50 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.

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.

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.

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.

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.

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).

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.

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).

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.

Provided herein are improved methods and systems for sequencing cell-free nucleic acids (cfNA) in a sample to generate sequencing reads. In some cases, the sample contains target cfNA from a pathogen and contaminant nucleic acids from a sample collection site. The methods and systems described herein can distinguish the sequencing reads originated from the target cfNA from contaminants nucleic acids and thereby accurately identify the target cfNA in the sample.

The invention relates to an early disease detection device based on high-depth cell-free nucleic acid sequencing, and the device can comprise a library preparation part which is used for preparing a library set comprising a whole genome sequencing library, a whole epigenome sequencing library, a transcriptome sequencing library and an epitranscriptome sequencing library based on cell-free nucleic acid extracted from blood; the sequencing part is used for sequencing the library set to generate high-depth cell-free nucleic acid sequencing data; the signal calculation part is used for mapping the high-depth cell-free nucleic acid sequencing data to a genome reference sequence so as to determine the position of the high-depth cell-free nucleic acid sequencing data on a genome, and calculating at least one disease-specific signal value based on the mapped high-depth cell-free nucleic acid sequencing data; a prediction signal value generation section for processing the at least one disease-specific signal value to generate a disease prediction signal value; and a prediction model generation unit for generating a disease prediction model using the marker information relating to the blood and the disease prediction signal value to detect the occurrence or absence of a disease in the input blood and the type of the disease.

Disclosed herein are methods, compositions, and devices for diagnosing and treating diseases, including cardiovascular diseases as well as such diseases and dysfunctions associated with administration of cancer therapy. Methods include sequencing a set of regions in a cell-free nucleic acid molecule and detecting one or more biomarkers indicative of cardiovascular disease and dysfunction.

The present invention provides methods for using drain fluid obtained from medical procedures to assess diagnostic biomarkers indicative of disease obtained from drain fluid. In some embodiments, the diagnostics biomarkers are cell-free nucleic acids. In some embodiments, the disease is cancer. In some embodiments, a library of cfDNA is prepared and / or sequenced. In some embodiments, the methods comprise extracting nucleic acid from surgical drain fluid; conducting a size selection procedure to isolate cell-free nucleic acid from the surgical drain fluid; and detecting the cell-free nucleic acid.

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.

Provided herein are methods and systems for detection of precancer or cancer. The method may comprise using cell-free nucleic acids. The methods may comprise assaying nucleic acids. The method may comprise extracting cell-free nucleic acids. The methods may comprise generating libraries based at least on cell-free nucleic acids.

Systems and methods for cancer subject tumor fraction estimation comprise obtaining a first plurality of nucleic acid fragment sequences from the subject's liquid biological sample. The first plurality of sequences represent cell-free nucleic acids in the liquid sample. A second plurality of nucleic acid fragment sequences is obtained from the subject's tumor sample. The second plurality of sequences represent nucleic acid molecules in the tumor. Smoothed noise rates, each determined using nucleic acid fragment sequences from non-cancer samples mapping to a corresponding allele position in a plurality of allele positions, are obtained. Variant allele counts and coverages are determined for the allele positions using the first plurality of sequences. Solid variant allele fractions are determined for the plurality of allele positions using the second plurality of sequences. The subject tumor fraction is calculated using the smoothed noise rates, variant allele counts, coverages, and solid variant allele fractions.

Described herein are methods for determining the status of a homologous recombination repair defect (HRD) in a subject, comprising using a sample containing a cell-free nucleic acid, such as cell-free DNA (cfDNA). Because such nucleic acids are in a small amount in a sample, such as blood, techniques are described herein for analyzing characteristics present in cell-free nucleic acids to provide a metric related to the presence or absence of homologous recombination repair defects in a given subject.

The present disclosure provides methods for using drainage fluid obtained from a medical procedure to assess diagnostic biomarkers indicative of a disease obtained from the drainage fluid. In some embodiments, the diagnostic biomarker is cell-free nucleic acid. In some embodiments, the disease is cancer. In some embodiments, a library of cfDNA is prepared and / or sequenced. In some embodiments, the methods comprise: extracting nucleic acid from surgical drainage fluid; performing a size selection procedure to isolate cell-free nucleic acid from the surgical drainage fluid; and detecting the cell-free nucleic acid.

A disease classification method includes determining, using a predictive model, whether cell-free nucleic acid samples are tumor-derived or non-tumor derived based on at least one of the cell-free nucleic acid score or a tumor fraction regression (TFR) score satisfying a respective threshold. The TFR score is determined based on a quantification of an observed tumor-associated aberrant methylation of each of a plurality of cell-free nucleic acid samples using a TFR model. The TFR score includes a fraction of molecules of the plurality of cell-free nucleic acid samples that indicate a tumor. The cell-free nucleic acid score is indicative of the presence of a tumor, and is based on at least one of epigenetic factors or genomic alterations of the cell-free nucleic acid samples.