample state vector. Based on the modified Hamming distance, the analytics system marks the first fragment and the second fragment as either duplicate fragments or non-duplicate fragments.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
41.
SAMPLE CONTAMINATION DETECTION OF CONTAMINATED FRAGMENTS WITH CPG-SNP CONTAMINATION MARKERS
Methods and systems for detecting contaminated fragments in a biological sample for cancer classification are disclosed. The system identifies CpG-SNP contamination markers. The CpG-SNP contamination markers include at least one SNP that affects a CpG site. The CpG-SNP contamination markers may include additive CpG-SNP sites and/or subtractive CpG-SNP sites. Additive CpG-SNP sites include an SNP that creates a new CpG site. Subtractive CpG-SNP sites include an SNP that removes a preexisting CpG site. Hybrid sites may include additional sites. A multiple CpG-SNP contamination marker comprises two or more CpG-SNP sites. A CpG-SNP & indel contamination marker comprises at least one CpG-SNP site and an indel site. For a given sample, the system identifies contamination markers for which the sample is homozygous. The system determines fragments having a haplotype that is different from the homozygous haplotype of the sample to be contamination fragments.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
42.
SYSTEMS AND METHODS FOR DETECTING DISEASE SUBTYPES
Systems and methods for detecting a subtype of a disease state and for determining the development of a resistance mechanism in a disease are disclosed. One method may include: receiving, at an input component of the system, a set of sequence reads associated with a nucleic acid sample; generating, using a processor of the system and via analysis of the set of sequence reads, methylation data; and analyzing, using the processor, the methylation data to identify the subtype of the disease state. Another method may include: obtaining methylation data from a targeted methylation sequencing assay, applying the methylation data to a trained machine learning model, and receiving an output indicating whether MRD is present in a test subject and/or whether a resistance mechanism has been developed by a disease. Other aspects are described and claimed.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
Methods and systems for detecting contaminated fragments in a biological sample for cancer classification are disclosed. The system identifies CpG-SNP contamination markers. The CpG-SNP contamination markers include at least one SNP that affects a CpG site. The CpG-SNP contamination markers may include additive CpG-SNP sites and/or subtractive CpG-SNP sites. Additive CpG-SNP sites include an SNP that creates a new CpG site. Subtractive CpG-SNP sites include an SNP that removes a preexisting CpG site. Hybrid sites may include additional sites. A multiple CpG-SNP contamination marker comprises two or more CpG-SNP sites. A CpG-SNP & indel contamination marker comprises at least one CpG-SNP site and an indel site. For a given sample, the system identifies contamination markers for which the sample is homozygous. The system determines fragments having a haplotype that is different from the homozygous haplotype of the sample to be contamination fragments.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
44.
METHYLATION-BASED AGE PREDICTION AS FEATURE FOR CANCER CLASSIFICATION
Methods and systems are disclosed for covariate prediction from methylation features. A system identifies a feature set of genomic regions by training one or more regressions to evaluate a covariance score of a genomic region. The system may select the feature set with the highest indicativeness scores and may consider other selection criteria. The system trains an age prediction model using training samples with reported chronological age label(s). The system can further utilize the chronological age prediction to predict a likelihood of cancer in a test sample. To do so, the system may compare the predicted covariate value and/or label to the reported value and/or label. In one embodiment, the system may utilize an age residual threshold to determine whether there is a strong likelihood of presence of cancer. In other embodiments, the system may utilize the predicted chronological age value as a feature to a cancer classifier.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 20/40 - Population geneticsLinkage disequilibrium
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
45.
DYNAMICALLY SELECTING SEQUENCING SUBREGIONS FOR CANCER CLASSIFICATION
Methods and systems for segmenting sequencing regions obtained from a sample interval are disclosed, sample contamination detection are disclosed. In particular, an analytics system accesses test sequences from a sample. The test sequences each include a sequencing region which, in aggregate, form an aggregate sequencing region. The analytics system segments sequencing regions from the aggregate sequencing region into sequencing subregions. Several methods of segmenting sequencing regions into sequencing subregions are disclosed: (1) maximizing cancer vs. non-cancer methylation beta differences, (2) minimizing cancer vs. non-cancer methylation beta differences, (3) segmentation based on CpG density in regions, (4) dynamic generation of sequencing subregions based on mutual information scores and cancer classification propensity. The analytics system applies selects sequencing subregions and applies a cancer classifier to those subregions to identify cancer presence in the sample.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
Methods and systems for segmenting sequencing regions obtained from a sample interval are disclosed. sample contamination detection are disclosed. In particular, an analytics system accesses test sequences from a sample. The test sequences each include a sequencing region which, in aggregate, form an aggregate sequencing region. The analytics system segments sequencing regions from the aggregate sequencing region into sequencing subregions. Several methods of segmenting sequencing regions into sequencing subregions are disclosed: (1) maximizing cancer vs. non-cancer methylation beta differences, (2) minimizing cancer vs. non-cancer methylation beta differences, (3) segmentation based on CpG density in regions, (4) dynamic generation of sequencing subregions based on mutual information scores and cancer classification propensity. The analytics system applies selects sequencing subregions and applies a cancer classifier to those subregions to identify cancer presence in the sample.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Methods of identifying changes in genomic DNA copy number are disclosed. This disclosure provides methods for detecting chromosomal aberrations in a subject using Hidden Markov modeling. In some cases, methods provided herein use de novo sequence assembly to detect chromosomal aberrations in a subject. The methods can be used to detect copy number changes in cancerous tissue compared to normal tissue. The methods can be used to diagnose cancer and other diseases associated with chromosomal anomalies.
Various embodiments are directed to detecting infection-causing microbial cell-free DNA from a biological sample based on their size profiles and/or end signatures, in which the detection of infection-causing microbial DNA can be performed without no template control (NTC) samples. Embodiments can include identifying the infection-causing pathogen-derived microbial DNA based on sizes of microbial cell-free DNA molecules. Embodiments can also include identifying from the infection-causing pathogen-derived microbial DNA based on end signatures of microbial cell-free DNA molecules. Embodiments can also include applying a machine-learning algorithm to a plurality of vectors that represent end signatures of the microbial cell-free DNA molecules, to identify the infection-causing pathogen-derived microbial DNA. By detecting the infection-causing pathogen-derived microbial DNA, a level of infection for the biological sample can be predicted.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
49.
SYSTEMS AND METHODS FOR MANAGING ACCESS TO A RESOURCE
Systems and methods of providing access to a resource via an access management system may include: receiving, at an authorization server, a login request to a user profile from a client application, wherein the login request comprises a set of login credentials; transmitting, from the authorization server to an identity provider, the set of login credentials; authenticating, upon validation of the set of login credentials by the identity provider, the user; receiving, at the authorization server and subsequent to the authenticating, an authentication request from the client application; issuing, subsequent to validating the authentication request and by the authorization server, an access token to the client application; detecting, at a resource server, a request from the client application to access a resource, wherein the request comprises the access token; and enabling, by the resource server and responsive to validating the access token, the client application access to the resource.
Methods for generating a sequencing library from a sample comprising a plurality of single-stranded DNA molecules are provided, along with methods of using the generated sequencing library for detecting cancer, determining cancer stage, monitoring cancer progression, and/or determining a cancer classification from a test sample obtained from a subject.
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
51.
TUMOR FRACTION ESTIMATION USING METHYLATION VARIANTS
A computer-implemented method for generating a tumor fraction estimate from a DNA sample of a subject is disclosed. The method may include receiving a dataset of methylation sequence reads from the sample of the subject. The method may also include dividing the dataset into a plurality of variants. The method may further include determining methylation states of the plurality of variants. The method may further include filtering the plurality of variants based on a bank of reference sequence reads to generate a filtered subset of variants. The bank may include reads generated from non-cancer samples and biopsy samples of a plurality of tissues of reference individuals. The counts of the methylation states of variants in the filtered subset are determined and input to a model that is trained based on recurrence rates of the variants in the reference sequence reads. The tumor fraction estimate may be generated by the model.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
52.
ENRICHMENT OF MUTATED CELL FREE NUCLEIC ACIDS FOR CANCER DETECTION
Provided herein are methods of enriching mutated cell free nucleic acids for detection and diagnosis of cancer. Also provided are methods using a CRISPR-Cas system to target and deplete unwanted more abundant cell free nucleic acid sequences thereby enriching for less abundant sequences.
Methods and systems for detecting contaminated fragments in a biological sample for cancer classification are disclosed. The system identifies multiple SNP site contamination markers and indel site contamination markers. The multiple SNP site contamination markers include at least two SNP sites within a threshold distance, having population haplotype frequency within a range of threshold frequencies, excluding guanine-adenine polymorphisms and/or cytosine-thymine polymorphisms, ensuring Hardy-Weinberg equilibrium, or any combination of the parameters above. The indel site contamination markers include indel sequences that are within a threshold length, having high complexity, having population haplotype frequency within a range of threshold frequencies, ensuring Hardy-Weinberg equilibrium, or any combination of the parameters above. The system identifies contamination markers for which the sample is homozygous. The system estimates the contamination level of the sample by identifying fragments having a haplotype that is different than the homozygous haplotype of the respective contamination marker site.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
The present disclosure relates to an improved method for analyzing sequencing data to detect cross-sample contamination in a test sample. Determining cross-contamination in a test sample can be informative for determining that the test sample will be less likely to correctly identify the presence of cancer in the subject. Pre-determined single nucleotide polymorphisms selected from: an allele present in a select database or a genotyping SNP associated with a sample type are used to identify. A sample is determined to be contaminated using the determined contamination probabilities of the one or more pre- determined SNPs.
YISSUM RESEARCH DEVELOPMENT COMPANY OF THE HEBREW UNIVERSITY OF JERUSALEM LTD. (Israel)
HADASIT MEDICAL RESEARCH SERVICES AND DEVELOPMENT LTD. (Israel)
GRAIL, LLC (USA)
Inventor
Kaplan, Tomer
Dor, Yuval
Shemer, Ruth
Glaser, Benjamin
Abstract
The present disclosure relates generally to compositions and methods for determining cell type based on a methylation profile of associated DNA. For cell free DNA, such determination can be used to identify disease or conditions relating to the cell type. For tumor cells, such determination is useful for identifying their primary origin.
C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
56.
COMPOSITIONS AND METHODS FOR IDENTIFYING CELL TYPES
YISSUM RESEARCH DEVELOPMENT COMPANY OF THE HEBREW UNIVERSITY OF JERUSALEM LTD. (Israel)
HADASIT MEDICAL RESEARCH SERVICES AND DEVELOPMENT LTD. (Israel)
GRAIL, LLC (USA)
Inventor
Kaplan, Tomer
Dor, Yuval
Shemer, Ruth
Glaser, Benjamin
Abstract
The present disclosure relates generally to compositions and methods for determining cell type based on a methylation profile of associated DNA. For cell free DNA, such determination can be used to identify disease or conditions relating to the cell type. For tumor cells, such determination is useful for identifying their primary origin.
C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
Provided herein are methods for enriching a biological sample for a target nucleic acid, and analyzing the nucleic acid. In some cases, a biological sample is enriched for target nucleic acids associated with a cancer or tumor. In some cases, a biological sample is enriched for target nucleic acids, and the target nucleic acids vary in length. In some cases, one or more probes are used to enrich the biological sample for the target nucleic acid. In some cases, one or more probes hybridize to one or more ends of a target nucleic acid.
Methods and systems for detecting contaminated fragments in a biological sample for cancer classification are disclosed. The system identifies multiple SNP site contamination markers and indel site contamination markers. The multiple SNP site contamination markers include at least two SNP sites within a threshold distance, having population haplotype frequency within a range of threshold frequencies, excluding guanine-adenine polymorphisms and/or cytosine-thymine polymorphisms, ensuring Hardy-Weinberg equilibrium, or any combination of the parameters above. The indel site contamination markers include indel sequences that are within a threshold length, having high complexity, having population haplotype frequency within a range of threshold frequencies, ensuring Hardy-Weinberg equilibrium, or any combination of the parameters above. The system identifies contamination markers for which the sample is homozygous. The system estimates the contamination level of the sample by identifying fragments having a haplotype that is different than the homozygous haplotype of the respective contamination marker site.
A computer platform is provided that permits selling users to list items for sale and to allow a number of experts to review and verify authenticity of these items. In some embodiments, the system may be capable of queuing items to be listed within a management system, and experts are permitted to review particular items. Also, it is appreciated that certain experts have particular expertise to evaluate items of certain types, and therefore, in some implementations, the system is configured to more accurately match experts with particular items to be reviewed.
Methods and systems for detecting contaminated fragments in a biological sample for cancer classification are disclosed. The system identifies multiple SNP site contamination markers and indel site contamination markers. The multiple SNP site contamination markers include at least two SNP sites within a threshold distance, having population haplotype frequency within a range of threshold frequencies, excluding guanine-adenine polymorphisms and/or cytosine-thymine polymorphisms, ensuring Hardy-Weinberg equilibrium, or any combination of the parameters above. The indel site contamination markers include indel sequences that are within a threshold length, having high complexity, having population haplotype frequency within a range of threshold frequencies, ensuring Hardy-Weinberg equilibrium, or any combination of the parameters above. The system identifies contamination markers for which the sample is homozygous. The system estimates the contamination level of the sample by identifying fragments having a haplotype that is different than the homozygous haplotype of the respective contamination marker site.
Aspects of the invention include methods for preparing sequencing libraries, performing sequencing procedures that can correct for process-related errors, and identifying rare variants that are or may be indicative of cancer.
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
Various embodiments are directed to applications (e.g., classification of biological samples) of the analysis of the count and size of cell-free nucleic acids, e.g., plasma DNA and serum DNA, including nucleic acids from pathogens, such as viruses. Embodiments of one application can predict if a subject previously treated for a pathology will relapse at a future time point. Targeted sequencing (e.g., specifically designed capture probes, amplification primers) can be used to identify DNA across the entire viral genome.
Methods for measuring subpopulations of target molecules (e.g., polypeptides and/or cell-free ribonucleic acid) are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.
A system and method are disclosed for training a cancer classifier. The method includes, for each training sample comprising a plurality of methylation sequence reads: for each methylation sequence read, applying a probabilistic noise model, corresponding to a genomic region of a plurality of genomics regions that the methylation sequence read overlaps with, to the methylation sequence read to determine an anomaly score indicating a likelihood of observing the methylation pattern in healthy samples. Each probabilistic noise model is trained with methylation sequence reads from healthy samples. The method includes determining a feature vector comprising a feature for each genomic region based on a count of methylation sequence reads overlapping the genomic region with an anomaly score below a threshold anomaly score. The method includes training the cancer classifier with the feature vectors of the training samples to determine a cancer prediction based on an input feature vector.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
65.
METHYLATION FRAGMENT PROBABILISTIC NOISE MODEL WITH NOISY REGION FILTRATION
A system and method are disclosed for training a cancer classifier. The method includes, for each training sample comprising a plurality of methylation sequence reads: for each methylation sequence read, applying a probabilistic noise model, corresponding to a genomic region of a plurality of genomics regions that the methylation sequence read overlaps with, to the methylation sequence read to determine an anomaly score indicating a likelihood of observing the methylation pattern in healthy samples. Each probabilistic noise model is trained with methylation sequence reads from healthy samples. The method includes determining a feature vector comprising a feature for each genomic region based on a count of methylation sequence reads overlapping the genomic region with an anomaly score below a threshold anomaly score. The method includes training the cancer classifier with the feature vectors of the training samples to determine a cancer prediction based on an input feature vector.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
66.
METHYLATION FRAGMENT PROBABILISTIC NOISE MODEL WITH NOISY REGION FILTRATION
A system and method are disclosed for training a cancer classifier. The method includes, for each training sample comprising a plurality of methylation sequence reads: for each methylation sequence read, applying a probabilistic noise model, corresponding to a genomic region of a plurality of genomics regions that the methylation sequence read overlaps with, to the methylation sequence read to determine an anomaly score indicating a likelihood of observing the methylation pattern in healthy samples. Each probabilistic noise model is trained with methylation sequence reads from healthy samples. The method includes determining a feature vector comprising a feature for each genomic region based on a count of methylation sequence reads overlapping the genomic region with an anomaly score below a threshold anomaly score. The method includes training the cancer classifier with the feature vectors of the training samples to determine a cancer prediction based on an input feature vector.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
67.
METHODS FOR ANALYSIS OF TARGET MOLECULES IN BIOLOGICAL FLUIDS
Methods for measuring subpopulations of target molecules (e.g., polypeptides and/or cell-free ribonucleic acid) are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.
C12Q 1/6809 - Methods for determination or identification of nucleic acids involving differential detection
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
68.
METHODS FOR ANALYSIS OF TARGET MOLECULES IN BIOLOGICAL FLUIDS
Methods for measuring subpopulations of target molecules (e.g., polypeptides and/or cell-free ribonucleic acid) are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12Q 1/6809 - Methods for determination or identification of nucleic acids involving differential detection
69.
METHODS FOR DETECTING DISEASE USING ANALYSIS OF RNA
Methods for measuring subpopulations of ribonucleic acid (RNA) molecules are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
A simulation system performs microsimulations to model the impact of one or more early cancer detection screenings for a plurality of participants to simulate a randomized controlled trial (RCT). In one instance, the microsimulations are performed using parallel processing techniques. The microsimulation simulates the impact of early detection screenings on individual trajectories of the participants. In particular, while most screening modalities are for single cancer types, the microsimulation herein simulates the effect of a detection model on individual trajectories for participant populations having multiple types of cancer using, for example, multi-cancer early detection (MCED) screenings that are capable of detecting multiple types of cancer.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
71.
SOMATIC VARIANT COOCCURRENCE WITH ABNORMALLY METHYLATED FRAGMENTS
Systems and methods for identifying variant alleles as somatic or germline are provided. Reference and variant alleles for a genomic position are identified. Methylation states and sequences of nucleic acid fragment sequences that map to the genomic position are obtained from a sample of a subject. Using the sequences of nucleic acid fragment sequences, each nucleic acid fragment sequence that has the reference allele is assigned to a reference subset, and each nucleic acid fragment sequence that has the variant allele is assigned to a variant subset. One or more indications of the methylation states across the nucleic acid fragment sequences in the variant subset and an indication of the number of nucleic acid fragment sequences in the reference subset versus the variant subset are applied to a trained binary classifier. An identification of the variant allele at the genomic position as somatic or germline is obtained from the classifier.
Systems and methods for identifying variant alleles as somatic or germline are provided. Reference and variant alleles for a genomic position are identified. Methylation states and sequences of nucleic acid fragment sequences that map to the genomic position are obtained from a sample of a subject. Using the sequences of nucleic acid fragment sequences, each nucleic acid fragment sequence that has the reference allele is assigned to a reference subset, and each nucleic acid fragment sequence that has the variant allele is assigned to a variant subset. One or more indications of the methylation states across the nucleic acid fragment sequences in the variant subset and an indication of the number of nucleic acid fragment sequences in the reference subset versus the variant subset are applied to a trained binary classifier. An identification of the variant allele at the genomic position as somatic or germline is obtained from the classifier.
A simulation system performs microsimulations to model the impact of one or more early cancer detection screenings for a plurality of participants to simulate a randomized controlled trial (RCT). In one instance, the microsimulations are performed using parallel processing techniques. The microsimulation simulates the impact of early detection screenings on individual trajectories of the participants. In particular, while most screening modalities are for single cancer types, the microsimulation herein simulates the effect of a detection model on individual trajectories for participant populations having multiple types of cancer using, for example, multi-cancer early detection (MCED) screenings that are capable of detecting multiple types of cancer.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 15/00 - ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
G16B 25/00 - ICT specially adapted for hybridisationICT specially adapted for gene or protein expression
G16B 35/00 - ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
Provided herein are methods and systems for identifying chimeric nucleic acid fragments, e.g., organism-pathogen chimeric nucleic acid fragments and chromosomal rearrangement chimeric nucleic acid fragments. Also provided herein are methods and systems relating to determining a pathogen integration profile or a chromosomal rearrangement in a biological sample and determining a classification of pathology based at least in part on a pathogen integration profile or a chromosomal rearrangement in a biological sample. In certain aspects of the present disclosure, cell-free nucleic acid molecules from a biological sample are analyzed.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
Methods for preparing sequencing libraries from a DNA-containing test sample, as well as methods for correcting sequencing-derived errors, are provided.
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
C12Q 1/6848 - Nucleic acid amplification reactions characterised by the means for preventing contamination or increasing the specificity or sensitivity of an amplification reaction
G16B 35/00 - ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
C12N 15/66 - General methods for inserting a gene into a vector to form a recombinant vector using cleavage and ligationUse of non-functional linkers or adaptors, e.g. linkers containing the sequence for a restriction endonuclease
77.
Read-Tier Specific Noise Models for Analyzing DNA Data
Noise models for processing nucleic acid datasets can stratify processed sequence reads into different read tiers. Each read tier can be defined based on whether a potential variant location is at an overlapping region and/or a complementary region of the sequence reads. A processing system can determine, for each read tier, a stratified sequencing depth at the variant location. The processing system can determine, for reach read tier, one or more noise parameters conditioned on the stratified sequencing depth of the read tier. The noise parameters can be associated with a noise distribution. The processing system can generate an output for each noise model based on the noise parameters conditioned on the stratified sequencing depth. The processing system can combine the output for each stratified noise model to generate a combined result, which can represent a likelihood that an event would be as or more extreme than the observed data.
Disclosed herein are systems and methods for localization of a disease state (e.g., tissue of origin of cancer) using nucleic acid samples. In an embodiment, a method comprises receiving a plurality of cancer signals of a sample, each cancer signal indicating a probability that the sample is associated with a different disease state of a plurality of disease states. The method determines a first cancer signal having a greatest probability among the plurality of cancer signals. In accordance with a determination that the first cancer signal satisfies a criterion, the method associates the sample with a first disease state. In accordance with a determination that the first cancer signal does not satisfy the criterion, the method determines a second cancer signal having a second greatest probability among the plurality of cancer signals, and associates the sample with the first disease state and a second disease state.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
79.
METHODS OF IDENTIFYING SOMATIC MUTATIONAL SIGNATURES FOR EARLY CANCER DETECTION
Aspects of the invention include methods and systems for identifying somatic mutational signatures for detecting, diagnosing, monitoring and/or classifying cancer in a patient known to have, or suspected of having cancer. In various embodiments, the methods of the invention use a non-negative matrix factorization (NMF) approach to construct a signature matrix that can be used to identify latent signatures in a patient sample for detection and classification of cancer. In some embodiments, the methods of the invention may use principal components analysis (PCA) or vector quantization (VQ) approaches to construct a signature matrix.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
80.
CONDITIONAL TISSUE OF ORIGIN RETURN FOR LOCALIZATION ACCURACY
Disclosed herein are systems and methods for localization of a disease state (e.g., tissue of origin of cancer) using nucleic acid samples. In an embodiment, a method comprises receiving a plurality of cancer signals of a sample, each cancer signal indicating a probability that the sample is associated with a different disease state of a plurality of disease states. The method determines a first cancer signal having a greatest probability among the plurality of cancer signals. In accordance with a determination that the first cancer signal satisfies a criterion, the method associates the sample with a first disease state. In accordance with a determination that the first cancer signal does not satisfy the criterion, the method determines a second cancer signal having a second greatest probability among the plurality of cancer signals, and associates the sample with the first disease state and a second disease state.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
A method for extracting information from a dataset, e.g., a document, includes: receiving the dataset at an information handling device, optionally, extracting, via optical character recognition implemented by a processor of the information handling device, textual information associated with the dataset, and classifying the dataset into one of a plurality of classes. Classifying the dataset may include computing a similarity score for each of the plurality of classes for each of a plurality of window regions of the dataset, calculating a subset of highest similarity scores for each of the plurality of classes for each of the plurality of window regions, determining overall similarity scores for each of the plurality of classes, and classifying the dataset as corresponding to a class with a highest overall similarity score.
Nuclease activity can affect the methylation level and fragmentation of cfDNA. Certain levels of nuclease activity may be correlated with certain levels of methylation in certain regions. Methylation level in certain genomic regions can be analyzed to classify nuclease activity. Methylation statuses of different genomic regions compared to methylation statuses of other genomic regions can determine a level of a condition (e.g., a disease such as cancer or disorder) in a subject. Nuclease activity can be monitored through analysis of methylation statuses of different sites. The efficacy of a treatment can also be determined using methylation levels at certain genomic regions. The number of fragments from genomic regions that are hypomethylated or hypermethylated in a reference genome can be used to provide information (e.g., fractional concentration) on the sample itself. The size distribution of extrachromosomal circular DNA can also be used to analyze a biological sample. Systems are also described.
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
Disclosed herein are systems and methods for localization of a disease state (e.g., tissue of origin of cancer) using nucleic acid samples. In an embodiment, a method comprises receiving a plurality of cancer signals of a sample, each cancer signal indicating a probability that the sample is associated with a different disease state of a plurality of disease states. The method determines a first cancer signal having a greatest probability among the plurality of cancer signals. In accordance with a determination that the first cancer signal satisfies a criterion, the method associates the sample with a first disease state. In accordance with a determination that the first cancer signal does not satisfy the criterion, the method determines a second cancer signal having a second greatest probability among the plurality of cancer signals, and associates the sample with the first disease state and a second disease state.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
Methods and systems for determining a subject's likelihood of responding to a treatment by assessing the subject's cell-free DNA (cfDNA) sample include receiving sequence data gathered from sequencing the cfDNA sample, generating a feature matrix of values that correspond to synonymous and nonsynonymous mutations detected in the sequence data, and predicting, based on analysis of the feature matrix at a TMB prediction model, a tumor mutational burden (TMB) for a tissue of interest at the subject. The predicted TMB is evaluated to determine whether a set of criteria indicating a likely response to treatment is met. The set of criteria can include criterion(s) that are met when the predicted TMB is high, when the predicted TMB corresponds to a predicted tumoral heterogeneity indicative of homogeneous tissue, when the predicted TMB corresponds to a tumor fraction indicative of a positive responder, or any combination thereof.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
85.
PREPARATION OF NUCLEIC ACID SAMPLES FOR SEQUENCING
Compositions and methods are provided for amplifying nucleic acids, including cell free nucleic acid fragments, in preparation for sequencing. Methods are provided for making circularized nucleic acid templates having the structure [T]-[PS1]-[L]-[PS2] or [PS1]-[L]-[PS2]-[T'], where (a) T is a target nucleic acid and T' is a complement to a target nucleic acid; (b) each of PS1 and PS2 is a nucleic acid primer site; (c) L is a linker having a primer extension reaction terminating organic molecule; and the structure is circularized by binding a 5' end thereof to a 3' end thereof. Target sequences in the circularized templates are amplified by binding to PS1 a primer complimentary to PS1 and binding to PS2 a primer complimentary to PS2 and copying the target sequences by a primer extension reaction. Advantages include a reduction in ligation steps, which can result in fewer clean up steps and improved library conversion efficiency.
Methods for preparing a sequencing library from a DNA-containing test sample are provided. In some embodiments, the methods involve rescuing a partially ligated DNA fragment to enhance library preparation conversion efficiencies. In some embodiments, the methods involve improving recovery of duplex sequence information from double-stranded DNA.
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
C12N 15/66 - General methods for inserting a gene into a vector to form a recombinant vector using cleavage and ligationUse of non-functional linkers or adaptors, e.g. linkers containing the sequence for a restriction endonuclease
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Providing temporary use of on-line non-downloadable software
and applications for use in studying, diagnosing or
screening for cancer; providing temporary use of on-line
non-downloadable cloud computing software for use in
studying, diagnosing or screening cancer; all of the
foregoing services provided to physicians, nurses, and other
healthcare clinicians. Genetic testing and reporting for medical purposes; medical
testing for diagnostic or treatment purposes; medical
screening; medical diagnostic testing, monitoring and
reporting services; providing medical information regarding
genetics via a website; genetic analysis and reporting
services for medical purposes; all of the foregoing services
provided to physicians, nurses, and other healthcare
clinicians.
88.
DETECTING CANCER, CANCER TISSUE OF ORIGIN, AND/OR A CANCER CELL TYPE
The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]
89.
SYSTEMS AND METHODS FOR USING A CONVOLUTIONAL NEURAL NETWORK TO DETECT CONTAMINATION
A method for training a convolutional neural net for contamination analysis is provided. A training dataset is obtained comprising, for each respective training subject in a plurality of subjects, a variant allele frequency of each respective single nucleotide variant in a respective plurality of single nucleotide variants, and a respective contamination indication. First and second subsets of the plurality of training subjects have first and second contamination indication values, respectively. A corresponding first channel comprising a first plurality of parameters that include a respective parameter for a single nucleotide variant allele frequency of each respective single nucleotide variant in a set of single nucleotide variants in a reference genome is constructed for each respective training subject. An untrained or partially trained convolutional neural net is trained using, for each respective training subject, at least the corresponding first channel of the respective training subject as input against the respective contamination indication.
The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for detection of cancer tissue of origin (e.g., types of cancer).
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C12Q 1/6809 - Methods for determination or identification of nucleic acids involving differential detection
The present description provides a hematological disorder (HD) assay panel for targeted detection of methylation patterns or variants specific to various hematological disorders, such as clonal hematopoiesis of indeterminate potential (CHIP) and blood cancers, such as leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm. Further provided herein includes methods of designing, making, and using the HD assay panel for detection of various hematological disorders.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
93.
DIFFERENTIAL TAGGING OF RNA FOR PREPARATION OF A CELL-FREE DNA/RNA SEQUENCING LIBRARY
In various aspects, the present disclosure provides methods, compositions, reactions mixtures, kits, and systems for sequencing both RNA and DNA from a single source sample. In some embodiments, RNA is treated so as to differentiate RNA sequences from DNA sequences derived from the same sample. In some embodiments, the RNA and DNA are cell-free polynucleotides.
Systems and methods described herein include detecting a presence or absence of HPV in a biological sample having cell-free nucleic acids from a subject and potentially cell-free nucleic acids from an HPV strain. Based on a detection of HPV viral nucleic acids in the biological sample, an HPV-based multiclass classifier that predicts a score for each HPV-associated cancer type is applied. The HPV-based multiclass classifier is trained on a training set of HPV-positive cancer samples. An HPV-associated cancer associated with the biological sample is determined based on the scores predicted by the HPV multiclass classifier.
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
Methods for measuring subpopulations of cell-free ribonucleic acid (RNA) molecules are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.
Systems and methods described herein include detecting a presence or absence of HPV in a biological sample having cell-free nucleic acids from a subject and potentially cell-free nucleic acids from an HPV strain. Based on a detection of HPV viral nucleic acids in the biological sample, an HPV-based multiclass classifier that predicts a score for each HPV-associated cancer type is applied. The HPV-based multiclass classifier is trained on a training set of HPV-positive cancer samples. An HPV-associated cancer associated with the biological sample is determined based on the scores predicted by the HPV multiclass classifier.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
Methods for measuring subpopulations of cell-free ribonucleic acid (RNA) molecules are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.
C12Q 1/6809 - Methods for determination or identification of nucleic acids involving differential detection
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
(1) Providing temporary use of on-line non-downloadable software and applications for use in studying, diagnosing or screening for cancer; providing temporary use of on-line non-downloadable cloud computing software for use in studying, diagnosing or screening cancer; all of the foregoing services provided to physicians, nurses, and other healthcare clinicians.
(2) Genetic testing and reporting for medical purposes; medical testing for diagnostic or treatment purposes; medical screening; medical diagnostic testing, monitoring and reporting services; providing medical information regarding genetics via a website; genetic analysis and reporting services for medical purposes; all of the foregoing services provided to physicians, nurses, and other healthcare clinicians.
99.
SYSTEMS AND METHODS FOR CANCER CONDITION DETERMINATION USING AUTOENCODERS
A method for discriminating a cancer state is provided. A first dataset is obtained for a plurality of subjects having a first cancer state. Each subject has a plurality of nucleic acid methylation fragments with methylation patterns comprising CpG site methylation states. An autoencoder including an encoder and decoder is trained by evaluating the error in the autoencoder reconstruction of the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the first dataset. A second dataset is obtained for a plurality of subjects having a second cancer state. A plurality of features is identified by inputting the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the second dataset into the trained autoencoder and computing a score determined by the autoencoder reconstruction of the methylation pattern. The plurality of features is used to train a supervised model that discriminates a cancer state.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
100.
GENERATING CANCER DETECTION PANELS ACCORDING TO A PERFORMANCE METRIC
A system generates a cancer detection panel. The system is configured to generate an assay having a minimized size and number of genomic regions while still detecting the presence of cancer at or above a specific performance threshold. To select the genomic regions for the panel, the system employs a classification model. The classification model receives a set of genomic regions that may be associated with disease presence. The model then determines a sensitivity score for each genomic region and ranks the regions according to their score. The sensitivity score is based on a likelihood that variations in the genomic region are indicative of cancer. The model then selects genomic regions for the panel based on their rank. The model only selects as many genomic indicators as are needed for desired detection performance. The genomic regions can be associated with solid or liquid cancers, viral regions, or cancer hotspots.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
