67 results about "Cell-Free Nucleic Acids" patented technology

Provided are systems and methods for cell-free nucleic acid sequencing to assess a condition.Generally, cell-free nucleic acid samples are used to perform methyl sequencing, targeting specific regions associated with abnormal methylation.The methylation of cell-free nucleic acid molecules can be evaluated based on sequencing results.Various features can be derived from methylation evaluation and used in computational models to assess cell-free nucleic acid samples for a condition.

Disclosed herein in some embodiments are methods, compositions, and systems for distinguishing between ulcerative colitis (UC), Crohn's disease (CD) and other Inflammatory Bowel Disorders (IBD) by sequencing cell free nucleic acids. In some embodiments, microbial cell-free nucleic acid sequencing can provide data that can determine whether UC, CD, or other IBD are asymptomatic, in remission, or active. In some embodiments, microbial cell-free nucleic acid sequencing can provide data that can determine whether an active form of UC, CD, or other IBD is mild, moderate, or severe.

To provide a method of screening a pathogen-associated disorder in a subject, a method of prognosticating a pathogen-associated disorder in a subject, and a system therefor.SOLUTION: Provided herein are methods and systems for stratifying risk for a subject to develop a pathogen-associated disorder based on analysis of cell-free nucleic acid molecules from a biological sample of the subject. In various examples, screening frequency is determined based on the risk analysis. Also provided herein are methods and systems for analyzing variant patterns of a pathogen genome in cell-free nucleic acid molecules.SELECTED DRAWING: Figure 2

Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid. Also provided herein are methods for partitioning one or more genomic regions of a reference genome into a plurality of portions according to one or more features.

The invention provides a method for the analysis of a biological sample to determine multiple types of information therefrom in a streamlined, combined workflow, where all information is obtained in a sequencing-based analysis. The information includes the presence and concentration of specific plasma proteins in a blood sample: the number, location, and types of histone modifications associated with cell-free DNA obtained from the same sample: the sequence of cfRNA and cfDNA in the cell-free DNA sample; and epigenetic information pertaining to the cell-free DNA, such as hydroxy methylation and methylation profiles, i.e., the distribution of 5-hydroxymethylcytosine (5hmC) and 5-methylcy tosine (5mC) residues, respectively. The invention additionally pertains to a classical sequencing-based method for analyzing a biological sample to determine one or more non-classical sequence features of the sample. Compositions, kits, and related methods are also provided, including an embodiment in which truncated sequencing adapters are used in conjunction with barcoded PCR primers.

A computer-implemented method comprising: (a) obtaining, by a computer system, sequencing data derived from a plurality of cellfree nucleic acid molecules obtained or derived from a subject; (b) proce

The present disclosure provides methods and systems for screening or detecting a colorectal cancer or following colorectal disease progression that may be applied to cell-free nucleic acids such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, and detecting colon proliferative cell disorder-associated signals in the sequencing information and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and / or monitoring response to treatment, tumor load, relapse, or colorectal cancer development.

Methods and systems for producing a filtered read sequence information data set by identifying one or more split sequence reads in a set of test sequence reads obtained from cell-free nucleic acid (cfNA) in a biological sample obtained from a subject, wherein each split sequence read comprises at least one breakpoint; and, suppressing, in the set of test sequence reads, (i) at least a portion of one or more of the split sequence reads and / or at least a portion of one or more of the test sequence reads that comprise at least one sequence variant within a selected number of nucleotides from a given breakpoint, thereby producing the filtered sequence information data set, or, (ii) one or more base calls of the split sequence reads and / or one or more base calls of the test sequence reads that comprise at least one sequence variant within a selected number of nucleotides from a given breakpoint, thereby producing the filtered sequence information data set.

Processes and materials to detect neoplasms from a biopsy are described. Processes and materials to build a sequencing library are described. Processes and material to perform targeted sequencing are described. Processes and materials to mitigate confounding sources are described. Cell-free nucleic acids can be sequenced and the sequencing result can be utilized to detect sequences derived from a neoplasm.

Disclosed herein are systems and methods for determining a disease state of an individual. The systems can be configured to perform the methods disclosed herein, and these methods can include receiving sequencing data for one or more cell-free nucleic acid fragments obtained or derived from a biological sample of the individual. These methods can also include determining a methylation profile for the individual based on identifying abnormal patterns of methylation features. A trained machine learning model can be used to generate an indication of the disease state.

Disclosed herein are methods and systems for targeted detection of circulating tumor DNA (ctDNA) molecules. In some cases, a methylated DNA depleted molecular sequencing library can be generated and used to reliably detect ctDNA in cell-free DNA samples at lower sequencing depths and at lower costs than existing methods.

Processes and materials to detect cancer, transplant rejection, or fetal genetic abnormalities from a biopsy are described. In some cases, cell-free nucleic acids can be sequenced, and the sequencing result can be utilized to detect sequences indicative of a neoplasm, transplant rejection, or fetal genetic abnormality. Detection of somatic variants occurring in phase and / or insertions and deletions (indels) can indicate the presence of cancer, transplant rejection, or fetal genetic abnormalities in a diagnostic scan, and a clinical intervention can be performed.

Methods and systems for targeted detection of circulating tumor DNA (ctDNA) molecules are disclosed herein. A cell-free nucleic acid may be subjected to conditions to increase its methylation level, thereby yielding a hypermethylated cell-free nucleic acid, and subsequently identifying a sequence of the hypermethylated cell-free nucleic acid.

The present disclosure provides methods and systems for screening or detecting colorectal cancer or subsequent colorectal disease progression, which can be applied to cell-free nucleic acids, such as cell-free DNA. The methods can train a machine learning model using detection of methylation signals within single sequencing reads in identified genomic regions as input features, and generate a classifier suitable for stratifying a population of individuals. The methods can include extracting DNA from a cell-free sample obtained from a subject, transforming the DNA for methylation sequencing, generating sequencing reads, and detecting colon proliferative cell disorder related signals in the sequencing information, and training a machine learning model to provide a discriminator capable of distinguishing between groups such as healthy, cancer, etc. in a population of subjects, or distinguishing between disease subtypes or stages. The methods can be used, for example, to predict, prognosticate, and / or monitor response to treatment, tumor burden, recurrence, or progression of colorectal cancer.

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

Disclosed is a method of diagnosing cancer and predicting the type of cancer based on a single nucleotide variant in a cell-free nucleic acid including extracting nucleic acids from a biological sample to obtain sequence information, extracting cancer-specific single nucleotide variants through filtering based on aligned reads, calculating the regional mutation density of single nucleotide variants and the frequency of mutation signature of single nucleotide variants, and inputting the calculated values into a trained s: artificial intelligence model to analyze output values. This method is capable of exhibiting high sensitivity and accuracy compared to other methods of diagnosing cancer and predicting the type of cancer using genetic information of cell-free nucleic acids, and of ensuring the same level of sensitivity and accuracy as cancer-tissue-cell-based methods, and can be usefully applied to other analyses using single nucleotide variants in cell-free nucleic acids.

In alternative embodiments, provided are primer-based nucleic acid amplification methods capable of detecting the presence of a nucleic acid sequence including at nucleotide level resolution by means of the production of a reporter. In alternative embodiments, provided are hybridization-based nucleic acid detection methods capable of single nucleotide resolution. In alternative embodiments, provided are methods for detecting a target nucleic acid sequence in a sample, which can comprises a biological sample, and optionally the biological sample comprises a cell free nucleic acid (cfNA), optionally a cell-free DNA (cfDNA) or a cell-free RNA (cfRNA), or a circulating cell-free DNA (ccfDNA) or a circulating cell-free RNA (ccfRNA), or an environmental DNA (eDNA) or an environmental RNA (eRNA).

A method of detecting a combination of nucleic acid biomarkers in a human subject can include: obtaining a nucleic acid sample from the human subject; selecting the combination of nucleic acid biomarkers; analyzing a transcriptome of the human subject for the combination of nucleic acid biomarkers in the nucleic acid sample from the human subject; detecting in the nucleic acid sample the presence of the combination of nucleic acid biomarkers, wherein each nucleic acid biomarker in the combination of nucleic acid biomarkers has a variation from a transcription standard.

Disclosed herein are methods, compositions, and devices for use in the early detection of cancer. The methods include preparing cell-free nucleic acid molecules from a subject for sequencing, sequencing a panel of regions in the cell-free nucleic acid molecules, and detecting one or more markers that are indicative of a cancer.

The present invention provides a computer-implemented method for detecting variant nucleic acid from a cell-free nucleic acid-containing sample. The method comprises (a) providing data representing fragment sizes of nucleic acid fragments obtained from said sample and / or representing a measure of deviation from copy number neutrality of the nucleic acid fragments obtained from said sample; b) processing the data from step a) according to a classification algorithm, wherein said classification algorithm operates to classify sample data into one of at least a first class containing the variant nucleic acid and a second class not containing the variant nucleic acid, based on a plurality of cell-free nucleic acid fragment size features and / or a deviation from copy number neutrality feature; and c) outputting the classification of the sample from step b, thereby determining whether the sample contains the variant nucleic acid or not, or a probability that the sample contains the variant nucleic acid. Related methods are also provided.

Disclosed herein are methods, compositions, and devices for diagnosing and treating cancer. The method includes sequencing a set of regions in a cell-free nucleic acid molecule and detecting one or more biomarkers indicative of cancer.

Described herein are methods, compositions, and systems useful for detecting transplant rejection and associated abnormal conditions in solid organ transplant recipients, such as pancreatic transplant recipients, pancreatic and kidney transplant recipients, and simultaneous pancreatic and kidney transplant recipients. Methods described herein may involve combined assessment of blood gene expression profiles from an assessment of particular, related mRNA transcript levels and donor-derived cell-free nucleic acids (dd-cfDNA) or each an independent assessment of the mRNA transcript level as well as an independent assessment of the dd-cfDNA. Genes that correlate with pancreatic transplant rejection in simultaneous pancreatic and kidney transplant recipients are also disclosed.

The present disclosure provides methods and systems for detecting colorectal cancer by nucleic acid methylation analysis. In particular, the present disclosure provides methods and systems for screening or detecting colorectal cancer or subsequent colorectal disease progression, which can be applied to cell-free nucleic acids, such as cell-free DNA. The method may train a machine learning model using detection of methylation signals within a single sequencing read in an identified genomic region as an input feature and generate a classifier suitable for layering a population of individuals. The method may include extracting DNA from a cell-free sample obtained from a subject, transforming the DNA for methylation sequencing, generating a sequencing read, and detecting a colonic proliferative cell disorder related signal in sequencing information, and training a machine learning model to provide a discriminator, the discriminator is capable of differentiating groups, such as health, cancer, or differentiating disease subtypes or stages, in a population of subjects. The methods are useful, for example, in predicting, prognosing, and / or monitoring response to treatment, tumor load, recurrence, or progression of colorectal cancer.

In alternative embodiments, provided are primer-based nucleic acid amplification methods capable of detecting the presence of a nucleic acid sequence including at nucleotide level resolution by means of the production of a reporter. In alternative embodiments, provided are hybridization-based nucleic acid detection methods capable of single nucleotide resolution. In alternative embodiments, provided are methods for detecting a target nucleic acid sequence in a sample, which can comprises a biological sample, and optionally the biological sample comprises a cell free nucleic acid (cfNA), optionally a cell-free DNA (cfDNA) or a cell-free RNA (cfRNA), or a circulating cell-free DNA (ccfDNA) or a circulating cell-free RNA (ccfRNA), or an environmental DNA (eDNA) or an environmental RNA (eRNA).

In embodiments described herein, methylation information is determined relative to a classification region of a reference genome associated with the presence of a homologous recombination repair defect in a subject. Many computing techniques may be used to analyze the methylation information to provide metrics related to the presence or absence of homologous recombination repair defects in a given subject.

Disclosed herein are methods, compositions, and devices for diagnosing and treating cancer. The methods include sequencing a set of regions in cell-free nucleic acid molecules and detecting one or more biomarkers indicative of cancer and cancer subtype typing ability.

To provide a method, a device, a composition, and a kit for the analysis of a microbiome in an individual or each component of the microbiome.SOLUTION: A method for determining the presence and the possession rate of a microorganism sequence in a sample of cell-free nucleic acid from a non-microorganism host has: (i) preparing a sample of cell-free nucleic acid from an individual, (ii) performing determination of high throughput sequence of the nucleic acid, (iii) performing bioinformatics analysis and subtracting a host sequence from the analysis, and (iv) determining the presence and the possession rate of a microorganism sequence for microbiome evaluation of the non-microorganism host.SELECTED DRAWING: None

To provide methods for classifying genetic mutations detected in cell-free nucleic acids as tumor or non-tumor origin.SOLUTION: Methods for differentiating tumor and non-tumor origin nucleic acid variants in cell-free nucleic acid (cfNA) samples include generating a tumor variant dataset comprising a population of reference tumor-related genetic variants. The tumor variant dataset comprises frequency of observance data among reference samples that comprises reference bodily fluid (e.g., plasma) samples and reference non-bodily fluid (e.g., non-plasma) samples for tumor-related genetic variants in the population of reference tumor-related genetic variants. The methods further include determining ratios of the frequency of observance data between the reference samples for tumor-related genetic variants in the population of reference tumor-related genetic variants to produce a relative prevalence dataset.SELECTED DRAWING: Figure 2

To provide methods useful in determining the cellular origin of a cell-free nucleic acid (cfNA) fragment from a cfNA sample, such as a liquid biopsy sample.SOLUTION: The methods disclosed herein generally improve the specificity and / or sensitivity of an assay for detecting diseased cell nucleic acid (e.g., cancer cell DNA) in a cfNA sample by identifying variant alleles that are produced by non-target cells, such as hematopoietic stem cells in certain embodiments. Still other aspects include, inter alia, related systems and computer-readable media.SELECTED DRAWING: None

The present disclosure provides a method for enriching for multiple genomic regions using a first bait set that selectively hybridizes to a first set of genomic regions of a nucleic acid sample and a second bait set that selectively hybridizes to a second set of genomic regions of the nucleic acid sample. These bait set panels can selectively enrich for one or more nucleosome-associated regions of a genome, said nucleosome-associated regions comprising genomic regions having one or more genomic base positions with differential nucleosomal occupancy, wherein the differential nucleosomal occupancy is characteristic of a cell or tissue type of origin or disease state.