EccDNA residues as cancer biomarkers

Abstract

Provided herein are methods and systems that include using sequence readings of linear DNA molecules naturally present in a biological sample to classify a set of linear DNA molecules as eccDNA residues, i.e. Linear DNA molecules produced by in vivo opening by eccDNA molecules. In various embodiments, the characteristics of the classified eccDNA residues can be analyzed to determine the characteristics of the biological sample or the subject from which the biological sample was obtained. Examples of a characteristic that may be determined include classification of a lesion, such as a cancer level, or an inferred age of a subject.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to the following provisional application, which is a non-provisional application of the following provisional application: U.S. Provisional Application No. 63 / 535,236, filed August 29, 2023, entitled “eccDNA Remants as a Cancer Biomarker”, the entire contents of which are incorporated herein by reference for all purposes. Background Technology

[0003] Extrachromosomal DNA (ecDNA) is covalently circular DNA found outside of chromosomes. Much research on these DNA molecules has focused on ecDNA shorter than 1 kb, sometimes referred to as extrachromosomal circular DNA (eccDNA) (Yi E, Chamorro GonzálezR, Henssen AG & Verhaak RGW, Nat Rev Genet. 23, (2022): 760). Sin et al. found that eccDNA can be found in maternal plasma DNA of pregnant women and has a size distribution with two major peak clusters at 202 bp and 338 bp, which is significantly different from the 166 bp peak observed from linear cfDNA molecules (Sin STK et al., Proc. Natl. Acad. Sci. USA 117, (2020): 1658). Fetal-derived eccDNA exhibits a relatively shorter size and lower methylation level compared to maternally derived eccDNA (Sin STK et al., Proc. Natl. Acad. Sci. USA 117, (2020): 1658; Sin STK et al., Clin. Chem. 67, (2021): 788) indicates that the generation of eccDNA molecules may be related to their tissue origin. Recent analyses of tumor tissues have shown the presence of much larger ecDNAs, ranging from 50 kb to 5 Mb, and these ecDNAs contribute to oncogenic remodeling through chimeric circularization and the reintegration of circular DNA into the linear genome (Koche RP et al., Nat. Genet. 52, (2020): 29).

[0004] Some methods for eccDNA identification are based on removing background linear DNA molecules via exonucleases, followed by restriction endonucleases (e.g., ...). Msp I) or transposases cleave intact circular DNA molecules to facilitate sequencing (Sin STK et al., Proc. Natl. Acad. Sci. USA 117, (2020): 1658). Other methods use rolling circle amplification (RCA) to remove linear DNA with exonucleases, leaving intact circular DNA molecules, followed by acoustic treatment, sequencing aptamer ligation, and sequencing (Shibata Y et al., Science 336, (2012): 82). It is noteworthy that each of these existing methods includes a step of in vitro enrichment of eccDNA (e.g., by removing linear DNA or RCA), since it is presumed that all data generated by random whole-genome sequencing without such enrichment steps originates from DNA molecules that are always linear. Summary of the Invention

[0005] Various embodiments are provided for identifying linear DNA molecules, which are products of in vivo cleavage of circular DNA molecules (e.g., eccDNA molecules). The identification of these specific linear DNA molecules (referred to as eccDNA remnants) may involve classifying the linear DNA in a sample using sequence information (e.g., sequence reads). Notably, the classification of eccDNA remnants in a sample can utilize sequence information from, for example, random whole-genome sequencing. Therefore, in vitro operations involving enrichment and / or lysis of the circular DNA in the sample prior to sequencing are unnecessary. The sample may be a biological sample, such as a plasma sample obtained from an object, and may include cellular DNA and / or cell-free DNA.

[0006] An exemplary purpose of classifying eccDNA residues in biological samples from an object is to determine the characteristics of the biological sample or object. An exemplary characteristic that can be determined is the classification of a lesion, such as cancer. Another exemplary characteristic is the proportionate concentration of clinically relevant DNA inferred from the classified eccDNA residues. Determining the characteristics of a biological sample or object may involve analyzing the classified eccDNA residues to determine the count of the eccDNA residues. Optionally or additionally, determining the characteristics may involve determining the size distribution of the eccDNA residues and / or the eccDNA molecules from which the eccDNA residues originate. In some examples, determining the characteristics may additionally or optionally include determining the normalized genomic coverage of the eccDNA residues, determining the frequency of nucleotide motif patterns associated with the eccDNA residues, and / or determining the methylation density of the eccDNA residues.

[0007] In some embodiments, this disclosure relates to methods for analyzing biological samples from a subject, wherein the biological sample comprises a plurality of free-floating DNA molecules, each independently having a 5' end sequence and a 3' end sequence. For each of the plurality of free-floating DNA molecules, the method includes receiving one or more sequence reads having at least a 5' end sequence and a 3' end sequence to obtain 5' end sequence reads and 3' end sequence reads; mapping the 5' end sequence and 3' end sequence to a reference genome; and based on the mapping, classifying the free-floating DNA molecules as having been cleaved from a circular DNA molecule in vivo, thereby identifying a set of eccDNA remnants. The method further includes analyzing the set of eccDNA remnants to determine the characteristics of the biological sample or subject.

[0008] These and other embodiments of this disclosure are described in detail below. For example, other embodiments relate to systems, apparatuses, and computer-readable media associated with the methods described herein.

[0009] Other features and advantages of this disclosure will be realized by referring to the remainder of the specification, including the drawings and claims. Further features and advantages of this disclosure, as well as the structure and operation of various embodiments of this disclosure, are described in detail below in conjunction with the drawings. In the drawings, the same reference numerals may denote the same or functionally similar elements.

[0010] Brief description of the attached figures

[0011] Figure 1 A schematic diagram of eccDNA residues is shown, which are linear DNA molecules formed by in vivo breakage (e.g., cleavage) of eccDNA molecules.

[0012] Figure 2 Examples of the formation of eccDNA molecules from genomic DNA in vivo, the formation of eccDNA residues from eccDNA molecules in vivo, and a workflow for analyzing and classifying eccDNA residues by mapping received sequence reads are shown.

[0013] Figure 3 A schematic illustration of exemplary mapped sequence reads is shown to demonstrate that linear DNA molecules include deletions, insertions, inversions, or duplications, rather than being eccDNA remnants.

[0014] Figure 4 A flowchart illustrates a method for classifying whether a free-floating DNA molecule is an eccDNA residue based on a mapping of sequence reads from free-floating DNA molecules.

[0015] Figure 5A flowchart illustrates a method for determining the characteristics of a biological sample or an object from which a biological sample is obtained, the determination being based on the analysis of linear DNA molecules classified as eccDNA residues by mapping sequence reads of linear DNA molecules.

[0016] Figure 6 The data table presents sequence reads from hepatitis B virus (HBV) carriers and hepatocellular carcinoma (HCC) patients, as well as data related to eccDNA residues identified by mapping based on sequence reads.

[0017] Figure 7 The diagram shows a comparison between the normalized counts of eccDNA residues detected in HBV carrier biosamples and the normalized counts of eccDNA residues detected in HCC patient biosamples.

[0018] Figure 8A A graph is shown that plots the size-frequency distribution of small eccDNA residues detected using the method presented in this paper.

[0019] Figure 8B The diagram shows the size-frequency distribution of small eccDNA molecules as measured using paired-end sequencing.

[0020] Figure 9 The chart shows a comparison between the percentage of eccDNA remnants larger than 1 kb detected in HBV carrier biosamples and the percentage of eccDNA remnants larger than 1 kb detected in HCC patient biosamples.

[0021] Figure 10 The diagram shows the size-frequency distribution of eccDNA residues detected in HBV carrier biosamples and HCC patient biosamples.

[0022] Figure 11 A data table is presented relating to sequence reads from non-NPC subjects and NPC patients, and to eccDNA residues identified based on sequence read mapping.

[0023] Figure 12 The chart shows a comparison between the percentage of eccDNA residues larger than 1 kb detected in non-NPC subject biosamples and the percentage of eccDNA residues larger than 1 kb detected in NPC patient biosamples.

[0024] Figure 13The diagram shows the genomic distribution of eccDNA residues detected in HBV carrier samples and HCC patient samples.

[0025] Figure 14 Illustrations showing fragments I, II, III, and IV, which are associated with nucleotide motif patterns of eccDNA remnants, with designated start and end positions in the reference genome, wherein fragments I and II are separated by adjacent spacer region S1, while fragments III and IV are separated by adjacent spacer region S2.

[0026] Figure 15 A table is presented, listing frequency data of trinucleotide motif 4-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0027] Figure 16 A table is presented, listing frequency data of dinucleotide motif 4-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0028] Figure 17 A table is presented, listing frequency data of tetranucleotide motif 4-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0029] Figure 18 A table is presented listing frequency data for dinucleotide motif 4-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients, where the fragments in the patterns are located in relation to... Figure 16 Those different genomic locations.

[0030] Figure 19 A table is presented, listing frequency data of trinucleotide motif 3-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0031] Figure 20 A table is presented, listing frequency data of tetranucleotide motif 3-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0032] Figure 21 A table is presented, listing frequency data of trinucleotide motif 2-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0033] Figure 22 A table is presented listing frequency data for tetranucleotide motif 2-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0034] Figure 23 A table is presented, listing frequency data of dinucleotide motif 2-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0035] Figure 24 A table is presented, listing frequency data of trinucleotide motif 1-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0036] Figure 25 A table is presented, listing frequency data of tetranucleotide motif 1-fragment patterns associated with eccDNA fragments from HBV carriers and HCC patients.

[0037] Figure 26 The table shows frequency data for dinucleotide, trinucleotide, and tetranucleotide motif 1-fragment patterns at different genomic locations relative to the start position of eccDNA fragments from HBV carriers and HCC patients.

[0038] Figure 27 The table presents frequency data for dinucleotide, trinucleotide, and tetranucleotide motif 1-fragment patterns at different genomic locations relative to the termination position of eccDNA fragments from HBV carriers and HCC patients.

[0039] Figure 28 The chart shows a comparison between the percentage of methylation density of eccDNA residues of various sizes detected in HBV carrier biosamples and HCC patient biosamples.

[0040] Figure 29 The chart shows the area under the curve (AUC) values ​​used to distinguish HBV carrier biosamples from HCC patient biosamples using eccDNA residues with sizes below (top) or above (bottom) of various cutoff sizes.

[0041] Figure 30 The chart shows a comparison between the percentage of methylation density of eccDNA residues larger than 1 kb detected in HBV carrier biosamples and HCC patient biosamples.

[0042] Figure 31 The chart shows a comparison between the percentage of methylation density of linear DNA and eccDNA residues detected in biological samples.

[0043] Figure 32A flowchart is shown for a method for determining the cancer grade of an object, which is based on the analysis of linear DNA molecules classified as eccDNA residues by mapping sequence reads of linear DNA molecules from the object's biological sample.

[0044] Figure 33 A graph is shown that illustrates the correlation between the percentage of eccDNA remnants longer than 1 kb in a biological sample and the proportion of tumor DNA in the biological sample.

[0045] Figure 34 A block diagram of an exemplary measurement system according to the provided implementation is shown.

[0046] Figure 35 A block diagram of an exemplary computer system according to the provided implementation is shown.

[0047] Figure 36 A flowchart illustrating a method for estimating the proportionate concentration of clinically relevant DNA in biological samples is provided.

[0048] Figure 37 A flowchart illustrating a method for determining calibration data points based on measurements taken from calibration samples is provided.

[0049] the term

[0050] the term" biological samples "" refers to the information obtained from " object(For example, any sample containing one or more nucleic acid molecules of interest, such as a human or other animal, such as a pregnant woman, an individual with cancer or other medical condition, or an individual suspected of having cancer or other medical condition, an organ transplant recipient, or an object suspected of having a disease process involving an organ (e.g., the heart in a myocardial infarction, the brain in a stroke, or the hematopoietic system in anemia). Biological samples can be bodily fluids, such as blood, plasma, serum, urine, vaginal fluid, fluid from edema of the scrotum (e.g., testes), vaginal douches, pleural effusion, ascites, cerebrospinal fluid, saliva, sweat, tears, sputum.) Examples of suitable samples include bronchoalveolar lavage fluid, breast milk, aspirated fluid from different parts of the body (e.g., thyroid, breast), intraocular fluid (e.g., aqueous humor), and amniotic fluid. Fecal samples may also be used. In various embodiments, the majority of DNA in the biological sample enriched for cell-free DNA (e.g., plasma samples obtained via centrifugation) may be cell-free, for example, more than 50%, 60%, 70%, 80%, 90%, 95%, or 99% of the DNA may be cell-free. Centrifugation protocols for enriching cell-free DNA from biological samples may include, for example, centrifuging the biological sample at 3,000 mL / min. The liquid fraction of the centrifuged sample is obtained by centrifuging at g × 10 minutes, followed by a further centrifugation at, for example, 30,000 g for 10 minutes to remove residual cells. As part of the analysis of the biological sample, a statistically significant number of cell-free DNA molecules can be analyzed (e.g., to provide accurate measurement results). In some embodiments, at least 1,000 cell-free DNA molecules are analyzed. In other embodiments, at least 10,000, 50,000, 100,000, 500,000, 1,000,000, or 5,000,000 or more cell-free DNA molecules can be analyzed. At least the same number of sequence reads can be analyzed. Any quantity described herein can be any of the numbers listed above. Example sample sizes can include 30, 50, 100, 200, 300, 500, 1,000, 5,000, or 10,000 or more nanograms, or 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 ml.

[0051] abbreviation" eccDNA "eccDNA" refers to extrachromosomal circular DNA, which is a covalently closed circular DNA molecule, including DNA derived from the genome. eccDNA molecules can have any size.

[0052] “ eccDNA remnants "It is a linear molecule, which is the product of the cleavage of eccDNA molecules in vivo."

[0053] the term" Connection point "", Connecting loci "and" Connecting sites"" refers to a position within a nucleic acid molecule where two sequences with a segregating position (i.e., coordinates) in a reference (e.g., a reference genome) are adjacent to each other. Therefore, although these sequences are not adjacent to each other in a reference (e.g., a reference genome), they are adjacent to each other at the junction points of the nucleic acid molecule. As an example, junction points can be created when a genomic DNA fragment is circularized, thereby joining the ends of the fragment to form a circular DNA molecule, such as an eccDNA molecule.

[0054] the term" Sequence readings A sequence read is a string of nucleotides obtained from any part or all of a nucleic acid molecule. For example, a sequence read can be a short nucleotide string (e.g., 20 to 150 nucleotides) sequenced from a nucleic acid fragment, a short nucleotide string at one or both ends of a nucleic acid fragment, or sequencing of an entire nucleic acid fragment present in a biological sample. Sequence reads can be obtained in various ways, such as using sequencing technologies or using probes (e.g., in hybridization arrays or capture probes that can be used in microarrays) or amplification technologies (e.g., polymerase chain reaction (PCR) or linear or isothermal amplification using a single primer). Example sequencing technologies include massively parallel sequencing, targeted sequencing, Sanger sequencing, sequencing by ligation, ion semiconductor sequencing, and single-molecule sequencing (e.g., sequencing using nanopores), or single-molecule real-time sequencing (e.g., from Pacific...). Biosciences). This type of sequencing can be random sequencing or targeted sequencing (e.g., enriching such regions by using capture probes that hybridize with a specified region or by amplifying certain regions). Exemplary probe-based techniques include real-time PCR and digital PCR (e.g., droplet digital PCR). As part of biological sample analysis, a statistically significant number of sequence reads can be analyzed, for example, at least 1,000 sequence reads can be analyzed. As other examples, at least 5... 000, 10,000, 50,000, 100,000, 500,000, 1,000,000, or 5,000,000 or more sequence readings. Furthermore, the number of sequence readings determined for embodiments of this disclosure can be at least 1,000, 5,000, 10,000, 50,000, 100,000, 100,000, 500,000, 1,000,000, or 5,000,000.

[0055] Sequence readings may include the "..." associated with the ends of a DNA molecule, such as the 5' end or the 3' end. End sequence "or" Ending sequenceThe 5' end sequence can correspond to the outermost N bases of a molecule, such as 1 to 30 bases at the end of a DNA molecule. If the sequence read corresponds to the entire DNA molecule, the sequence read can include two end sequences. When paired-end sequencing provides two sequence reads corresponding to the ends of a DNA molecule, each sequence read can include one end sequence. The sequence read that includes the 5' end sequence of the DNA molecule is referred to herein as the "5' end sequence". 5 ' End sequence readings Sequence reads, including the 3' end sequence of a DNA molecule, are referred to in this paper as "...". 3 ' End sequence readings The sequence reading corresponding to the entire DNA molecule is "". 5 ' End sequence readings "and" 3 ' End sequence readings "Both."

[0056] the term" Mapping "or" Alignment "Associating a sequence with a location or coordinates (e.g., genomic coordinates) in a reference (e.g., a reference genome) that has a known reference sequence, wherein the sequence is similar to the known reference sequence at that location in the reference. This can be done according to..." Mapping quality This is used to measure or report similarity. In one example of mapping quality used in this paper, the mapping quality X of a sequence relative to the reported position or coordinates in the reference indicates that the probability of the sequence mapping to a different position is no greater than 10^(-X / 10). For example, a mapping quality of 30 indicates that the probability of the sequence mapping to another position is less than 0.1%.

[0057] “ Reference genome The "reference sequence" or "reference sequence" can be the entire genome sequence of a reference organism, one or more parts of a reference genome that may be continuous or non-contiguous, a common sequence of multiple reference organisms, a compiled sequence based on different components of different organisms, or any other suitable reference sequence. As an example, the reference genome / sequence length can be at least 1,000, 10,000, 50,000, 100,000, 500,000, 1,000,000, 5,000,000, 10,000,000, 50,000,000, 100,000,000, 500,000,000, one billion, or three billion nucleotides, such as the complete human genome or a duplicated human genome. The reference may also include information about reference variations known to be found in a population of organisms.

[0058] the term" Genome coverage "and" Genomic coverage"This typically involves the measurement of regions of the genome (e.g., a reference genome) represented by nucleic acid molecules (such as DNA molecules), groups of nucleic acid molecules, or sequences of such molecules." Standardized genome coverage "" is a value representing the frequency with which members or sequences of a population of nucleic acid molecules map to a specific genomic region, where the value is normalized relative to the frequency of those specific genomic regions appearing in the genome. One method for calculating the normalized genomic coverage of a population of nucleic acid molecules or sequences involves determining the percentage of the population that maps to a specific region of the genome (e.g., then dividing that percentage by the percentage of the total genome within those specific regions).

[0059] abbreviation" bp "bp" refers to a base pair. In some cases, "bp" can be used to indicate the length of a DNA fragment, even if the DNA fragment may be single-stranded and does not contain base pairs. In the context of single-stranded DNA, "bp" can be interpreted as providing the length of a nucleotide.

[0060] the term" Size pattern "and" Size distribution "This typically involves the size of DNA fragments in a biological sample. A size pattern can be a histogram showing the distribution of the amount of DNA fragments in various sizes. Various statistical parameters (also called size parameters or simply parameters) can distinguish one size pattern from another. One parameter is the percentage of DNA fragments of a particular size or size range relative to all DNA fragments or relative to another size or size range of DNA fragments."

[0061] the term" Nucleotide motif A motif is an arrangement of two or more nucleotides (e.g., two or more adjacent nucleotides) that are excessively present in a set of nucleotide sequences. The nucleotide motif may or may not be located in a conserved position in the set of nucleotide sequences.

[0062] "in the mammalian genome" DNA methylation "This typically refers to the addition of a methyl group (i.e., 5-methylcytosine) to the 5' carbon of a cytosine residue in a CpG dinucleotide. DNA methylation can occur in cytosine in other backgrounds, such as CHG and CHH, where H is adenine, cytosine, or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Non-cytosine methylation, such as N6-methyladenine, has also been reported."

[0063] "Each genomic locus (e.g., CpG site) " Methylation index "This can refer to the proportion of the number of DNA fragments showing methylation at that site (e.g., as determined by sequence readings or probes) to the total number of readings covering that site." First based state"Reading" can refer to whether a specific site is methylated at a specific location on a DNA fragment, or whether a specific site in the genome has a specific differential methylation state, such as hypermethylation or hypomethylation. "Reading" can include information obtained from a DNA fragment (e.g., the methylation state at the site). Readings can be obtained using reagents (e.g., primers or probes) that preferentially hybridize with DNA fragments with a specific methylation state. Typically, such reagents are applied after treatment with methods that differentially modify or differentially recognize DNA molecules based on their methylation state, such as bisulfite conversion, methylation-sensitive restriction enzymes, methylation-binding proteins, anti-methylcytosine antibodies, or single-molecule sequencing techniques that recognize methylcytosine and hydroxymethylcytosine.

[0064] "Region or set of sites" Methylation density"CpG methylation density" can refer to the number of readings showing methylation at sites within a region (also called a bin) or group of sites divided by the total number of readings at sites covering that region or group of sites. A region can include one or more sites of interest, including at least 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, and 1,000 sites. Sites can have specific characteristics, such as CpG sites. Therefore, the "CpG methylation density" of a region can refer to the number of readings showing CpG methylation divided by the total number of readings at CpG sites covering the region (e.g., specific CpG sites, CpG islands, or CpG sites within a larger region). For example, the methylation density per 100 kb bin in the human genome can be determined based on the proportion of the total number of unconverted cytosines (corresponding to methylated cytosines) at CpG sites after bisulfite treatment relative to all CpG sites covered by sequence readings mapped to a 100 kb region. This analysis can also be performed on other bin sizes, such as 500 kb. bp, 5 kb, 10 kb, 50-kb, or 1-Mb, etc. A region can be an entire genome or a chromosome or a portion of a chromosome (e.g., a chromosome arm). When a region includes only CpG sites, the methylation index of the CpG sites is the same as the methylation density of the region. "Ratio of methylated cytosine" can refer to the number of cytosine sites "C" that appear methylated (e.g., unconverted after bisulfite conversion) relative to the total number of cytosine residues analyzed in the region, i.e., including the total number of cytosine residues excluding the CpG background. Methylation indices, methylation density, and ratio of methylated cytosine are examples of "methylation level." Other methods known to those skilled in the art, besides bisulfite conversion, can be used to query the methylation status of DNA molecules, including (but not limited to) methylation-sensitive enzymes (e.g., methylation-sensitive restriction enzymes), methylation-binding proteins, and single-molecule sequencing using methylation-sensitive platforms (e.g., nanopore sequencing). (18910-18915) or by single-molecule real-time sequencing analysis by PacificBiosciences (Tse et al. Proc Natl Acad Sci USA 2021; 118:e2019768118).

[0065] “ methylation level"Methylation" is an example of relative abundance, such as the relative abundance of methylated DNA molecules (e.g., at one or more specific sites) compared to other DNA molecules (e.g., all other DNA molecules or DNA molecules that are unmethylated only at one or more specific sites). The amount of other DNA molecules can act as a normalization factor. As another example, the intensity (e.g., fluorescence or electrical intensity) of methylated DNA molecules can be determined relative to the intensity of all DNA molecules or the intensity of unmethylated DNA molecules at one or more sites. Relative abundance may also include intensity per volume. Methylation levels can be determined using methylation-sensing analysis, such as methylation-sensing sequencing or PCR. Exemplary methylation-sensing sequencing may include bisulfite sequencing or, for example, single-molecule techniques using nanopores.

[0066] Differentially methylated regions (DMRs) are genomic regions (e.g., loci sets) spanning two or more biological samples that exhibit different DNA methylation states. These different DNA methylation states can be defined by specific differences in methylation indices or densities, such as (but not limited to) 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, etc. Differentially methylated sites can be defined in a similar manner.

[0067] the term" hypomethylation "Can refer to a site or set of sites (e.g., a region) having a methylation level below a specified threshold, such as 50%, 45%, 40%, 35%, 30%, 25%, or 20% of the methylation level. If the methylation level is below the threshold, the site in the genome can be considered unmethylated." Hypermethylation "Can refer to a specified value above the methylation level, such as a site or set of sites (e.g., a region) that is equal to or higher than 95%, 90%, 80%, 75%, 70%, 65%, or 60% of the methylation level. If the methylation level is greater than the threshold, the site in the genome can be considered methylated."

[0068] “ relative frequency "(also referred to simply as "frequency")" can refer to a proportion (e.g., percentage, fraction, or concentration). In particular, the relative frequency of a particular nucleotide motif (e.g., A, CG, TAG, etc.) or multiple such motifs can provide the proportion of free DNA fragments having that particular motif or combination of motifs.

[0069] As used herein, the term "parameter" refers to a numerical value that characterizes a quantitative dataset and / or a numerical relationship between quantitative datasets. For example, the ratio (or a function of the ratio) between a first quantity of a first nucleic acid sequence and a second quantity of a second nucleic acid sequence is a parameter. Parameters can be used to determine any classification described herein, such as any classification concerning fetus, cancer, or transplantation analysis. Standardized quantities (e.g., relative frequencies) are examples of parameters.

[0070] The terms “cutoff value” and “threshold” refer to predetermined numerical values ​​used in the operation. For example, a cutoff size can refer to a size that fragments exceeding which will be excluded. A threshold can be a value above or below which a specific classification applies. Any of these terms can be used in any of these situations. A cutoff value or threshold can be a “reference value” or derived from a reference value that represents a specific classification or distinguishes two or more classifications. A cutoff value can be predetermined with or without reference to the characteristics of a sample or object. For example, a cutoff value can be selected based on the age or sex of the test subject. A cutoff value can be selected after and based on the output of the test data. For example, a specific cutoff value can be used when the sequencing of a sample reaches a certain depth. As another example, a reference object with known classifications and measurement characteristics (e.g., methylation level, statistical size value, or count) of one or more conditions can be used to determine a reference level to distinguish different conditions and / or classifications of conditions (e.g., whether the subject has the condition). A reference value can be selected as a representative of a classification (e.g., the mean) or a value between two clusters of a measure (e.g., selected to obtain the desired sensitivity and specificity). As another example, reference values ​​can be determined based on statistical simulations of a sample. Any of these terms can be used in any of these contexts. Such reference values ​​can be determined in various ways, as those skilled in the art will understand. For example, a metric can be determined for two distinct groups of objects with different known classifications, and a reference value can be chosen as a representative of either a classification (e.g., the mean) or a value between the two groups of the metric (e.g., chosen to obtain the desired sensitivity and specificity). As another example, reference values ​​can be determined based on statistical simulations of a sample. Specific cutoff values, thresholds, reference values, etc., can be determined based on the desired accuracy (e.g., sensitivity and specificity).

[0071] A “separation value” corresponds to the difference or ratio involving two values, such as the difference or ratio of two proportional contributions or two methylation levels. A separation value is an example of a parameter. A separation value can be a simple difference or ratio. As examples, the direct ratio of x / y and x / (x+y) are separation values. Other examples are y / x and y / (x+y). Separation values ​​can include other factors, such as a doubling constant. As other examples, the difference or ratio can be a function of the following values, such as the difference or ratio of the natural logarithms (ln) of two values. Separation values ​​can include both differences and ratios, such as (xy) / (x+y). Separation values ​​can be compared to a threshold to determine whether the separation between two values ​​is statistically significant. A separation value is an example of a relative quantity. Separation values ​​can be compared to a threshold to determine whether the separation between two values ​​is statistically significant.

[0072] As used herein, the term "classification" refers to any number or other character associated with a specific characteristic of a sample. For example, the symbol "+" (or the word "positive") can indicate that a sample is classified as belonging to an object with a lesion. Classification can be binary (e.g., positive or negative) or have more classification levels (e.g., scales from 1 to 10 or 0 to 1), including probabilities. Different techniques used to determine classification can be combined, for example by majority voting or by requiring all initial / intermediate classifications to be the same (e.g., positive), to obtain a final classification from the initial or intermediate classifications used for each of the different techniques.

[0073] “ Clinically relevant DNA "Clinically relevant DNA" refers to DNA from a specific tissue source being measured, for example, to determine the proportional concentration of such DNA or to classify the phenotype of a sample (e.g., plasma). Examples of clinically relevant DNA include fetal DNA in maternal plasma or tumor DNA in patient plasma or other samples containing cell-free DNA. Another example includes measuring the amount of graft-associated DNA in the plasma, serum, or urine of a transplant patient. Further examples include measuring the proportional concentration of hematopoietic DNA and non-hematopoietic DNA in the recipient's plasma, or the proportional concentration of liver DNA fragments (or other tissues) in a sample, or the proportional concentration of brain DNA fragments in cerebrospinal fluid.

[0074] the term" Fetal DNA ratio concentration "Can be used with terminology" fetal DNA proportion )"and" fetal fetal DNA fraction The term "fetal DNA" is used interchangeably and refers to the proportion of fetal DNA molecules present in biological samples (e.g., maternal plasma or serum samples) derived from the fetus (Lo et al.). Am J Hum Genet. 1998;62:768-775; Lun et al, Clin Chem.2008;54:1664-1672). Similarly, tumor proportion or tumor DNA proportion can refer to the proportional concentration of tumor DNA in a biological sample, or tissue proportion can refer to the proportional concentration of DNA from one or more specific tissues (e.g., from a transplanted organ).

[0075] “ Calibration Samples "A calibration sample may correspond to a biological sample whose proportion of clinically relevant DNA (e.g., the proportion of tissue-specific DNA) is known or determined by calibration methods, such as using tissue-specific alleles, as in transplantation, where alleles present in the donor genome but lacking in the recipient genome can be used as markers for the transplanted organ. As another example, a calibration sample may correspond to a sample from which terminal motifs can be determined. Calibration samples can be used for both purposes."

[0076] “ Calibration data points "include" Calibration value The calibration data point is a measured or known proportion of clinically relevant DNA (e.g., DNA from a specific tissue type). Calibration values ​​can be determined based on the relative frequencies (e.g., aggregate values) of the calibration samples, for which the proportion of clinically relevant DNA is known. Calibration data points can be defined in various ways, such as as discrete points or as calibration functions (also known as calibration curves or calibration surfaces). The calibration function can be derived from additional mathematical transformations of the calibration data points. Proportional concentrations can be determined in various ways, such as using tissue-specific alleles, tissue-specific methylation values ​​or patterns, and the size distribution of samples with known proportional concentrations.

[0077] The term "cancer grade" can refer to the presence or absence of cancer, the stage of cancer, the size of the tumor, the presence or absence of metastasis, the total tumor burden in the body, the response of cancer to treatment, and / or other measures of cancer severity (e.g., cancer recurrence). Cancer grades can be numbers or other markers, such as symbols, letters, and colors. A grade can be zero. Cancer grades can also include a pre-existing condition (state) before it worsened or became cancerous. Cancer grades can be used in a variety of ways. For example, screening can check for the presence of cancer in someone who previously had no known cancer. Assessment can investigate someone who has been diagnosed with cancer to monitor cancer progression over time, investigate the effectiveness of treatments, or determine prognosis. In one implementation, prognosis can be expressed as the likelihood of a patient dying from cancer, or the likelihood or extent of cancer progression after a specified period or time, or the likelihood or extent of cancer metastasis. Testing can refer to 'screening' or it can refer to checking whether someone has suggestive features of cancer (e.g., symptoms or other positive tests) has cancer.

[0078] "Disease grade" can refer to the quantity, extent, or severity of lesions associated with an organism. An object's health status can be considered a disease-free classification. Grades can be as described above for cancer. Another example of disease is transplant rejection. Other examples of disease can include autoimmune attacks (e.g., lupus nephritis damaging the kidneys or multiple sclerosis damaging the central nervous system), inflammatory diseases (e.g., hepatitis), fibrotic processes (e.g., cirrhosis), fatty infiltration (e.g., fatty liver disease), degenerative processes (e.g., Alzheimer's disease), and ischemic tissue damage (e.g., myocardial infarction or stroke). An object's health status can be considered a disease-free classification.

[0079] "Machine learning model (ML model)" can refer to a software module configured to run on one or more processors to provide categorical or numerical characteristics of one or more samples. Sample data (e.g., training data) can be used to generate ML models to make predictions on test data. One example is an unsupervised learning model. Another example type of model is supervised learning, which can be used with embodiments of this disclosure. Example supervised learning models can include various methods and algorithms, including analytical learning, statistical models, artificial neural networks, backpropagation, boosting (a common algorithm), Bayesian statistics, case-based reasoning, decision tree learning, inductive logic programming, Gaussian process regression, genetic programming, data grouping, kernel estimator, learning automata, learning classification systems, minimum information length (decision trees, decision graphs, etc.), multilinear subspace learning, naive Bayes classifier, maximum entropy classifier, conditional random fields, nearest neighbor algorithm, probabilistic approximate correct learning (PAC), ripple down rule, knowledge acquisition methods, symbolic machine learning algorithms, subsymbolic machine learning algorithms, minimum complexity machine (MCM), random forests, classifier ensembles, ordered classification, data preprocessing, handling imbalanced datasets, statistical relation learning, or Proaftn (a multi-criteria classification algorithm). Models may include linear regression, logistic regression, deep recurrent neural networks (e.g., Long Short-Term Memory, LSTM), hidden Markov models (HMM), linear discriminant analysis (LDA), k-means clustering, density-based spatial clustering with noise (DBSCAN), random forest algorithms, support vector machines (SVM), or any model described herein. Various cost / loss functions and optimization techniques defining the error from known labels (e.g., least squares and absolute difference from the known classification) can be used in various ways to train supervised learning models, such as using backpropagation, steepest descent, conjugate gradients, and Newton and quasi-Newton techniques.

[0080] the term" About "and" Approximately"About" can mean within an acceptable range of error for a particular value as determined by a person skilled in the art, the range of error depending in part on the manner in which the value is measured or determined, i.e., limitations of the measurement system. For example, according to practice in the art, "about" can mean within one or more standard deviations. Alternatively, "about" can mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. Alternatively, particularly with respect to biological systems or methods, the term "about" or "approximately" can mean within a certain order of magnitude of the value, within 5 times, or more preferably within 2 times. If a particular value is described in this application and claims, unless otherwise stated, it should be assumed that the term "about" means within an acceptable range of error for that particular value. The term "about" can have the meaning as commonly understood by a person skilled in the art. The term "about" can mean ±10%. The term "about" can mean ±5%.

[0081] Where a range of values ​​is provided, it should be understood that, unless the context explicitly specifies otherwise, each intermediate value between the upper and lower limits of that range, accurate to the tenths of the lower limit unit, is also specifically disclosed. Embodiments of this disclosure cover every smaller range between any stated values ​​or intermediate values ​​within the stated range and any other stated values ​​or intermediate values ​​within the stated range. The upper and lower limits of these smaller ranges may independently include or exclude them from the range, and each range including any boundary, no boundary, or two boundaries within a smaller range is also covered by this disclosure, subject to any specifically excluded boundaries in the stated range. Where a stated range includes one or both boundaries, ranges excluding any one or both of those included boundaries are also included in this disclosure.

[0082] Standard abbreviations can be used, such as bp, base pair; kb, kilobase; pi, picoliter; s or sec, second; min, minute; h or hr, hour; aa, amino acid; nt, nucleotide; etc.

[0083] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. While any methods and materials similar to or equivalent to those described herein may be used in practice and testing of embodiments of this disclosure, some potential and exemplary methods and materials may now be described.

[0084] Detailed description

[0085] This disclosure provides various methods, products, and systems for identifying and analyzing eccDNA residues, which are linear DNA molecules produced by the in vivo linearization of eccDNA molecules. eccDNA residues can be, for example, products of one or more molecular degradation processes occurring in a living organism, and are not the result of in vitro processing steps applied to a biological sample from the organism. eccDNA residues can be distinguished from other linear DNA molecules in the sample by examining the sequence information of the linear DNA molecules present in the biological sample. Characteristics of molecules classified as eccDNA residues can be determined, and these characteristics can advantageously provide information related to the biological sample or the object from which the sample originates. For example, eccDNA residues of a biological sample can be used as biomarkers for lesions (e.g., cancer), enabling new and useful methods for determining the classification of such lesions.

[0086] Existing techniques for analyzing linear DNA found in biological samples fail to recognize that this linear DNA may include eccDNA remnants. Therefore, it is presumed that sequencing information obtained from untreated biological samples is associated only with DNA that is consistently linear, and not with previously circular DNA. This sequencing data is processed and analyzed accordingly, where sequence mapping results inconsistent with linear DNA are considered artifacts or errors and are ignored. However, the methods, products, and systems presented herein recognize the importance of information from these previously considered worthless mapping results. The disclosed techniques apply this previously unused sequence mapping data to classify sequenced DNA molecules as eccDNA remnants. By identifying and analyzing eccDNA remnants that previous methods failed to identify and analyze, the provided methods, products, and systems advantageously provide insights that are otherwise lacking in determining the characteristics of biological samples and their source objects.

[0087] Another advantage of the methods, products, and systems provided is that they eliminate the operations previously thought necessary to obtain eccDNA information from biological samples. In earlier methods, before the realization that linearized eccDNA remnants could be found in biological samples, methods such as enzyme cleavage or acoustic shearing were used to break circular eccDNA. This in vitro eccDNA breaking linearized the circular eccDNA, allowing it to be sequenced using techniques designed for sequencing linear nucleic acid molecules. In contrast, the disclosed methods, products, and systems do not rely on any procedures or materials for opening circular eccDNA. Instead, information about the eccDNA molecule can be determined more directly by analyzing linear DNA molecules already present in the biological sample, and because this linear DNA can include eccDNA remnants.

[0088] Furthermore, earlier eccDNA analysis techniques often employed methods to reduce or remove linear DNA from a sample before linearizing it. This reduction or removal of linear DNA could involve, for example, digesting the linear DNA with an exonuclease, such as exonuclease V (ExoV). Such digestion reduces the background amount of linear DNA in the sample, thereby enriching the sample for circular eccDNA. However, for currently available methods, products, and systems, this enrichment is not only unnecessary but also counterproductive, as any digestion (e.g., degradation or other removal) of the linear DNA in the sample will also affect the eccDNA residues in a linear form. Since the disclosed methods, products, and systems therefore do not utilize materials or operations for reducing or removing linear DNA or for opening eccDNA, this disclosure provides the advantage of a simpler method that benefits from improved time and cost-effectiveness.

[0089] Before describing the invention in more detail, it should be understood that the invention is not limited to the specific embodiments described, as these embodiments can vary. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting, as the scope of the invention will be limited only by the appended claims. Efforts have been made to ensure the accuracy of the figures used (e.g., quantities, temperatures, etc.), but some experimental errors and biases should be taken into account. Unless otherwise stated, parts are parts by weight, molecular weight is weight average molecular weight, temperature is degrees Celsius, and pressure is atmospheric pressure or close to atmospheric pressure.

[0090] I. ECcDNA Residues

[0091] The eccDNA residues that are the subject of this disclosure are linear DNA molecules produced from the circular eccDNA molecules of the object through the object's natural mechanisms. For example, eccDNA residues can be produced through in vivo nucleic acid degradation processes, such as cleavage by nucleases (e.g., plasma nucleases). Endogenous endonucleases, such as deoxyribonuclease 1 (DNASE1), deoxyribonuclease 1-like 3 (DNASE1L3), and DNA fragmentation factor β subunit (DFFB), can cleave eccDNA. This cleavage can occur intracellularly and / or extracellularly, forming linearized DNA molecules derived from eccDNA. This disclosure therefore relates to linearized DNA molecules derived from previously intact eccDNA as eccDNA residues. In contrast, eccDNA residues do not include linearized DNA molecules produced from previously intact eccDNA molecules through synthetic or artificial methods.

[0092] Different endonucleases preferentially produce DNA molecules with different sizes and terminal motifs (Serpas L et al., Proc. Natl. Acad. Sci. USA 116, (2019):641; Jiang P et al., Cancer Discov . 10, (2020): 664; Han DSC et al., Am. J. Hum. Genet. (106, (2020):202), and their enzymatic activities can vary in different tissues. Therefore, analysis of the molecular characteristics of eccDNA residues generated in vivo can provide information about the tissue of origin. Additional information about eccDNA residue molecules includes, but is not limited to, quantity, size, motif, serrated ends, and others. These eccDNA characteristics provide new data sources for determining the properties of an object (e.g., the disease classification of the object). As an example, analysis of circulating eccDNA residues in a patient's plasma can be used for non-invasive cancer detection. Furthermore, analysis of eccDNA residues can allow determination of the proportion of clinically relevant DNA in a biological sample. This clinically relevant DNA may include, for example, DNA from a tumor, transplanted organ, or fetus, or may consist of, for example, DNA from a tumor, transplanted organ, or fetus.

[0093] II. Identification of eccDNA Residues

[0094] In some aspects, this disclosure provides various methods for identifying eccDNA residues. These methods can be used, for example, to classify whether a linear DNA molecule (e.g., a free linear DNA molecule) is a product of the opening (e.g., in vivo cleavage) of a circular DNA molecule (e.g., an eccDNA molecule). Therefore, these methods can distinguish eccDNA residues in a sample from other linear DNA molecules in the sample. This distinction between eccDNA residues and other linear DNA molecules relies on the similarity and differences in structure and composition between eccDNA residues, eccDNA molecules, and non-eccDNA residue linear DNA molecules.

[0095] like Figure 1 As illustrated in the diagram, eccDNA remnants are molecules that share a linear form with other linear DNA molecules, but also include the linker site features of the eccDNA. Because eccDNA is a circularized form of previously linear DNA (e.g., a linear region of genomic DNA), the eccDNA molecule contains a linker site at which the previously separated ends of the linear DNA become adjacent to each other. For example, in Figure 1In the eccDNA molecule 104, the previously separated terminal sequences 101 and 102 are adjacent to each other at the junction 103. In the eccDNA residue 105 formed by cutting the eccDNA molecule with plasma nuclease, the previously separated terminal sequences 101 and 102 remain connected at the junction 103. Other linear DNA 106 does not include a similar junction.

[0096] A. Sequencing and mapping linear DNA

[0097] Figure 2 A schematic illustration of the concepts based on the provided method for identifying eccDNA remnants by mapping sequence reads associated with linear DNA molecules is provided. Figure 2 In the exemplary illustration, sequences "a" 201 and "b" 202 of genome 203 are separated from each other within a genomic region (i.e., a chromosomal region). The genomic region spanning and including sequences "a" and "b" can form eccDNA 204 through a circularization reaction that occurs naturally within the subject. After this circularization, sequences "a" and "b" are directly adjacent to each other in the eccDNA molecule and together form a circular linker locus 205.

[0098] The eccDNA molecule can then be cleaved in vivo by digestion with an enzyme active in the body (e.g., an endonuclease). The endonuclease can be, for example, deoxyribonuclease 1 (DNASE1), deoxyribonuclease-1-like 1 (DNASE1L1), deoxyribonuclease-1-like 2 (DNASE1L2), deoxyribonuclease-1-like 3 (DNASE1L3), deoxyribonuclease 2 (DNASE2), endonuclease G (ENDOG), DNA fragmentation factor β subunit (DFFB), or another endonuclease active against circular DNA. The endonuclease can cleave the eccDNA at cleavage site 206. The opening of this circular DNA molecule at the cleavage site produces a linear DNA molecule 207 with a 5' end sequence "d" 208 and a 3' end sequence "c" 209, which is referred to herein as the eccDNA remnant. In some cases, cleavage of eccDNA produces eccDNA remnants that are shorter in size than the original eccDNA molecule. In other cases, cleavage produces eccDNA remnants that are the same size as the original eccDNA molecule. Because the eccDNA remnants produced by the subject can circulate in the subject's plasma, biological samples of the subject containing the subject's plasma may contain these eccDNA remnants.

[0099] For example, as in Figure 2As shown, eccDNA remnants in biological samples from an object can be sequenced to produce sequence information in the form of one or more sequence reads. Figure 2 In the exemplary workflow shown, sequencing aptamer 210 is ligated to the 5' end sequence "d" 208 and the 3' end sequence "c" 209 of the eccDNA residue 207. When sequencing is performed via nanopore sequencing, the sequencing aptamer may include a motor protein 211 for controlling the translocation of the eccDNA residue via a nanopore. After the sequencing aptamer is ligated to the eccDNA residue, the resulting construct can be sequenced, for example, via nanopore sequencing. The sequencing direction 212 is from the 5' end sequence "d" to the 3' end sequence "c".

[0100] although Figure 2 The illustration shows an exemplary workflow in which nanopore sequencing is used to generate one or more sequencing reads corresponding to eccDNA remnants, but other sequencing technologies or alternative methods can also be used to generate sequence reads. For example, single-molecule real-time (SMRT) sequencing, synthetic sequencing, ion semiconductor sequencing, and / or chain-termination sequencing can be applied to generate sequence reads. Thus, sequencing of eccDNA remnants and other linear DNA molecules in a biological sample can involve single-end sequencing or paired-end sequencing. Additionally or alternatively, methods using hybridization arrays, capture probes, amplification (e.g., polymerase chain reaction (PCR), linear amplification using a single primer, or isothermal amplification), and / or biophysical measurements (e.g., mass spectrometry) can be used to determine sequence information associated with eccDNA remnants.

[0101] One or more sequence reads generated by sequencing the eccDNA remnant 207 typically correspond to at least the 5' end sequence "d" 208 and the 3' end sequence "c" 209 of the eccDNA remnant. The sequence reads corresponding to the 5' end sequence of the eccDNA remnant are referred to herein as 5' end sequence reads, while the sequence reads corresponding to the 3' end of the eccDNA remnant are referred to herein as 3' end sequence reads. In some embodiments, such as Figure 2 As illustrated in the diagram, a single sequence read 213 spans the entire eccDNA residue and therefore corresponds to both the 5' and 3' end sequences of the eccDNA residue. In this case (e.g., nanopore sequencing), the single sequencing read is both the 5' and 3' end sequence reads. In other examples (e.g., paired-end sequencing), no single sequence read spans the entire eccDNA residue, and the 5' and 3' end sequence reads are distinct sequence reads.

[0102] In some cases, one or more sequence reads generated by sequencing eccDNA remnants are raw sequence reads preprocessed according to one or more operations. For example, duplicate reads can be removed from one or more sequence reads. As another example, sequences associated with sequencing aptamers can be removed from sequence reads. Additionally or alternatively, sequences associated with low-quality bases at the 3' end of the sequence reads can be similarly removed from the sequence reads. Another preprocessing operation may involve selecting and / or reversing a specified number of bases at one or both ends of the sequence read for alignment, i.e., mapping. For example, when paired-end sequencing is used to generate sequence reads, the reversal of some sequence reads prior to mapping is necessary to convert those reads into sequence reads with a sequence orientation that matches the sequence orientation of the linear DNA molecule being sequenced.

[0103] The methods provided for identifying eccDNA remnants typically involve mapping the 5' and 3' end sequences of the eccDNA remnants to a reference genome. Typically, the reference genome is an object-specific reference genome from which the eccDNA remnants originate. For example, when the eccDNA remnants are a component of a biological sample from a human object, the reference genome used for mapping the end sequence readings could be a human reference genome, such as hg19. Mapping the end sequence readings to the reference genome can involve using alignment techniques, such as Bowtie 2 (Langmead et al., Nat. Methods 9,(2012): 357). Other alignment techniques can also be used for mapping. In some cases, the alignment process for sequence end mapping may involve requiring the alignment to meet predetermined mapping quality conditions or thresholds. For example, a mapping quality greater than 30 may be required for the mapping of 5' end sequences and / or 3' end sequences, indicating that the probability of the end sequence mapping to a position other than the reported position is 10^(-3) = 0.1%. In other examples, a mapping quality greater than 10 may be required for 5' end sequences and / or 3' end sequences, such as greater than 15, greater than 20, greater than 25, greater than 35, greater than 40, greater than 45, or greater than 50.

[0104] In some cases, such as Figure 2 As shown, one or both of the 5' end sequence reads and the 3' end sequence reads may include a linker point 205 of the eccDNA remnant. In such cases, the end sequence read containing the linker point will include two fragments separated by the linker point, and the mapping of these fragments to the reference genome will show a discontinuity at the linker point. For example, in Figure 2In the mapping shown, sequence read 213 contains fragments “A” 214 and “B” 215. Fragment “A” of the sequencing read spans the portion of the eccDNA remnant from the 5' end sequence “d” 208 to sequence “b” 202, forming the 5' portion of the linking locus 205. Fragment “B” of the sequencing read spans the portion of the eccDNA remnant from sequence “a” 201 (which forms the 3' portion of the linking locus) to the 3' end sequence “c” 209. When fragments “A” and “B” of the sequencing reads are aligned with the reference genome 216, the mapping results indicate a discontinuity at the linking locus, such that the mapping for fragment “B” is neither directly connected to nor immediately following the mapping for fragment “A”. The characteristics of this discontinuous mapping and its use in identifying eccDNA remnants are described in more detail in Section II.B. In some cases, it may be required that the mapping quality of fragments separated from different junctions of sequence reads to the reference genome has a quality greater than 10, such as greater than 15, greater than 20, greater than 25, greater than 30, greater than 35, greater than 40, greater than 45 or greater than 50.

[0105] In some examples, the linker loci of a sequencing read are identified by a method that determines the optimal alignment of two adjacent fragments of the sequencing read with a reference genome in a discontinuous manner. Following this identification, “splitting sites” that separate these fragments in the sequencing read can be identified. These splitting sites then correspond to the linker sites or loci of the original eccDNA molecules from which the eccDNA remnants originated.

[0106] B. Classification of eccDNA based on linear DNA mapping

[0107] After receiving the sequence reads corresponding to a linear DNA molecule, and having mapped the 5' and 3' end sequences of the linear DNA molecule to a reference genome, the mapping result can be used, according to the provided method, to classify whether the linear DNA molecule is an eccDNA remnant. Typically, multiple criteria are used to determine whether the mapping result indicates that the linear DNA molecule is an eccDNA remnant. If the linear DNA molecule is an eccDNA remnant, the mapped genomic coordinates of the 5' end sequence will be greater than the mapped genomic coordinates of the 3' end sequence. Accordingly, in many examples, the 5' and 3' end sequences of the eccDNA remnant will be mapped to the same chromosome in the genome, while in some other examples, they will be mapped to different chromosomes in the genome. Furthermore, if the linear DNA molecule is an eccDNA remnant, the mapped genomic orientation of the 5' end sequence will be the same as that of the mapped genomic orientation of the 3' end sequence. Therefore, in some implementations, the provided method classifies linear DNA molecules as eccDNA residues if both of the following conditions are met: (1) the genomic coordinates of the 5' end sequence are greater than the genomic coordinates of the 3' end sequence, and (2) the genomic orientation of the 5' end sequence is the same as the genomic orientation of the 3' end sequence.

[0108] 1. Mapped genomic coordinates represent eccDNA remnants.

[0109] The genomic coordinates of a specific sequence or sequence fragment refer to its position in the reference genome. These genomic coordinate positions can be assigned numerical values ​​that increase in the direction from the 5' coordinate position to the 3' coordinate position. Therefore, if the first sequence is mapped to a position in the reference genome downstream of the position to which the second sequence is mapped in the 5' to 3' direction, the first sequence has larger genomic coordinates than the second sequence. Conversely, in this case, the first sequence has smaller genomic coordinates than the second sequence because the first sequence is mapped to an upstream position in the reference genome of the second sequence.

[0110] Figure 2 An example of sequence mapping results with genomic coordinates is provided, indicating that the linear DNA molecule is an eccDNA remnant. In the example shown, the 5' end sequence "d" 208 of sequencing read 213 is mapped to the corresponding sequence "d" in the reference genome 216. Similarly, the 3' end sequence "c" 209 of sequencing read is mapped to the corresponding sequence "c" in the reference genome. Because sequence "d" is downstream of sequence "c" in the reference genome in the 5' to 3' direction, the mapped genomic coordinates of the 5' end sequence "d" of the linear DNA molecule 207 are greater than the mapped genomic coordinates of the 3' end sequence "c" of the linear DNA molecule.

[0111] Where at least one received sequencing read associated with a linear DNA molecule includes an identified join site, optional or additional mapping criteria related to genomic coordinates can be used. For example, if the 5' end sequence of the read determines that it contains a join locus, then the read must include the 5' end sequence, the sequence forming the 5' portion of the join locus, and the sequence forming the 3' portion of the locus. (See reference...) Figure 2 In the example shown, sequencing readout 213 includes a 5' end sequence "d" 208, a 5' portion sequence "b" forming the junction 205, and a 3' portion sequence "a" forming the junction 205. In this case, the 3' portion sequence forming the junction (e.g., sequence "a") is mapped to genomic coordinates that are smaller than those mapped to the 5' portion sequence forming the junction (e.g., sequence "b"). Additionally, the 3' portion sequence forming the junction (e.g., sequence "a") is mapped to genomic coordinates that are smaller than those mapped to the 5' end sequence (e.g., sequence "d").

[0112] As another example, if it is determined that the 3' end sequence readout contains the connecting locus, then the readout must include the 3' end sequence, the sequence forming the 5' portion of the connecting locus, and the sequence forming the 3' portion of the locus. See again. Figure 2 In the example shown, sequencing readout 213 includes a 3' end sequence "c" 209, a 5' portion sequence "b" forming the junction 205, and a 3' portion sequence "a" forming the junction 205. In this case, the 5' portion sequence forming the junction (e.g., sequence "b") is mapped to genomic coordinates that are greater than those mapped to the 3' portion sequence forming the junction (e.g., sequence "a"). Additionally, the 5' portion sequence forming the junction (e.g., sequence "b") is mapped to genomic coordinates that are greater than those mapped to the 3' end sequence (e.g., sequence "c").

[0113] Furthermore, in cases where at least one received sequencing read associated with a linear DNA molecule includes an identified join point, the mapped genomic coordinates of fragments on either side of that identified join point can further indicate that the linear DNA molecule is an eccDNA remnant. Specifically, for such sequencing reads, fragments corresponding to more than 5' portions of the linear DNA molecule will be mapped to larger genomic coordinates than fragments corresponding to more than 3' portions of the linear DNA molecule. For illustration, for Figure 2Sequencing reads 213, fragment "A" 214 corresponds to a further 5' portion of the linear DNA molecule 207, while fragment "B" 215 corresponds to a further 3' portion of the linear DNA molecule. Mapping these fragments to the reference genome 216 shows that fragment "A" has larger genomic coordinates than fragment "B", which provides further evidence that the linear DNA molecule is an eccDNA remnant.

[0114] 2. Mapped genomic orientation indicates eccDNA remnants

[0115] The genomic orientation of a specific sequence or sequence fragment refers to the direction in which the sequence fragment is mapped to the reference genome. For example, when a sequence corresponding to the 5' to 3' region of a sequenced DNA molecule is optimally aligned with a region of the reference genome in the same 5' to 3' orientation, the sequence can be described as having a forward or positive genomic orientation. Conversely, when a sequence corresponding to the 5' to 3' region of a sequenced DNA molecule is optimally aligned with a region of the reference genome in the opposite 3' to 5' orientation, the sequence can be described as having a reverse or negative genomic orientation.

[0116] Figure 2 An example of sequence mapping results showing genomic orientation indicating that a linear DNA molecule is an eccDNA remnant is provided. In the example shown, the 5' end sequence "d" 208 of sequencing read 213 is mapped to the reference genome 216 in a forward genomic orientation. Similarly, the 3' end sequence "c" 209 of sequencing read is also mapped to the reference genome in a forward orientation. These same genomic orientations, together with the genomic coordinate information described in Section II.B.1, can be used to identify linear DNA molecule 207 as an eccDNA remnant.

[0117] Where at least one received sequencing read associated with a linear DNA molecule includes an identified join site, optional or additional mapping criteria related to genome orientation can be used. For example, if a 5' end sequence read is determined to contain a join locus, then the read must necessarily include the 5' end sequence, the sequence forming the 5' portion of the join locus, and the sequence forming the 3' portion of the locus. (See reference...) Figure 2 In the example shown, sequencing readout 213 includes a 5' end sequence "d" 208, a 5' portion sequence "b" forming the junction 205, and a 3' portion sequence "a" forming the junction 205. In this case, the 3' portion sequence forming the junction (e.g., sequence "a") has the same genomic orientation as the 5' portion sequence forming the junction (e.g., sequence "b"). Additionally, the 3' portion sequence forming the junction (e.g., sequence "a") has the same genomic orientation as the 5' end sequence (e.g., sequence "d").

[0118] As another example, if it is determined that the 3' end sequence contains the linker locus, then the reading must include the 3' end sequence, the sequence forming the 5' portion of the linker locus, and the sequence forming the 3' portion of the locus. See again. Figure 2 In the example shown, sequencing readout 213 includes a 3' end sequence "c" 209, a 5' portion sequence "b" forming the junction 205, and a 3' portion sequence "a" forming the junction 205. In this case, the 5' portion sequence forming the junction (e.g., sequence "b") has the same genomic orientation as the 3' portion sequence forming the junction (e.g., sequence "a"). Additionally, the 5' portion sequence forming the junction (e.g., sequence "b") has the same genomic orientation as the 3' end sequence (e.g., sequence "c").

[0119] Furthermore, when at least one received sequencing read associated with a linear DNA molecule includes an identified join point, the mapped genomic orientation of the fragments on either side of that identified join point can further indicate that the linear DNA molecule is an eccDNA remnant. Specifically, for such sequencing reads, fragments corresponding to more than 5' portions of the linear DNA molecule will have the same genomic orientation as fragments corresponding to more than 3' portions of the linear DNA molecule. For illustration, for Figure 2 Sequencing reads 213, fragment "A" 214 corresponds to a further 5' portion of the linear DNA molecule 207, while fragment "B" 215 corresponds to a further 3' portion of the linear DNA molecule. Mapping these fragments to the reference genome 216 shows that fragment "A" has the same forward genomic orientation as fragment "B", providing further evidence that the linear DNA molecule is an eccDNA remnant.

[0120] 3. The mapping results do not indicate eccDNA residues.

[0121] The specific sequence mapping results described in sections II.B.1 and II.B.2 can be used, according to the provided methods, to classify linear DNA molecules as eccDNA remnants, because different sequence mapping results can be viewed as sequence reads associated with linear DNA molecules that are not eccDNA remnants. For example, the mapping characteristics of eccDNA remnants differ from the mapping configurations of other linear DNA structural variants that also exhibit mapping discontinuities. These alternative linear structural variants include, for example, Figure 3 The deletions, insertions, inversions, and duplications of genomic DNA fragments shown (van Belzen IAM, Schönhuth A, Kemmeren P & Hehir-Kwa, npj Precis. Oncol. 5, (2021): 1).

[0122] In the case of linear DNA molecules resulting from deletions of genomic DNA segments, if the sequence readings of the linear DNA molecule include sequences adjacent to the deleted genomic segment, the mapping of those sequence readings will show discontinuities. In this case, adjacent sequence fragments in the readings (e.g., Figure 3 Sequence fragments 301 and 302 in the diagram will be optimally aligned with non-adjacent genomic coordinates, as is the case with mappings associated with eccDNA remnants. However, unlike the eccDNA mapping results, the genomic coordinates of the 5' end sequence (e.g., end sequence 303) will be smaller than, but not larger than, the genomic coordinates of the 3' end sequence (e.g., end sequence 304). This difference can be used to distinguish between linear eccDNA remnants and linear DNA molecules resulting from genomic deletions.

[0123] In cases where linear DNA molecules are generated by sequence insertion, if the sequence readings of the linear DNA molecule include sequences adjacent to and within the inserted sequence, the mapping of these sequence readings will show discontinuities. In this case, one or more sequence fragments (e.g., Figure 3 Sequence fragments 305 and / or 306 in the reference genome will align optimally with the reference genome, while adjacent sequence fragments (e.g., sequence fragment 307) will not align. Relatedly, sequence reads may also include non-adjacent sequence fragments (e.g., sequence fragments 305 and 306) mapped to adjacent sequences in the reference genome. However, in the case of insertion, the genomic coordinates of the 5' end sequence (e.g., end sequence 308) will be smaller than, but not larger than, the genomic coordinates of the 3' end sequence (e.g., end sequence 309). This difference can be used to distinguish between linear eccDNA remnants and linear DNA molecules resulting from insertion.

[0124] In the case of linear DNA molecules resulting from inversions of genomic DNA segments, if the sequence readings of the linear DNA molecule include sequences adjacent to and within the inverted genomic segment, the mapping of these sequence readings will show discontinuities. In this case, adjacent sequence fragments in the readings (e.g., Figure 3Sequence fragments 310 and 311, or 312 and 313, will be optimally aligned with genomic coordinates that are not adjacent to each other, as is the case with the mapping associated with eccDNA remnants. However, these sequence fragment pairs (e.g., sequence fragments 310 and 311, or 312 and 313) will have opposite genomic orientations due to inversions. In contrast, sequence fragments from eccDNA remnants do not show different genomic orientations. Furthermore, for readings of linear DNA molecules with inversions, the genomic coordinates of the 5' end sequence (e.g., end sequence 314) will be smaller than, but not larger than, the genomic coordinates of the 3' end sequence (e.g., end sequence 315). This property will be maintained even if the linear DNA molecule includes an inversion at the 5' end (in which case, e.g., the genomic coordinates of sequence fragment 311 are smaller than those of sequence fragment 315), or an inversion at the 3' end (in which case, e.g., the genomic coordinates of sequence fragment 314 are smaller than those of sequence fragment 312). These differences can be used to distinguish between linear eccDNA residues and linear DNA molecules produced by genomic inversions.

[0125] In the case of linear DNA molecules generated from repetitions of genomic DNA segments, if the sequence readings of the linear DNA molecule include the sequence within each repetition, the mapping of those readings will show discontinuities. In this case, for example, adjacent sequence fragments in the readings (e.g., Figure 3 Sequence fragments 316 and 317 in the sequence will be optimally aligned with non-adjacent genomic coordinates, as is the case with mappings associated with eccDNA remnants. Although different from eccDNA remnant sequence reads, sequence reads of linear DNA molecules generated by repetitions may include pairs of sequence fragments (e.g., sequences 317 and 318, or 316 and 319) each mapped to the same genomic coordinates. Furthermore, for reads of linear DNA molecules with repetitions, the genomic coordinates of the 5' end sequence (e.g., end sequence 318) will be smaller than, but not larger than, the genomic coordinates of the 3' end sequence (e.g., end sequence 320). These differences can be used to distinguish between linear eccDNA remnants and linear DNA molecules generated by genomic repetitions.

[0126] C. Method

[0127] Figure 4 A flowchart of a method 400 for determining whether a linear DNA molecule in a biological sample is an eccDNA residue, according to an embodiment of this disclosure, is shown. Various examples of method 400 are described in sections II.A and II.B. Method 400 may be performed, in part or in whole, using a computer system.

[0128] At box 410, one or more sequence reads are obtained, wherein the one or more sequence reads include the 5' end sequence and the 3' end sequence of a linear DNA molecule in the biological sample. In some examples, obtaining sequence reads includes receiving a biological sample. The biological sample may include cell-free DNA. For example, the biological sample may be a purified sample, for example, to isolate a majority of cell-free portions (e.g., plasma). Other pretreatment steps may also be performed on the biological sample. In some examples, obtaining sequencing reads includes sequencing the linear DNA molecules present in the sample. Sequencing may be random sequencing of all linear DNA molecules in the biological sample, rather than targeted sequencing of a specific molecule, and may be performed using any sequencing technology described in Section II.A. In some cases, the biological sample is a cell-free biological sample and / or the linear DNA molecules are free linear DNA molecules. For example, the biological sample may include plasma or be composed of plasma. The linear DNA molecules of the biological sample are naturally present in the biological sample in a linear form, and the provided methods do not include operations designed to linearize circular DNA molecules of the biological sample, such as enzymatic cutting or mechanical shearing. In some cases, a obtained sequence read includes the 5' end sequence and the 3' end sequence of the linear DNA molecule. In other cases, one obtained sequence readout includes the 5' end sequence of the linear DNA molecule, and another obtained sequence readout includes the 3' end sequence of the linear DNA molecule. The obtained sequence readouts may each independently have a length of at least 25 bp, at least 45 bp, at least 75 bp, at least 150 bp, at least 250 bp, at least 500 bp, at least 1 kb, at least 3 kb, at least 10 kb, at least 30 kb, or at least 100 kb.

[0129] At box 420, the 5' end sequence and the 3' end sequence of the linear DNA molecule obtained in box 410 are mapped to a reference genome. The mapping can be performed as described in section II.A and may involve independently determining the optimal alignment of each of the 5' and 3' end sequences to the reference genome. In some cases, the alignments need to independently meet mapping quality conditions, e.g., having a mapping quality of at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, or at least 50. In some cases, the mapping of the end sequences includes determining the genomic coordinates of each end sequence. Additionally or alternatively, the mapping of the end sequences may include determining the genomic orientation of each end sequence. In some examples, the provided method also includes additional mapping of one or more sequences of the linear DNA molecule other than the 5' and 3' end sequences, and determining the genomic coordinates and / or genomic orientation of these additionally mapped sequences. In some cases, the mapping further includes identifying whether the sequence readings include a linker locus, and optionally determining the genomic coordinates of that linker locus.

[0130] At box 430, based on the mapping in box 420, linear DNA molecules are classified according to whether they were cut from circular DNA molecules in vivo (i.e., whether the linear DNA molecules are eccDNA remnants). The classification of linear DNA molecules can be performed as described in section II.B. For example, classification may include comparing the genomic coordinates of the 5' end sequence of the linear DNA molecule with the genomic coordinates of the 3' end sequence of the linear DNA molecule. Additionally or alternatively, classification may include comparing the genomic orientation of the 5' end sequence with the genomic orientation of the 3' end sequence. As an example, if (1) the genomic coordinates of the 5' end sequence are greater than the genomic coordinates of the 3' end sequence, and (2) the genomic orientation of the 5' end sequence is the same as the genomic orientation of the 3' end sequence, then the linear DNA molecule may be classified as an eccDNA remnant. In some cases, classification may include comparing the genomic coordinates and / or genomic orientation of the mapped sequences other than the 5' and 3' end sequences, or classification may also include comparing the genomic coordinates and / or genomic orientation of the mapped sequences other than the 5' and 3' end sequences.

[0131] III. Analyze eccDNA residues to determine their characteristics.

[0132] In some aspects, this disclosure also provides various methods for determining the characteristics of a biological sample, or the characteristics of an object from which a biological sample is obtained, wherein said characteristics are determined by analyzing eccDNA residues in the biological sample. Because existing methods for determining the characteristics of a biological sample or object do not take into account or analyze eccDNA residues present in the biological sample, the methods disclosed herein advantageously provide a new source of information that can be used, for example, to identify the health status of an object and / or to identify, classify, or treat lesions in an object.

[0133] The methods provided for determining the characteristics of a biological sample or object typically involve classifying all or at least a portion of the linear DNA molecules in the biological sample based on whether each of them is an eccDNA remnant. In this way, a group of linear DNA molecules classified as eccDNA remnants can be identified. The classification of each linear DNA molecule can be performed as described in Section II. The number of classified linear DNA molecules can be, for example, at least 100, at least 300, at least 1000, at least 3000, at least 10,000, at least 30,000, at least 100,000, at least 300,000, at least 1,000,000, or at least 3,000,000. Once the group of classified eccDNA remnants has been identified, the eccDNA remnants in the group can be analyzed, for example, to determine the ensemble value of the group members. In some cases, the ensemble value can be compared to a reference value to determine the characteristics of the biological sample or object. In some cases, the determination of the characteristics of the biological sample or object involves comparing the ensemble value to a threshold and determining whether the ensemble value exceeds or falls below the threshold.

[0134] A. Counting

[0135] In some examples, the provided methods for determining the characteristics of a biological sample, or the characteristics of an object from which a biological sample is obtained, include determining a count of linear DNA molecules in the biological sample that are classified as eccDNA remnants. The count can be a raw value representing the absolute number (i.e., a numerical quantity) of members in a set of all-classified eccDNA remnants from the biological sample. Optionally, the count can be a processed value. For example, the count can be a standardized value representing the relative number of members in a set of all-classified eccDNA remnants from the biological sample. In some cases, the count is a relative number obtained by dividing the absolute number of classified eccDNA remnants in the biological sample by the absolute number of multiple linear DNA molecules for which sequence readings are received and mapped. In this way, the eccDNA remnants can be standardized using the count of linear DNA molecules among multiple linear DNA molecules. In some examples, such a standardized count can be based on eccDNA remnants per million mapped readings (e ccDNA Remnants P er M Illion-mapped reads (EPM) are used for reporting. Other methods for processing raw or standardized eccDNA residue counts may additionally or optionally include dividing the count by the volume of the biological sample, thereby converting the raw or standardized count into a concentration value.

[0136] The provided method may further include using defined counts (e.g., defined normalized counts) to determine characteristics of a biological sample or object. For example, the count of defined eccDNA residues in a specific biological sample may be compared to the count of a previously defined reference sample. Similarly, the defined eccDNA residue count may be compared to a reference value. The defined count may also be compared to two or more reference values. In some examples, two or more reference values ​​are used to create a standard curve fitted to the reference values, and the defined count is compared to the standard curve. In other examples, the defined eccDNA residue count is compared to a threshold, where a defined count less than or greater than the threshold indicates a specific characteristic of the biological sample or object.

[0137] B. Size Distribution

[0138] In some examples, the provided methods for determining the characteristics of a biological sample, or the characteristics of an object from which a biological sample is obtained, include determining the size distribution of linear DNA molecules in the biological sample classified as eccDNA remnants. This size distribution also represents, or alternatively represents, the estimated size distribution of the original eccDNA molecules corresponding to the eccDNA remnants. The size distribution may include information about the size (e.g., estimated size) of all or at least a portion of the linear DNA molecules classified as eccDNA molecules. In some cases, the size distribution includes one or more counts (e.g., absolute or relative or normalized counts) of eccDNA remnants having a size (e.g., estimated size) greater than and / or less than one or more thresholds. For example, the size distribution may include absolute and / or relative counts of eccDNA remnants or original eccDNA molecules having estimated sizes greater than 100 bp, greater than 300 bp, greater than 1 kb, greater than 3 kb, greater than 10 kb, greater than 30 kb, and / or greater than 100 kb. Additionally or alternatively, the size distribution may include absolute and / or relative counts of eccDNA remnants or original eccDNA molecules with estimated sizes less than 100 kb, less than 30 kb, less than 10 kb, less than 1 kb, less than 300 bp, or less than 100 bp. In some cases, the size distribution includes one or more percentages of eccDNA remnants or original eccDNA molecules, each of which has a size within a predetermined size range, and each of which has a lower boundary, an upper boundary, or both. For example, the size distribution may include a size-frequency distribution.

[0139] In some examples, for each of all or at least a portion of the linear DNA molecules classified as eccDNA remnants, at least one sequence reading includes the 5' end sequence and the 3' end sequence of the linear DNA molecule. For example, in Figure 2 In the example shown, sequence read 213 includes the 5' end sequence 208 and the 3' end sequence 209 of the linear DNA molecule 207. In such a case, it can be assumed that the sequence read includes the entire sequence of the linear DNA molecule (e.g., an eccDNA remnant), and the length of the sequence read will be equal to the length of the linear DNA molecule. Therefore, if the sequence read includes the 5' end and 3' end sequences of the linear DNA molecule classified as an eccDNA remnant, the size of the eccDNA remnant can be determined by measuring the length of the sequencing read, i.e., calculating the distance between the 5' end of the 5' end sequence and the 3' end of the 3' end sequence.

[0140] In some examples, for all or at least a portion of each of the linear DNA molecules classified as eccDNA remnants, at least one sequence reading includes a junction, i.e., a site in the sequence where nucleotides at two separate genomic locations are directly adjacent to each other. For example, in Figure 2 In the example shown, sequence readout 213 includes a join point 205 of eccDNA molecule 204, where nucleotides of sequence fragments 202 and 201 are directly adjacent to each other in the sequence readout, but mapped to separate genomic coordinates in reference genome 216. In such a case, the nucleotides flanking the join point represent the 5' and 3' ends of the genomic sequence circularized to form the eccDNA molecule. As a result, the distance between these nucleotides in the reference genome is equal to the length of the original eccDNA molecule. Therefore, if the sequence readout of a linear DNA molecule classified as an eccDNA remnant includes a join point, the size of the original DNA molecule can be determined by calculating the distance between the genomic locations (i.e., genomic coordinates) of the nucleotides forming the 5' and 3' portions of the join point.

[0141] In some examples, for a specific linear DNA molecule classified as an eccDNA remnant, no sequence readings include the junction, or the 5' and 3' end sequences of the linear DNA molecule. In such cases, the length of the linear DNA molecule can be assumed to be greater than the combined length of the reading including the 5' end sequence (i.e., the 5' end sequence reading) and the reading including the 3' end sequence (i.e., the 3' end sequence reading), minus any overlapping regions shared by the 5' and 3' end sequences. Therefore, the size of the eccDNA remnant or the original eccDNA molecule can be estimated by determining the length of the 5' end sequence reading, determining the length of the 3' end sequence reading, summing these two lengths, and approximating the size of the eccDNA remnant as greater than this sum minus the length of any overlapping regions shared by the 5' and 3' end sequences. In some examples of the provided methods, such estimates and approximations are not used to determine the size distribution, but rather the size distribution is determined solely using data from sequence readings that include the junction and, optionally further, the 5' and 3' end sequences of the same linear DNA molecule.

[0142] The provided method may further include using a defined size distribution to determine the characteristics of a biological sample or object. For example, a defined size distribution of the eccDNA residue of a particular biological sample or a presumed size distribution of the original eccDNA molecules can be compared to a previously determined size distribution of a reference sample. Similarly, a defined size distribution of the eccDNA residue or a presumed size distribution of the original eccDNA molecules can be compared to reference values. A defined size distribution can also be compared to two or more reference values. In some examples, a standard curve fitted to the reference values ​​is created using two or more reference values, and the defined size distribution is compared to the standard curve. In other examples, the defined size distribution of the eccDNA residue or the presumed size distribution of the eccDNA molecules is compared to a threshold, wherein if the defined size distribution is less than or greater than the threshold, it indicates a specific property of the biological sample or object.

[0143] C. Genome distribution

[0144] In some examples, the provided methods for determining the characteristics of a biological sample, or the characteristics of an object from which a biological sample is obtained, include determining the genomic distribution of linear DNA molecules classified as eccDNA remnants in the biological sample. In these examples, the methods typically include identifying a set of eccDNA remnants according to the classification techniques described in Section II.B. The method further includes determining whether each eccDNA remnant in the set belongs to a subset of one or more regions within a class of genomic elements mapped to a reference genome. In some cases, the eccDNA remnant is classified as belonging to a subset when its starting location is mapped to a specific class of genomic elements. Classes may include any one or more types of genomic elements generally known to be present in genomic DNA. For example, categories may include 5' untranslated regions, 3' untranslated regions, exons, introns, 2 kb upstream regions of genes (Gene2kbU), 2 kb downstream regions of genes (Gene2kbD), CpG islands, 2 kb upstream regions of CpG islands (CGI2kbU), 2 kb downstream regions of CpG islands (CGI2kbD), Alu repeat regions, or any combination of these types of genomic elements.

[0145] These examples further include determining a count of a subset of eccDNA remnants. The count can be an absolute count, a relative count, or a normalized count. In some cases, the count is normalized to produce a normalized genomic distribution of the eccDNA remnants. Normalization can correlate the count with the theoretical distribution of all genomic DNA belonging to a particular category of genomic elements. Therefore, the normalization process may include determining the percentage of the genome covered by that category and dividing the percentage of eccDNA remnants mapped to that category by the percentage of the genome covered by that category.

[0146] The provided method may further include using a standardized genomic distribution to determine characteristics of a biological sample or object. For example, a determined standardized genomic distribution of the eccDNA residue of a specific biological sample may be compared to a previously determined standardized genomic distribution of the eccDNA residue from a reference sample. Similarly, a determined standardized genomic distribution of the eccDNA residue may be compared to a reference distribution. The determined standardized genomic distribution may also be compared to two or more reference distributions. In some examples, two or more reference distributions are used to create a standard curve fitting the reference distributions, and the determined standardized genomic distribution is compared to the standard curve. In other examples, the determined standardized genomic distribution of the eccDNA residue is compared to a threshold distribution, where a determination of the standardized genomic distribution being less than or greater than the threshold distribution indicates a specific characteristic of the biological sample or object.

[0147] D. Nucleotide motif frequency

[0148] In some examples, the methods provided for determining the characteristics of a biological sample, or the characteristics of an object from which a biological sample is obtained, include determining the frequency of one or more nucleotide motif patterns appearing in linear DNA molecules classified as eccDNA remnants. In these examples, the methods typically include identifying a group of eccDNA remnants according to the classification techniques described in Section II.B. As part of these classification techniques, the start position (i.e., the genomic location of the upstream edge of the eccDNA remnant) and the end position (i.e., the genomic location of the downstream edge of the eccDNA remnant) of each remnant can be determined. Since these start and end positions represent the locations where these fragments were excised from the genome that existed prior to the formation of the eccDNA, the DNA sequence (i.e., the eccDNA sequence and / or the genomic sequence) within a specific distance of the start and end positions can include frequently occurring nucleotide motif features associated with fragment excision.

[0149] Figure 14An example of the start and stop positions of an eccDNA molecule is shown. In this particular example, both the start and stop positions are flanked by a pair of trinucleotide motifs, with a 4-bp spacer between them. Specifically, trinucleotide motif I is upstream of the eccDNA start position, trinucleotide motif II is downstream of the eccDNA start position, and motifs I and II are adjacent to the spacer S1, which includes the start position. Similarly, trinucleotide motif III is upstream of the eccDNA stop position, trinucleotide motif IV is downstream of the eccDNA stop position, and motifs III and IV are adjacent to the spacer S2, which includes the stop position. Trinucleotide motifs I, II, III, and IV together constitute the nucleotide motif pattern.

[0150] Although Figure 14 Examples include a 4-motif pattern of trinucleotides, but other configurations of patterns and motifs can be used with the provided method. For example, nucleotide motif patterns can each independently include only single-nucleotide motifs or multi-nucleotide motifs, such as motifs of at least two nucleotides, at least three nucleotides, at least four nucleotides, at least six nucleotides, at least seven nucleotides, at least eight nucleotides, at least nine nucleotides, at least ten nucleotides, or more than ten nucleotides. Furthermore, nucleotide motifs can each independently include a variety of multiple nucleotides, such that each motif can independently be a dinucleotide motif, trinucleotide motif, tetranucleotide motif, pentanucleotide motif, hexanucleotide motif, heptanucleotide motif, octanucleotide motif, nonanucleotide motif, decanucleotide motif, or a larger nucleotide motif.

[0151] Although in some examples, such as Figure 14 As shown, the nucleotide pattern may include a polynucleotide motif located simultaneously on both sides of the start and stop positions of the eccDNA fragment, but other configurations of the nucleotide motif may also be used with the provided method. In some examples, at least one nucleotide motif of the nucleotide motif pattern is located upstream of the start position of the eccDNA. In some examples, each nucleotide motif of the nucleotide motif pattern is located upstream of the start position of the eccDNA. In some examples, at least one nucleotide motif of the nucleotide motif pattern is located downstream of the start position of the eccDNA and upstream of the stop position of the eccDNA. In some examples, each nucleotide motif of the nucleotide motif pattern is located downstream of the start position of the eccDNA and upstream of the stop position of the eccDNA. In some examples, at least one nucleotide motif of the nucleotide motif pattern is located downstream of the stop position of the eccDNA. In some examples, each nucleotide motif of the nucleotide motif pattern is located downstream of the stop position of the eccDNA.

[0152] In some examples, the method includes determining the frequency of a single nucleotide motif pattern appearing for the set of eccDNA remnants. In other examples, the method includes determining the frequency of multiple nucleotide motif patterns appearing for the set of eccDNA remnants. In the latter case, the frequency may be the sum or average of multiple frequencies, each frequency corresponding to one of multiple nucleotide motif patterns.

[0153] Each nucleotide motif is located in a corresponding segment of a reference genome (e.g., a segment upstream of the eccDNA start position or downstream of the eccDNA stop position) or an eccDNA remnant (e.g., a segment downstream of the eccDNA start position and a segment upstream of the eccDNA stop position). Each segment may be independently located within a specified distance from the start position (i.e., the corresponding 5' end of the eccDNA remnant in this set of eccDNA remnants) or from the stop position (i.e., the corresponding 3' end of the eccDNA remnant in this set of eccDNA remnants). The specified distance may be, for example, 1 bp, 2 bp, 3 bp, 4 bp, 5 bp, 6 bp, 7 bp, 8 bp, 9 bp, 10 bp, 15 bp, 20 bp, 25 bp, 30 bp, 40 bp, 50 bp, 60 bp, 80 bp, 100 bp, 150 bp, 200 bp, or greater than 200 bp.

[0154] The provided method may further include using determined frequencies of one or more nucleotide motif patterns appearing for the set of eccDNA remnants to determine characteristics of a biological sample or object. For example, the determined frequencies of eccDNA remnant appearance for a specific biological sample may be compared to previously determined frequencies of eccDNA remnant appearance from a reference sample. Similarly, the determined frequencies of eccDNA remnant appearance may be compared to reference frequencies. The determined frequencies may also be compared to two or more reference frequencies. In some examples, two or more reference frequencies are used to create a standard curve fitting the reference frequencies, and the determined frequencies are compared to the standard curve. In other examples, the determined frequencies of the set of eccDNA remnants are compared to a threshold frequency, where a determined frequency less than or greater than the threshold frequency indicates a specific characteristic of the biological sample or object.

[0155] E. Methylation density

[0156] In some examples, the provided methods for determining the characteristics of a biological sample, or the characteristics of an object from which a biological sample is obtained, include determining the methylation density of at least a subset of linear DNA molecules in a biological sample classified as eccDNA remnants. In these examples, the methods typically include identifying a group of eccDNA remnants according to the classification techniques described in Section II.B. The methods typically further include, for example, determining the size of at least a portion of the group of eccDNA remnants using the methods described in Section III.B. Based on these determined sizes, a subset of the group of eccDNA remnants is identified, wherein each eccDNA remnant in the subset independently has a size within a specified range.

[0157] In some examples, each DNA residue in the subset of eccDNA residues independently has a size smaller than the maximum size. The maximum size can be, for example, approximately 300 bp, approximately 400 bp, approximately 500 bp, approximately 600 bp, approximately 700 bp, approximately 800 bp, approximately 900 bp, approximately 1000 bp, approximately 1200 bp, or approximately 1500 bp. In other examples, each DNA residue in the subset of eccDNA residues independently has a size larger than the minimum size. The minimum size can be, for example, approximately 600 bp, approximately 700 bp, approximately 800 bp, approximately 900 bp, approximately 1000 bp, approximately 1200 bp, approximately 1500 bp, approximately 2000 bp, approximately 2500 bp, or approximately 3000 bp.

[0158] These examples of methods further include determining the methylation state at one or more sites for each eccDNA fragment in a subset of eccDNA fragments. Based on the determined methylation state, the methylation density of the subset of eccDNA fragments is then determined. The provided methods may further include using the methylation density determined for that subset to determine characteristics of a biological sample or object. For example, the determined methylation density of a subset of eccDNA fragments from a particular biological sample may be compared to a previously determined methylation density of eccDNA fragments from a reference sample. Similarly, the determined methylation density of a subset of eccDNA fragments from a sample may be compared to a reference methylation density. The determined methylation density may also be compared to two or more reference methylation densities. In some examples, two or more reference methylation densities are used to create a standard curve fitting the reference methylation densities, and the determined methylation density is compared to the standard curve. In other examples, the determined methylation density of a subset of eccDNA fragments from a sample is compared to a threshold methylation density, wherein if the determined methylation density is less than or greater than the threshold methylation density, it indicates a specific property of the biological sample or object.

[0159] F. Method

[0160] Figure 5 A flowchart of a method 500 for analyzing a biological sample from an object based on the analysis of eccDNA residues to determine the characteristics of the biological sample or object, according to an embodiment of this disclosure, is shown. Various examples of method 500 are described in sections III.A and III.B. Method 500 may be performed, in part or in whole, using a computer system.

[0161] At box 510, one or more sequence reads are obtained, wherein the one or more sequence reads include the 5' end sequence and the 3' end sequence of a linear DNA molecule in the biological sample. Box 510 can be used in conjunction with... Figure 4 The method shown in box 410 of 400 is performed in a similar manner. As in box 410, in some examples, obtaining sequence reads includes receiving a biological sample. The biological sample may include cell-free DNA. For example, the biological sample may be a purified sample, for instance, to isolate a majority of cell-free portions (e.g., plasma). Other pretreatment steps may also be performed on the biological sample. In some examples, obtaining sequencing reads includes sequencing the linear DNA molecules present in the sample. Sequencing may be random sequencing of all linear DNA molecules in the biological sample, rather than targeted sequencing of specific molecules, and may be performed using any sequencing technology described in Section II.A. In some cases, the biological sample is a cell-free biological sample and / or the linear DNA molecules are free linear DNA molecules. For example, the biological sample may include plasma or be composed of plasma. The linear DNA molecules of the biological sample are naturally present in the biological sample in a linear form, and the provided method does not include operations designed to linearize circular DNA molecules of the biological sample, such as enzymatic cutting or mechanical shearing. In some cases, an obtained sequence read includes the 5' and 3' end sequences of the linear DNA molecules. In other cases, one obtained sequence readout includes the 5' end sequence of the linear DNA molecule, and another obtained sequence readout includes the 3' end sequence of the linear DNA molecule. The obtained sequence readouts may each independently have a length of at least 25 bp, at least 45 bp, at least 75 bp, at least 150 bp, at least 250 bp, at least 500 bp, at least 1 kb, at least 3 kb, at least 10 kb, at least 30 kb, or at least 100 kb.

[0162] At box 520, the 5' end sequence and the 3' end sequence of the linear DNA molecule obtained in box 510 are mapped to a reference genome. Box 520 can be performed in a manner similar to box 420 of method 400. As with box 420, the mapping can be performed as described in section II.A and can involve independently determining the optimal alignment of each of the 5' and 3' end sequences with the reference genome. In some cases, the alignments need to independently meet mapping quality conditions, for example, having a mapping quality of at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, or at least 50. In some cases, the mapping of the end sequences includes determining the genomic coordinates of each end sequence. Additionally or alternatively, the mapping of the end sequences may include determining the genomic orientation of each end sequence. In some examples, the provided method also includes additional mapping of one or more sequences of the linear DNA molecule other than the 5' and 3' end sequences, and determining the genomic coordinates and / or genomic orientation of these additionally mapped sequences. In some cases, mapping further includes identifying whether the sequence reads include a linker locus, and optionally determining the genomic coordinates of that linker point.

[0163] At box 530, based on the mapping in box 520, the linear DNA molecule is classified according to whether it was cut from a circular DNA molecule in vivo (i.e., whether the linear DNA molecule is an eccDNA remnant). Box 530 can be performed in a similar manner to box 430 of method 400. As with box 430, the classification of linear DNA molecules can be performed as described in section II.B. For example, classification may include comparing the genomic coordinates of the 5' end sequence of the linear DNA molecule with the genomic coordinates of the 3' end sequence of the linear DNA molecule. Additionally or alternatively, classification may include comparing the genomic orientation of the 5' end sequence with the genomic orientation of the 3' end sequence. As an example, if (1) the genomic coordinates of the 5' end sequence are greater than the genomic coordinates of the 3' end sequence, and (2) the genomic orientation of the 5' end sequence is the same as the genomic orientation of the 3' end sequence, then the linear DNA molecule can be classified as an eccDNA remnant. In some cases, classification may include a comparison of the genomic coordinates and / or genomic orientation of the mapped sequences other than the 5' and 3' end sequences, or in addition to the 5' and 3' end sequences, classification may also include a comparison of the genomic coordinates and / or genomic orientation of the mapped sequences.

[0164] In box 540, members of a group of all linear DNA molecules classified as eccDNA remnants in box 530 are analyzed to determine the characteristics of the biological sample or the object from which the biological sample was obtained. In some cases, all members of the group of eccDNA remnants are analyzed. In other cases, a portion of the group of eccDNA remnants is analyzed. In some examples, the analysis of eccDNA remnants can be used to determine the ensemble value of the group of eccDNA remnants. One example of such an ensemble property is the count of eccDNA remnants within the group, such as an absolute count or a relative or normalized count. Another example of such an ensemble property is the size distribution of eccDNA remnants within the group. Other examples of ensemble properties include genomic distribution, nucleotide motif frequencies, and methylation density.

[0165] Set values ​​can be used to determine the characteristics of a sample or object. In some cases, the characteristics of a sample or object are determined using the count of the set of eccDNA remnants, for example, by comparing the count to a reference value and determining whether the count is greater than, less than, or equal to the reference value. In other cases, the characteristics of a sample are determined using the size distribution of the set of eccDNA remnants, for example, by determining the percentage of the set of eccDNA remnants that exceeds a size threshold (e.g., a predetermined size threshold), and then optionally determining whether that percentage is greater than, less than, or equal to a reference percentage value. Still in other cases, the characteristics of a sample are determined using the normalized genomic coverage of the eccDNA remnants, for example, where the normalized genomic coverage is calculated by counting a subset of eccDNA fragments mapped to a region within a class of genomic elements and dividing that count by the percentage of the genome in the region. In some cases, the characteristics of a sample are determined using the frequency of one or more nucleotide motif patterns appearing for the set of eccDNA remnants, for example, where at least one nucleotide motif pattern comprises multiple nucleotide motifs, such as trinucleotide motifs, dinucleotide motifs, or tetranucleotide motifs. In other cases, the methylation status of eccDNA residues with sizes within a specified size range, such as smaller than the maximum size (e.g., 1000 bp) or larger than the minimum size (e.g., 800 bp), is used to determine the characteristics of the sample.

[0166] IV. Exemplary characteristics of the sample or object

[0167] The provided method for analyzing eccDNA residues in biological samples can be used to determine a variety of useful properties of biological samples or the objects from which biological samples are obtained. Because this method uses information related to eccDNA residues detected in the sample, i.e., those classified from other linear DNA molecules, and because previous methods did not utilize this information, the provided method enables pathways that are otherwise unavailable to be used for analyzing samples and / or evaluating objects. For example, this method can provide improved techniques for non-invasive medical or diagnostic analysis.

[0168] A. Lesion Classification

[0169] In some examples, the methods provided for analyzing eccDNA remnants in biological samples can be used to identify, classify, or treat lesions in subjects. The analyzed eccDNA remnants can come from biological samples obtained from subjects screened for one or more specific lesions. Including eccDNA remnants in the determination of lesion classification can improve detection accuracy, for example, because other lesion classifications do not use information related to eccDNA remnants. Furthermore, because chromosomes can repair themselves after releasing eccDNA, sequence copies of chromosomes (i.e., the genome) may no longer show specific aberrations associated with the lesion, while the corresponding eccDNA remnants may show such aberrations.

[0170] The classification of lesions determined by the provided method can include a disease grade, such as cancer grade, for example, where the subject is undergoing cancer screening. The disease grade can be a disease grade specific to a particular organ, such as the liver. For example, the lesion classification can include a liver disease grade, which can be determined as, for example, cancer, hepatitis B virus (HBV) infection, or no disease. In another example, the disease grade includes an indication of whether a transplanted organ has been rejected.

[0171] Determining the disease grade may include, for example, comparing aggregate values ​​(e.g., counts or size distributions) of classified eccDNA residues with reference values ​​and determining the disease grade based on that comparison. Reference values ​​may be determined based on a population of objects with known disease grades. Reference values ​​(e.g., cutoff values) may be selected to optimize the specificity and sensitivity of predicting disease grades. Therefore, reference values ​​may be determined using a training set of samples from objects who all have the disease, do not have the disease, or a combination of both. Thus, reference values ​​may be determined based on reference separation values ​​determined from samples of objects with known disease grades.

[0172] In some cases, the provided methods may further include treating the disease or condition indicated by the lesion classification, thereby improving the disease or condition (e.g., by eliminating the disease or by reducing its severity). Treatment may be selected and / or provided based on the identified disease or condition grade, the identified variant, and / or the tissue of origin. For example, the identified variant may be targeted with a specific drug or chemotherapy. Additionally or alternatively, the grade of the condition indicated by the lesion classification may be used to determine what level of aggressive treatment should be used.

[0173] Machine learning models can also be used in conjunction with the provided methods to, for example, determine the severity of a disease. Exemplary models include, but are not limited to, those using linear regression, logistic regression, neural networks (e.g., deep recurrent neural networks), Bayesian classifiers, hidden Markov models (HMMs), linear discriminant analysis (LDA), k-means clustering, density-based spatial clustering with noise (DBSCAN), decision trees (e.g., random forest algorithms), and support vector machines (SVMs). Models may include supervised learning models. Supervised learning models can include various methods and algorithms, including analytical learning, artificial neural networks, backpropagation, boosting (a common algorithm), Bayesian statistics, case-based reasoning, decision tree learning, inductive logic programming, Gaussian process regression, genetic programming, data grouping, kernel estimation, learning automata, learning classification systems, minimum information length (decision trees, decision graphs, etc.), multilinear subspace learning, simple Bayesian classifiers, maximum entropy classifiers, conditional random fields, nearest neighbor algorithm, probabilistic approximate correct learning (PAC), ripple descent rules, knowledge acquisition methods, symbolic machine learning algorithms, subsymbolic machine learning algorithms, support vector machines, minimum complexity machines (MCM), random forests, classifier ensembles, ordered classification, data preprocessing, handling imbalanced datasets, statistical relation learning, or Proaftn (a multi-criteria classification algorithm).

[0174] B. DNA proportional concentration

[0175] In some examples, the provided method for analyzing eccDNA residues in biological samples can be used to determine the proportion of certain DNAs of interest in the biological sample. The DNA of interest may be, for example, clinically relevant DNA. The DNA of interest may originate from one or more specific tissues. In some examples, the provided method can be used to determine the proportion of DNA from tumor tissue present in the biological sample. In other examples, the provided method can be used to determine the proportion of DNA from transplanted tissue present in the biological sample. Still in other examples, the provided method can be used to determine the proportion of DNA from fetal tissue present in the biological sample. Because previous methods for determining DNA proportion concentration did not take into account or analyze the presence of eccDNA residues in the biological sample, the method disclosed herein advantageously provides a new source of information that can be used to provide a more accurate assessment of DNA proportion concentration through non-invasive sampling and assay.

[0176] In some examples, determining the DNA proportion concentration involves comparing a reference value to a set of values ​​(e.g., counts or size distributions) of classified eccDNA residues from a biological sample of the object. The reference value can be determined based on the level of a set of values ​​measured in one or more other objects or the same object having one or more known DNA proportion concentrations. A reference value (e.g., a cutoff value) can be selected to optimize the specificity and / or sensitivity of the estimated DNA proportion concentration. Therefore, a reference value can be determined using a training set of samples obtained from objects with different DNA proportion concentrations. For example, a reference value can be determined based on a reference separation value determined from samples from objects with different known DNA proportion concentrations.

[0177] V. Cancer Detection Methods

[0178] In some aspects, this disclosure provides various methods for determining the cancer grade of a subject, wherein the cancer grade is determined by analyzing eccDNA residues in a biological sample from the subject as described in Section IV.A. Because previous methods for determining the cancer grade of a subject did not take into account or analyze eccDNA residues present in the biological sample, the methods disclosed herein advantageously provide a new source of information that can be used to provide a more accurate assessment of the cancer grade through non-invasive sampling and assay.

[0179] Biological samples can be obtained from subjects (e.g., subjects with or suspected of having cancer) at various time points and analyzed independently at those time points. In some examples, samples are obtained from subjects before and after cancer treatment (e.g., targeted therapy, immunotherapy, chemotherapy, surgery). In other examples, samples are obtained from subjects before and after cancer diagnosis. In further examples, samples are obtained from subjects before and after cancer progression, before and after metastasis, before and after disease severity increases, and / or before and after complications occur.

[0180] In some cases, the method further includes providing treatment appropriate to the cancer grade determined in the subject. Various treatments can be administered. Treatment may include any suitable therapy, including the use of drugs, chemotherapy, radiation therapy, immunotherapy, hormone therapy, stem cell transplantation, surgery, or other suitable cancer treatments. Cancer treatment can be, for example, targeted therapy using precise drugs tailored to specific characteristics of the disease (e.g., the specific genetic composition of the tumor). Based on the determined cancer grade, a treatment plan can be developed to reduce the risk of harm to the subject. The method may include treating the subject according to the treatment plan.

[0181] A. Results using eccDNA residue counting and size distribution

[0182] As an example, the provided method for determining cancer grade is used to analyze biological samples from subjects who are HBV carriers or hepatocellular carcinoma (HCC) patients. The analytical methods described in the preceding sections are used to detect and classify eccDNA residues in biological samples using sequence reads obtained using a linear DNA sequencing protocol. Specifically, nanopore sequencing data previously obtained from patients with different cancer grades are analyzed according to aspects of the provided method, and this analysis successfully identifies different eccDNA residue characteristics (e.g., differences in count and size distribution) associated with the different cancer grades of these subjects.

[0183] Figure 6This example shows nanopore sequencing data based on biological samples from 6 HBV carriers and 8 patients with HCC. A median of 19,500,531 (range: 6,603,199 to 33,347,334) sequencing read counts, i.e., mapped fragments, were received from the HBV subjects, and a median of 72,966,770 (range: 22,475,592 to 101,757,021) sequencing read counts were received from the HCC patients. Using the methods described in Sections II.C and III.F, based on the mapping of sequence reads, a median of 126 (range: 43 to 173) eccDNA remnants were classified in each sample from the HBV subjects. Similarly, a median of 280 (range: 95 to 568) eccDNA remnants were classified in each sample from the HCC subjects.

[0184] Similarly, Figure 6 As shown, the raw counts of eccDNA residues determined based on sequencing reads from each of 14 biological samples are normalized relative to the counts of the mappable reads used in these determinations. The resulting relative counts are... Figure 6 The report uses eccDNA residue per million mapable readings (EPM) and shows that 5 to 10 EPMs were classified (i.e., detected) in HBV carrier samples, while 2 to 7 EPMs were classified in HCC patient samples.

[0185] Figure 7 Is drawn from Figure 6 The data in the table are plotted as follows. The vertical y-axis of the plot represents the normalized count of eccDNA residues (EPM) per million mappable readings, and the data points represent the counts in different HBV carrier samples (left) and HCC patient samples (right). The plotted data show that the relative eccDNA residue count in the HCC group was significantly lower than that in the HBV carrier group (P = 0.0079, Wilcoxon rank-sum test).

[0186] Similarly, as described in Chapter III.B, the size and size distribution of eccDNA remnants for each of the 14 biological samples were determined. As part of this analysis, the size patterns of small eccDNA remnants identified in this experiment were compared with Illumina data of small eccDNA size patterns. Figure 8A and Figure 8B The charts show that the size patterns determined by these two methods are consistent with each other, and both identified two main peaks in the range of 200 bp and 350 bp.

[0187] Refer again Figure 6The determined eccDNA residue size distribution of 14 biological samples was used to calculate the percentage of eccDNA residues larger than 1 kb in each sample.

[0188] Figure 9 The calculated data are presented graphically, comparing the percentage of these large eccDNAs between HBV carriers and HCC patients. The vertical y-axis of the graph represents the percentage of eccDNA remnants longer than 1 kb, and the data points represent the percentage values ​​for different HBV carrier samples (left) and HCC patient samples (right). The plotted data shows complete separation between these groups regarding the percentage of eccDNA remnants longer than 1 kb (P = 0.0006, Wilcoxon rank-sum test).

[0189] Figure 10 The chart plots the size distribution of classified eccDNA remnants in HBV carrier biosamples and HCC patient biosamples, showing a size range from less than 400 bp to greater than 3 kb. While the majority of classified eccDNA remnants in HCC patient samples were greater than 400 bp, almost half of the classified eccDNA remnants in HBV carrier samples were less than 400 bp. More specifically, 55.9%, 26.8%, 14.4%, and 11.9% of eccDNA remnants in HBV carrier samples had sizes greater than 400 bp, 1 kb, 2 kb, and 3 kb, respectively. In contrast, 78.3%, 59.2%, 47.5%, and 43.5% of eccDNA remnants in HCC patient samples had sizes greater than 400 bp, 1 kb, 2 kb, and 3 kb, respectively.

[0190] Figure 11 This presents nanopore sequencing data from another example of using eccDNA residue size distribution to detect or classify cancer. Figure 11 The tables include data based on biological samples from three non-nasopharyngeal carcinoma (NPC) subjects showing persistently positive EBV DNA levels, as well as from nine NPC patients. eccDNA in each of these 12 samples was classified based on sequence read mapping using the methods described in Sections II.C and III.F. Additionally, the size and size distribution of eccDNA residues in each sample were determined as described in Section III.B.

[0191] Figure 12 Provided according to Figure 11The data from the table were used to create a graph comparing the frequency of large eccDNA remnants between non-NPC subjects and NPC patients. The vertical y-axis of the graph represents the percentage of eccDNA remnants longer than 1 kb, and the data points represent the percentage values ​​for different non-NPC subject samples (left) and NPC patient samples (right). Figure 12 As shown, the median proportions of eccDNA remnants longer than 1 kb were 44.0% and 52.4% for non-NPC and NPC subjects, respectively. Therefore, there was an 8% increase in NPC cases compared to non-NPC subjects. This, along with the results of the HCC example described above, and... Figures 6 to 10 Together with the results shown, these findings demonstrate that the size distribution and / or count of eccDNA residues can provide information for the detection of different cancer types, where such detection can have significant diagnostic value.

[0192] B. Genome Distribution Results

[0193] In another example, the total population of plasma eccDNA remnants identified according to chapter VA for HBV carriers and HCC patients is mapped onto various categories of genomic elements. These categories include a range of genomic features, including but not limited to 5' UTR, 3' UTR, exons, introns, 2 kb upstream of a gene (Gene2kbU), 2 kb downstream of a gene (Gene2kbD), CpG islands, 2 kb upstream of a CpG island (CGI2kbU), 2 kb downstream of a CpG island, Alu, etc. The distribution of eccDNA remnants across these categories is quantified using a metric called “normalized genome coverage,” which is calculated as the percentage of eccDNA mapped to genomic elements of that category divided by the percentage of the genome covered by that particular category.

[0194] Figure 13This is a graph comparing the normalized genome coverage between HBV carriers and HCC patients in different categories. The vertical y-axis of the graph represents the normalized genome coverage of eccDNA residues, and the horizontal x-axis lists genomic elements or regions for several different categories. For each of these different categories, the normalized genome coverage of HBV carrier samples (left) and HCC patient samples (right) is plotted. Notably, for the 3' UTR region, the normalized genome coverage of HBV carriers is 1.38, while that of HCC patients is 1.00, indicating a significant difference between the two types of eccDNA residue samples. Furthermore, for exon regions, the normalized genome coverage of HBV carriers (1.67) is also significantly higher than that of HCC patients (1.42). The opposite pattern was observed in the Gene2kbU category, where the normalized genome coverage of HBV carriers was 1.06, while that of HCC patients was 1.26. These observations show that, among the different categories where standardized genome coverage differs significantly between HCC patients and HBV carriers, higher standardized genome coverage indicates cancer detection in some categories, while lower standardized genome coverage indicates cancer detection in others.

[0195] These results suggest that analyzing eccDNA remnants to determine their normalized genomic coverage can provide useful information for cancer detection. For example, comparisons of normalized genomic coverage across different categories can yield in-depth insights into potential genomic changes between HBV carriers and HCC patients, which could help identify specific genes or regulatory mechanisms that play a role in disease progression.

[0196] C. Results using nucleotide sequence motifs

[0197] In other examples, nucleotide motifs and motif patterns located flanking the linker sites of eccDNA remnants were investigated, where the motifs and patterns indicate the locations where these fragments were cut from the genome prior to eccDNA formation. For these non-limiting examples, the DNA sequences analyzed included those within 50 base pairs upstream and downstream of the start and end positions of the eccDNA remnant linker sites.

[0198] Figure 14 This shows a segment of DNA (e.g., genomic DNA) from which the eccDNA fragment has been removed. Figure 14The diagram indicates the start and end genomic locations of the excised eccDNA. Four trinucleotide fragments identified as I, II, III, and IV are also shown, with fragments I and II located opposite each other at the start genomic location, and fragments III and IV located opposite each other at the end genomic location. Fragments I and II are separated by spacer region S1, which includes the eccDNA start genomic location. Fragments III and IV are separated by spacer region S2, which includes the eccDNA end genomic location. Figure 14 In the specific example shown, S1 and S2 are each 4 bp in length. Furthermore, the position of spacer S1 is such that the nucleotides of fragment I closest to the start position are 3 bp from the start position, and the nucleotides of fragment II closest to the start position are 2 bp from the start position. Similarly, the position of spacer S2 is such that the nucleotides of fragment IV closest to the stop position are 3 bp from the stop position, and the nucleotides of fragment III closest to the stop position are 2 bp from the stop position.

[0199] Figure 15 A pair of tables are shown, listing data from the analysis. Figure 14 The results are shown as an example of the results for fragments I, II, III, and IV at specified positions. In this analysis, the sequences of each of the four trinucleotide fragments (I, II, III, and IV) were compared between HBV carriers (left table) and HCC patients (right table). The left and right tables list the 20 most frequent combinations of these trinucleotide fragments in HBV carriers and HCC patients, respectively. For example, in HBV carriers, the combination “CTT” (fragment I), “TGC” (fragment II), “CTA” (fragment III), and “GCT” (fragment IV) occurred at a frequency of 0.4207, while in HCC patients, the frequency of this combination increased significantly to 0.8321. This significant increase in frequency indicates a significant association with HCC. Similarly, for the combination “CTT”, “TGC”, “TAT”, and “CTA”, the frequency increased from 0.1803 in HBV carriers to 0.3782 in HCC patients. This approximately two-fold overrepresentation suggests that the presence of combinations of these motif patterns could serve as a key feature in disease progression and as a biomarker for cancer diagnosis.

[0200] Figure 16 and Figure 17 Each example presents a pair of tables listing results from other examples, such as... Figure 14 and 15The motifs shown are located around the start and end positions of the eccDNA genome, but with lengths of 2 nt and 4 nt, instead of the pattern of four 3 bp sequence motifs. Specifically, this was generated by analyzing four dinucleotide fragments (I, II, III, and IV). Figure 16 The data. In this case, the nucleotide of dinucleotide fragment I closest to the start position is 3 bp upstream of the start position, and the nucleotide of dinucleotide fragment II closest to the start position is 2 bp downstream of the start position. The nucleotide of dinucleotide fragment IV closest to the stop position is 3 bp downstream of the stop position, and the nucleotide of dinucleotide fragment III closest to the stop position is 2 bp upstream of the stop position. In order to generate Figure 17 The data were analyzed from four tetranucleotide fragments (I, II, III, and IV). In this case, the nucleotide of tetranucleotide fragment I, closest to the start position, was 3 bp upstream of the start position, and the nucleotide of tetranucleotide fragment II, closest to the start position, was 2 bp downstream of the start position. The nucleotide of tetranucleotide fragment IV, closest to the stop position, was 3 bp downstream of the stop position, and the nucleotide of tetranucleotide fragment III, closest to the stop position, was 2 bp upstream of the end position.

[0201] Figure 16 and Figure 17 Data shows that combinations of dinucleotide motif patterns or tetranucleotide motif patterns can also be used to distinguish between cancer and non-cancer samples. For example, Figure 16 The results showed that the combination of "TT" (fragment I), "TG" (fragment II), "TA" (fragment III), and "GC" (fragment IV) occurred at a frequency of 0.4808 in HBV carriers, while the frequency of this combination significantly increased to 1.1094 in HCC patients. Similarly, Figure 17 The results showed that for combinations of "ACTT" (fragment I), "TGCT" (fragment II), "CCTA" (fragment III), and "GCTA" (fragment IV), the frequency increased from 0.4207 in HBV carriers to 0.8321 in HCC patients. These results indicate that the size of the analyzed motifs can vary without losing their ability to detect cancer.

[0202] Figure 18 This shows a pair of tables listing the results from an example that analyzes data located at... Figure 16 Patterns of dinucleotide fragments (I, II, III, and IV) at different positions were analyzed. This was achieved by analyzing the formation of patterns from dinucleotide fragments I, II, III, and IV. Figure 18The data shows that the nucleotide closest to the start position of dinucleotide fragment I is 4 bp upstream of the start position, the nucleotide closest to the start position of dinucleotide fragment II is 3 bp downstream of the start position, the nucleotide closest to the end position of dinucleotide fragment III is 3 bp upstream of the end position, and the nucleotide closest to the end position of dinucleotide fragment IV is 4 bp downstream of the end position. Figure 18 The data shows that the position of the motifs in the analysis can change without losing the ability to detect cancer. For example, Figure 18 The combination of “CT” (fragment I), “GC” (fragment II), “CT” (fragment III) and “CT” (fragment IV) occurred at a frequency of 0.4207 in HBV carriers, while the frequency of this combination increased to 0.8321 in HCC patients.

[0203] Figures 19 to 25 A table is provided listing the results from additional examples, each analyzing patterns composed of fewer than four motif fragments. Although the trinucleotides analyzed in these additional examples... Figure 19 , Figure 21 and Figure 24 ), tetranucleotide ( Figure 20 , Figure 22 and Figure 25 ) and dinucleotides ( Figure 23 The motif fragments are respectively related to those previously found in Figure 15 , Figure 17 and Figure 16 The motif segments described previously are the same, but the analysis of these motif segments is considered to be a three-motif pattern. Figure 19 and Figure 20 ), two motif patterns ( Figures 21 to 23 ) and single motif fragments ( Figure 24 and Figure 25 ).exist Figure 19 In the study, data analysis of the patterns of three trinucleotide fragments (I, II, and III; I, II, and IV; I, III, and IV; and II, III, and IV) was sufficient to identify at least a 4-fold difference in frequency between HBV and HCC samples. Similarly, Figure 20 Data indicates that analyzing the patterns of three tetranucleotide fragments can also identify at least a 4-fold difference between HBV and HCC. Results from other examples confirm the effectiveness of using trinucleotides ( Figure 21 ), tetranucleotide ( Figure 22 ) or dinucleotides ( Figure 23 The patterns of two motif fragments can detect at least a 3-fold difference between HBV and HCC samples. Furthermore, even using only a single motif fragment according to the provided method, it is possible to distinguish between cancer and non-cancer samples. Figure 24and Figure 25 ).

[0204] Figure 26 and Figure 27 Data from another example is provided to confirm that analysis of a single motif fragment near the eccDNA remnant linker site can detect cancer status in a biological sample or object. In this example, the single motif fragment analyzed is not initially identified as part of a larger pattern of multiple fragments, such as... Figures 15 to 25 The same as in the previous example. Instead, the fragments analyzed in this example include those located at the starting position of eccDNA remnants in the genome (…). Figure 26 ) or termination position ( Figure 27 A single dinucleotide, trinucleotide, or tetranucleotide motif within a specified distance (e.g., 50 bp) upstream or downstream. Figure 26 and Figure 27 The data presented in the table indicate that significant frequency differences of at least 10-fold can be observed using this provided method.

[0205] D. Results using methylation density

[0206] In other examples, the methylation density of eccDNA fragments across different size ranges was investigated. Nanopore sequencing enables direct, real-time sequencing and detection of DNA base modifications, including but not limited to 5mC, 5hmC, 6mA, and / or 4mC, without the need for additional chemical transformations or experimental preparation, distinguishing nanopore sequencing from methods such as bisulfite sequencing. Leveraging nanopore sequencing data allows for the characterization of the methylation profiles of eccDNA remnants identified from the plasma of HBV carriers and HCC patients. These eccDNA remnants (MD... 残余物 The total methylated CpG density was calculated using the following equation:

[0207] Where M represents the count of methylated CpG sites in all eccDNA residues, and U represents the count of unmethylated CpG sites in the same plasma sample.

[0208] Figure 31 This chart compares the methylation density values ​​of linear DNA and eccDNA residues. The vertical y-axis of the chart represents the percentage of methylation density, and the data points represent the percentage values ​​of linear DNA (left) and eccDNA residues (right) identified in biological samples from HBV carriers. Figure 31As shown, the median methylation density of eccDNA residues differed significantly from that of linear DNA (Wilkerson rank-sum test, P = 0.00008). This difference indicates that the methylation of eccDNA is not entirely similar to that of conventional linear free DNA. Therefore, the observed behavior of linear DNA methylation (e.g., the difference between linear DNA methylation from disease and non-disease samples) differs from the behavior of eccDNA residue methylation.

[0209] Figure 28 A graph showing calculated methylation density values ​​for eccDNA remnants containing at least one CpG site is presented. The vertical y-axis of the graph represents the methylation density values ​​for eccDNA remnants longer than 1 kb, and the horizontal x-axis lists several different eccDNA remnant size ranges. For each of these different size ranges, the methylation density of eccDNA remnants from an HBV carrier sample (left) and an HCC patient sample (right) is plotted. Figure 28 As shown, the methylation density of eccDNA remnants from HBV carriers and HCC patients generally decreases in the 150 bp to 1000 bp range. The median methylation densities of eccDNA remnants from HBV carriers with sizes ranging from 150 bp to 250 bp, 250 bp to 450 bp, and 450 bp to 1000 bp were 70.8%, 60.0%, and 62.4%, respectively, while the median methylation densities of eccDNA remnants from HCC patients with the same size range were 90.0%, 63.8%, and 57.9%, respectively. Conversely, once the length exceeds 1000 bp, the methylation density of eccDNA remnants increases with increasing length. For eccDNA remnants ranging in size from 1000 bp to 2000 bp and > 2000 bp from HBV carriers, the median methylation densities were 70.2% and 70.1%, respectively, compared to 66.8% and 70.7% for eccDNA remnants from HCC patients. This observed pattern suggests variation in the original fragment length of the eccDNA remnants.

[0210] Figure 28The data further showed that for smaller eccDNA fragments, such as those in the 150–250 bp and 250–450 bp size ranges, the methylation density values ​​of eccDNA fragments from HCC patient samples were higher than those from HBV carrier samples. Furthermore, for larger eccDNA fragments, such as those in the 1000–2000 bp size range, the methylation density values ​​of eccDNA fragments from HCC patient samples were higher than those from HBV carrier samples. These findings suggest the existence of one or more cutoff or threshold sizes associated with differences in eccDNA methylation in cancer-related states. For example, the results indicate an upper cutoff value below which eccDNA remnants from cancer samples have a higher methylation density than eccDNA remnants from non-cancer samples. The results also indicate a lower cutoff value above which eccDNA remnants from cancer samples have a lower methylation density than eccDNA remnants from non-cancer samples.

[0211] Figure 29 A pair of graphs are presented showing the area under the curve (AUC) values ​​related to different sizes of eccDNA remnants used to distinguish between HCC patient samples and HBV carrier samples using methylation density values. The vertical y-axis of the graphs represents the AUC value used to distinguish the two sample types, and the horizontal x-axis lists several different eccDNA remnant size ranges based on either an upper cutoff value (top graph) or a lower cutoff value (bottom graph). The data in the top graph shows that HCC can be detected using methylation density values ​​of eccDNA remnants smaller than 600 bp, smaller than 800 bp, or smaller than 1000 bp. This data further indicates that, in terms of the upper size limit, the most accurate cancer detection based on eccDNA remnant methylation density is achieved using eccDNA remnants with a size upper limit of approximately 600 bp to approximately 1000 bp (e.g., an upper size limit of approximately 800 bp). The data in the bottom graph shows that HCC can be detected using methylation density values ​​of eccDNA remnants larger than 800 bp, larger than 1000 bp, or larger than 2000 bp. This data further demonstrates that, in terms of size lower limits, the most accurate cancer detection based on the methylation density of eccDNA residues is achieved using eccDNA residues with a size lower limit of approximately 800 bp to approximately 2000 bp (e.g., a size lower limit of approximately 1000 bp).

[0212] Figure 30A graph showing an example of applying a lower eccDNA residue size limit to detect cancer using methylation density values. The vertical y-axis of the graph represents the percentage of methylation density for eccDNA residues longer than 1 kb, and the data points represent percentage values ​​for different HCC patient samples (left) and HBV carrier samples (right). More specifically, this example involves analyzing 5 HCC samples with at least 20% tumor DNA and 14 samples without HCC. Figure 30 As shown, the median methylation density of eccDNA remnants larger than 1 kb differed between non-HCC (71.3%) and HCC (65.8%) samples. This difference suggests that large eccDNA remnants exhibit low methylation in HCC cases and may therefore provide a distinguishing feature for cancer detection, such as the identification and differentiation of HCC samples from non-HCC samples.

[0213] Therefore, these results together demonstrate the ability of the presented method to detect and classify eccDNA remnants in biological samples using linear sequencing data. These results further demonstrate how the analysis of the aggregate characteristics of these detected eccDNA remnants, such as their count, size distribution, genomic distribution, nucleotide motif patterns, and / or methylation patterns, can be used to classify lesions and determine the cancer grade of an object.

[0214] E. Method

[0215] Figure 32 A flowchart is shown for a method 3200 for analyzing a biological sample from an object to determine the object's cancer grade based on the analysis of eccDNA residues according to an embodiment of this disclosure. Various examples of the operation of method 3200 are described in sections II, III, and O. Method 3200 can be performed partially or entirely using a computer system.

[0216] At box 3210, one or more sequence reads are obtained, wherein the one or more sequence reads include the 5' end sequence and the 3' end sequence of a linear DNA molecule in the biological sample. Box 3210 can be used in conjunction with... Figure 4 Block 410 of method 400 shown Figure 5The method shown in box 510 of 500 is performed in a similar manner. As with boxes 410 and 510, in some examples, obtaining sequence reads involves receiving a biological sample. The biological sample can be obtained from a subject undergoing cancer screening. The biological sample may include cell-free DNA. For example, the biological sample may be a purified sample, for example, to isolate a majority of cell-free portions (e.g., plasma). Other pretreatment steps may also be performed on the biological sample. In some examples, obtaining sequencing reads involves sequencing the linear DNA molecules present in the sample. Sequencing may be random sequencing of all linear DNA molecules in the biological sample, rather than targeted sequencing of specific molecules, and may be performed using any sequencing technology described in Section II.A. In some cases, the biological sample is a cell-free biological sample and / or the linear DNA molecules are free linear DNA molecules. For example, the biological sample may include plasma or be composed of plasma. The linear DNA molecules of the biological sample are naturally present in the biological sample in a linear form, and the provided method does not include operations designed to linearize circular DNA molecules in the biological sample, such as enzymatic cutting or mechanical shearing. In some cases, an obtained sequence read includes the 5' end sequence and the 3' end sequence of the linear DNA molecule. In other cases, one obtained sequence readout includes the 5' end sequence of the linear DNA molecule, and another obtained sequence readout includes the 3' end sequence of the linear DNA molecule. The obtained sequence readouts may each independently have a length of at least 25 bp, at least 45 bp, at least 75 bp, at least 150 bp, at least 250 bp, at least 500 bp, at least 1 kb, at least 3 kb, at least 10 kb, at least 30 kb, or at least 100 kb.

[0217] At box 3220, the 5' end sequence and the 3' end sequence of the linear DNA molecule obtained in box 3210 are mapped to a reference genome. Box 3220 can be performed in a manner similar to box 420 of method 400 and box 520 of method 500. As with boxes 420 and 520, the mapping can be performed as described in section II.A and can involve independently determining the optimal alignment of each of the 5' and 3' end sequences to the reference genome. In some cases, the alignments need to independently meet mapping quality conditions, for example, having a mapping quality of at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, or at least 50. In some cases, the mapping of the end sequences includes determining the genomic coordinates of each end sequence. Additionally or alternatively, the mapping of the end sequences may include determining the genomic orientation of each end sequence. In some examples, the provided method also includes additional mapping of one or more sequences of a linear DNA molecule, in addition to the 5' and 3' end sequences, and determining the genomic coordinates and / or genomic orientation of these additionally mapped sequences. In some cases, the mapping further includes identifying whether the sequence readout includes a linker locus, and optionally determining the genomic coordinates of that linker locus.

[0218] At box 3230, based on the mapping in box 3220, the linear DNA molecule is classified according to whether it was cut from the circular DNA molecule in vivo (i.e., whether the linear DNA molecule is an eccDNA remnant). Box 3230 can be performed in a manner similar to box 430 of method 400 and box 530 of method 500. As with boxes 430 and 530, the classification of the linear DNA molecule can be performed as described in section II.B. For example, classification may include comparing the genomic coordinates of the 5' end sequence of the linear DNA molecule with the genomic coordinates of the 3' end sequence of the linear DNA molecule. Additionally or alternatively, classification may include comparing the genomic orientation of the 5' end sequence with the genomic orientation of the 3' end sequence. As an example, if (1) the genomic coordinates of the 5' end sequence are greater than the genomic coordinates of the 3' end sequence, and (2) the genomic orientation of the 5' end sequence is the same as the genomic orientation of the 3' end sequence, then the linear DNA molecule can be classified as an eccDNA remnant. In some cases, classification may include a comparison of the genomic coordinates and / or genomic orientation of the mapped sequences other than the 5' and 3' end sequences, or in addition to the 5' and 3' end sequences, classification may also include a comparison of the genomic coordinates and / or genomic orientation of the mapped sequences.

[0219] In box 3240, members of all linear DNA molecules classified as eccDNA remnants in box 3230 are analyzed to determine the cancer grade of the object from which the biological sample was obtained. In some cases, all members of the classified group of eccDNA remnants are analyzed. In other cases, a portion of the classified group of eccDNA remnants is analyzed. In some examples, the analysis of eccDNA remnants can be used to determine the ensemble value of the group of eccDNA remnants. One example of such an ensemble property is the count of eccDNA remnants within the group, such as an absolute count or a relative or normalized count. Another example of such an ensemble property is the size distribution of eccDNA remnants within the group. Another example of such an ensemble property is the genomic distribution of eccDNA remnants within the group. Another example of such an ensemble property is the frequency of one or more nucleotide motif patterns appearing in eccDNA remnants within the group. Another example of such an ensemble property is the methylation state of eccDNA remnants within the group that have a size within a specified size range.

[0220] Set values ​​can be used to determine the cancer grade of an object. In some cases, the cancer grade of the object is determined using the count of the set of eccDNA remnants, for example, by comparing the count to a reference value and determining whether the count is greater than, less than, or equal to the reference value. In other cases, the cancer grade of the object is determined using the size distribution of the set of eccDNA remnants, for example, by determining the percentage of the set of eccDNA remnants that exceeds a size threshold (e.g., a predetermined size threshold), and then optionally determining whether that percentage is greater than, less than, or equal to a reference percentage value. Still in other cases, determining the cancer grade of an object includes using a normalized genomic coverage of the eccDNA remnants, for example, where the normalized genomic coverage is calculated by counting a subset of eccDNA fragments mapped to a region within a class of genomic elements and dividing that count by a percentage of the genome in the region. In some cases, determining the cancer grade of an object includes using the frequency of one or more nucleotide motif patterns appearing for the set of eccDNA remnants, for example, where at least one of the nucleotide motif patterns includes multiple nucleotide motifs, such as trinucleotide motifs, dinucleotide motifs, or tetranucleotide motifs. In other cases, determining the cancer grade of an object involves using the methylation status of eccDNA residues that are within a specified size range, such as smaller than the maximum size (e.g., 1000 bp) or larger than the minimum size (e.g., 800 bp).

[0221] VI. Methods for determining the proportional concentration of DNA

[0222] In some aspects, this disclosure provides various methods for determining the proportional concentration of DNA of interest (e.g., clinically relevant DNA) in a biological sample from a subject, wherein the proportional concentration is determined by analyzing eccDNA residues in the biological sample as described in Section IV.A. The DNA of interest may be, for example, DNA specifically derived from tumor tissue, transplanted tissue, or fetal tissue. Because previous methods for determining DNA proportional concentration did not take into account or analyze eccDNA residues present in the biological sample, the methods disclosed herein advantageously provide a new source of information that can be used to provide a more accurate assessment of DNA proportional concentration through non-invasive sampling and assay.

[0223] As an example, cell-free DNA (cfDNA) extracted from the plasma of nine HCC patients was used for nanopore sequencing. Analysis of the resulting sequence reads after alignment with a reference genome revealed a median of 52,774,950 mapped fragments, ranging from 22,475,592 to 147,920,695. Subsequently, the tumor proportion in the nine HCC patients was estimated using ichorCNA (Adalsteinsson et al. Nat Commun. 2017;8:1324), finding a range of 7.95% to 47.5%. The eccDNA remnants in each sample from HCC subjects were then classified based on the mapping of sequence reads using the methods described in Sections II.C and III.F. The size and size distribution of the eccDNA remnants in each biosample were determined, as described in Section III.B, and the determined eccDNA remnant size distribution for each biosample was used to calculate the percentage of eccDNA remnants larger than 1 kb in each sample.

[0224] Figure 33 A graph comparing the percentage of these large (i.e., > 1 kb) eccDNA fragments in each sample to the tumor proportion of the sample is presented. The vertical y-axis of the graph represents the percentage of eccDNA remnants longer than 1 kb, and the horizontal x-axis represents the tumor proportion, with data points plotted for each of the nine HCC samples. Notably, a positive correlation was observed between the percentage of eccDNA remnants longer than 1 kb and the tumor proportion (Pearson r = 0.76; p = 0.017). These findings suggest that eccDNA remnants of a specific size (e.g., > 1 kb) may reflect the proportionate concentration of clinically relevant DNA.

[0225] A. Use of calibration data points

[0226] Figure 36This is a flowchart of method 300, illustrating a method for estimating the proportional concentration of clinically relevant DNA in a biological sample according to an embodiment of the invention. The biological sample includes clinically relevant DNA and other DNA. The biological sample can be obtained from a patient (e.g., a female subject pregnant with a fetus). In another embodiment, the patient may have or be suspected of having a tumor. In one embodiment, the biological sample can be received at a machine (e.g., a sequencing machine) that outputs measurement data (e.g., sequence reads) that can be used to determine the size of DNA fragments. Method 300 can be performed entirely or partially using a computer system, as with other methods described herein.

[0227] At box 310, the amount of eccDNA residue corresponding to various characteristic values ​​(e.g., size, number of residues, methylation density, or number of motifs) is measured. For each of the multiple sizes, the amount of multiple DNA fragments from the biological sample corresponding to that size can be measured. For example, the number of DNA fragments with lengths greater than 100 bp, greater than 300 bp, greater than 1 kb, greater than 3 kb, greater than 10 kb, greater than 30 kb, and / or greater than 100 kb can be measured. These quantities can be stored as a histogram. In one embodiment, the size of each of multiple nucleic acids from the biological sample is measured, which can be done on an individual basis (e.g., by single-molecule sequencing) or on a population basis (e.g., by electrophoresis). The size can correspond to a range. Therefore, the quantity can be the amount of DNA fragments with sizes within a specific range.

[0228] Multiple eccDNA remnants can be randomly or preferentially selected from one or more predetermined regions of the genome. For example, targeted enrichment can be performed, as described above. In another embodiment, the eccDNA remnants can be randomly sequenced (e.g., using universal sequencing), and the resulting sequence reads can be aligned to the genome corresponding to the target (e.g., a reference human genome). The eccDNA remnants whose sequences align to one or more predetermined regions can then be used to determine their size.

[0229] In various implementations, size can be mass, length, or other suitable size measure. Measurements can be performed in various ways as described herein. For example, paired-end sequencing and alignment of the eccDNA residues can be performed, or electrophoresis can be used. Statistically significant numbers of eccDNA residues can be measured to provide an accurate size pattern of the biological sample. Examples of statistically significant numbers of eccDNA residues include greater than 100,000; 1,000,000; 2,000,000, or other suitable values, depending on the required precision.

[0230] In one implementation, data obtained from physical measurements (e.g., paired-end sequencing or electrophoresis) can be received and analyzed on a computer to complete the measurement of the size of the eccDNA residue. For example, sequence reads from paired-end sequencing can be analyzed (e.g., by alignment) to determine the size. As another example, the size can be determined by analyzing the electrophoretic pattern produced by electrophoresis. In one implementation, the analysis of the eccDNA residue does indeed include the actual process of sequencing or electrophoresis of the eccDNA residue, while other implementations only analyze the resulting data.

[0231] At box 320, a first value for the first parameter is calculated based on the amount of eccDNA residues of various sizes. In one respect, the first parameter provides a statistical measure (e.g., a histogram) of the size pattern of DNA fragments in a biological sample. If the parameter is determined based on the size of multiple eccDNA residues, this parameter can be referred to as the size parameter.

[0232] The first parameter can take various forms. Such a parameter is the number of eccDNA residues with a specific characteristic value divided by the total number of fragments, which can be obtained from a histogram (any data structure that provides an absolute or relative count of fragments with a specific characteristic value). As another example, the parameter can be the number of fragments with a specific characteristic value or within a specific range divided by the number of fragments with another characteristic value or range. This division can serve as a standardization to interpret the different numbers of eccDNA residues analyzed in different samples. Standardization can be achieved by analyzing the same number of eccDNA residues in each sample, which effectively provides the same result as dividing by the total number of fragments analyzed. Other examples of parameters are described in this article.

[0233] At box 330, one or more first calibration data points are obtained. Each first calibration data point may indicate a proportional concentration of clinically relevant DNA corresponding to a specific value (calibration value) of a first parameter. The proportional concentration may be specified as a specific concentration or a concentration range. The calibration value may correspond to a value of the first parameter (e.g., a specific size parameter) determined based on multiple calibration samples. Calibration data points may be determined based on calibration samples with known proportional concentrations, which can be measured using various techniques described herein. At least some calibration samples will have different proportional concentrations, but some calibration samples may have the same proportional concentration.

[0234] In various implementations, one or more calibration points can be defined as a discrete point, a set of discrete points, as a function, as both a discrete point and a function, or any other combination of discrete or continuous sets of values. As an example, calibration data points can be determined based on a calibration value for a size parameter (e.g., the number of segments within a specific size or size range) of a sample with a specific proportional concentration. Multiple histograms can be used, each with a different histogram for each calibration sample, and some of these calibration samples may have the same proportional concentration.

[0235] In one implementation, measurements of the same parameter from multiple samples at the same proportional concentration can be combined to determine calibration data points for a specific proportional concentration. For example, the average value of the parameter can be obtained from sample data at the same proportional concentration to determine a specific calibration data point (or to provide a range corresponding to a calibration data point). In another implementation, multiple data points with the same calibration value can be used to determine an average proportional concentration.

[0236] In one implementation, characteristic values ​​of eccDNA residues are measured for multiple calibration samples. A calibration value for the same parameter is determined for each calibration sample, where the parameter can be plotted based on the known proportional concentration of the sample. A function can then be fitted to the data points of the plot, where the function fit defines the calibration data points used to determine the proportional concentration of new samples.

[0237] At box 340, the first value is compared with the calibration value of at least one calibration data point. The comparison can be made in various ways. For example, the comparison could be whether the first value is higher or lower than the calibration value. The comparison could involve a comparison with a calibration curve (composed of calibration data points), and thus the comparison could identify points on the curve with the first value of the first parameter. For example, the calculated value X of the first parameter (determined by the measured characteristic value of DNA in the new sample) can be used as input to a function F(X), where F is the calibration function (curve). The output of F(X) is a proportional concentration. An error range can be provided, which can be different for each X value, thus providing a range of values ​​as the output of F(X).

[0238] In step 350, the proportional concentration of clinically relevant DNA in the biological sample is estimated based on a comparison. In one embodiment, it can be determined whether a first value of the first parameter is higher or lower than a cutoff calibration value, and thus whether the estimated proportional concentration of the immediate sample is higher or lower than the proportional concentration corresponding to the cutoff calibration value. For example, if the calculated first value X1 of the biological sample is higher than the calibration value X... C Then the proportion concentration FC1 of the biological sample can be determined to be higher than that corresponding to X. C proportional concentration FC CThis comparison can be used to determine if a sufficient proportion of a biological sample is present for further testing, such as testing for fetal aneuploidy. The relationship between "above" and "below" can depend on how the parameters are defined. In such implementations, only a single calibration data point may be required.

[0239] In another implementation, the comparison is performed by inputting a first value into a calibration function. The calibration function can effectively compare the first value with a calibration value by identifying points on the curve corresponding to the first value. The estimated proportional concentration is then provided as the output value of the calibration function.

[0240] In one implementation, values ​​for more than one parameter of a biological sample can be determined. For example, a second value for a second parameter can be determined, corresponding to a different statistical measure of the size pattern of eccDNA residues in the biological sample. The second value can be determined using measurements of the same or different characteristic values ​​of DNA fragments. Each parameter can correspond to a different calibration curve. In one implementation, different values ​​can be independently compared with different calibration curves to obtain multiple estimated proportion concentrations, which can then be averaged or used to provide a range as output.

[0241] In another embodiment, a multidimensional calibration curve can be used, where different values ​​of the parameters can be efficiently input into a single calibration function for the output proportional concentration. The single calibration function can be generated by function fitting of all data points obtained from the calibration samples. Therefore, in one embodiment, the first and second calibration data points can be points on a multidimensional curve, where the comparison includes identifying multidimensional points with coordinates corresponding to a first value and one or more second values.

[0242] B. Determine calibration data points

[0243] Figure 37 This is a flowchart of a method 1300 for determining calibration data points based on measurements performed from calibration samples, according to an embodiment of the present invention. The calibration samples include clinically relevant DNA and other DNA.

[0244] At box 1310, multiple calibration samples are received. Calibration samples can be obtained as described herein. Each sample can be analyzed individually by a separate experiment or by some identification method (e.g., barcoding eccDNA residues) to identify which sample the molecule originated from. For example, calibration samples can be received at a machine (e.g., a sequencing machine) that outputs measurement data (e.g., sequence reads) that can be used to determine the characteristics of eccDNA residues, or at an electrophoresis machine.

[0245] At box 1320, the proportional concentration of clinically relevant DNA is measured in each of the multiple calibration samples. In various embodiments for measuring fetal DNA concentration, paternal genetic sequences or fetal-specific epigenetic markers may be used. For example, paternal alleles will be missing in the pregnant woman's genome and can be detected in maternal plasma at a percentage proportional to the proportional concentration of fetal DNA. Fetal-specific epigenetic markers may include DNA sequences that display fetal or placental-specific DNA methylation patterns in maternal plasma.

[0246] At box 1330, the amount of eccDNA residue from each calibration sample is measured for various characteristics (e.g., size, number of residues, methylation density, or number of motifs). Characteristic values ​​can be measured as described herein. Characteristic values ​​can be counted, plotted, used to create histograms, or otherwise classified to obtain data on characteristic value patterns in the calibration samples.

[0247] At box 1340, a calibration value for the parameter is calculated based on the amount of DNA fragments with multiple characteristics (e.g., size). A calibration value can be calculated for each calibration sample. In one embodiment, the same parameter is used for each calibration value. However, embodiments may use multiple parameters as described herein. For example, the cumulative proportion of eccDNA residues less than 1000 bases can be used as a parameter, and samples with different proportions may have different calibration values. Calibration data points can be determined for each sample, where each calibration data point includes the calibration value and the measured proportion concentration of the sample. These calibration data points can be used in method 300 or can be used to determine the final calibration data points (e.g., defined by function fitting).

[0248] At box 1350, a function is defined that approximates the calibration value across multiple proportional concentrations. For example, a linear function can be fitted to the calibration value as a function of the proportional concentrations. The linear function can be defined for the calibration data points used in method 300.

[0249] In some implementations, calibration values ​​for multiple parameters can be calculated for each sample. The calibration values ​​for a sample can define multidimensional coordinates (where each dimension corresponds to each parameter), which, along with the proportionate concentration, provide data points. Therefore, in one implementation, a multidimensional function can be fitted to all multidimensional data points. Thus, a multidimensional calibration curve can be used, where different values ​​of the parameters can be efficiently input into a single calibration function that outputs the proportionate concentration. And the single calibration function can be generated by fitting a function to all data points obtained from the calibration sample.

[0250] VII. Treatment

[0251] A. Further screening methods

[0252] Based on any classification, such as the proportion of lesion or clinically relevant DNA concentrations, individuals may be referred to for additional screening methods, such as chest X-ray, ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography. Such screening can be performed for cancer.

[0253] B. Treatment Options

[0254] The embodiments of this disclosure can accurately predict disease recurrence, thereby facilitating early intervention and selection of appropriate treatments to improve disease outcomes and overall survival in subjects. For example, enhanced chemotherapy can be selected for subjects whose corresponding samples predict disease recurrence. In another example, biological samples from subjects who have completed initial treatment can be sequenced to identify viral DNA that predicts disease recurrence. In such examples, alternative treatment regimens (e.g., higher doses) and / or different treatments can be selected for subjects because their cancer may have developed resistance to the initial treatment.

[0255] The implementation plan may also include treating the subject in response to a classification indicating lesion recurrence. For example, if the prediction corresponds to a localized lesion, surgery may be selected as a possible treatment. In another example, if the prediction corresponds to distant metastasis, chemotherapy may be additionally selected as a possible treatment. In some implementation plans, treatments include surgery, radiation therapy, chemotherapy, immunotherapy, targeted therapy, hormone therapy, stem cell transplantation, or precision medicine. To reduce the risk of harm to the subject and increase overall survival, a treatment plan may be developed based on the determined recurrence classification. The implementation plan may further include treating the subject according to the treatment plan.

[0256] C. Treatment type

[0257] The implementation plan may further include treating the patient's lesion after the classification of the subject has been determined. Treatment may be provided based on the determined lesion grade, the proportionate concentration of clinically relevant DNA, or the tissue of origin. For example, a specific drug or chemotherapy may be used to target the mutation. The tissue of origin may be used to guide surgery or any other form of treatment. Furthermore, the lesion grade can be used to determine the degree of invasiveness when using any type of treatment, and the degree of invasiveness may also be determined based on the lesion grade. Lesions (e.g., cancer) may be treated with chemotherapy, drugs, diet, therapy, and / or surgery. In some implementation plans, the more a parameter (e.g., amount or size) deviates from a reference value, the more aggressive the treatment.

[0258] Treatment may include resection. For bladder cancer, treatment may include transurethral bladder tumor resection (TURBT). This procedure is used for diagnosis, classification, and treatment. During TURBT, a surgeon inserts a cystoscope into the bladder through the urethra. The tumor is then removed using instruments with small wire loops, lasers, or high-energy electricity. For patients with non-muscle-invasive bladder cancer (NMIBC), TURBT can be used to treat or eliminate the cancer. Another treatment may include radical cystectomy and lymph node dissection. Radical cystectomy is the removal of the entire bladder and possibly surrounding tissues and organs. Treatment may also include urinary diversion. Urinary diversion is when the bladder is removed as part of treatment, and the physician creates a new pathway for urine to drain out of the body.

[0259] Treatment may include chemotherapy, which uses drugs to destroy cancer cells, typically by preventing them from growing and dividing. Drugs may involve, for example (but not limited to), mitomycin-C (available as a general medication), gemcitabine (Gemzar), and thiotepa (Tepadina) for intrabladder chemotherapy. Systemic chemotherapy may involve, for example (but not limited to), cisplatin, gemcitabine, methotrexate (Rheumatrex, Trexall), vinblastine (Velban), doxorubicin, and cisplatin.

[0260] In some implementations, treatment may include immunotherapy. Immunotherapy may include immune checkpoint inhibitors that block a protein called PD-1. Inhibitors may include (but are not limited to) atezolizumab (Tecentriq), nivolumab (Opdivo), avelumab (Bavencio), durvalumab (Imfinzi), and pembrolizumab (Keytruda).

[0261] Treatment implementation plans can also include targeted therapy. Targeted therapy is a treatment that targets cancer-specific genes and / or proteins that contribute to cancer growth and survival. For example, erdafitinib is an orally administered drug approved for the treatment of people with locally advanced or metastatic urothelial carcinoma with mutations in the FGFR3 or FGFR2 gene, which has cancer cells that are continuously growing or spreading.

[0262] Some treatments may include radiation therapy. Radiation therapy is a method of destroying cancer cells using high-energy X-rays or other particles. In addition to each individual treatment, combinations of the treatments described herein may also be used. In some implementations, combinations of treatments may be used when the value of a parameter exceeds a threshold that exceeds a reference value. Information regarding treatments in the references is incorporated herein by reference.

[0263] VIII. System

[0264] On the other hand, this disclosure provides various systems, such as measurement systems and / or computer systems, for performing the methods described herein or the individual or combined operations of these methods.

[0265] Figure 34 A measurement system 3400 according to one embodiment of the present disclosure is shown. The system includes a sample 3405, such as a free DNA molecule, within an analytical device 3410, through which an analysis 3408 can be performed. For example, the sample 3405 can be contacted with reagents of the analysis 3408 to provide a signal of physical characteristic 3415 (e.g., the sequence of a linear DNA molecule). An example of the analytical device may be a flow channel comprising probes and / or primers or droplets through which the analysis moves (where the droplets comprise the analysis). The physical characteristic 3415 of the sample (e.g., fluorescence intensity, voltage, or current) is detected by a detector 3420. The detector 3420 can perform measurements at time intervals (e.g., periodic time intervals) to obtain data points constituting a data signal. In one embodiment, an analog-to-digital converter converts the analog signal from the detector into digital form at multiple times. The analytical device 3410 and the detector 3420 may form an analytical system, such as a sequencing device for sequencing linear DNA molecules from a biological sample according to the embodiments described herein. Data signal 3425 is transmitted from detector 3420 to logic system 3430. As an example, data signal 3425 can be used to determine sequence information. Data signal 3425 may include various measurements taken simultaneously, such as different colors or different electrical signals of fluorescent dyes applied to different molecules of sample 3405, and therefore data signal 3425 may correspond to multiple signals. Data signal 3425 may be stored in local memory 3435, external memory 3440, or storage device 3445.

[0266] The logic system 3430 may be a computer system, ASIC, microprocessor, graphics processing unit (GPU), etc., or may include a computer system, ASIC, microprocessor, graphics processing unit (GPU), etc. It may also include a display (e.g., a monitor, LED display, etc.) and user input devices (e.g., a mouse, keyboard, buttons, etc.), or be connected to a display and user input devices. The logic system 3430 and other components may be part of a stand-alone or network-connected computer system, or may be directly connected to or incorporated into a device including detector 3420 and / or analysis device 3410 (e.g., a sequencing device). The logic system 3430 may also include software executing in processor 3450. The logic system 3430 may include a computer-readable medium storing instructions for controlling the measurement system 3400 to perform any of the methods described herein. For example, the logic system 3430 may provide commands to a system including analysis device 3410 to enable sequencing operations to be performed. Such physical operations may be performed in a specific order, such as adding and removing reagents in a specific order. Such physical operations can be performed using robotic systems (e.g., robotic arms) that can be used to obtain samples and perform analysis.

[0267] The measurement system 3400 may also include a reporting device 3455, which can display the results of any of the methods described herein (e.g., as determined using the measurement system). The reporting device 3455 can communicate with a reporting module within a logic system 3430, which can merge, organize, and send reports to the reporting device 3455. The reporting device 3455 can display information indicating the characteristics of molecules, for example, classified as eccDNA residues in sample 3405, wherein the characteristics can advantageously provide information relating to the biological sample or the object from which the sample originates. The reporting module can display information from any one or more of the detection and / or determination steps in methods 400, 500, and / or 1100, as described in sections II.C, III.C, and VB. The information can be displayed by the reporting device 3455 in any format that can be recognized and understood by a user of the measurement system 3400. For example, the information can be displayed by the reporting device 3455 in a format that can be displayed, printed, or transmitted, or any combination thereof.

[0268] The measurement system 3400 may also include a treatment device 3460 capable of providing treatment to an object. The treatment device 3460 may determine treatment and / or be used to perform treatment. Examples of such treatments may include surgery, radiation therapy, chemotherapy, immunotherapy, targeted therapy, hormone therapy, and stem cell transplantation. A logic system 3430 may be connected to the treatment device 3460, for example, to provide results of the methods described herein. The treatment device may receive input from, for example, imaging devices and other devices (e.g., for controlling treatment, such as controlling a robotic system).

[0269] Any computer system mentioned in this article can utilize any suitable number of subsystems. Examples of such subsystems are shown in [the document / examples]. Figure 35 In computer system 10. In some embodiments, the computer system includes a single computer device, wherein a subsystem may be a component of the computer device. In other embodiments, the computer system may include multiple computer devices having internal components, each of which is a subsystem. The computer system may include desktop and laptop computers, tablet computers, mobile phones, and other mobile devices.

[0270] Shown Figure 35 The subsystems in the display are interconnected via system bus 75. Additional subsystems, such as printer 74, keyboard 78, one or more storage devices 79, monitor 76 (e.g., display screen, such as LED) connected to display adapter 82, and other devices, are displayed. Peripheral devices and I / O devices connected to input / output (I / O) controller 71 can be connected to the computer system via a variety of devices known in the art, such as input / output (I / O) port 77 (e.g., USB, FireWire®). For example, I / O port 77 or external interface 81 (e.g., Ethernet, Wi-Fi, etc.) can be used to connect computer system 10 to a wide area network (e.g., the Internet), a mouse input device, or a scanner. The interconnection via system bus 75 allows central processing unit 73 to exchange information with each subsystem and control the execution of multiple instructions from system memory 72 or storage device 79 (e.g., fixed disk, such as hard disk drive, or optical disk), as well as information exchange between subsystems. System memory 72 and / or storage device 79 may include computer-readable media. Another subsystem is the data collection device 85, such as a camera, microphone, accelerometer, etc. Any data mentioned herein can be output from one component to another and can also be output to the user.

[0271] A computer system may include multiple identical components or subsystems, connected together, for example, via an external interface 81, an internal interface, or via a removable storage device that can be connected to and moved from one component to another. In some embodiments, the computer system, subsystem, or device may communicate via a network. In such cases, one computer may be considered a client and another a server, where each computer may be part of the same computer system. Clients and servers may each include multiple systems, subsystems, or components. In various embodiments, the method may involve a variety of numbers of clients and / or servers, including at least 10, 20, 50, 100, 200, 500, 1,000, or 10,000 devices. The method may include a variety of numbers of communications between devices, including at least 100, 200, 500, 1,000, 10,000, 50,000, 100,000, 500,000, or one million communications. Such communications can involve at least 1 MB, 10 MB, 100 MB, 1 GB, 10 GB, or 100 GB of data.

[0272] Aspects of the implementation may be implemented using hardware circuitry (e.g., application-specific integrated circuits or field-programmable gate arrays) and / or using computer software stored in a modular or integrated manner in memory with a general-purpose programmable processor in the form of logical control, and thus the processor may include memory storing software instructions for configuring the hardware circuitry and an FPGA or ASIC having configuration instructions. As used herein, the processor may include a single-core processor, a multi-core processor on the same integrated chip, or single board or network hardware, and multiple processing units on dedicated hardware. Based on this disclosure and the teachings provided herein, those skilled in the art will appreciate other ways and / or methods of implementing embodiments of this disclosure using hardware and combinations of hardware and software.

[0273] Any software component or function described in this application may be implemented in the form of software code using, for example, conventional or object-oriented techniques. This software code is executed by a processor using any suitable computer language (e.g., Java, C, C++, C#, Objective-C, Swift) or scripting language (e.g., Perl or Python). The software code may be stored as a series of instructions or commands on a computer-readable medium for storage and / or transmission. Suitable non-transitory computer-readable media may include random access memory (RAM), read-only memory (ROM), magnetic media (e.g., hard disk or floppy disk) or optical media (e.g., closed-disk CD, digital optical disc (DVD), or Blu-ray disc), flash memory, and similar devices. The computer-readable medium may be any combination of such devices. Additionally, the order of operations may be reconfigured. A process may terminate upon completion of its operations, but may have additional steps not included in the figure. A process may correspond to a method, function, program, subroutine, subroutines, etc. When a process corresponds to a function, its termination may correspond to the function returning to the calling function or the main function.

[0274] Such programs can also be encoded and transmitted using carrier signals suitable for transmission over wired, optical, and / or wireless networks (including the Internet) conforming to various protocols. Therefore, computer-readable media can be constructed using data signals encoded by such programs. Computer-readable media encoded with program code (e.g., as software) can be packaged with compatible devices or provided separately from other devices (e.g., for download via the Internet). Any such computer-readable media can reside on or within a single computer product (e.g., a hard disk drive, CD, or an entire computer system) and can reside on or within different computer products within a system or network. Computer systems may include monitors, printers, or other suitable displays for providing users with any of the results mentioned herein.

[0275] Any method described herein can be performed wholly or partially using a computer system including one or more processors configured to perform the steps described herein. Any operation performed via the processor can be executed in real time. The term "..." real time"This can refer to a computational operation or process completed within a certain time limit. The time limit can be 30 seconds, 1 minute, 10 minutes, 30 minutes, 1 hour, 4 hours, 1 day, or 7 days. Therefore, an implementation can refer to a computer system configured to perform the steps of any method described herein, potentially having different components that perform the corresponding steps or groups of corresponding steps. Although presented in the form of numbered steps, the steps of the methods herein can be performed simultaneously or at different times or in different orders. Furthermore, a portion of these steps can be used in conjunction with a portion of other steps of other methods. Additionally, all or some steps can be optional. Moreover, any step of any method can be performed using modules, units, circuits, or other means of a system for performing these steps."

[0276] The specified details of a particular embodiment may be combined in any suitable manner without departing from the spirit and scope of the embodiments of this disclosure. However, other embodiments of this disclosure may refer to specific embodiments relating to each individual aspect or a particular combination of these individual aspects.

[0277] The foregoing description of exemplary embodiments of this disclosure has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit this disclosure to the precise form described, and many modifications and variations are possible in light of the foregoing teachings.

[0278] Unless explicitly stated otherwise, the use of “a,” “an,” or “the” is intended to mean “one or more.” Unless explicitly stated otherwise, the use of “or” is intended to mean “inclusive of or” rather than “exclusive of or.” Referring to the “first” component does not necessarily require the provision of the second component. Furthermore, unless explicitly stated otherwise, referring to the “first” or “second” component does not limit the components mentioned to a particular position or order. The term “based on” is intended to mean “at least partially based on.”

[0279] The claims are drafted to exclude any optional elements. Therefore, this statement is intended to serve as a basis for the use of exclusive terms such as “solely,” “only,” and similar terms, or for the use of negative limitations, in conjunction with the description of the claimed elements.

[0280] All patents, patent applications, publications, and specifications mentioned herein are incorporated herein by reference in their entirety for all purposes. No prior art is acknowledged. In the event of any conflict between this application and the references provided herein, this application shall prevail.

Claims

1. A method for analyzing a biological sample from an object, said biological sample comprising a plurality of free, linear DNA molecules each independently containing a 5' end sequence and a 3' end sequence, said method comprising: For each of the multiple free linear DNA molecules: Receive one or more sequence readings that include at least the 5' end sequence and the 3' end sequence to obtain 5' end sequence readings and 3' end sequence readings; Map the 5' end sequence and the 3' end sequence to the reference genome; and Based on the mapping, the free linear DNA molecules are classified as either cut from circular DNA molecules in vivo, thereby identifying a group of eccDNA residues. and Analyze the eccDNA residues of the group to determine the characteristics of the biological sample or object.

2. The method of claim 1, wherein mapping the 5' end sequence and the 3' end sequence to a reference genome comprises: Determine the genomic coordinates and genomic orientation of the 5' end sequence; and Determine the genomic coordinates and genomic orientation of the 3' end sequence.

3. The method of claim 2, wherein the classification includes: The genomic coordinates of the 5' end sequence are compared with the genomic coordinates of the 3' end sequence; and The genomic orientation of the 5' end sequence is compared with that of the 3' end sequence.

4. The method of claim 3, wherein the classification further comprises: The free-floating DNA molecule is classified as a member of the eccDNA residues of the group if both of the following conditions are met: (1) The genomic coordinates of the 5' end sequence are greater than the genomic coordinates of the 3' end sequence, and (2) The genomic orientation of the 5' end sequence is the same as that of the 3' end sequence.

5. The method of any one of claims 1 to 4, wherein for at least a portion of the eccDNA residue, the 5' end sequence reading and the 3' end sequence reading do not contain junctions where the nucleotides at two separate genomic locations are directly adjacent to each other.

6. The method of any one of claims 1 to 4, wherein, independently for each of the plurality of free-floating DNA molecules, a sequence reading comprises the 5' end sequence and the 3' end sequence.

7. The method of any one of claims 1 to 6, wherein mapping the 5' end sequence and the 3' end sequence to the reference genome is each independently based on satisfying mapping quality conditions.

8. The method of any one of claims 1 to 7, wherein analysis of the eccDNA residues of the group comprises: Determine the count of eccDNA residues in the group; and The counts are used to determine the characteristics of the biological sample or object.

9. The method of claim 8, wherein using the count includes comparing the count with a reference value.

10. The method of claim 8 or 9, wherein the count of the eccDNA residues in the group is normalized relative to the count of the free DNA molecules in the plurality of free DNA molecules.

11. The method of any one of claims 1 to 7, wherein analysis of the eccDNA residues of the group comprises: Determine the size distribution of the original eccDNA molecules corresponding to the eccDNA residues of the group; and The size distribution is used to determine the characteristics of the biological sample or object.

12. The method of claim 11, wherein: For at least a portion of the eccDNA remnant, at least one of the 5' end sequence read and the 3' end sequence read contains a junction point where the nucleotides at the two separate genomic locations are directly adjacent to each other, and Determining the size distribution includes, for each of the 5' end sequence readings or the 3' end sequence readings that include the eccDNA residue of the junction, calculating the distance between the two separated genomic locations.

13. The method of claim 11 or 12, wherein the statistical values ​​of the size distribution are used to determine the characteristic.

14. The method of claim 13, wherein the statistic is the percentage of eccDNA residues in the group that exceed a size threshold.

15. The method of any one of claims 1 to 7, wherein analyzing the eccDNA residues of the group comprises: For each eccDNA residue in the group, it is determined based on the mapping whether the eccDNA residue is a member of a subset of the eccDNA residues, wherein each member of the subset of the eccDNA residues is mapped to one or more regions within a class of genomic elements of the reference genome; Determine the count of a subset of the eccDNA residues; The count is normalized relative to the total size of one or more regions in the reference genome; and The standardized counts are used to determine the characteristics of the biological sample or object.

16. The method of claim 15, wherein normalizing the count comprises dividing the count by a percentage of the reference genome within the one or more regions.

17. The method of claim 15 or 16, wherein the category of said genomic element includes 5' untranslated region, 3' untranslated region, exon, intron, 2 kb upstream region of a gene (Gene2kbU), 2 kb downstream region of a gene (Gene2kbD), CpG island, 2 kb upstream region of a CpG island (CGI2kbU), 2 kb downstream region of a CpG island (CGI2kbD), Alu repeat region, or combinations thereof.

18. The method of any one of claims 1 to 7, wherein analyzing the eccDNA residues of the group comprises: Determine the frequency of one or more nucleotide motif patterns appearing in the eccDNA remnants of the group, each nucleotide motif pattern comprising one or more nucleotide motifs, each nucleotide motif located in a corresponding segment of the reference genome or a corresponding segment of the eccDNA remnant, each segment independently within a specified distance from the corresponding 5' end sequence of the eccDNA remnant of the group or the corresponding 3' end sequence of the eccDNA remnant; and The frequency is used to determine the characteristics of the biological sample or object.

19. The method of claim 18, wherein, The specified distance is 50 bp.

20. The method of claim 18 or 19, wherein at least one of the one or more nucleotide motif patterns comprises a plurality of nucleotide motifs.

21. The method of any one of claims 18 to 20, wherein each nucleotide motif is independently a trinucleotide motif, a dinucleotide motif, or a tetranucleotide motif.

22. The method of any one of claims 1 to 7, wherein analyzing the eccDNA residues of the group comprises: Identify a subset of the eccDNA residues in the group, each eccDNA residue in the subset having an independent size within a specified size range; For each eccDNA residue in the subset, determine the methylation state at one or more sites of the eccDNA residue; Based on the determined methylation state, the methylation density of the subset is determined; and The methylation density is used to determine the characteristics of the biological sample or object.

23. The method of claim 22, wherein each eccDNA residue of the subset independently has a size smaller than the maximum size, wherein the maximum size is optionally 1000 bp.

24. The method of claim 22, wherein each eccDNA residue of the subset independently has a size greater than a minimum size, wherein the minimum size is optionally 800 bp.

25. The method of any one of claims 1 to 24, wherein the characteristics of the object include cancer grade.

26. The method of any one of claims 1 to 22, wherein the characteristics of the object include the proportionate concentration of the DNA of interest.

27. The method of claim 26, wherein the DNA of interest includes tumor tissue DNA, transplanted tissue DNA, or fetal tissue DNA.

28. The method of any one of claims 1 to 27, wherein the method further comprises sequencing the plurality of free-floating DNA molecules to obtain the sequence reads.

29. The method of any one of claims 1 to 28, wherein the biological sample comprises plasma.

30. The method of any one of claims 1 to 29, wherein the plurality of free-floating DNA molecules comprises at least 1,000 free-floating DNA molecules.

31. A computer product comprising a non-transitory computer-readable medium storing a plurality of instructions, which, when executed, cause a computer system to perform the method as claimed in any one of claims 1 to 30.

32. A system comprising: The computer product as claimed in claim 31; and One or more processors for executing instructions stored on the computer-readable medium.

33. A system comprising means for performing the method of any one of claims 1 to 30.

34. A system comprising one or more processors configured to perform the method of any one of claims 1 to 30.

35. A system comprising modules that respectively perform the steps of the method according to any one of claims 1 to 30.

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