Patent Application
20240043935
Cell-free DNA (cfDNA) is a rich source of information that can be applied to the diagnosis and prognostication of many physiological and pathological conditions such as pregnancy and cancer (Chan, K. C. A. et al. (2017), New England Journal of Medicine 377, 513-522; Chiu, R. W. K. et al. (2008), Proceedings of the National Academy of Sciences of the United States of America 105, 20458-20463; Lo, Y. M. D. et al., (1997), The Lancet 350, 485-487). Cell-free DNA molecules in various bodily fluids (e.g., plasma, serum, urine, saliva, semen, peritoneal fluid, cerebrospinal fluid) may include a mixture of DNA molecules originating from various tissues. One mechanism whereby such cfDNA molecules are released is through cell death (e.g., apoptosis or necrosis). Selected cell populations, e.g., lymphocytes and neutrophils, have also been shown to secrete DNA molecules into bodily fluids. cfDNA molecules consist of fragmented DNA molecules. The correlation between cfDNA fragmentation patterns and nucleosome structures has been illustrated in many studies (Sun et al. Proc Natl Acad Sci USA. 2018; 115:E5106; Snyder et al. Cell. 2016; 164:57-68). Though circulating cfDNA is now commonly used as a non-invasive biomarker and is known to circulate in the form of short fragments, the physiological factors governing the fragmentation and molecular profile of cfDNA remain elusive.
Cell-free DNA may be analyzed to understand the epigenomic status. Epigenomic status of DNA may indicate regulation of genes, tissue origin, or diseases. The amount of histone modifications is an epigenomic factor. Conventional techniques to detect histone modifications involve using specific antibodies, relatively large amounts of sample, and more complicated sample handling. A simpler and more efficient technique is desired for determining epigenomic status of DNA. These and other needs are addressed.
The present disclosure describes various techniques, such as measuring quantities (e.g., relative frequencies) of sequence motifs and sizes of cell-free DNA fragments in a biological sample of an organism for measuring a property of the sample (e.g., fractional concentration of a tissue type or a characteristic of the tissue type), measuring an amount of histone modifications, determining a condition of the organism based on such measurements, and enriching a biological sample for clinically-relevant DNA. Different tissue types exhibit different patterns for chromatin structures. The present disclosure provides various uses for deducing the chromatin structures based on the measures of the relative frequencies of sequence motifs and/or sizes of cell-free DNA, e.g., in mixtures of cell-free DNA from various tissues. DNA from one of a particular tissue may be referred to as clinically-relevant DNA.
Various examples can quantify amounts of sequence motifs representing the ending sequences of DNA fragments (i.e., end motifs). For example, embodiments can determine one or more relative frequencies of a set of one or more sequence motifs for ending sequences of DNA fragments. In various implementations, preferred sets of end motifs can be determined through using another technique (e.g., cfChIP-seq [cell-free Chromatin immunoprecipitation followed by sequencing) to measure an epigenomic status (e.g., histone modification) of chromatin in a particular region of a subject. The preferred sets of end motifs can be selected based on appearing more frequently in one or more regions with a particular epigenomic status compared to other end motifs. The particular epigenomic status can be associated with a particular tissue type or clinically-relevant DNA.
In various implementations, the relative frequencies of a preferred set can be used to measure a classification of a property (e.g., fractional concentration of clinically-relevant DNA) of a new sample, a condition (e.g., a gestational age of a fetus or a level of pathology) of the organism, or a measure of epigenomic status (e.g., histone modification amount). Accordingly, embodiments can provide measurements to inform physiological alterations, including cancers, autoimmune diseases, transplantation, and pregnancy.
As further examples, a preferred set of sequence end motif(s) can be used in a physical enrichment and/or an in silico enrichment of a biological sample for cell-free DNA fragments that are clinically-relevant. The enrichment can use sequence end motifs that are preferred for one or more genomic regions having particular histone modification(s). The particular histone modifications at the one or more genomic regions may be preferred for certain clinically-relevant tissue, such as fetal, tumor, or transplant. The physical enrichment can use one or more probe molecules that detect a particular set of sequence end motifs such that the biological sample is enriched for clinically-relevant DNA fragments. For the in silico enrichment, a group of sequence reads of cell-free DNA fragments having one of a set of preferred ending sequences for clinically-relevant DNA can be identified. Certain sequence reads can be stored based on a likelihood of corresponding to clinically-relevant DNA, where the likelihood accounts for the sequence reads including the preferred sequence end motifs. The stored sequence reads can be analyzed to determine a property of the clinically-relevant DNA the biological sample.
In some embodiments, the amount of DNA fragments in a certain size range can be used to determine the amount of a histone modification in cell-free DNA. The amount of histone modification deduced through the size information can be used to determine tissue fraction, a classification of a level of a disorder, and a status of a tissue or organ transplant.
Additionally, while a histone modification in a specific genomic region may indicate the DNA being of a specific type of tissue, histone modifications in many genomic regions may be the result of several different tissues. Using the histone modifications in genomic regions contributed by several different tissues may allow for more accurate analysis of a biological sample than using only histone modifications in genomic regions resulting from a single tissue. For example, using histone modifications contributed by several different tissues may result in more accurate analysis of the tissue origin and of the level of a disorder.
These and other embodiments of the disclosure are described in detail below. For example, other embodiments are directed to systems, devices, and computer readable media associated with methods described herein.
Reference to the remaining portions of the specification, including the drawings and claims, will realize other features and advantages of the present disclosure. Further features and advantages of the present disclosure, as well as the structure and operation of various embodiments of the present disclosure, are described in detail below with respect to the accompanying drawings. In the drawings, like reference numbers can indicate identical or functionally similar elements.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
A “tissue” corresponds to a group of cells that group together as a functional unit. More than one type of cells can be found in a single tissue. Different types of tissue may consist of different types of cells (e.g., hepatocytes, alveolar cells or blood cells), but also may correspond to tissue from different organisms (mother vs. fetus) or to healthy cells vs. tumor cells.
A “biological sample” refers to any sample that is taken from a subject (e.g., a human (or other animal), such as a pregnant woman, a person with cancer, or a person suspected of having cancer, an organ transplant recipient or a subject suspected of having a disease process involving an organ (e.g., the heart in myocardial infarction, or the brain in stroke, or the hematopoietic system in anemia) and contains one or more nucleic acid molecule(s) of interest. The biological sample can be a bodily fluid, such as blood, plasma, serum, urine, vaginal fluid, fluid from a hydrocele (e.g., of the testis), vaginal flushing fluids, pleural fluid, ascitic fluid, cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolar lavage fluid, discharge fluid from the nipple, aspiration fluid from different parts of the body (e.g., thyroid, breast), intraocular fluids (e.g., the aqueous humor), etc. Stool samples can also be used. In various embodiments, the majority of DNA in a biological sample that has been enriched for cell-free DNA (e.g., a plasma sample obtained via a centrifugation protocol) can be cell-free, e.g., greater than 50%, 60%, 70%, 80%, 90%, 95%, or 99% of the DNA can be cell-free. The centrifugation protocol can include, for example, 3,000 g×10 minutes, obtaining the fluid part, and re-centrifuging at for example, 16,000 g for another 10 minutes to remove residual cells. As part of an analysis of a biological sample, at least 1,000 cell-free DNA molecules can be analyzed. As other examples, at least 10,000 or 50,000 or 100,000 or 500,000 or 1,000,000 or 5,000,000 cell-free DNA molecules, or more, can be analyzed.
“Clinically-relevant DNA” can refer to DNA of a particular tissue source that is to be measured, e.g., to determine a fractional concentration of such DNA or to classify a phenotype of a sample (e.g., plasma). Examples of clinically-relevant DNA are fetal DNA in maternal plasma or tumor DNA in a patient's plasma or other sample with cell-free DNA. Another example includes the measurement of the amount of graft-associated DNA in the plasma, serum, or urine of a transplant patient. A further example includes the measurement of the fractional concentrations of hematopoietic and nonhematopoietic DNA in the plasma of a subject, or fractional concentration of a liver DNA fragments (or other tissue) in a sample or fractional concentration of brain DNA fragments in cerebrospinal fluid.
A “sequence read” refers to a string of nucleotides sequenced from any part or all of a nucleic acid molecule. For example, a sequence read may be a short string of nucleotides (e.g., nucleotides) sequenced from a nucleic acid fragment, a short string of nucleotides at one or both ends of a nucleic acid fragment, or the sequencing of the entire nucleic acid fragment that exists in the biological sample. A sequence read may be obtained in a variety of ways, e.g., using sequencing techniques or using probes, e.g., in hybridization arrays or capture probes, or amplification techniques, such as the polymerase chain reaction (PCR) or linear amplification using a single primer or isothermal amplification. As part of an analysis of a biological sample, at least 1,000 sequence reads can be analyzed. As other examples, at least 10,000 or 50,000 or 100,000 or 500,000 or 1,000,000 or 5,000,000 sequence reads, or more, can be analyzed.
A sequence read can include an “ending sequence” associated with an end of a fragment. The ending sequence can correspond to the outermost N bases of the fragment, e.g., 2-bases at the end of the fragment. If a sequence read corresponds to an entire fragment, then the sequence read can include two ending sequences. When paired-end sequencing provides two sequence reads that correspond to the ends of the fragments, each sequence read can include one ending sequence.
A “sequence motif” of “sequence end signature” may refer to a short, recurring pattern of bases in nucleic acid fragments (e.g., cell-free DNA fragments). A sequence motif can occur at an end of a fragment, and thus be part of or include an ending sequence. An “end motif” can refer to a sequence motif for an ending sequence that preferentially occurs at ends of nucleic acid, e.g., DNA, fragments, potentially for a particular type of tissue. An end motif may also occur just before or just after ends of a fragment, thereby still corresponding to an ending sequence.
The term “fractional fetal DNA concentration” is used interchangeably with the terms “fetal DNA proportion” and “fetal DNA fraction,” and refers to the proportion of fetal DNA molecules that are present in a biological sample (e.g., maternal plasma or serum sample) that is derived from the fetus (Lo et al, Am J Hum Genet. 1998; 62:768-775; Lun et al, Clin Chem. 2008; 54:1664-1672).
A “relative frequency” (also referred to just as “frequency”) may refer to a proportion (e.g., a percentage, fraction, or concentration). In particular, a relative frequency of a particular end motif pair (e.g., A<>A) can provide a proportion of cell-free DNA fragments that have that particular pair of ending sequences.
An “aggregate value” may refer to a collective property, e.g., of relative frequencies of a set of end motifs. Examples include a mean, a median, a sum of relative frequencies, a variation among the relative frequencies (e.g., entropy, standard deviation (SD), the coefficient of variation (CV), interquartile range (IQR) or a certain percentile cutoff (e.g. 95th or 99th percentile) among different relative frequencies), or a difference (e.g., a distance) from a reference pattern of relative frequencies, as may be implemented in clustering. As another example, an aggregate value can comprise an array/vector of relative frequencies, which can be compared to a reference vector (e.g., representing a multidimensional data point).
A “calibration sample” can correspond to a biological sample whose fractional concentration of clinically-relevant nucleic acid (e.g., tissue-specific DNA fraction) is known or determined via a calibration method, e.g., using an allele specific to the tissue, such as in transplantation whereby an allele present in the donor's genome but absent in the recipient's genome can be used as a marker for the transplanted organ. As another example, a calibration sample can correspond to a sample from which end motifs can be determined. A calibration sample can be used for both purposes. Multiple calibration samples may be used As an example, a first calibration sample can correspond to a biological sample, which has measurable histone modification levels across various genomic regions of interest. A second calibration sample can correspond to a biological sample, which has measurable fragmentomic features across various genomic regions of interest. The first and second calibration samples can be used together for determining the calibration values.
A “calibration data point” includes a “calibration value” and a measured or known characteristic value of a target tissue type or a fractional concentration of the clinically-relevant nucleic acid (e.g., DNA of particular tissue type). The calibration value can be determined from various types of data measured from nucleic acid molecules of a sample, e.g., amounts of end motifs or fragment sizes. The calibration value corresponds to a parameter that correlates to the desired property, e.g., characteristic value of a target tissue type or a fractional concentration of the clinically-relevant DNA. For example, a calibration value can be determined from relative frequencies (e.g., an aggregate value) of end signatures as determined for a calibration sample, for which the desired property is known. The calibration data points may be defined in a variety of ways, e.g., as discrete points or as a calibration function (also called a calibration curve or calibration surface). The calibration function could be derived from additional mathematical transformation of the calibration data points. In some embodiments, a “calibration data point” may include a “calibration value” and a measured or known characteristic values (e.g., fragmentomic features) of a group of genomic regions of interest (e.g., characterized by certain levels of histone modifications).
A “separation value” corresponds to a difference or a ratio involving two values, e.g., two fractional contributions or two methylation levels. The separation value could be a simple difference or ratio. As examples, a direct ratio of x/y is a separation value, as well as x/(x+y). The separation value can include other factors, e.g., multiplicative factors. As other examples, a difference or ratio of functions of the values can be used, e.g., a difference or ratio of the natural logarithms (ln) of the two values. A separation value can include a difference and a ratio.
A “separation value” and an “aggregate value” (e.g., of relative frequencies) are two examples of a parameter (also called a metric) that provides a measure of a sample that varies between different classifications (states), and thus can be used to determine different classifications. An aggregate value can be a separation value, e.g., when a difference is taken between a set of relative frequencies of a sample and a reference set of relative frequencies, as may be done in clustering.
The term “classification” as used herein refers to any number(s) or other characters(s) that are associated with a particular property of a sample. For example, a “+” symbol (or the word “positive”) could signify that a sample is classified as having deletions or amplifications. The classification can be binary (e.g., positive or negative) or have more levels of classification (e.g., a scale from 1 to 10 or 0 to 1). As further examples, the levels of classification can correspond to a fractional concentration or a value for a characteristic, e.g., of a sample or of a target tissue type.
The term “parameter” as used herein means a numerical value that characterizes a quantitative data set and/or a numerical relationship between quantitative data sets. For example, a ratio (or function of a ratio) between a first amount of a first nucleic acid sequence and a second amount of a second nucleic acid sequence is a parameter.
The terms “cutoff” and “threshold” refer to predetermined numbers used in an operation. For example, a cutoff size can refer to a size above which fragments are excluded. A threshold value may be a value above or below which a particular classification applies. Either of these terms can be used in either of these contexts. A cutoff or threshold may be “a reference value” or derived from a reference value that is representative of a particular classification or discriminates between two or more classifications. Such a reference value can be determined in various ways, as will be appreciated by the skilled person. For example, metrics (parameters) can be determined for two different cohorts of subjects with different known classifications, and a reference value can be selected as representative of one classification (e.g., a mean) or a value that is between two clusters of the metrics (e.g., chosen to obtain a desired sensitivity and specificity). As another example, a reference value can be determined based on statistical simulations of samples. A particular value for a cutoff, threshold, reference, etc. can be determined based on a desired accuracy (e.g., a sensitivity and specificity). A parameter can be compared to cutoff value, threshold value, reference value, or calibration value to determine a classification Such a process for determining such values can be performed as part of training a machine learning model, e.g., which receives a training vector of a set of one or more parameters. And the comparison of a parameter(s) to any of such values can be accomplished by inputting the parameter(s) into a machine learning model, e.g., that was trained that was trained using the parameter values determined from other subjects, e.g., ones with or without a condition, abnormality, or pathology or ones with a known parameter values (e.g., a calibration value).
The term “level of cancer” can refer to whether cancer exists (i.e., presence or absence), a stage of a cancer, a size of tumor, whether there is metastasis, the total tumor burden of the body, the cancer's response to treatment, and/or other measure of a severity of a cancer (e.g., recurrence of cancer). The level of cancer may be a number or other indicia, such as symbols, alphabet letters, and colors. The level may be zero. The level of cancer may also include premalignant or precancerous conditions (states). The level of cancer can be used in various ways. For example, screening can check if cancer is present in someone who is not previously known to have cancer. Assessment can investigate someone who has been diagnosed with cancer to monitor the progress of cancer over time, study the effectiveness of therapies or to determine the prognosis. In one embodiment, the prognosis can be expressed as the chance of a patient dying of cancer, or the chance of the cancer progressing after a specific duration or time, or the chance or extent of cancer metastasizing. Detection can mean ‘screening’ or can mean checking if someone, with suggestive features of cancer (e.g., symptoms or other positive tests), has cancer. A “level of disease” is similar to “level of cancer” but can refer to a disease rather than cancer.
A “level of abnormality” can refer to the amount, degree, or severity of abnormality associated with an organism, where the level can be as described above for cancer. An example of abnormality is pathology associated with the organism. Another example of abnormality is a rejection of a transplanted organ. Other example abnormalities can include autoimmune attack (e.g., lupus nephritis damaging the kidney or multiple sclerosis), inflammatory diseases (e.g., hepatitis), fibrotic processes (e.g., cirrhosis), fatty infiltration (e.g., fatty liver diseases), degenerative processes (e.g., Alzheimer's disease) and ischemic tissue damage (e.g., myocardial infarction or stroke). A heathy state of a subject can be considered a classification of normal.
The term “gestational age” can refer to a measure of the age of a pregnancy which is taken from the beginning of the woman's last menstrual period (LMP), or the corresponding age of the gestation as estimated by a more accurate method if available. Such methods include adding 14 days to a known duration since fertilization (as is possible in in vitro fertilization), or by obstetric ultrasonography.
A “pregnancy-associated disorder” includes any disorder characterized by abnormal relative expression levels of genes in maternal and/or fetal tissue or by abnormal clinical characteristics in the mother and/or fetus. These disorders include, but are not limited to, preeclampsia (Kaartokallio et al. Sci Rep. 2015; 5:14107; Medina-Bastidas et al. Int J Mol Sci. 2020; 21:3597), intrauterine growth restriction (Faxén et al. Am J Perinatol. 1998; 15:9-13; Medina-Bastidas et al. Int J Mol Sci. 2020; 21:3597), invasive placentation, pre-term birth (Enquobahrie et al. BMC Pregnancy Childbirth. 2009; 9:56), hemolytic disease of the newborn, placental insufficiency (Kelly et al. Endocrinology. 2017; 158:743-755), hydrops fetalis (Magor et al. Blood. 2015; 125:2405-17), fetal malformation (Slonim et al. Proc Natl Acad Sci USA. 2009; 106:9425-9), HELLP syndrome (Dijk et al. J Clin Invest. 2012; 122:4003-4011), systemic lupus erythematosus (Hong et al. J Exp Med. 2019; 216:1154-1169), and other immunological diseases of the mother.
A “site” (also called a “genomic site”) corresponds to a single site, which may be a single base position or a group of correlated base positions, e.g., a CpG site or larger group of correlated base positions. A “locus” may correspond to a region that includes multiple sites. A locus can include just one site, which would make the locus equivalent to a site in that context.
The term “about” or “approximately” can mean within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 1 or more than 1 standard deviation, per the practice in the art. 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 processes, the term “about” or “approximately” can mean within an order of magnitude, within 5-fold, and in some versions within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value should be assumed. The term “about” can have the meaning as commonly understood by one of ordinary skill in the art. The term “about” can refer to ±10%. The term “about” can refer to ±5%.
Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. It is also to be understood that the endpoints of the range provided are included in the range. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within embodiments of the present disclosure. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither, or both limits are included in the smaller ranges is also encompassed within the present disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the present disclosure.
Standard abbreviations may be used, e.g., bp, base pair(s); kb, kilobase(s); pi, picoliter(s); s or sec, second(s); min, minute(s); h or hr, hour(s); aa, amino acid(s); nt, nucleotide(s); and the like.
Unless defined otherwise, 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 belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the embodiments of the present disclosure, some potential and exemplary methods and materials may now be described
Epigenomic status of different regions of chromatin (DNA and proteins) may indicate the expression activities of genes, tissue origin, or diseases. A histone modification is an example of an epigenomic factor where measurements of the amount of histones having a particular epigenomic status can be used in various ways. Techniques to detect histone modifications include cfChIP-seq (cell-free Chromatin immunoprecipitation followed by sequencing), which has some disadvantages. The cfChIP-seq technique requires 1-2 ml or more of sample, which is a large sample compared to the hundreds of microliters or less used when just sequencing is performed. In addition, cfChIP-seq uses more complicated and time-consuming sample techniques, compared with procedures of conventional plasma cfDNA-seq. In the cfChIP-seq procedure, the target epigenome is linked to proteins (e.g., histone modification). Proteins are unstable compared to DNA. Freeze, thaw, and storage conditions affect the stability of protein more than that of DNA.
This disclosure shows that certain end motifs of cell-free DNA (i.e., sequences at ends of the naturally fragmented DNA), sizes, and/or other fragmentomic features are highly correlated with histone modifications. The amount of these end motifs can indicate the amount of a histone modification in a sample, and therefore a subject. As a result, the end motifs can be used to indicate the activity of genes, tissue origin, or disease, avoiding the disadvantages of cfChIP-seq. Analyzing end motifs can use sequencing techniques that do not require the extra steps of cfChIP-seq. As a result, embodiments of the present invention can use less than 100 μl of biological sample, which can include about 500 pg of cell-free DNA. Sampling handling for sequencing is much simpler than with cfChIP-seq techniques. Samples do not need to be frozen to temperatures of less than −80° C. Samples can be shipped farther distances from a clinic to a laboratory. In addition, analyzing end motifs can be applied to study multiple, different epigenome types from a single measurement, rather than limited by the specific histone modification tied to the specific antibody used in a particular cfChIP-seq assay.
Measuring certain end motifs of cell-free DNA can therefore provide an improved technique of determining an epigenomic status of a particular region of chromatin, e.g., corresponding to a particular region of a reference genome. Additionally, measuring certain end motifs can also determine different properties of a sample where such a property is associated with the epigenomic status of the particular region, such as fractional concentration of a tissue type, classification of a disorder, gestational age, nutrition status of an organ, size of an organ, or other properties. These properties may be determined using the epigenomic status determined from the end motifs or directly from the end motifs.
Samples can be physically or in silico enriched for certain end motifs that are more frequently associate with certain epigenomic statuses, including histone modifications. Enrichment of samples may allow for more accurate measurements of a property of a sample, measuring an amount of histone modification, or determining a condition of an organism.
Histone modifications have various functions in the cell. One function is regulating gene expression. Gene expression may be promoted or inhibited. For example, the amount of H3K4me3 is correlated with transcriptional activity. In some cases, a histone modification may increase chromatin compaction and reduce transcription (e.g., H3K36me3).
A. Histone Modifications Determined Using cfChIP-Seq
Plasma DNA pool is a mixture of DNA molecules released from various tissues, among which certain molecules would be bound to histone proteins accompanied with certain histone modifications. Histone proteins include H1 (linker histones), H2A/B, H3, and H4 (core histones). DNA molecules together with histone proteins would form nucleosome structures (Zhou et al. Nat Struct Mol Biol. 2019; 26:3-13). The coiling of DNA around histones is largely due to electrostatic affinity between the positively charged histones and the negatively charged phosphate backbone of DNA. Histone modifications include but are not limited to histone methylation, acetylation, phosphorylation, and ubiquitylation, etc. (Barth et al. Trends Biochem. Sci. 2010; 35:618-626). Histone methylation could occur at different lysine residues of a histone. The methylation of each lysine residue can involve one, two, or three methyl groups so that the lysine residue would be mono-, di-, or tri-methylated, respectively. Examples of histone methylation include but not limited to the tri-methylation of the lysine (K) residue 4 at the N terminus of histone H3 (H3K4me3), mono-methylation of the lysine (K) residue 4 at the N terminus of histone H3 (H3K4me1) for transcriptional activation, H3K27me3 and H3K9me3 for transcriptional inactivation, and H3K36me3 associated with transcribed regions in gene bodies. H3K9me2 was reported to be a signal for heterochromatin formation in gene-poor chromosomal regions with tandem repeat structures, such as satellite repeats, telomeres, and pericentromeres. Histone acetylation includes, but not limited to, H3K27ac, H3K9ac, and H3K14ac, etc.
Plasma cfDNA molecules bound by histones with certain modifications may be isolated via chromatin immunoprecipitation. Those immunoprecipitated plasma cfDNA molecules can be analyzed using different technologies. In one embodiment, they can be analyzed by DNA sequencing.
B. Selected End Motifs Indicate Histone Modifications
Using fragmentomic features, including but not limited to plasma DNA end motifs and sizes, we developed new approaches for analyzing histone modifications in plasma without the requirement of immunoprecipitation. The regions relatively enriched with histone modifications would generate differential fragment end motif patterns when compared with those regions that lack histone modifications. Thus, the patterns of fragment end motifs could be used for deducing histone modifications. End motif could be defined as one or more nucleotides at one end of a cell-free DNA fragment. The number of nucleotides (nt) at each of fragment ends used for analysis could be, for example, but not limited to, 1 nt, 2 nt, 3 nt, 4 nt, 5 nt, 6 nt, 7 nt, 8 nt, 9 nt, and 10 nt or above. Plasma DNA fragment size could be measured in various ways. In one embodiment, plasma DNA fragment size can be measured by the number of nucleotides present in a plasma DNA molecule. In another embodiment, plasma DNA fragment size can be measured using paired-end sequencing, aligning the sequences to a genome, and then deducing the size from the genome coordinates of the aligned sequences. In embodiments, tissue- or disease-specific histone modification levels are deduced from cfDNA end motif or size frequency, or etc., enabling the monitoring of the physiology or pathology of one or more tissues, or the detection of monitoring of disease status.
The regions with histone modifications may include but not limited to repetitive regions, X chromosome inactivation regions, chromatin structures [e.g., open and closed chromatin structures], pseudogenes, CTCF, DNase I hypertensive sites [DHS], actively transcribed regions and inactively transcribed regions, G quadruplex, etc. For example, selected end motifs in a region with a DNase I hypersensitive site may be used for informing the amount of histone modifications associated with that DNase I hypersensitive site. As another example, sizes of DNA fragments in an X chromosome inactivation region may inform the amount of histone modifications of X-chromosomal genes.
Particular regions may be associated with a particular tissue type. In some instances, a certain property of the region may occur more often for a particular tissue type. As an example, a region being open chromatin (i.e., a large gap between histones) may occur more often for a particular tissue type than other tissue types. Other properties may include the region being a repetitive region, an X chromosome inactivation region, a closed chromatin structure, a pseudogene, CTCF, DHS, actively transcribed region, inactively transcribed region, or G quadruplex. Particular regions may be associated with a specific particular tissue type and no other tissue types. In other embodiments, particular regions may be associated with several different tissue types. The prevalence of that region property may be related to the contribution of the particular tissue type and the relative strength of the particular tissue to be associated with the region property. Deconvolution may be used to determine the tissue contribution from these regions, similar to what is described for histone modifications below.
1. Determining End Motifs Associated with Histone Modifications
Different histone modifications may confer different accessibilities of DNA nucleases, thus resulting in the characteristic fragmentations. Selective cleavage of DNA by nucleases through cfDNA fragmentation occurs in TSS and CpG islands, which have a particular epigenetic status (Han et al., Genome Res. 2021:31:2008-2021). Fragmentation patterns of cell-free DNA may be informative for inferring the histone modifications present in plasma DNA molecules. In embodiments, we analyzed nucleases cutting preference for cfDNA within regions of interest, which could be indicated by the pattern of cfDNA end motifs. The fragment end motif could be defined by one or more nucleotides at one end of a cell-free DNA fragment. For example, we determined the proportions of cfDNA molecules carrying a particular 4-mer end motif (a total of 256 types).
Regions involving histone modifications may be grouped into different categories according to the magnitudes of ChIP signal.
We analyzed 4-mer end motif frequencies across the 9 categories defined according to the different levels of H3K4me3 signal for samples without immunoprecipitation.
The results also show that many of the end motifs with higher rankings in cfChIP-seq have C and G nucleotides adjacent to each other. H3K4me3 sites appear to be enriched with CG sequences.
Accordingly, the end motifs with the largest ranking difference occur at a higher rate in the regions associated with H3K4me3 than occur without cfChIP, genome-wide, or relative to a random group of DNA fragments.
2. Testing Correlation with cfChIP-Seq Signal
A higher frequency of the 24 end motifs from
Because end motif frequency can identify epigenome status and different cells have different epigenome statuses, end motif frequency may be used to identify the tissue origin, determine a fractional concentration of a tissue in the sample, estimate characteristics of tissues, or determine levels of a disorder. End motif frequencies can also measure amounts of histone modifications.
A. Estimating Fractional Concentration of Tissue of Origin
The genomic regions where H3K4me3 signals are high for placenta are known (
1. Results
2. Example Method for Determining Fractional Concentration
At block 1710, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads include ending sequences corresponding to ends of the plurality of cell-free DNA fragments.
In some embodiments, process 1700 may include sequencing the cell-free DNA fragments in the biological sample to obtain the plurality of sequence reads. In embodiments, the volume of the biological sample may be 100 μl or less, including 80 to 100 μl to 80 μl or 30 to 50 μl. The biological sample may use a volume smaller than the volume used in cfChIP-seq.
In some embodiments, process 1700 may include probe-based techniques to measure the amount of motifs. Techniques may include qPCR, digital PCR, digital droplet PCR, etc. As an example, cfDNA molecules can be subjected to the process of DNA end pair, A-tailing, and common adaptor ligation. The adaptor-ligated molecules can be partitioned, e.g., into different reactions, such as droplets. A pair of PCR primers can be designed in a way that one primer could bind to the common adaptor region and the other could bind to the specific region of interest. DNA molecules would be amplified inside a reaction (e.g., droplet) by the pair of PCR primers. The fluorescent probe specific to a certain end motif can be hydrolyzed and emit fluorescent signals, thus enabling the detection of the presence of a specific motif as well as the quantification of a specific motif. For digital PCR, the number of reactions positive for a particular end motif can be counted and used to determine the amount of DNA fragments with that end motif in the region analyzed. For real-time PCR, the intensity of each signal can be used as a measure of an amount of DNA fragments ending with a particular motif. The two intensities can be compared to each other.
At block 1720, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions has a histone modification associated with a target tissue type. The target tissue type may include the placenta, liver, heart, neutrophils, monocytes, B cells, adipose, NK cells, or any tissue type described herein. The histone modification may be H3K4me3, H3K4me1, H3K4me2, H3K27me3, H3K27ac, H3K36me3, H3K9me2, H3K9me3, H3S10P, H3R2me, H3T2P, H3K14ac, H3K9ac, H3K79me2, H3K79me3, H4K5ac, H4K8ac, H4K12ac, H4K16ac, H4K20me, H2BK120ub, H2AK119ub. The one or more genomic regions may include transcription start sites, promoter regions, enhancer regions, super enhancer regions, gene bodies, repetitive sequences, satellite repeats, telomeres, pericentromeres, mitotic chromosomes, transcriptional end sites. exon, intron, insulator, etc. The one or more genomic regions may have amounts of histone modification that are statistically significantly different from the amounts of histone modifications in other genomic regions or the average amount of modifications in other genomic regions or across all genomic regions. The sequence reads may be aligned to a reference genome (e.g., human reference genome) to determine if the sequence reads are located in the one or more genomic regions.
At block 1730, one or more sequence motifs corresponding to one or more ending sequences of a corresponding cell-free DNA fragment are determined for each sequence read of the group of sequence reads. The one or more sequence motifs may be correspond to a single nucleotide, a two-nucleotide sequence, a three-nucleotide sequence, a four-nucleotide sequence, a five-nucleotide sequence, a six-nucleotide sequence, a seven-nucleotide sequence, an eight-nucleotide sequence, or a sequence having more than eight nucleotides. The one or more sequence motifs may each have the same number of nucleotides. In some embodiments, the sequence motif includes the nucleotide at the end of the cell-free DNA fragment. The sequence motif may be at the 5′ end of the cell-free DNA fragment. In some embodiments, the sequence motif may be at the 3′ end. In embodiments, the one or more sequence motifs may include sequence motifs at the 3′ end and at the 5′ end. If a whole fragment is sequenced, two sequence motifs may be determined.
At block 1740, one or more relative frequencies of a set of the one or more sequence motifs are determined. The set of the one or more sequence motifs occurs at a higher rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation. The chromatin immunoprecipitation may be cell-free chromatin immunoprecipitation followed by sequencing (cfChIP-seq) or may be cellular chromatin immunoprecipitation followed by sequencing. Sequencing without chromatin immunoprecipitation may include genome-wide sequencing. The set of the one or more sequence motifs correspond to sequence motifs having a similar relative frequency, such as a peak group in
At block 1750, an aggregate value of the one or more relative frequencies is determined. Example aggregate values are described throughout the disclosure, e.g., including an entropy value (a motif diversity score or variance), a sum of relative frequencies, and a multidimensional data point corresponding to a vector of counts for a set of motifs (e.g., a vector 256 counts for 256 motifs of possible 4-mers or 64 counts for 64 motifs of possible 3-mers). When the set of one or more sequence motifs includes a plurality of sequence motifs, the aggregate value can include a sum of the relative frequencies of the set. In some embodiments, the aggregate value may be an estimation of the histone modifications. The levels of histone modifications can be determined by various types of data, e.g., amounts of end motifs or fragment sizes.
At block 1760, the aggregate value is compared to one or more calibration values. The one or more calibration values are determined from one or more calibration samples whose fractional concentrations of cell-free DNA fragments from the target tissue type are known.
The one or more calibration values may be determined through determining aggregate values for sequence motifs of the one or more calibration samples. For example, the aggregate value determined from the biological sample may be a first aggregate value determined from one or more first relative frequencies. One or more second relative frequencies of the set of the one or more sequence motifs in the one or more genomic regions may be determined for each calibration sample of the one or more calibration samples. A second aggregate value may be determined for the one or more second relative frequencies for each calibration sample of the one or more calibration samples. Each of the one or more second aggregate values may thereby be associated with a known concentration of the calibration samples. The calibration value may include the one or more second aggregate values. For example, the calibration values may be points along a line or a curve relating known concentrations with the second aggregate values.
In some embodiments, the one or more calibration values may be determined from a function relating known concentrations with second aggregate values. The first aggregate value may be inputted into the function to return a fractional concentration. The first aggregate value is then used as the calibration value. The comparison of the aggregate value is comparing the aggregate value to the calibration value used in the function and determining that the aggregate value is the same as the calibration value.
At block 1770, a fractional concentration of cell-free DNA fragments from the target tissue type is determined using the comparison. The fractional concentration may be the known fractional concentration associated with the calibration value, which may have a value close to or equal to the first aggregate value. In some embodiments, the fractional concentration may be determined from a function or a line with the one or more calibration values. The function or line may relate known fractional concentrations to the one or more calibration values. The fractional concentration of the target tissue type can be used to determine characteristics of the tissue type and/or the subject from which the biological sample is obtained.
A classification of a disorder or disease may be determined using the fractional concentration. For example, if the target tissue type is the placenta, the method may further include determining a classification of a pregnancy-associated disorder or a gestational age using the fractional concentration. The fractional concentration may be compared to a cutoff value determined from samples from reference subjects having a certain classification of the pregnancy-associated disorder or having a certain gestational age. A pregnancy-associated disorder may include pre-eclampsia, intrauterine growth restriction, invasive placentation and pre-term birth, hemolytic disease of the newborn, placental insufficiency, hydrops fetalis, fetal malformation, HELLP syndrome, systemic lupus erythematosus, and other immunological diseases of the mother. The pregnancy-associated disorder may be associated with the fetus or the mother.
In some embodiments, a classification of a level of cancer may be determined using the fractional concentration. The fractional concentration may be compared to a cutoff value determined from samples from reference subjects having a certain classification of the level of cancer.
a) Fractional Concentration of a Second Target Tissue Type
In some embodiments, fractional concentrations of multiple tissue types can be determined. Different tissues can show different histone modification amounts in different genomic regions (e.g., as described in section V.A). A biological sample, such as a plasma sample, may have DNA fragments from different tissues. The DNA fragments may therefore include fragments associated with the histone modification in different genomic regions. Each genomic region may have sequence motifs associated with the histone modification. The sequence motifs in different genomic regions can be used to determine fractional concentrations of the different tissues in the biological sample. The amounts of the sequence motifs are correlated with the fractional concentrations of the tissues. The method can be repeated for a second target tissue to determine the fractional concentration of the second target tissue.
For example, the steps described above may be for a first target tissue type. The one or more genomic regions associated with the first target tissue type may be one or more first genomic regions. The group of sequence reads located in the one or more first genomic regions may be a first group of sequence reads. The histone modification in the one or more first genomic regions may be a first histone modification. The set of the one or more sequence motifs may be a set of one or more first sequence motifs. The relative frequency may be a first relative frequency. The aggregate value may be a first aggregate value. The one or more calibration samples may be one or more first calibration samples. The fractional concentration may be a first fractional concentration.
The method may further include identifying a second group of sequence reads located in one or more second genomic regions in a similar manner as block 1720. Each of the one or more second genomic regions may have a second histone modification associated with a second target tissue type. The one or more second genomic regions may be the same as or different from the one or more first genomic regions.
For each sequence read of the second group of sequence reads, one or more second sequence motifs corresponding to the one or more ending sequences of a corresponding cell-free DNA fragment may be determined, similar to block 1730.
One or more second relative frequencies of a set of the one or more second sequence motifs may be determined, similar to block 1740. The set of the one or more second sequence motifs may occur at a higher rate in chromatin immunoprecipitation sequencing for the second histone modification associated with the one or more second genomic regions than in sequencing without chromatin immunoprecipitation. Sequence motifs that appear more frequently in ChIP-sequencing may be used because those sequence motifs may be associated with the second histone modification (similar to
A second aggregate value of the one or more second relative frequencies may be determined, similar to block 1750.
The one or more second aggregate values may be compared to one or more second calibration values in a similar manner as block 1760.
The one or more second calibration values may be determined from one or more second calibration samples whose fractional concentrations of DNA fragments from the second target tissue type are known. A second fractional concentration of cell-free DNA fragments from the second target tissue type may be determined using the comparison, similar to block 1770.
b) Determining Sequence Motifs
The set of the one or more sequence motifs can be determined in a manner similar to the procedure described with
Process 1700 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described elsewhere herein.
Although
B. Estimating Characteristic Value of Target Tissue
The values of various characteristics of target tissues can be estimated using sequence motifs associated with histone modifications. The characteristics can describe the health of the tissue, the age of the tissue, or a level of disease in the tissue. For example, the determined characteristic can include a particular gestational age or range (e.g., 8 weeks, 9-12 weeks). In another example, the determined characteristic can be a size or nutrition status of an organ corresponding a particular tissue type.
At block 1810, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads include ending sequences corresponding to ends of the plurality of cell-free DNA fragments. Block 1810 may be performed in a similar manner as block 1710.
At block 1820, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions has a histone modification associated with a target tissue type. Block 1820 may be performed in a similar manner as block 1720.
At block 1830, one or more sequence motifs corresponding to one or more ending sequences of a corresponding cell-free DNA fragment are determined for each sequence read of the group of sequence reads. Block 1830 may be performed in a similar manner as block 1730.
At block 1840, one or more relative frequencies of a set of the one or more sequence motifs are determined. The set of the one or more sequence motifs occurs at a higher rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation. Block 1840 may be performed in a similar manner as block 1740.
At block 1850, an aggregate value of the one or more relative frequencies is determined. Block 1850 may be performed in a similar manner as block 1750.
At block 1860, the aggregate value is compared to one or more calibration values. The one or more calibration values are determined from one or more calibration samples whose values for the characteristic of the target tissue type are known. The comparison may be performed using a machine learning model, which may be any machine learning model described herein. The calibration values may be determined using the machine learning model.
The one or more calibration values may be determined in the same manner as block 1760, but using calibration samples whose values for the characteristic of the target tissue type are known. For example, the aggregate value determined from the biological sample may be a first aggregate value determined from one or more first relative frequencies. One or more second relative frequencies of the set of the one or more sequence motifs in the one or more genomic regions may be determined for each calibration sample of the one or more calibration samples. A second aggregate value may be determined for the one or more second relative frequencies for each calibration sample of the one or more calibration samples. Each of the one or more second aggregate values may thereby be associated with a value of the characteristic of the calibration samples. The calibration value may include the one or more second aggregate values. For example, the calibration values may be points along a line or a curve relating known values of the characteristic with the second aggregate values.
At block 1870, a first value for a characteristic of the target tissue type is estimated using the comparison. The first value for the characteristic may be the known first value associated with the calibration value, which may have a value close to or equal to the aggregate value. In some embodiments, the first value for the characteristic may be determined from a function or a line with the one or more calibration values. The function or line may relate known first values to the one or more calibration values.
The target tissue type may be liver or hematopoietic cells. The target tissue type may be fetal tissue. In some embodiments, the biological sample may be obtained from a pregnant female subject, and the target tissue type may be placental tissue. In some embodiments, the target tissue type may be an organ that has cancer. The target tissue type may be any organ described herein. The characteristic may be a level of cancer or a nutrition status of an organ. For example, the nutrition status of the organ may be if the organ is healthy or not, including any intermediate levels measuring health of the organ. As another example, the characteristic may be gestational age. In another example, the determined characteristic can be the concentration of a particular tissue type (e.g., liver cells) relative to the concentration of the other tissue type (e.g., hematopoietic cells).
In some embodiments, process 1800 may include using size frequencies along with relative frequencies of sequence motifs. Process 1800 may include measuring sizes of the cell-free DNA fragments using the sequence reads. Process 1800 may further include determining one or more size frequencies of the sequence reads for one or more size ranges, which may be any size range described herein. An aggregate value for the one or more size frequencies may be determined. The aggregate value may be a sum of size frequencies or any value analogous to the aggregate value for the relative frequencies of sequence motifs. In some embodiments, the aggregate value may be an estimation of the histone modifications. The levels of the histone modifications can be determined by various types of data, e.g., amounts of end motifs or fragment sizes. The aggregate value for the one or more size frequencies may be compared to calibration values that are determined with calibration samples whose values for the characteristic of the target tissue type are known. Estimating the first value for the characteristic may include using the comparison of the aggregate value for size frequencies, Similar to the comparison of the aggregative value for relative frequencies of sequence motifs.
Process 1800 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described herein.
C. Measuring Amount of Histone Modification
Sequence motifs may be used to determine the amount of a histone modification. As shown with
1. Example Method for Determining Amount of Histone Modification Using Sequence Motifs
At block 1910, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads include ending sequences corresponding to ends of the plurality of cell-free DNA fragments. Block 1910 may be performed in a similar manner as block 1710.
At block 1920, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions has a histone modification associated with a target tissue type. Block 1920 may be performed in a similar manner as block 1720.
At block 1930, one or more sequence motifs corresponding to one or more ending sequences of a corresponding cell-free DNA fragment are determined for each sequence read of the group of sequence reads. Block 1930 may be performed in a similar manner as block 1730.
At block 1940, one or more relative frequencies of a set of the one or more sequence motifs are determined. The set of the one or more sequence motifs occurs at a higher rate or a lower rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation. Block 1940 may be performed in a similar manner as block 1740.
At block 1950, an aggregate value of the one or more relative frequencies is determined. Block 1950 may be performed in a similar manner as block 1750.
At block 1960, the aggregate value is compared to one or more calibration values. The one or more calibration values are determined from one or more calibration samples whose amounts of histone modifications are known. The amounts of histone modification in the one or more calibration samples may be known from performing ChIP-sequencing on each of the one or more calibration samples.
The one or more calibration values may be determined in the same manner as block 1760 or block 1860 but using calibration samples whose amounts of histone modifications are known. For example, the aggregate value determined from the biological sample may be a first aggregate value determined from one or more first relative frequencies. One or more second relative frequencies of the set of the one or more sequence motifs in the one or more genomic regions may be determined for each calibration sample of the one or more calibration samples. A second aggregate value may be determined for the one or more second relative frequencies for each calibration sample of the one or more calibration samples. Each of the one or more second aggregate values may thereby be associated with an amount of the histone modification of the calibration samples. The calibration value may include the one or more second aggregate values. For example, the calibration values may be points along a line or a curve relating known values of the characteristic with the second aggregate values.
At block 1970, an amount of histone modification in the one or more genomic regions is determined using the comparison. The amount of histone modification may be the known amount with the calibration value, which may have a value close to or equal to the aggregate value. In some embodiments, the amount of the histone modification may be determined from a function or a line with the one or more calibration values. The function or line may relate known amounts of the histone modification to the one or more calibration values. The amount of histone modification may be in the target tissue type.
Process 1900 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described herein.
Although
2. Example Method Using Fragmentomic Features
At block 2010, a plurality of sequence reads of the cell-free DNA fragments is received. Block 2010 may be performed in a similar manner as block 1710.
At block 2020, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions has a histone modification associated with a target tissue type. Block 2020 may be performed in a similar manner as block 1720.
At block 2030, a value of a fragmentomic feature of each cell-free DNA fragment corresponding to each sequence read in the group of sequence reads is determined. Fragmentomic feature may include fragment size, end motif, jagged-end (overhangs of one strand over the other), end nucleotide, topological form, and/or nucleosomal footprint. The fragmentomic feature may be any fragmentomic feature described herein.
For example, as described with
As another example, the fragmentomic feature may be a size, and the one or more value ranges are one or more size ranges, as described in section IV.E.
As an example, the fragmentomic feature may be the topological form, and the one or more value ranges are one or more topological forms. The topological form may be circular or linear.
As an example, the fragmentomic feature is the nucleosomal footprint, and the one or more value ranges are one or more nucleosomal footprints. The nucleosomal footprint represents the binding pattern of the nucleosome to genomic DNA. The spaces between nucleosomes can be a value of the nucleosomal footprint.
At block 2040, one or more relative frequencies of cell-free DNA fragments having values of the fragmentomic feature in a set of one or more value ranges are determined. The set of the one or more value ranges occurs at a differential rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation. The differential rate may be higher or lower and may be by a statistically significant amount. Block 2040 may be performed in a similar manner as block 1740 but using one or more value ranges of the fragmentomic feature instead of the one or more sequence motifs. In other embodiments, the set of the one or more value ranges determined by sequencing samples without cell-free chromatin immunoprecipitation are determined by focusing on genomic regions containing differential rates with higher or lower histone modification signals predetermined from other reference samples or databases.
At block 2050, an aggregate value of the one or more relative frequencies is determined. The aggregate value may be a sum of the one or more relative frequencies or a statistical measure (e.g., mean, median, mode, percentile) of the one or more relative frequencies.
At block 2060, the aggregate value is compared to one or more calibration values. The one or more calibration values are determined from one or more calibration samples whose amounts of histone modifications are known. The amounts of histone modification in the one or more calibration samples may be known from performing cfChIP-sequencing on each of the one or more calibration samples. The one or more calibration values may be determined in the same manner as block 1960 but using frequencies of one or more value ranges of a fragmentomic feature instead of one or more sequence motifs.
At block 2070, an amount of the histone modification in the biological sample is determined using the comparison. The amount of histone modification may be in the target tissue type. Block 2070 may be performed in a similar manner as block 1970.
The amount of histone modification may be used to determine a fractional concentration of a target tissue, a classification of a level of a disorder, or a classification of a transplant status of a target tissue type (e.g., as described with process 2000).
Although
3. Determining Fractional Concentrations Using Deconvolution
The fractional concentrations of multiple tissue types can be determined through a deconvolution process.
The plasma DNA ChIP signals across those informative genomic regions were compared with the patterns of ChIP signals across different tissues, deducing the proportional DNA contributions related to H3K27ac into plasma from different tissues. Graph 2120 shows the deduced proportional DNA contribution of different tissues.
Based on
A system of linear equations, one for each region, can be solved to determine the fractional concentrations for each tissue in a cell-free mixture, such as a plasma sample.
The set of linear equations is for m genomic regions and n tissues. HA represents the total histone modification amount in genomic region A in the sample, as may be measured using one or more sequence motifs. HB represents the total histone modification amount in genomic region B. HA and HB may represent the same or different histone modifications. Hm represents the total histone modification amount in genomic region m. The fractional concentration for target tissue 1 is f1, for target tissue 2 is f2, and for target tissue n is fn. Target tissue 1 is known to have an amount h1,A in genomic region A, an amount h1,B in genomic region B, and an amount h1,m in genomic region m. Target tissue 2 is known to have an amount h2,A in genomic region A, an amount h2,B in genomic region B, and an amount h2,m in genomic region m. Target tissue n is known to have an amount hn,A in genomic region A, an amount hn,B in genomic region B, and an amount hn,m in genomic region m. In some embodiments, the matrix H may represent the histone modification amounts as measured using one or more sequence motifs. H and h may not need to be directly calculated to solve for fractional concentrations if there are appropriate sequence motif amounts to use.
The amounts of histone modifications in target tissues in certain genomic regions (e.g., h1,A, h1,B, etc.) may be relative amounts. These amounts may be determined from a calibration sample. For instance, a calibration sample having half target tissue 1 and half target tissue 2 may show certain ratio of histone modification amounts, and that ratio can be used for h1,A and h1,B.
The number of equations should be more or equal to the number of target tissues in order to solve for the fractional concentrations. The number of equations can equal the number of genomic regions and therefore the number of genomic regions can equal the number of target tissues. If the sum of the fractional concentrations is known (e.g., sum is 1), then the number of genomic regions can equal the number of regions minus 1. With the histone modification amounts in each genomic region measured through using sequence motifs, the fractional concentrations can be determined by solving the system of equations.
Accordingly, in some embodiments, multiple tissue types may have the same or similar sequence motifs associated with histone modifications in the same genomic regions. The fractional concentration of each of these multiple tissue types can be determined through a deconvolution process. The deconvolution process may include solving a set of linear or nonlinear equations, such as the ones described herein.
The amount of histone modification may be determined as described with process 1900. In process 1900, the group of sequence reads is a first group of sequence reads. The one or more genomic regions are one or more first genomic regions. The set of the one or more sequence motifs is a set of one or more first sequence motifs. The one or more relative frequencies are one or more first relative frequencies. The aggregate value is a first aggregate value. The one or more calibration values are one or more first calibration values. The amount of histone modification is a first amount of histone modification. An example of the first amount is HA in the equations described above.
A second amount of histone modification in one or more second genomic regions may be determined for the system of linear equations. An example of the second amount is HB. The histone modification may be associated with a first tissue type and the second tissue type in the one or more first genomic regions.
The histone modification may be associated with the first tissue type and the second tissue type in one or more second genomic regions. For example, the one or more first genomic regions may be regions associated with region X in
A second group of sequence reads located in the one or more second genomic regions is identified. The identification may be performed in a similar manner as described with block 1920. Each of the one or more second genomic regions may have the histone modification associated with the first tissue type and the second tissue type. In some embodiments, the histone modification in the one or more second genomic regions may have a histone modification that is different from the one in the one or more first genomic regions.
For each sequence read of the second group of sequence reads, one or more second sequence motifs corresponding to one or more ending sequences of a corresponding cell-free DNA fragment are determined. The determination may be performed in a similar manner as described with block 1930.
One or more second relative frequencies of a set of the one or more second sequence motifs are determined. The set of the one or more second sequence motifs occurs at a higher rate in ChIP-seq for the histone modification associated with the one or more second genomic regions than in sequencing without chromatin immunoprecipitation. The determination may be performed in a similar manner as described with block 1940.
A second aggregate value of the one or more second relative frequencies is determined. The determination may be performed in a similar manner as described with block 1950.
The second aggregate value is compared to one or more second calibration values. The comparison may be performed in a similar manner as block 1960.
The second amount of histone modification in the one or more second genomic regions is determined using the comparison. The determination may be performed in a similar manner as block 1970.
The first fractional concentration of the first tissue type and the second fractional concentration of the second tissue type is determined by solving a system of linear or nonlinear equations. The system of linear equations may be the set of equations described herein. The system of linear equations may include the first amount of histone modification (e.g., HA), the second amount of histone modification (e.g., HB), and parameters specifying relative amounts of the respective histone modification for each tissue type in the one or more first genomic regions and the one or more second genomic regions (e.g., h1,A, h1,B, h2,A, h2,B). The first fractional concentration may be f1, and the second fractional concentration may be f2.
Biological samples may include more than two target tissue types. Methods for determining the fractional concentrations of two target tissue types can be extended for three or more tissue types.
In embodiments, the histone modification may be associated with a third tissue type in the one or more first genomic regions and the one or more second genomic regions. The histone modification may be associated with the first tissue type, the second tissue type, and the third tissue type in one or more third genomic regions. The process may involve performing similar steps as described for the second tissue type. The process may include determining a third amount of histone modification (e.g., Hm where m is C) in the one or more third genomic regions in the same manner as determining the second amount of histone modification. The third fractional concentration of the third tissue type may be determined by solving the system of linear or nonlinear equations. The system of linear equations may include the third amount of histone modification and parameters for relative amounts for each tissue type in the one or more third genomic regions.
D. Classifying Level of Disorder
Sequence motifs may be used to classify a level of a disorder. The disorder may be specific to a particular tissue type or may apply to the subject. Sequence motifs may indicate an amount or presence of a histone modification, and that amount or presence of a histone modification may be associated with a particular level of disorder. The amount or presence of the histone modification, however, may not need to be determined in order to use the sequence motifs to classify a level of a disorder.
At block 2310, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads include ending sequences corresponding to ends of the plurality of cell-free DNA fragments. Block 2310 may be performed in a similar manner as block 1710.
At block 2320, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions has a histone modification associated with one or more target tissue types. Block 2320 may be performed in a similar manner as block 1720.
At block 2330, one or more sequence motifs corresponding to one or more ending sequences of a corresponding cell-free DNA fragment are determined for each sequence read of the group of sequence reads. Block 2330 may be performed in a similar manner as block 1730.
At block 2340, one or more relative frequencies of a set of the one or more sequence motifs are determined. The set of the one or more sequence motifs occurs at a higher rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation. Block 2340 may be performed in a similar manner as block 1740.
At block 2350, an aggregate value of the one or more relative frequencies is determined. Block 2350 may be performed in a similar manner as block 1750.
At block 2360, the aggregate value is compared to one or more calibration values. The one or more calibration values are determined from one or more calibration samples whose classifications of the level of a disorder are known.
The one or more calibration values may be determined in the same manner as block 1760, block 1860, or block 1960, but using calibration samples whose classifications of the level of the disorder are known. For example, the aggregate value determined from the biological sample may be a first aggregate value determined from one or more first relative frequencies. One or more second relative frequencies of the set of the one or more sequence motifs in the one or more genomic regions may be determined for each calibration sample of the one or more calibration samples. A second aggregate value may be determined for the one or more second relative frequencies for each calibration sample of the one or more calibration samples. Each of the one or more second aggregate values may thereby be associated with a classification of the level of the disorder. The calibration value may include the one or more second aggregate values. For example, the calibration values may be points along a line or a curve relating known classifications of the level of the disorder with the second aggregate values.
At block 2370, a classification of a level of a disorder is determined using the comparison. The classification of the level of the disorder may be the known classification with the calibration value, which may have a value close to or equal to the aggregate value. In some embodiments, the classification of the level of the disorder may be determined from a function or a line with the one or more calibration values. The function or line may relate known classifications to the one or more calibration values. In some embodiments, the classification may be a level of an abnormality.
The disorder may be in the target tissue type. The disorder may be cancer of the target tissue type. The cancer may include hepatocellular carcinoma (HCC), colorectal cancer (CRC), or any cancer described herein. In some embodiments, the disorder is a pregnancy-associated disorder. The disorder may be a blood disorder. The disorder may be any disorder described herein.
In embodiments, process 2300 may include using size frequencies, as described with process 1800.
Process 2300 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described herein.
Although
A. Size Information for Deducing ChIP Signal
Plasma DNA size information can be used for detecting and quantifying histone modifications present in plasma DNA molecules. Like the relationship between cfDNA end motifs information and histone modification level, size information of cfDNA molecule may be influenced by histone modification level, i.e., epigenetic status. We analyzed the size information of cfDNA molecules within regions of interest. Those regions involving histone modifications can be grouped into different categories according to the magnitudes of ChIP signal. For example, the regions were first sorted according to the magnitudes of ChIP signal and then were empirically classified into 9 categories (e.g.,
B. Deduced ChIP Signals and Fetal Fraction
We further used a linear regression model to build a model (i.e., recalibration formula) for deducing the H3K4me3 ChIP signal in a region of interest or in a set of regions of interest. As an example, we trained a model for each sample for deducing the ChIP signals based on a size range of 250-350 bp, namely Y=aX+b where ‘Y’ represented the log-transformed ChIP signal, ‘X’ represented the percentage of cfDNA molecules within a size range of 250-350 bp from a particular genomic region of interest or a set of regions of interest for which histone modifications were to be determined. ‘a’ and were the slope and intercept, respectively. In one embodiment, we determined the percentage of cfDNA molecules within a size range of 250-350 bp from those placental-specific regions in terms of H3K4me3. We analyzed 30 plasma DNA samples of pregnant women. The size range of 250-350 bp was chosen for illustrative purposes. Other size ranges may also be used. Size ranges can be selected using a machine learning model.
In
We further used a linear regression model to build a model (i.e., recalibration formula) for deducing the H3K27ac ChIP signal in a region of interest or in a set of regions of interest. As an example, we trained a model for each sample for deducing the ChIP signals based on a size range of 250-350 bp, namely Y=aX+b where ‘Y’ represents the log-transformed ChIP signal, ‘X’ represents the percentage of cfDNA molecules within a size range of 250-350 bp from a particular genomic region of interest or a set of regions of interest for which histone modifications were to be determined. ‘a’ and ‘b’ represent the slope and intercept, respectively. In one embodiment, we determined the percentage of cfDNA molecules within a size range of 250-350 bp from those placental-specific regions in terms of H3K27ac. We analyzed 30 plasma DNA samples of pregnant women.
We analyzed different size ranges for deducing the H3K27ac ChIP signals and correlated the deduced H3K27ac ChIP signals with the tissue DNA fraction determined by SNP-based approach. We analyzed 30 plasma DNA samples of pregnant women. The size ranges of bp, 160-225 bp, and 230-350 bp were used for illustrative purposes. Other size ranges may also be used in some other embodiments.
As shown in
C. Deduced ChIP Signals and Cancer
In one embodiment, we explored whether the deduced ChIP signal of histone modification from plasma DNA without immunoprecipitation would be informative for cancer detection. We analyzed 34 patients with hepatocellular carcinoma (HCC), 17 subjects with chronic hepatitis B virus (HBV) and 8 healthy control samples.
The ROC analysis revealed that the deduced H3K27ac ChIP signal using the cumulative frequency of molecules within a size range of 230-350 bp in liver-specific H3K27ac regions achieved a significantly higher area under the receiver operating characteristic curve (AUC) of 0.934 for differentiating patients with HCC at the intermediate and advanced stages from patients without HCC, compared to that within a size range of 50-150 bp (AUC: 0.586) (P=0.001; Delong's test).
D. Deduced ChIP Signals and Transplants
We further analyzed plasma DNA sequencing results without immunoprecipitation for a cohort of 14 liver transplantation patients. The size ranges of 50-150 bp, 160-225 bp, and 230-350 bp were used for illustrative purposes. Other size ranges may also be used in some other embodiments.
As shown in
E. Example Method for Determining Histone Modification Using Sizes
At block 3610, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads may be obtained by random massively parallel sequencing. The plurality of sequence reads may be obtained using paired-end sequencing.
At block 3620, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions has a histone modification associated with one or more target tissue types. Block 3620 may be performed in a similar manner as block 1720.
At block 3630, a size of each cell-free DNA fragment corresponding to each sequence read in the group of sequence reads is measured. The size of a fragment can be measured using paired-end sequencing, aligning the sequence to a genome, and then deducing the size from the genome coordinates of the aligned sequences. In some embodiments, the size of a fragment may be measured by sequencing the entire fragment and then determining the size from the sequence.
At block 3640, one or more relative frequencies of cell-free DNA fragments having sizes in a set of one or more size ranges are determined. The set of the one or more size ranges may occur at a differential rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation. The differential rate may be higher or lower and may be by a statistically significant amount. The one or more size ranges may include 50 to 100 bp, 100 to 150 bp, 150 to 200 bp, 200 to 250 bp, 250 to 300 bp, 300 to 350 bp, 350 to 400 bp, 400 to 450 bp, 450 to 500 bp, over 500 bp, or any combination thereof.
At block 3650, an aggregate value of the one or more relative frequencies is determined. The aggregate value may be a sum of the one or more relative frequencies or a statistical measure (e.g., mean, median, mode, percentile) of the one or more relative frequencies.
At block 3660, the aggregate value is compared to one or more calibration values. The one or more calibration values are determined from one or more calibration samples whose amounts of histone modifications are known. The amounts of histone modification in the one or more calibration samples may be known from performing cfChIP-sequencing on each of the one or more calibration samples. The one or more calibration values may be determined in the same manner as block 1960 but using frequencies of one or more size ranges instead of one or more sequence motifs.
At block 3670, an amount of the histone modification in the biological sample is determined using the comparison. The amount of histone modification may be in the target tissue type. Block 3670 may be performed in a similar manner as block 1970.
The amount of histone modification may be used to determine a fractional concentration of a target tissue, a classification of a level of a disorder, or a classification of a transplant status of a target tissue type. The amount of histone modification can be determined using sequence motifs, fragmentomic features, or any other technique, in addition to size ranges.
In some embodiments, the amount of the histone modification may be compared to one or more second calibration values. The one or more second calibration values may be determined from one or more second calibration samples whose fractional concentrations of a target tissue type and amounts of histone modification are known. A fractional concentration of the target tissue type may be determined using the comparison of the amount of the histone modification to the one or more second calibration values.
In some embodiments, the amount of the histone modification may be compared to one or more third calibration values. The one or more third calibration values may be determined from one or more third calibration samples whose level of a disorder and amounts of histone modification are known. A classification of a level of a disorder is determined using the one or more third calibration values. The disorder may be any disorder described herein.
In some embodiments, the amount of the histone modification is compared to one or more fourth calibration values. The one or more fourth calibration values may be determined from one or more fourth calibration samples whose transplant status and amounts of histone modification are known. A classification of a transplant status of the target tissue type is determined using the one or more fourth calibration values. Classifications of a transplant status include whether the transplanted organ is rejected by the subject.
Although
V. Tissue Contributions Deduced from Histone Modifications
The characteristic size profile of cfDNA shows a modal frequency at approximately 166 bp, with smaller molecules forming a series of peaks in a 10-bp periodicity (Lo et al. Sci Transl Med. 2010; 2:61ra91). Such size patterns of plasma DNA fragments suggest the presence of histone proteins bound to cfDNA molecules. One recent study revealed the presence of histone modifications associated with cfDNA molecules in plasma, using cell-free chromatin immunoprecipitation followed by sequencing (cfChIP-seq) (Sadeh et al. Nat Biotechnol. 2021; 39:586-598). However, Sadeh et al's study did not provide any approach for deducing the percentage contribution of chromatin modifications from various tissues/organs.
Sadeh et al. analyzed the average number of reads per kilobase across genomic regions associated with a tissue-specific histone modification of a tissue as a signal to indicate the contribution from that tissue. The tissue-specific regions deduced from reference tissues were considered as independent factors when analyzing those signals (Sadeh et al. 2021). One limitation of the method described by Sadeh et al. is that when a tissue lacks the tissue-specific histone modifications or the number of regions showing tissue-specific histone modifications is not sufficient in a tissue, the DNA contribution from the tissue cannot be accurately deduced. The method in Sadeh relies on the absolute signals of histone modifications in plasma regarding a tissue-specific region. However, the relative strength of the signals of histone modifications in each reference tissue was not taken into account in this approach by Sadeh et al., likely leading to the inaccurate analysis or no analysis.
For example, the reads per kilobase in a genomic region related to a histone modification for a tissue may be governed by at least two factors: the first factor is the percentage of DNA (including DNA not related to a histone modification) contributed by such a tissue, and the second factor is the level of histone modification present in that tissue. The analysis adjusted by the level of histone modification present in that tissue is important for the tissue contribution analysis based on histone modifications. Sadeh et al. attempted to analyze percentage contribution from the liver using linear regression. The plasma DNA of healthy subjects was considered to have 0% liver contribution, and DNA from liver tissue was considered to have 100% liver contribution. The differences in histone modifications between the liver tissues and plasma DNA of healthy subjects were used to determine the liver contribution in other plasma DNA samples (Sadeh et al. Nat Biotechnol. 2021). Such an analysis did not use histone modification signals from two or more tissues. Plasma DNA includes contributions from various tissues, and the liver contributions to plasma may vary across healthy subjects. Thus, the assumption for linear regression analysis may not hold true under the circumstances.
Hence, the contributions from two or more tissues being analyzed cannot be accurately deduced in Sadeh et al.'s approach. The strength of histone modification signal from each tissue is important in quantitatively analyzing signals present in plasma cfDNA. The strength of histone modification signal may refer to the percentage of cells harboring the histone modification of interest in a tissue, which can be measured by the depth of sequencing read coverage present in ChIP-seq. The approaches, by not using the signals of histone modifications across different tissues, would greatly deteriorate the performance in determining the contributions of cfDNA with histone modifications into plasma from different tissues.
In this disclosure, we developed approaches of comparing the relative signals of histone modifications plasma DNA with the signals from reference tissues to deduce the percentage contribution from each cell type or tissue, herein referred to as plasma DNA tissue mapping by histone modifications. In one embodiment, such comparison would consider the signals of modified histone from various tissues as covariates to deconvolute the percentage contributions from various tissues to plasma, for example, but not limited to, using quadratic programming, non-negative least squares (NNLS), etc. Sun et al. demonstrated that comparing methylation signals of plasma DNA with methylation signals of various tissues allowed deduction of the percentage contributions of DNA molecules into plasma across tissues through the use of quadratic programming (Sun et al., Proc Natl Acad Sci USA. 2018; 115:E5106). However, the histone modification would occur at amino acid sequences of histone proteins, where the signal properties of modified signal are distinct from DNA methylation. The procedures of signal processes in DNA methylation analysis could not be used for modified histones. Histone modifications involve post-translational modification of a histone protein, which impacts their interactions with DNA. By contrast, the DNA methylation is a biochemical process where a DNA base, usually cytosine, is enzymatically methylated at the 5-carbon position. Histone modification and methylations involve different types of biochemical machinery. In some embodiments of the disclosure, one could deduce the contribution of histone modification into plasma through comparing the number of DNA immunoprecipitated via one or more antibodies of interest with the counterpart measures across various reference tissues. In contrast to the approach used by Sadeh et al.'s study in which only the tissue-specific histone modifications were informative, the approach present in this disclosure could make use of both tissue-specific histone modifications and tissue-variable histone modifications.
A. Plasma DNA Tissue Mapping by Histone Modifications
In embodiments, the percentage contribution of DNA into plasma from various cell types could be determined by comparing the profile of plasma DNA histone modifications with profiles of histone modifications derived from a number of organs, tissues, or cells. For example, one could apply H3K27ac ChIP-seq to a number of tissues including, but not limited to, neutrophils, megakaryocytes, T cells, B cells, erythrocytes, monocytes, natural killer cells, or cells from the liver, colon, adipose tissues, brain, pancreas, placenta, heart, lung, kidney, spleen, bladder, stomach, etc. One could determine informative genomic regions carrying tissue-specific histone modifications (e.g., H3K27ac). An informative genomic region refers to a region that preferentially enriched a certain histone modification (e.g., H3K27ac) in a particular tissue (e.g., the liver) but was relatively depleted of such modification in other tissues. Such regions could be referred to tissue-specific histone modification regions (e.g., tissue-specific H3K27ac regions). In some embodiments, an informative genomic region referred to a region that showed variable signals of certain histone modification (e.g., H3K27ac) across tissues of interest. The variable signals could be defined by the coefficient of variation (CV) of the histone signal that exceeded but not limited to 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, etc. and the difference in modified histone signal between maximum and minimum exceeded a certain cutoff, such as but not limited 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100, 500, 1000, 5000, 10,000 reads per kilobase, etc. Such regions can be defined as tissue-variable histone modification regions (e.g., tissue-variable H3K27ac regions).
As different pathological or physiological states would alter chromatin status in certain cell types, we conjectured that the analysis of histone modifications of cfDNA molecules would allow noninvasive detection and monitoring of diseases, for example, fetal abnormalities in pregnant women, cancer, autoimmune diseases, the presence of transplant rejection, blood disorders, etc.
B. Examples of Plasma DNA Tissue Mapping by Histone Modifications
Deduced histone modification signals can be used to determine fetal DNA fraction, to determine specific tissue contributions to the sample, to classify subjects as pregnant or non-pregnant, and to classify subjects with a likelihood of a disorder (e.g., cancer).
1. Biological Samples from Pregnant Females
We recruited 19 pregnant samples, with a median gestation age of 38 weeks. Plasma was isolated from whole blood within 6 hours of sample collection through sequential steps of centrifugation: centrifugation at 1,600 g for 10 minutes followed by re-centrifugation of the plasma portion at 16,000 g for another 10 minutes. Plasma could be stored at −80° C. We used two types of histone modifications (H3K27ac and H3K4me3) as examples. Antibody conjugated beads were incubated with plasma by rotating overnight at 4° C. and washing with wash buffer, and the immunoprecipitated DNA was ligated with barcoded adapters on beads. The DNA was eluted, followed by the amplification through PCR. DNA library was sequenced in multiplex sequencing together with several other libraries by the Illumina platform (e.g., Nextseq 500 or NovaSeq 6000), with a median of 4.30 million paired-end reads (range: 0.10-30.73). We performed H3K27ac ChIP-seq for 19 pregnant samples, 13 non-pregnant samples, and 12 samples with hematological diseases (10 beta-thalassemia major samples, 1 iron deficiency anemia sample, and 1 aplastic anemia sample). Moreover, we performed H3K4me3 ChIP-seq for 12 pregnant women, 4 non-pregnant healthy subjects and 4 patients with hematological diseases (2 with beta-thalassemia major, 1 with iron deficiency anemia, and 1 with aplastic anemia sample).
The fetal DNA fraction in maternal plasma for each pregnant woman was calculated based on a single nucleotide polymorphism (SNP)-based approach (Lo et al. Sci Transl Med. 2010; 2:61ra91). The genotypes regarding the maternal buffy coat and placental tissue samples were obtained using microarray-based genotyping technology (Illumina Infinium Omni 2.5-8 array), and informative SNPs were identified (i.e., where the mother was homozygous (denoted as AA genotype), and the fetus was heterozygous (denoted as AB genotype)). Fetal-specific DNA fragments were identified according to the DNA fragments carrying fetal-specific alleles at informative SNP sites. In this scenario, the B allele was fetal-specific, and the DNA fragments carrying the B allele were deduced to be originated from fetal tissues. The number of fetal-specific molecules (p) carrying the fetal-specific alleles (B) was determined. The number of molecules (q) carrying the shared alleles (A) was determined. The fetal DNA fraction across all cell-free DNA samples would be calculated by 2p/(p+q)*100%.
ChIP-seq data for various tissues were obtained from public databases for illustration purposes. The public databases used herein included, but not limited to, the Blueprint project (blueprint-epigenome.eu/), the ENCODE project (encodeproject.org/), and the Roadmap project (roadmapepigenomics.org/). In total, we obtained H3K27ac ChIP-seq results from 18 tissue types, including but not limited to neutrophils, monocytes, B cells, T cells, natural killer cells, erythroblast cells, and megakaryocytes, the liver, brain, pancreas, placenta, heart, colon, lung, adipose, kidney, spleen, and bladder), with a median of 22.5 million paired-end/single-end reads (range: 12-45 million). Additionally, we obtained H3K4me3 ChIP-seq data from 19 tissues, including but not limited to neutrophils, monocytes, B cells, T cells, natural killer cells, erythroblast cells, megakaryocytes, the liver, brain, pancreas, placenta, heart, colon, lung, adipose, kidney, spleen, bladder, and stomach, with a median of 25 million paired-end reads (range: 7-32 million).
Based on ChIP-seq data from various tissues, we determined informative genomic regions which carried tissue-specific histone modifications. In one embodiment, one could analyze a number of genomic regions that were known to be enriched in a particular type of histone modifications. For example, H3K4me3 was known to preferentially occur at regions nearby transcriptional start sites (i.e., promoter regions). Hence, one could determine ChIP signals across regions nearby a transcriptional start site (TSS). In one embodiment, the ChIP signal for a region of interest can be determined by the percentage of sequencing reads overlapping such a region among the total mapped reads. In another embodiment, the ChIP signal for a region of interest can be determined by the percentage of sequencing reads overlapping with such a region among the total mapped reads related to all regions of interest. The ChIP signals would be adjusted by GC biases and mapping biases, expressing as fragments per kilobase per million (i.e., FPKM) analyzed fragments.
In one embodiment, according to the ChIP signals identified from a number of tissues/organs, a human reference genome would be classified as regions with the presence of certain histone modifications (e.g., H3K27ac) (denoted as regions of interest [ROIs]), and regions with the absence of such said histone modifications (denoted as background regions). ChIP-seq reads of plasma DNA present in background regions might be due to non-specific antibody (Ab) binding during the experimental process, which was considered as background noise. The raw ChIP signal of an ROI was determined as the number of fragments for which the end fell within that ROI. In some embodiments, the raw ChIP signal of a ROI was determined as the number of fragments for which at least one or more nucleotides in a molecule overlapped with that ROI. The raw signal of a ROI can be deducted by the background noise across background regions surrounding such a ROI being interrogated.
Taking H3K27ac as an example, we divided the genome into non-overlapping 5-Mb windows. For each 5-Mb window, we calculated the raw signals in ROIs (N regions) that were bound by H3K27ac according to the ChIP results shown in the ENCODE and Blueprint projects. The remaining regions (M regions) were deemed background regions for determining the noise. Poisson distribution could be used for estimating the average sequence depth per one kilobase (kb) across M background regions, referred to as estimated background noise. The raw ChIP signals across N ROIs deducted by the estimated background noise (i.e., noise-deducted ChIP signals) would be used for the downstream analysis. To minimize the influence of sequencing depths on the comparison of ChIP signals between samples, we determined the scaling factors of sequencing depth across samples using sequencing reads from those regions that were shown to be bound by H3K27ac across various samples. The noise-deducted ChIP signals would be adjusted by the corresponding scaling factors of sequencing depth. In one embodiment, one could further express the ChIP signals aforementioned as fragments per kilobase per million (FPKM). In some embodiments, for the background noise estimation, a number of overlapping windows could be used. The window sizes could be, but not limited to, 10 kb, 50 kb 100 kb, 500 kb, 1 Mb, 2 Mb, 3 Mb, 4 Mb, 5 Mb, 10 Mb, etc.
The regions carrying tissue-specific histone modifications (i.e., tissue-specific regions) can be determined using the following criteria:
In one embodiment, the selected regions were not necessarily restricted to tissue-specific regions. One could use region(s) showing a high variability in histone modification signals across the panel of tissues of interest for analysis (tissue-variable regions). These regions could be determined using the following criteria:
For plasma ChIP-seq data of H3K27ac, the number of plasma DNA fragments with their 5′ end overlapping each tissue-specific region of H3K27ac was determined. The normalized ChIP signal in FPKM was calculated for each tissue-specific region accordingly. Similarly, for plasma DNA ChIP data of H3K4me3, the number of plasma DNA fragments with their 5′ end overlapping each tissue-specific region of H3K4me3 was determined. The normalized ChIP signal was calculated for each tissue-specific region accordingly. Comparing the ChIP signals of plasma DNA to ChIP signals from various tissues allowed us to deduce the DNA contribution into the plasma DNA pool that is related to histone modifications of interest.
In one embodiment, the measured ChIP signal levels of DNA molecules were recorded in a vector (X) and the retrieved reference ChIP signal levels across different tissues were recorded in a matrix (M). The proportional contributions (P) from different tissues to plasma DNA pool were deduced by quadratic programming:
i=Σk(pk×Mik),
where
The aggregated DNA contribution related to a particular type of histone modifications from all cell types would be constrained to be 100%:
Σkpk=100%,
Furthermore, any contribution from a cell type would be required to be non-negative:
p
k≥0,∀k
Hence, pk could be deduced by, but not limited to, quadratic programming with a program written in Python (python.org) or R language (r-project.org). In some other embodiments, one could use, but not limited to, linear or non-linear regression, non-negative least squares, Bayesian framework, etc. In some embodiments the regions used for tissue contribution deduction could be tissue-specific regions only, or tissue-variable regions only, or the combination of both tissue-specific and tissue-variable regions.
2. Simultaneous Tissue Contribution Analysis
The particular tissue contribution of interest can be determined based on the deduced H3K27ac histone modification signals (ChIP signals in this disclosure) related to the tissue-specific histone modification regions. In one embodiment, the amount of histone modification may be deduced by fragmentomic features. In one embodiment, one can use various tissue-specific histone modification regions to analyze contributions from multiple tissue types simultaneously. As an example, we analyzed the plasma DNA samples of 8 healthy subjects. For each sample, we deduced the H3K27ac ChIP signal for the regions carrying histone modifications of H3K27ac specific to different tissues. The H3K27ac ChIP signals were deduced using the cumulative frequency of molecules within a size range of 230 to 350 bp.
Comparing the deduced H3K27ac ChIP signals across various tissue specific regions, neutrophil-specific regions showed the highest median levels compared to other tissues, suggesting neutrophils as the major contributor for plasma cfDNA. The contribution of each tissue may related to the ChIP signal. For example, one may determine that monocytes and megakaryocytes may be the next major contributors. The tissues with the least contribution may be placenta and colon. These observations were in line with the previous studies for healthy individuals, by which neutrophils were proved to be the major contributor of the plasma DNA (K. Sun, et al., Proc Natl Acad Sci USA. 2015; 112; E5503-E5512).
3. Classifying Pregnant Subjects
ChIP signals may be used to determine the fetal DNA fraction or for differentiating pregnant and non-pregnant subjects.
4. Samples from Subjects with Cancer
In one embodiment, although there was no colon specific H3K4me3 regions (
C. Detecting and Monitoring Diseases
Histone modification levels measured by embodiments in this disclosure can be used to determine a classification of a likelihood of a blood disorder and a classification of a level of cancer, including whether the cancer has metastasized. Biological samples from subjects with beta-thalassemia major were analyzed for histone modification levels. Beta-thalassemia major is an example of a blood disorder. Other blood disorders would be expected to have similar anomalous results at least because blood disorders may have abnormal contributions from cells in the blood. Biological samples from subjects with colorectal cancer (CRC), were analyzed for histone modification levels. CRC is an example of a cancer. Other cancers would be expected to have similar histone modification levels when the cancer is localized to a tissue or when the cancer metastasized to another tissue.
1. Blood Disorders
To demonstrate the clinical utility with the use of histone modification-based plasma DNA tissue deconvolution, we recruited patients with hematological diseases such as, but not limited to, beta-thalassemia major, iron deficiency anemia, aplastic anemia, and idiopathic thrombocytopenia purpura. We applied H3K27ac based immunoprecipitation assay followed by massively parallel sequencing to those plasma DNA samples.
In addition, we used a published ddPCR assay to measure erythroid DNA in those plasma DNA samples using a differentially methylated region that was hypomethylated in erythroblasts but hypermethylated in other cell types (Lam et al. Clin Chem. 2017; 63:1614-1623).
2. Cancer with Metastasis
The deduced ChIP signal of histone modification from plasma DNA without immunoprecipitation can be used to differentiate between localized cancer and metastatic cancer. We analyzed a cohort of 4 localized colorectal cancer (CRC) patients, 7 CRC patients with liver metastasis, and 8 healthy control samples. For each sample, we deduced the H3K27ac ChIP signals for colon- and liver-specific regions. The H3K27ac ChIP signals were deduced using the cumulative frequency of molecules within a size range of 230 to 350 bp.
D. Example of Urine DNA Tissue Mapping
We have illustrated that the relative tissue contribution to the plasma DNA pool can be deduced by comparing the profile of plasma DNA histone modifications with profiles of histone modifications derived from a number of organs, tissues, or cells. We further demonstrated that these methods present in this disclosure could be extended to urine samples.
The urine DNA samples showed significantly higher percentage contributions of kidney (median: 10.66%) and bladder (median: 4.98%) than counterparts in plasma DNA samples (median of kidney: 0.00%, median of bladder: 0.00%), which is expected from urine samples. These results demonstrate that urine samples can be used to determine tissue contribution using deduced histone modification levels.
E. Example Method for Determining Fractional Concentration
At block 5110, N genomic regions are identified. N is an integer greater than 1. The N genomic regions may be regions that are known to carry tissue-specific histone modifications. The region may be determined by criteria described herein. For instance, the region may have a histone modification level for a tissue that is greater than a cutoff amount. The cutoff amount may be a normalized ChIP signal, be based on a relative percentage difference, and/or based on a coefficient of variation across all tissue types. The region may be any region of interest described herein.
At block 5120, for each of M tissue types, N tissue-specific histone modifications levels at the N genomic regions are obtained. N is greater than or equal to M. The histone modification may be H3K27ac, H3K4me3, or any histone modification described herein. The tissue histone modification levels form a matrix A of dimensions N by M. One of the M tissue types corresponds to a first tissue type. The first tissue type may be fetal, erythroblast, any tissue listed in
At block 5130, an input data vector b is received. The input data vector b may include N mixture histone modification levels at the N genomic regions. The N mixture histone modification levels may be measured from a plurality of cell-free DNA molecules in a biological sample of a subject. The biological sample may be any biological sample described herein. The N mixture histone modification levels may be measured by cell-free chromatin immunoprecipitation followed by sequencing (cfChIP-seq), by determining one or more relative frequencies of a set of one or more sequence motifs in the plurality of cell-free DNA molecules, or by determining one or more relative frequencies of one or more size ranges in the plurality of cell-free DNA molecules. Relative frequencies of fragmentomic features other than sequence motifs and size ranges can also be used. The mixture histone modification levels may be determined by any method described herein.
At block 5140, a fractional concentration of the first tissue type is determined, using a computer system and using matrix A and input data vector b. The fractional contribution may be determined using quadratic programming.
Process 5100 may include determining classifications using the fractional concentration. For example, the first tissue type may be a fetal tissue, and process 5100 may further include determining a classification of a pregnancy in the subject using the fractional concentration of the first tissue type. The classification of the pregnancy may be whether the pregnancy exists, a gestational age (e.g., trimester) of the fetus, or a level (e.g., existence) of a pregnancy-associated disorder.
As another example, process 5100 may include determining a classification of a disease using the fractional concentration of the first tissue type. For example, the disease may be beta-thalassemia major, iron deficiency anemia, aplastic anemia, or idiopathic thrombocytopenia purpura. The first tissue type may be erythroblasts, monocytes, brain, T cells, neutrophils, megakaryocytes, or any other tissue described herein. The level of the disease may be whether the disease exists or a severity of the disease. The disease may be a disease (e.g., cancer) of the first tissue type.
Although
F. Example Method for Determining Classification of Pregnancy or Disease
At block 5210, N genomic regions are identified. Block 5210 may be performed in the same manner as block 5110.
At block 5220, for each of M tissue types, N tissue-specific histone modifications levels at the N genomic regions are obtained. N is greater than or equal to M. Block 5220 may be performed in the same manner as block 5120.
At block 5230, an input data vector b is received. Block 5230 may be performed in the same manner as block 5130.
At block 5240, either a classification of a pregnancy in the subject or a classification of a disease in the subject may be determined using a computer system, the matrix A, and the input data vector b. The classification of the pregnancy or the classification of the disease may be any classification described with process 5100. Process 5200 may determine the classification without determining a fractional concentration of a tissue type.
Determining the classification of the pregnancy or the classification of the disease may include inputting the matrix A and the input data vector b into a model (e.g., a machine learning model). The model may be trained by receiving the matrix A and a plurality of training input data vectors b obtained from a plurality of biological samples of a plurality of training subjects. Each training subject may have a known classification of a condition of the training subject. The condition may be a status of a pregnancy or a known classification of the disease or any condition described herein. A plurality of training samples may be stored. Each training sample may include one of the plurality of training input data vectors b and a first label indicating the known classification of the condition. Parameters of the model may be optimized, using the plurality of training samples, based on outputs of the model matching or not matching corresponding labels of the first labels when the matrix A and the plurality of training input data vectors b are input to the model. An output of the model may specify the classification of the condition. The classification of the condition may be determined using the model.
The model may include a convolutional neural network (CNN). The CNN may include a set of convolutional filters configured to filter the plurality of input data vectors b. The filter may be any filter described herein. The number of filters for each layer may be from 10 to 20, 20 to 30, 30 to 40, 40 to 50, 50 to 60, 60 to 70, 70 to 80, 80 to 90, 90 to 100, 100 to 150, 150 to 200, or more. The kernel size for the filters can be 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, from 15 to 20, from 20 to 30, from 30 to 40, or more. The CNN may include an input layer configured to receive the filtered plurality of input data vectors b. The CNN may also include a plurality of hidden layers including a plurality of nodes. The first layer of the plurality of hidden layers coupled to the input layer. The CNN may further include an output layer coupled to a last layer of the plurality of hidden layers and configured to output an output data structure. The output data structure may include the properties.
The model may include a supervised learning model. Supervised learning models may include different approaches and algorithms including analytical learning, artificial neural network, backpropagation, boosting (meta-algorithm), Bayesian statistics, case-based reasoning, decision tree learning, inductive logic programming, Gaussian process regression, genetic programming, group method of data handling, kernel estimators, learning automata, learning classifier systems, minimum message length (decision trees, decision graphs, etc.), multilinear subspace learning, naive Bayes classifier, maximum entropy classifier, conditional random field, Nearest Neighbor Algorithm, probably approximately correct learning (PAC) learning, ripple down rules, a knowledge acquisition methodology, symbolic machine learning algorithms, subsymbolic machine learning algorithms, support vector machines, Minimum Complexity Machines (MCM), random forests, ensembles of classifiers, ordinal classification, data pre-processing, handling imbalanced datasets, statistical relational learning, or Proaftn, a multicriteria classification algorithm The model may linear regression, logistic regression, deep recurrent neural network (e.g., long short term memory, LSTM), Bayes classifier, hidden Markov model (HMM), linear discriminant analysis (LDA), k-means clustering, density-based spatial clustering of applications with noise (DB SCAN), random forest algorithm, support vector machine (SVM), or any model described herein.
As part of training a machine learning model, the parameters of the machine learning model (such as weights, thresholds, e.g., as may be used for activation functions in neural networks, etc.) can be optimized based on the training samples (training set) to provide an optimized accuracy in classifying the modification of the nucleotide at the target position. Various form of optimization may be performed, e.g., backpropagation, empirical risk minimization, and structural risk minimization. A validation set of samples (data structure and label) can be used to validate the accuracy of the model. Cross-validation may be performed using various portions of the training set for training and validation. The model can comprise a plurality of submodels, thereby providing an ensemble model. The submodels may be weaker models that once combined provide a more accurate final model.
Although
In embodiments, one or both of fragment sizes and sequence motifs can be used to classify a pregnancy or disorder. For example, end motifs can be used as described elsewhere in this application, including in section III.D and process 2300 of
A. Example Results
The arrays also include frequencies of all molecules with the 9 H3K27ac-associated end motifs (the molecules not being limited to any fragment size when considering end motifs. H3K27ac-associated end motifs include, but are not limited to CCGG, CCGC, GCGG, TCGG, TCGC, CCGA, CCCG, GCGC, and/or CCGT. The H3K27ac-associated end motifs may be defined by end motifs that are overrepresented in regions with high H3K27ac signal compared to regions with low H3K27ac signal in the sequenced result of plasma DNA samples without immunoprecipitation. For example, the overrepresentation may be a fold change in an end motif frequency of 1×, 2×, 3×, 4×, 5×, 6×, 7×, 8×, 9×, 10×, 20×, 30×, 50×, etc, when comparing the results of plasma DNA samples in regions with high and low H3K27ac signal. In some embodiments, the H3K27ac-associated end motifs may be defined by those motifs that are overrepresented in the sequenced result of plasma DNA samples with immunoprecipitation compared to the result of plasma DNA samples without immunoprecipitation. For example, the overrepresentation may be a fold change in an end motif frequency of 1×, 2×, 3×, 4×, 5×, 6×, 7×, 8×, 9×, 10×, 20×, 30×, 50×, etc, when comparing the results of plasma DNA samples with and without immunoprecipitation.
The data from all arrays (i.e., one larger array or a matrix) can be input into a machine learning model to differentiate between non-HCC and HCC subjects. A machine learning model may include, but is not limited to, support vector machine, random forest, convolutional neural network, or any model described herein. In this example, there are a total of 130 features for one type of tissue-specific H3K27ac-related region. With the four different tissue-specific regions, there are 520 features.
B. Example Method
At block 5610, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads may include ending sequences corresponding to ends of the cell-free DNA fragments.
At block 5620, a group of sequence reads located in one or more genomic regions is identified. Each of the one or more genomic regions may have a histone modification associated with one or more target tissue types. The one or more target tissue types may include an organ that has cancer or fetal tissue. In some embodiments, the one or more target tissue types may include liver, neutrophils, megakaryocytes, or erythroblasts. The histone modification may be H3K4me1, H3K4me2, H3K27me3, H3K27ac, H3K36me3, H3K9me2, H3K9me3, H3S10P, H3R2me, H3T2P, H3K14ac, H3K9ac, H3K79me2, H3K79me3, H4K5ac, H4K8ac, H4K12ac, H4K16ac, H4K20me, H2BK120ub, or H2AK119ub.
At block 5630, one or more sequence motifs corresponding to one or more ending sequences of a corresponding cell-free DNA fragment are determined for each sequence read of the group of sequence reads. The set of the one or more sequence motifs may include 1 to 5, 5 to 11 to 15, 15 to 20, or 20 to 25 sequence motifs. The cell-free DNA fragments may consist of fragments with a sequence motif of the set of the one or more sequence motifs.
At block 5640, sizes of the cell-free DNA fragments using the sequence reads are measured. The cell-free DNA fragments may have sizes with a predetermined size range. The predetermined size range may be any size range described herein, including 230-350 nt.
At block 5650, one or more sequence motif frequencies of a set of the one or more sequence motifs are determined for each of the one or more target tissue types. The set of the one or more sequence motifs occurs at a higher rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for the histone modification associated with the one or more genomic regions than in sequencing without chromatin immunoprecipitation.
At block 5660, one or more size frequencies of the sequence reads for one or more size ranges are determined for each of the one or more target tissue types.
At block 5670, the one or more sequence motif frequencies and the one or more size frequencies for each of the one or more target tissue types are input into a machine learning model. The machine learning model may include support vector machine, random forest, or convolutional neural network. The machine learning model may be any machine learning model disclosed herein, including a similar model to one described with process 5200.
The machine learning model may be trained by receiving a training data set. The training data set may include for each of the one or more target tissue types, training sequence motif frequencies of the set of the one or more sequence motifs and training size frequencies of cell-free DNA fragments from a plurality of biological samples of a plurality of training subjects. Each training subject may have a known classification of a condition.
The machine learning model may also be trained by storing a plurality of training samples. Each training sample may include for each of the one or more target tissue types, one or more training sequence motif frequencies of the set of the one or more sequence motifs occurring in cell-free DNA fragments in the training sample. Each training sample may include for each of the one or more target tissue types, training size frequencies of the cell-free DNA fragments in the training sample. Each training sample may also include a first label indicating a known classification of a condition.
The machine learning model may be trained by optimizing, using the plurality of training samples, parameters of the machine learning model based on outputs of the machine learning model matching or not matching corresponding labels of the first labels when the sequence motif frequencies and the size frequencies for each of the one or more target tissue types are input to the machine learning model. An output of the machine learning model may specify the classification of the condition.
In some embodiments, process 5600 may include, for each sequence motif of the set of the one or more sequence motifs, determining a size parameter of fragments having the respective sequence motif. A size parameter may be a statistical value (e.g., mean, median, mode, percentile) of the fragments having the respective sequence motif. Process 5600 may further include inputting the one or more size parameters into the machine learning model. The machine learning model in these embodiments may be trained with training samples including the determined size parameters.
At block 5680, a classification of a condition of a subject is determined using the machine learning model. The condition may be a pregnancy. For example, the classification of the pregnancy may provide a gestational age or the existence or severity of a pregnancy-associated disorder, including any pregnancy-associated disorder described herein. The condition may be a disease. The classification of the disease may be the existence or severity of the disease. The disease may be cancer, including hepatocellular carcinoma (HCC) or any cancer described herein.
In some embodiments, process 5600 may be modified such that either the sequence motif frequencies or the size frequencies are used. For example, process 5600 may include using only the size frequencies of molecules within a certain size range (e.g., first column in
Process 5600 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described elsewhere herein.
Although
The preference of DNA fragments associated with a certain epigenome status to exhibit a particular set of end motifs can be used to enrich a sample for DNA with that particular epigenome status. Accordingly, embodiments can enrich a sample for clinically-relevant DNA, including DNA from a particular tissue. For example, only DNA fragments having a particular ending sequence may be sequenced, amplified, and/or captured using an assay. As another example, filtering of sequence reads can be performed.
A. Physical Enrichment
Physical enrichment may be performed in various ways, e.g., via targeted sequencing or PCR, as may be performed using particular primers or adapters. If a particular end motif of an ending sequence is detected, then an adapter can be added to the end of the fragment. Then, when sequencing is performed, only DNA fragments with the adapter will be sequenced (or at least predominantly sequenced), thereby providing targeted sequencing.
As another example, primers that hybridize to the particular set of end motifs can be used. Then, sequencing or amplification can be performed using these primers. Capture probes corresponding to the particular end motifs can also be used to capture DNA molecules with those end motifs for further analysis. Some embodiments can ligate a short oligonucleotide to the end of a plasma DNA molecule. Then, a probe can be designed such that it would only recognize a sequence that is partially the end motif and partially the ligated oligonucleotide
Some embodiments can use CRISPR-based diagnostic technology, e.g. using a guide RNA to localize a site corresponding to a preferred end motif for the clinically-relevant DNA and then a nuclease to cut the DNA fragment, as may be done using Cas-9 or Cas-12. For example, an adapter can be used to recognize the end motif, and then CRISPR/Cas9 or Cas-12 can be used to cut the end motif/adaptor hybrid and create a universal recognizable end for further enrichment of the molecules with the desired ends.
At block 5710, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads include ending sequences corresponding to ends of the plurality of cell-free DNA fragments. One or more sequence motifs may correspond to one or more ending sequences of each cell-free DNA fragment. Block 5710 may be performed in a similar manner as block 1710.
At block 5720, a set of the one or more sequence motifs is identified. The set of the one or more sequence motifs occur at a higher rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for a histone modification in the clinically-relevant DNA than in sequencing without chromatin immunoprecipitation. Identifying the sequence motifs may be similar to the procedure described with process 1700 and with
At block 5730, the plurality of cell-free DNA fragments may be subjected to one or more probe molecules that detect the set of one or more sequence motifs in the ending sequences of the plurality of cell-free DNA fragments, thereby obtaining detected DNA fragments. Such use of probe molecules can result in obtaining detected DNA fragments. In one example, the one or more probe molecules can include one or more enzymes that interrogate the plurality of cell-free DNA fragments and that append a new sequence that is used to amplify the detected DNA fragments. In another example, the one or more probe molecules can be attached to a surface for detecting the sequence motifs in the ending sequences by hybridization.
At block 5740, the detected DNA fragments are used to enrich the biological sample for the clinically-relevant DNA fragments. In some embodiments, using the detected DNA fragments to enrich the biological sample may include amplifying the detected DNA fragments. In some embodiments, using the detected DNA fragments to enrich the biological sample for the clinically-relevant DNA fragments may include capturing the detected DNA fragments and discarding non-detected DNA fragments.
Process 5700 may further include analyzing the enriched biological sample to determine a tissue of origin or a classification of a level of a disease. Analyzing the enriched biological sample may include sequencing DNA fragments in the enriched biological sample.
Process 5700 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described herein.
Although
B. In Silico Enrichment
The in silico enrichment can use various criteria to select or discard certain DNA fragments. Such criteria can include end motifs, open chromatin regions, size, sequence variation, methylation and other epigenetic characteristics. Epigenetic characteristics include all modifications of the genome that do not involve a change in DNA sequence. The criteria can specify cutoffs, e.g., requiring certain properties, such as a particular size range, methylation metric above or below a certain amount, combination of methylation status of more than one CpG sites (e.g., a methylation haplotype (Guo et al, Nat Genet. 2017; 49: 635-42)), etc., or having a combined probability above a threshold. Such enrichment can also involve weighting DNA fragments based on such a probability.
As examples, the enriched sample can be used to classify a pathology (as described above), as well as to identify tumor or fetal mutations or for tag-counting for amplification/deletion detection of a chromosome or chromosomal region. For instance, if a particular end motif or a set of end motifs are associated with liver cancer (i.e., a higher relative frequency than for non-cancer or other cancers), then embodiments for performing cancer screening can weight such DNA fragments higher than DNA fragments not having this preferred one or this preferred set of end motifs.
At block 5810, a plurality of sequence reads of the cell-free DNA fragments is received. The plurality of sequence reads include ending sequences corresponding to ends of the plurality of cell-free DNA fragments. One or more sequence motifs may correspond to one or more ending sequences of each cell-free DNA fragment. Block 5810 may be performed in a similar manner as block 1710.
The plurality of sequence reads may be located in one or more predetermined genomic regions, wherein each of the one or more predetermined genomic regions has a histone modification associated with one or more target tissue types. The sequence reads may be aligned to a reference genome to determine their locations. The identification of sequence reads in these locations may be performed in a similar manner as block 1720.
At block 5820, one or more sequence motifs corresponding to one or more ending sequences of the cell-free DNA fragment are determined for each sequence read of a group of sequence reads. Block 5820 may be performed in a similar manner as block 1730.
At block 5830, a set of the one or more sequence motifs is identified. The set of the one or more sequence motifs occur at a higher rate in chromatin immunoprecipitation followed by sequencing (ChIP-seq) for a histone modification in the clinically-relevant DNA than in sequencing without chromatin immunoprecipitation. Identifying the sequence motifs may be similar to the procedure described with process 1700 and with
At block 5840, a group of the sequence reads that have the set of one or more sequence motifs in ending sequences is identified. This can be viewed as a first stage of filtering.
At block 5850, a likelihood that the sequence read corresponds to the clinically-relevant DNA based on an ending sequence of the sequence read including a sequence motif of the set of one or more sequence motifs is determined for each sequence read of the group of sequence reads. For instance, for each sequence read of the group of the sequence reads, a likelihood that the sequence read corresponds to the clinically-relevant DNA can be determined based on an ending sequence of the sequence read including a sequence motif of the set of one or more sequence motifs.
At block 5860, the likelihood is compared to a threshold for each sequence read of the group of sequence reads. As an example, the threshold can be determined empirically. For instance, various thresholds can be tested for samples that a concentration of the clinically-relevant DNA can be measured for a group of sequence reads. An optimal threshold can maximize the concentration while maintaining a certain percentage of the total number of sequence reads. The threshold could be determined by one or more given percentiles (5th, 10th, 90th, or 95th) of the concentrations of one or more end motifs present in the healthy controls or in control groups exposed to similar etiological risk factors but without diseases. The threshold could be a regression or probabilistic score.
At block 5870, the sequence read is stored when the likelihood exceeds the threshold for each sequence read of the group of sequence reads. The sequence read can be stored in memory (e.g., in a file, table, or other data structure), thereby obtaining stored sequence reads. Sequence reads having a likelihood below the threshold can be discarded or not stored in the memory location of the reads that are kept, or a field of a database can include a flag indicating the read had a lower threshold so that later analysis can exclude such reads. As examples, the likelihood can be determined using various techniques, such as odds ratio, z-scores, or probability distributions.
At block 5880, the stored sequence reads are analyzed to determine a property of the clinically-relevant DNA the biological sample. For example, the property may be any described herein, including with other flowcharts. For instance, the property of the clinically-relevant DNA the biological sample can be a fractional concentration of the clinically-relevant DNA. As another example, the property can be a level of pathology of a subject from whom the biological sample was obtained, where the level of pathology is associated with the clinically-relevant DNA. As another example, the property can be a gestational age of a fetus of a pregnant female from whom the biological sample was obtained.
Other criteria can be used to determine the likelihood. Sizes of the plurality of cell-free DNA fragments can be measured using the sequence reads. The likelihood that a particular sequence read corresponds to the clinically-relevant DNA can be further based on a size of the cell-free DNA fragment corresponding to the particular sequence read.
Methylation can also be used. Thus, embodiments can measure one or more methylation statuses at one or more sites of a cell-free DNA fragment corresponding to a particular sequence read. The likelihood that the particular sequence read corresponds to the clinically-relevant DNA can be further based on the one or more methylation statuses. As a further example, whether a read is within an identified set of open chromatin regions can be used as a filter.
Process 5800 may include additional implementations, such as any single implementation or any combination of implementations described herein and/or in connection with one or more other processes described herein.
Although
Assay device 5910 and detector 5920 can form an assay system, e.g., a sequencing system that performs sequencing according to embodiments described herein. A data signal 5925 is sent from detector 5920 to logic system 5930. As an example, data signal 5925 can be used to determine sequences and/or locations in a reference genome of nucleic acid molecules (e.g., DNA and/or RNA). Data signal 5925 can include various measurements made at a same time, e.g., different colors of fluorescent dyes or different electrical signals for different molecule of sample 5905, and thus data signal 5925 can correspond to multiple signals. Data signal 5925 may be stored in a local memory 5935, an external memory 5940, or a storage device 5945. The assay system can be comprised of multiple assay devices and detectors.
Logic system 5930 may be, or may include, a computer system, ASIC, microprocessor, graphics processing unit (GPU), etc. It may also include or be coupled with a display (e.g., monitor, LED display, etc.) and a user input device (e.g., mouse, keyboard, buttons, etc.). Logic system 5930 and the other components may be part of a stand-alone or network connected computer system, or they may be directly attached to or incorporated in a device (e.g., a sequencing device) that includes detector 5920 and/or assay device 5910. Logic system 5930 may also include software that executes in a processor 5950. Logic system 5930 may include a computer readable medium storing instructions for controlling measurement system 5900 to perform any of the methods described herein. For example, logic system 5930 can provide commands to a system that includes assay device 5910 such that sequencing or other physical operations are performed. Such physical operations can be performed in a particular order, e.g., with reagents being added and removed in a particular order. Such physical operations may be performed by a robotics system, e.g., including a robotic arm, as may be used to obtain a sample and perform an assay.
Measurement system 5900 may also include a treatment device 5960, which can provide a treatment to the subject. Treatment device 5960 can determine a treatment and/or be used to perform a treatment. Examples of such treatment can include surgery, radiation therapy, chemotherapy, immunotherapy, targeted therapy, hormone therapy, and stem cell transplant. Logic system 5930 may be connected to treatment device 5960, e.g., to provide results of a method described herein. The treatment device may receive inputs from other devices, such as an imaging device and user inputs (e.g., to control the treatment, such as controls over a robotic system).
Any of the computer systems mentioned herein may utilize any suitable number of subsystems. Examples of such subsystems are shown in
The subsystems shown in
A computer system can include a plurality of the same components or subsystems, e.g., connected together by external interface 81, by an internal interface, or via removable storage devices that can be connected and removed from one component to another component. In some embodiments, computer systems, subsystem, or apparatuses can communicate over a network. In such instances, one computer can be considered a client and another computer a server, where each can be part of a same computer system. A client and a server can each include multiple systems, subsystems, or components.
Aspects of embodiments can be implemented in the form of control logic using hardware circuitry (e.g. an application specific integrated circuit or field programmable gate array) and/or using computer software stored in a memory with a generally programmable processor in a modular or integrated manner, and thus a processor can include memory storing software instructions that configure hardware circuitry, as well as an FPGA with configuration instructions or an ASIC. As used herein, a processor can include a single-core processor, multi-core processor on a same integrated chip, or multiple processing units on a single circuit board or networked, as well as dedicated hardware. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and appreciate other ways and/or methods to implement embodiments of the present disclosure using hardware and a combination of hardware and software.
Any of the software components or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Java, C, C++, C#, Objective-C, Swift, or scripting language such as Perl or Python using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions or commands on a computer readable medium for storage and/or transmission. A suitable non-transitory computer readable medium can include random access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk) or Blu-ray disk, flash memory, and the like. The computer readable medium may be any combination of such devices. In addition, the order of operations may be re-arranged. A process can be terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.
Such programs may also be encoded and transmitted using carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet. As such, a computer readable medium may be created using a data signal encoded with such programs. Computer readable media encoded with the program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer readable medium may reside on or within a single computer product (e.g. a hard drive, a CD, or an entire computer system), and may be present on or within different computer products within a system or network. A computer system may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.
Any of the methods described herein may be totally or partially performed with a computer system including one or more processors, which can be configured to perform the steps. Any operations performed with a processor (e.g., aligning, determining, comparing, computing, calculating) may be performed in real-time. The term “real-time” may refer to computing operations or processes that are completed within a certain time constraint. The time constraint may be 1 minute, 1 hour, 1 day, or 7 days. Thus, embodiments can be directed to computer systems configured to perform the steps of any of the methods described herein, potentially with different components performing a respective step or a respective group of steps. Although presented as numbered steps, steps of methods herein can be performed at a same time or at different times or in a different order. Additionally, portions of these steps may be used with portions of other steps from other methods. Also, all or portions of a step may be optional. Additionally, any of the steps of any of the methods can be performed with modules, units, circuits, or other means of a system for performing these steps.
The specific details of particular embodiments may be combined in any suitable manner without departing from the spirit and scope of embodiments of the disclosure. However, other embodiments of the disclosure may be directed to specific embodiments relating to each individual aspect, or specific combinations of these individual aspects.
The above description of example embodiments of the present disclosure has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form described, and many modifications and variations are possible in light of the teaching above.
A recitation of “a”, “an” or “the” is intended to mean “one or more” unless specifically indicated to the contrary. The use of “or” is intended to mean an “inclusive or,” and not an “exclusive or” unless specifically indicated to the contrary. Reference to a “first” component does not necessarily require that a second component be provided. Moreover, reference to a “first” or a “second” component does not limit the referenced component to a particular location unless expressly stated. The term “based on” is intended to mean “based at least in part on.”
The claims may be drafted to exclude any element which may be optional. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely” “only”, and the like in connection with the recitation of claim elements, or the use of a “negative” limitation.
All patents, patent applications, publications, and descriptions mentioned herein are incorporated by reference in their entirety for all purposes. None is admitted to be prior art. Where a conflict exists between the instant application and a reference provided herein, the instant application shall dominate.
The present application claims priority to and is a non-provisional of U.S. Provisional Application No. 63/393,725, entitled “EPIGENETICS ANALYSIS OF CELL-FREE DNA,” filed on Jul. 29, 2022, the disclosure of which is incorporated by reference in its entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 63393725 | Jul 2022 | US |
