Method for estimation of cell-free DNA mixture proportions based on telomere-derived fragments
FIELD OF INVENTION
The invention relates to the field of liquid biopsy testing. The invention provides a method for estimation of the proportion of various components of analyzed DNA mixture with various clinical applications, such as non-invasive prenatal testing and oncology screening.
BACKGROUND OF THE INVENTION
Genome analysis
Genome is physically stored in a double helix DNA molecule, which consists of two strands, each carrying a sequence of nucleotides (A, T, G, C) called bases. A whole human genome is a sequence of roughly 3.2 billion DNA bases. The reference genome, an artificial genome composed by scientists, is the most common sequence of bases in human DNA. Genome of every individual differs from the reference genome by around 0.5% of bases, owing to genetic variations. These variations make each genotype unique and some of them can have significant impact on human health.
A standard procedure for obtaining individual variability is known. In the first step, collecting a biological sample is necessary (e.g. blood, saliva, etc.). The DNA molecule is then extracted and prepared for a process called sequencing. DNA sequencing is a biochemical process for determining the precise order of nucleotide bases within the DNA molecule. When using the massive parallel sequencing technology, the molecule must be fragmented and placed on a sequencing platform. Here the fragments are read in parallel creating digital sequences of DNA bases called reads. These reads are randomly ordered with unknown direction and unknown DNA strand of origin. If genomic reads belong to an organism with a known reference genome, such as human, they can be sorted in the process called mapping. Aim of the mapping is to reconstruct the original genomic sequence. Each read is aligned and thus mapped to the most probable region of origin on the reference genome. An aligned and mapped read is often simply called alignment. Set of aligned reads is de facto a digital copy of the DNA contained within the biological sample.
Aligned reads reveal differences between a sequenced and the reference genome, called genomic variants. Whole set or even specific selection of genomic variants is unique to each individual hence a genome is the ultimate person identifier. Although the majority of these variants have no apparent effect on an individual, genome-wide association studies have linked some of the variants to diseases, appearance or even the behavior of an individual.
Human genome is organized into 22 pairs of homologous chromosomes and one pair of sex chromosomes. In each pair, one chromosome is derived from the mother and the other one from the father. The maternal and paternal chromosomes in a homologous pair have the same gene at the same locus, nevertheless alleles of this gene can differ between the chromosomes. If both alleles are identical, the organism is said to be homozygous forthat locus. If they differ, the organism is said to be heterozygous for that locus.
Prenatal testing
The discovery of cell-free fetal DNA (cffDNA) in maternal plasma by Lo et al. in 1997 [1] has inspired various non-invasive prenatal screening (NIPS) applications, which avoids the ~ 1 :100 chance for miscarriage introduced by invasive sampling. 70%- of major birth defects are caused by genetic factors. The main causes of these hereditary diseases are chromosomal abnormalities and genetic mutations, while aneuploidy abnormalities account for most of chromosomal abnormalities in live infants [2,3].
CffDNA molecules circulate in the minority within a background of maternal cell-free DNA. Fetal fraction (FF), as a proportion of fetal fragments in analyzed DNA mixture of mother's blood, is known to be affected by gestational age, maternal weight, placental size and function, whether the pregnancy is singleton or twin and also whether a trisomy is present [4-10] and other various factors (e.g. fetal crown-rump length, serum pregnancy- associated plasma protein-A, serum free P-human chorionic gonadotropin, hypertension, smoking, cancer [4,6,11-13],
The relationship between FF in assisted reproductive pregnancy and that in natural conception is under continuous investigation. The concentrations of total circulating cell- free DNA and cffDNA and FF in assisted reproductive pregnancy were considered no different from those in the natural conception [14], However, singleton in vitro fertilization (IVF) fetuses were found to have a lower FF than naturally conceived fetuses in other studies [14-16], Some studies have found that low-molecular-weight heparin (LMWH) or enoxaparin use was associated with detection failure owing to a low FF. Treatment with LMWH may lead to apoptosis and thus decrease FF [17], However another study showed that LMWH itself had no impact on FF measurement or the final result, no matter what the dose tested, while the presence of an autoimmune disorder is an independent predictor of a non-reportable result. [18]. Technical factors like sample handling [19] and the choice of bioinformatics tools [20,21] can also influence FF and its estimation.
The concentration of cffDNA in the maternal plasma must exceed 3-4% to provide a low false negative rate [22] but a low FF may also indicate a higher risk of aneuploidy [23,24], However, a higher FF is not always better. An unusually elevated circulating levels of cffDNA have been reported in adverse pregnancy outcomes including preterm birth [25— 30], fetal growth restriction [31], gestational diabetes [32], preeclampsia [33-36], and abnormally invasive placenta [37], The underlying pathologies responsible for such quantitative changes has not been fully elucidated but has been suspected to be related to increased placental cell death or apoptosis [38]. Circulating fetal DNA levels may therefore be reflective of placental health, because cffDNA is thought to come from apoptotic placental trophoblastic cells [39],
Since cffDNA molecules are released into the bloodstream after cell death, they are mostly short in size less than 200 bp that show a fragmentation pattern resembling nuclease-cleaved nucleosomes; the distribution of molecules presents a succession of peaks, including a major 166-bp peak, a minor 143-bp peak and 10-bp periodic peaks below 143 bp. The most significant difference in the size distribution between fetal and maternal DNA in maternal plasma is that fetal DNA exhibits a reduction in the 166-bp peak and an increased proportion of DNA molecules of less than 143 bp [40-42], This observation means that cffDNA has probably undergone more processing or metabolism than the bulk of the circulating maternal DNA molecules. At 10-20 weeks of gestation (the most common time for NIPS) its concentration is about —10-15% of total cell-free DNA (cfDNA) in maternal plasma [4], During normal gestation, cffDNA undergoes a progressive rise, which peaks at term and then rapidly drops to undetectable levels postpartum [43,44], In support of this, increasing apoptosis of trophoblast cells has been observed at the end of pregnancy [45,46],
According to the current knowledge, fetal fragments cannot be unambiguously distinguished from the maternal ones. For these reasons NIPT still does not achieve accuracy of invasive methods like amniocentesis, and further improvements are necessary for their full replacement. In contrast to established screening methods [47], sampling of genetic material from the mother’s circulation does not pose any direct risk for the fetus [48].
The reliability of non-invasive prenatal testing (NIPT) is highly dependent on the accurate estimation of FF. Using different aspects of next-generation sequencing (NGS) technologies, several methods have been proposed up to date. These include Y chromosome-based DNA fragment estimation, machine learning algorithms, fragment length distribution estimation methods, differential methylation methods and quantification of single-nucleotide polymorphisms (SNP) [49],
Genomic methylation patterns change over time, so therefore it is no surprise that the C- methylation state of the fetal genome is different from that of the mother. Hypomethylated regions tend to have both low GC% and low gene density. However, since only 20-30% of CG islands in the human genome are unmethylated [50], their targeted analysis could make NIPT more cost-effective, but the additional laboratory work makes this option rather unattractive for routine diagnostic purposes, because this approach require splitting the sample in two, one part for the actual NGS analysis and another for determining the C-methylation [50-53].
SNP quantification methods involve measuring the presence of reads containing single base pair mutations from the fetus [54-56]. Two main factors are the most important in calculating the FF in these methods: the number of SNPs used in the analysis [56]and read depth (to identify variants both for mother and for mixture of fetus-mother DNA) [57]. In the FetalQuant (using high-depth sequencing data) [58] and in its update FetalQuantSD (using shallow-depth sequencing data) [56] are variants stored in compact VCF formats and further analyzed. Essentially, the variants that are present in the motherfetus mixture and not present in mother are categorized as fetus variants inherited from father and based on their frequency the FF is estimated. This FF is then cleaned from effects of sequential errors and other systematic bias. The SNPs must be common enough in the general population to be detectable so that they can be used. SNPs can be selected from various online databases, such as HapMap, gnomAD, or dbSNP [59].
Count based methods calculate disproportion of the number of reads mapped to chromosomes between mother and fetus genotypes. Although they are quite reliable, they can be used only on samples with male (the number of DNA fragments matching the sequence of the Y chromosome should be directly proportionate to the FF) [20,60,61] or trisomic (2 copies vs. 3 copies of an aberrant chromosome) fetuses [62-64]. In pregnancy with a healthy female fetus, pregnancy has to be determined by alternative methods.
As fetal DNA has generally shorter fragments than maternal DNA [65], authors of fragment length (FL) model presumes that maternal plasma samples with a higher fetal DNA fraction would have a higher proportion of short DNA fragments. FL model is then based on the size analysis of short and long DNA fragments in the maternal plasma with the assumption that ratio of short DNA fragments and long DNA fragments is correlated to proportion of fetal DNA fragments in maternal plasma. For example in fetal trisomy 21 , the proportion of shorter reads would increase due to the extra chromosome copy. Conversely, in monosomy X the proportion of longer fragments of maternal origin increases. Dataset of measured values (read lengths distribution of each mother) is then divided into two groups: a training group and a validation group and together they are used to create a linear regression representing the resulting FF from read lengths distribution [65-67].
Alternatively, FF may be estimated from the genomic location patterns that slightly differ between maternal and fetal fragments due to differences of their nucleosome positioning (SANEFALCON method) [68] and euchromatic DNA structure (SeqFF method) [69].
The SANEFALCON (Single reAds Nucleosome-basEd FetAL fraCtiON) method determines the fetal fraction through the distribution of reads mapped around nucleosome positions on autosomal chromosomes (independent of the fetal gender). Nucleosomedependent differences in degradation of maternal and fetal DNA lead to different start sites of sequence reads (i.e. fragment length). These changes correlate with the fetal fraction. SANEFALCON uses a linear regression from the nucleosome profile to predict the fetal fraction, with coefficients learned from a training set [68].
Due to high accuracy and no additional laboratory costs, the SeqFF method is a preferred method for samples with female fetus [20]. The basic principle involves discovering read overrepresentation in sub-chromosomal regions of 50 kbp. The FF is then determined using standard multivariate regression models and is then the average of the predictions of the models. Estimation of these weights requires a huge amount of training samples that are scarcely available for small laboratories, and so the method is applicable only for established tests with a large cohort of pre-analyzed samples.
There are also novel methods which use FF derived from the SeqFF method as an input. COMBO method is based on combining two approaches for FF estimation based on independent attributes - SVM: support vector machine estimator prediction based on fragment length profile and SeqFF method described above. The fragment length profiles may be utilized as a predictor of FF and achieve slightly lower precision compared to the favored methods based on positions of DNA fragments. Using a linear regression model, these two approaches were combined, whereby the best trained model reached 0.92 Pearson correlation when testing [41],
Patent AU2011218382B2 entitled "Methods for detecting fetal nucleic acids and diagnosing fetal abnormalities" by Efcavitch et al. claims detecting the presence or absence of fetal nucleic acid using a technique selected from sparse allele calling, targeted gene sequencing, identification of Y chromosomal material, enumeration, copy number analysis, and inversion analysis.
Patent US9493828B2 entitled "Methods for determining fraction of fetal nucleic acids in maternal samples" by Rava et al. claim gender-independent FF estimation method. It comprises using a predetermined set of SNP positions, counting reads mapped to these positions and identifying at least 10 informative SNPs by the allelic difference between the reads for each SNP. FF is calculated from the total number of mapped reads to a first allele and the total number of reads mapped to a second allele at each of the said informative SNPs.
Lo et al. in their patent US10208348B2 entitled "Determining percentage of fetal DNA in maternal sample" claim FF estimation method. Specifically, they claim a method for determining whether genomic imbalance such as chromosomal aneuploidy exists within a biological sample based on a FF threshold. The FF is computed from the same or different data used to determine the cut-off value. The threshold value can be computed from the formula: (3XF+2X(1-F))/(2XF+2X(1 -F)), where F is the FF. The method uses sites of genetic polymorphism where the mother is homozygous and the fetus is heterozygous. More specifically, these are sites where the fetus has one identical and one different allele when compared to a maternal pair of identical alleles, giving two different alleles in total. The FF is computed by comparison of the number of reads supporting the first allele and the number of reads supporting the second allele for a specific locus. Telomeres
Telomeres are protective DNA-protein structures at the end of chromosomes that guard against genome degradation and inappropriate activation of DNA response [70,71]. They can serve as a potential indicator for disease susceptibility and cellular aging, predictors of mortality, or a possible target for a particular treatment [72]. Human telomeric DNA is composed of tandem repeats (10-15 kb at birth) of double-stranded DNA nucleotide sequence 5 -TTAGGG-3', and a final 3' G-rich single-stranded overhang (150-200 bp long), linked by telomere-binding proteins [73,74], Telomere shortening can occur through two distinctive mechanisms [75], The first one takes place because DNA polymerase is unable to replicate the 3' end of the DNA strand fully, which causes the telomeres to physiologically shorten and lose approximately 30-150 bp with each cell division [76], The second is caused by oxidative stress resulting from an imbalance between antioxidant defenses and reactive oxygen species (ROS) production [77] that leads to DNA damage, and is considered as the leading factor responsible for the remaining telomere loss [78]. When telomeres erode to a critical length, cells become senescent, undergo morphological and genetic changes resulting in the cell cycle arrest or apoptosis and the loss of tissue function [79,80]. Senescent cells also produce inflammatory mediators that affect neighboring cells, leading to further damage within tissues and organs that accumulates over time. Thus, as individuals age, they acquire more senescent cells, accompanied by various age-related pathologies [70,81], Some cells overcome senescence by the acquisition of genetic mutations in the p53 gene or other checkpoint proteins. As a result, they continue to proliferate, acquire immortality, and proceed to carcinogenesis [82].
To acquire replicative immortality, cancerous cells need to overcome the shortening of telomeres [82,83]. Most cancers maintain telomere length by activating telomerase, while 4-11% use a telomerase-independent alternative lengthening of telomeres (ALT) mechanism. Length of telomere (TL) can be extended with the telomerase, a tightly regulated enzyme, which is active during human embryonic development, particularly around the time of blastulation [84], in the germline and stem cells, but is much lower or absent in most somatic cells [85-87].
TL is a dynamic marker that reflects not only genetic predispositions [88,89] but can be affected by individual's age [90], sex [91], hormones [92,93], exogenous life factors (e.g. stress [94], nutrition [95], exercise [96], obesity and weight loss [97], paternal age [98], alcohol dependence [99], tobacco smoking [100], socio-economic status [101]) and environmental exposures (e.g. air pollution [102], UV radiation [103]). It is closely related to longevity and a number of pathologies [104-106], such as cancer and cardiovascular diseases [107,108]. Therefore, TL at birth is a main predictor for TL throughout life [109- 111],
The effects of parental stress exposure on offspring TL could be directly mediated by parental germ-line telomere length prior to fertilization and its subsequent consequence on the TL inherited by the offspring [112]. By contrast, indirect effects of parental stress exposure may induce telomere shortening in offspring tissues through increases in maternally derived biological stress mediators during intrauterine life, or through alterations in parental behavior or care, which then affects offspring stress regulation and thereby induces changes in telomere biology [112].
The telomerase activity is strictly regulated in both placenta and embryo and appears to be important for successful fetal development [113,114], Phillippe proposed an interesting “cffDNA/telomere” hypothesis to provide a mechanistic explanation for normal parturition and the spontaneous preterm birth [44], This hypothesis described that telomere shortening induces apoptosis in placental tissues and subsequently leads to release of cffDNA. In human embryos, there has been observed a 50 % reduction in relative telomere length between the 6th and the 11th week of gestation [115]. During the remainder of human pregnancy, Menon et al. [116] have reported that the mean telomere lengths in cord blood cells and placental cells shorten as gestation progresses, with the shortest telomeres being found at term. Consistent with the progressive loss of telomere DNA, placental and fetal membrane cells have been observed to have weak or no telomerase enzyme activity, especially during late gestation [117-119].
Still, the telomeres have a certain degree of plasticity, and recent studies show that the uterine environment may influence fetal telomere length (TL) [109,120,121]. There is emerging evidence that factors such as maternal stress [122,123], inflammation [124], gestational diabetes [125], exposure to air pollution [126,127], tobacco smoke [128], and toxic metals [129] may negatively influence offspring TL. Telomeres may also be susceptible to poor or unbalanced nutrition [130]. Prepregnancy BMI has been found to be inversely associated with newborn TL [131], whereas maternal folate [120,132], vitamin C [133], and vitamin D [134] have been associated with longer telomeres in newborns.
DESCRIPTION OF THE INVENTION
The reliability of NIPT is dependent on a sufficient concentration of FF, which must exceed 3-4%. As it is currently not possible to reliably distinguish fetal DNA fragments from analyzed DNA mixtures of maternal cfDNA, several methods have been developed for the estimation of the overall FF proportion based on aggregated characteristics of all sequenced fragments instead. These methods use various aspects of NGS technologies to determine the fetal fraction, e.g. Y chromosome-based DNA fragment estimation, fragment length distribution estimation methods, differential methylation methods, quantification of sSNPs and machine learning algorithms. Our motivation is to develop new and independent FF estimation techniques based on telomere content estimation. The method can be used individually or in combination with alternative methods to achieve even higher precision. Telomeres are structures at the end of chromosomes that protect the genome from degradation and they are composed of tandem repeats (10-15 kb at birth) of double-stranded DNA nucleotide sequence 5 -TTAGGG-3', and a final 3' G- rich single-stranded overhang (150-200 bp long) and shortens with age.
One notable feature of telomeres is their ability to shorten over time due to the gradual loss of nucleotide sequences with each cell division. This gradual shortening can contribute to cellular aging and senescence, which is a crucial factor in the development of age-related diseases. Interestingly, the length of telomeres varies among different individuals and can be influenced by a range of factors, including genetics, environmental factors, and lifestyle choices. For example, research has shown that shorter telomeres are associated with a higher risk of chronic diseases such as cancer, cardiovascular disease, and diabetes.
In the context of prenatal testing, the length of telomeres can provide valuable information about the proportion of fetal DNA present in a maternal blood sample. The presented method is based on the detection of disproportion of telomere lengths between fetal and maternal genome. The length of telomeres is longer in younger fetal cells than in maternal cells. The higher amount of telomere derived fragments as expected based on observed factors, such the mother's age, gestational age, and BMI index, indicates the presence of fetal DNA in the sample. Consequently, the amount of the deviation corresponds to the amount of fetal fraction fragments, fetal fraction.
The described concept can be analogously utilized in various domains of the liquid based testing. As an example, the detection and characterization of the ongoing oncological disease is based on measuring the cell-free tumor DNA (cftDNA) fragments in the blood plasma of the patient. Analogously to the NIPT, the identification and quantification of tumor fragments is vital for the detection and characterization of ongoing oncology disease with application in its screening, monitoring and prognostics. As the genome of tumor tissue has typically different telomere length than healthy cells, the anomalies in the non-pregnant patient sample are indicative of on-going disease.
In this study, we present the feasibility of using telomere length as a novel marker for FF estimation in NIPT. We hypothesized that the telomere length in maternal blood could be used as a surrogate marker for FF estimation. We collected blood samples from pregnant women at various gestational ages, sequenced the samples using massively parallel sequencing and measured telomere length using appropriate methods, as described below. The following description will explain the main features of the method of the present invention, however, it does not imply that the invention must include all features and aspects described herein. The skilled person will get a full understanding of the present invention from the following description together with the examples, where some specific features and aspects will be explained in more detail.
The technical and scientific terms used herein have the same meaning as commonly understood by the persons skilled in the art of medicine, molecular genetics, prenatal non- invasive diagnostics, molecular biology and bioinformatics. Some specific terms are explained in the “Definitions” section.
The method of the invention comprises steps on the level of biology (molecular biology) and bioinformatics (This invention does not rely on an exact method for obtaining sequencing and mapping data, therefore this gathering process is not described here in detail. However, people skilled in the field of molecular biology know how to get these data using one of the available technologies. Described in more detail in the example):
1) Biological steps a) Obtaining samples of maternal blood - Peripheral blood of pregnant women is collected into tubes and then blood plasma is separated. b) Preparation of DNA sample and DNA library - DNA is isolated and standard fragment libraries were prepared from isolated DNA. c) Sequencing - Sequencing machine is used for whole genome sequencing (WGS) of prepared libraries using pair-end sequencing with read length of 35 bp.
2) Bioinformatic steps a) Mapping - Produced fastq files (two per sample) is directly mapped to the human reference genome. SAM files are converted to Binary Alignment Map (BAM) format, sorted and indexed. b) Assess the telomere content from BAM files calculating the number of telomere- derived fragments. c) training models to predict the FF using the telomere content values where the values of telomere content are used as an input variable for the regression model to predict the amount of FF.
Only these two inputs are required - sequenced fragments of cell-free DNA from pregnant mother’s blood and the human reference genome. The presented coverage of the sequenced reads across the genome is in the range between 0.2 - 1x which means that 0.2 - 1 of reads on average align to known reference bases.
Definitions
DNA The molecule inside cells that contains the genetic information responsible for the . development and function of an organism. DNA molecules allow this information to be passed from one generation to the next. base pair (bp) A fundamental unit of DNA consisting of two nucleobases bound to each other by hydrogen bonds. genome The complete set of genes or genetic material present in a cell or organism, which contains all of the information needed for a person to develop and grow. chromosome A structure found inside the nucleus of a cell. A chromosome is made up of proteins and DNA organized into genes. Each human cell normally contains 23 pairs of chromosomes. sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
DNA sequencing is the method that determines the order of the four nucleotides bases (adenine, thymine, cytosine, and guanine). massive parallel sequencing technology/NGS A high-throughput method used to determine a portion of the nucleotide sequence of an individual’s genome. This technique utilizes DNA sequencing technologies that are capable of processing multiple DNA sequences in parallel. Also called next-generation sequencing and NGS. telomere A region of repetitive DNA sequences at the end of a chromosome. Telomeres protect the ends of chromosomes from becoming frayed or tangled. Each time a cell divides, the telomeres become slightly shorter. Eventually, they become so short that the cell can no longer divide successfully, and the cell dies. telomere length As a normal cellular process, a small portion of telomeric DNA is lost with each cell division. When telomere length reaches a critical limit, the cell undergoes senescence and/or apoptosis. Telomere length may therefore serve as a biological clock to determine the lifespan of a cell and an organism.
DNA-polymerase Enzyme that creates DNA molecules by assembling nucleotides, the building blocks of DNA. This enzyme is essential to DNA replication and usually works in pairs to create two identical DNA strands from one original DNA molecule. telomerase The enzyme that repairs the telomeres of the chromosomes so that they do not become progressively shorter during successive rounds of chromosome replication. Telomerase activity is exhibited in gametes and stem and tumor cells. cell division The division of a cell into two daughter cells with the same genetic material. Another name for cell division is "mitosis." apoptosis A type of cell death in which a series of molecular steps in a cell lead to its death. This is one method the body uses to get rid of unneeded or abnormal cells. senescence A process by which a cell ages and permanently stops dividing but does not die. Overtime, large numbers of old (or senescent) cells can build up in tissues throughout the body. These cells remain active and can release harmful substances that may cause inflammation and damage to nearby healthy cells. Senescence may play a role in the development of cancer and other diseases. mitochondria An organelle found in large numbers in most cells, in which the biochemical processes of respiration and energy production occur. Mitochondria contain their own small chromosomes. mitochondrial DNA (mtDNA) the small circular chromosome found inside mitochondria. Generally, mitochondria, and therefore mitochondrial DNA, are inherited only from the mother. oxidative stress An imbalance between free radicals and antioxidants in the body. Free radicals are oxygen-containing molecules with an uneven number of electrons. The uneven number allows them to easily react with other molecules. Free radicals can cause large chain chemical reactions in the body because they react so easily with other molecules. When functioning properly, free radicals can help fight off pathogens. When there are more free radicals present than can be kept in balance by antioxidants, the free radicals can start doing damage to fatty tissue, DNA, and proteins, so that damage can lead to a vast number of diseases over time. Oxidative stress also contributes to aging. cancer A disease caused by an uncontrolled division of abnormal cells in a part of the body. Normally, human cells grow and multiply (through a process called cell division) to form new cells as the body needs them. When cells grow old or become damaged, they die, and new cells take their place. Sometimes this orderly process breaks down, and abnormal or damaged cells grow and multiply when they shouldn’t germline cells Gametes (sperm and egg) and the stem cells that divide to form gametes. stem cells A cell found in foetuses, embryos and some adult tissues that can give rise to a wide range of other cells. somatic cells The cells in the body other than sperm and egg cells (which are called germ cells). In humans, somatic cells are diploid, meaning they contain two sets of chromosomes, one inherited from each parent. genetic predisposition An increased chance or likelihood of developing a particular disease based on the presence of one or more genetic variants and/or a family history suggestive of an increased risk of the disease. Having a genetic predisposition does not mean an individual will develop the disease. Lifestyle and environmental factors can also affect an individual's risk of disease. cell-free fetal DNA (cffDNA) Fetal DNA that circulates freely in the maternal blood. cffDNA originates from placental trophoblasts. Fetal DNA is fragmented when placental microparticles are shed into the maternal blood circulation. They are significantly smaller than maternal DNA fragments. cffDNA generally reflects the genetic makeup of the developing baby (fetus). Two hours after delivery, cffDNA is no longer detectable in maternal blood. fetal fraction (FF) An important parameter in the analysis of noninvasive prenatal screening results, is the proportion of cffDNA present in the total maternal plasma cell- free DNA. It combines biological factors and bioinformatics algorithms to interpret noninvasive prenatal screening results and is an integral part of quality control. chromosome aberration A missing, extra, or irregular portion of chromosomal DNA - changes in chromosome number (gains and losses) and changes in structure (deletions, inversions, and exchanges). aneuploidy The presence of an abnormal number of chromosomes in a cell. An extra or missing chromosome is a common cause of some genetic disorders (also cancer). Aneuploidy originates during cell division when the chromosomes do not separate properly between the two cells (nondisjunction). Most cases of aneuploidy in the autosomes result in miscarriage, and the most common extra autosomal chromosomes among live births are 21 (Down syndrome), 18 (Edwards syndrome) and 13 (Patau syndrome). Autosomal aneuploidy is more dangerous than sex chromosome aneuploidy, as autosomal aneuploidy is almost always lethal to embryos that cease developing because of it. genetic marker A sequence of DNA with a known physical location on a chromosome. Genetic markers and genes that are close to each other on a chromosome tend to be inherited together. Genetic markers vary between individuals to the extent that they can be used to help find a nearby gene causing a certain disease or trait within a family. Examples of genetic markers are single polymorphism nucleotides (SNPs), restriction fragment length polymorphisms (RFLPs), variable number of tandem repeats (VNTRs), microsatellites, and copy number variants (CNVs). Genetic markers may or may not have a known function.
FASTQ file File containing all reads from a sequencer, together with its sequencing quality. This is the standard file format to store this data and it is usually compressed to save disk space. All the modern mapping software accept this format as input. mapping Alignment of the sequence information from NGS (i.e. DNA fragment the genomic position of which is unknown) with a matching sequence in reference human genome. This can be done several ways. Reads that do not map uniquely (map to several positions) are usually excluded from the analysis. The alignment is usually done by computer algorithms well known to the persons skilled in the art of molecular biology and bioinformatics.
SAM/BAM file File that contains aligned sequencing reads in a text format (SAM) or a compressed binary format (BAM). For every read it contains its mapped position on the reference genome (if the mapping for that read was successful), the mapping quality, sequencing quality (if provided), the location of the paired read (in case of pair-end sequencing), and various other information. It is a standard for storing aligned reads. Each SAM/BAM file is dependent on the reference genome used - this information is stored in the header of the SAM/BAM file. linear regression Linear model that assumes a linear relationship between the input variables and the single output variable and fits a linear equation to observed data. More specifically, the single output can be calculated from a linear combination of the input variables.
Whole genome sequencing (WGS) is an all-encompassing technique used to analyze full genomes.
SUMMARY OF THE INVENTION
For non-invasive prenatal testing, blood from pregnant women is collected and prepared for DNA isolation and sequencing. After sequencing, sequencing reads are aligned to the human reference genome (for example with bowtie2 mapping tool [135].
Then, individual BAM files of pregnant/nonpregnant samples are created using specific bioinformatic tools. Each read is processed using the Telomerehunter tool to determine telomere content in the sample. At first the Telomerehunter takes a BAM file as input, extracts telomeric reads and sorts them into four different fractions (intratelomeric, junction spanning, subtelomeric and intrachromosomal) depending on their mapping position. Subsequently, the telomere content is calculated from the number of intratelomeric reads normalized by the total number of reads with a GC composition similar to that of telomeres.
Sample Preparation and Sequencing
Blood from pregnant women is collected. Blood plasma is separated after collection and prepared for DNA isolation. Standard fragment libraries for massively parallel sequencing are prepared (for example using Illumina NextSeq 500/550 High Output Kit v2 (San Diego, CA, USA). Mapping of sequenced reads
Sequencing reads are aligned to the human reference genome (hg19). Subsequently, FASTQ files are mapped to human genome reference (preferably version GRCh38.p10), using Bowtie2 (v2.1.0) (Langmead and Salzberg, 2012) resulting in one Sequence Alignment Map (SAM) file for each sample. SAM files are converted to Binary Alignment Map (BAM) format, sorted and indexed using Samtools view, merge and index utilities (vO.1.19) (Li et al., 2009).
Telomere analysis for prediction of fetal fraction in samples
Each BAM file consisting of mapped reads from a sample is processed using the Telomerehunter tool to determine the telomere content in a sample. The Telomerehunter tool is used for estimating telomere content from human WGS data, whereas the telomere content is calculated as a number of intratelomeric reads per million reads with telomeric gc content. Furthermore, having the telomere content values the prediction accuracy of existing methods for determination of the FF in a sample can be increased by incorporating telomere content into prediction models. Subsequently, the values of telomere content are used as an input variable in linear regression to predict the FF amount The linear relationship between a telomere content and a FF in samples can be seen in Figure 1. Having the values of telomere content the existing methods for determining the FF amount can be improved by adding the information about the quantity of telomere reads. Statistical or machine learning methods suitable for regression problems are applied to the data consisting of FF amount calculated by existing algorithm and information about samples such as age of pregnant woman, gestational age, bmi index, telomere content and others to measure FFs.
BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 shows a positive correlation between telomere content and fetal fraction of samples (Pearson correlation = 0.233, p-value = 2.01 e-31).
Fig. 2 Representation of samples using the linear regression model to predict the fetal fraction amount in a sample based on its telomere content (Pearson correlation = 0.454, p-value = 4e-124).
Fig. 3 Representation of samples using the multiple linear regression model to predict the fetal fraction amount in a sample from SeqFF and telomere content values as independent variables (Pearson correlation = 0.906, p-value ~= 0).
EXAMPLES
Preparation of samples (NIPT)
Blood from pregnant women is collected. Blood plasma is separated after collection and prepared for DNA isolation. Standard fragment libraries for massively parallel sequencing are prepared (for example using Illumina NextSeq 500/550 High Output Kit v2 (San Diego, CA, USA).
Preparation of samples (NIPT)
Sample Preparation and Sequencing
Blood from pregnant women was collected. Blood plasma was separated after collection and prepared for DNA isolation. DNA isolation is performed according to TruSeq Nano Protocol (Illumina). Standard fragment libraries for massively parallel sequencing were prepared. Library preparation was performed according to TruSeq Nano Protocol (Illumina). Illumina NextSeq 500/550 High Output Kit v2 (San Diego, CA, USA) (75 cycles) was used for massively parallel sequencing of prepared libraries using pair-end sequencing with read length of 2*35bp on an Illumina NextSeq 500 platform (Available online: https://www.illumina.com/). Samples were sequenced on NextSeq 500 where each read has length 35bp and is read in both directions and output size is approximately 300-700 MB per sample generated in FASTQ format.
Mapping and Read Count Correction
Sequencing reads were aligned to the human reference genome (hg19). Subsequently, FASTQ files were mapped to human genome reference, version GRCh38.p10, using Bowtie2 (v2.1.0) (Langmead and Salzberg, 2012) resulting in one Sequence Alignment Map (SAM) file for each sample. SAM files were converted to Binary Alignment Map (BAM) format, sorted and indexed using Samtools view, merge and index utilities (vO.1.19) (Li et al., 2009).
Device
This device first maps reads from a sequenced sample to a human reference genome with Bowtie2 tool[135] which creates a BAM file representing aligned sequences. A BAM file is further processed by the Telomerehunter tool to obtain the information about telomeres from the analyzed sample. The Telomerehunter takes a BAM file as input, extracts and sorts telomeric reads and estimates the telomere content of the input sample, whereas GC biases are taken into account. The telomere content, which is calculated as the number of intratelomeric reads per million reads with telomeric gc content, is extracted from the results of the Telomerehunter tool.
As the last step, the regression models are trained on the training data to predict a fetal fraction (FF) amount in a sample and their prediction accuracy is subsequently calculated from the testing data. One of the models uses the telomere content for the FF prediction and the other model takes the telomere content and the SeqFF values as the independent variables of the multiple linear regression and calculates the FF as the outcome.
Analysis of the data verification/validation of the method
In our study, we gathered 2563 samples with male fetuses and determined the fetal fraction by measuring the abundance of chromosome Y. Subsequently, we utilized the Telomerehunter tool to assess the telomere content. Our findings demonstrated a significant positive correlation (Pearson correlation = 0.233, p-value = 2.01 e-31) between these two traits, providing evidence of their association (Fig. 1).
Other traits, such as maternal age, can influence the length of telomeres. To account for this, we collected relevant characteristics and combined them to create a predictive model that includes height, weight, DNA quantity, final sequencing library concentration, gestational age, maternal age, and BML To train this model, we divided our dataset into 80% for training (2050 samples) and 20% for testing (513 samples). Using linear regression, we trained the model to predict fetal fraction based on the informative characteristics (Fig. 2). Our model achieved a comparable level of accuracy on both the training (Pearson correlation = 0.452, p < 1.43e-98) and testing data (Pearson correlation = 0.464, p < 2.17e-27).
The predictive capabilities of our model have the potential to either function independently or enhance existing predictors. To showcase this potential, we employed multiple linear regression by utilizing the reference method SeqFF and telomere content values as independent variables, with the fetal fraction (FF) as the dependent variable (Fig. 3). Our findings indicated that the addition of information about telomere content improved the prediction accuracy of the FF amount when compared to the standalone SeqFF method (Pearson correlation = 0.903, p-value ~= 0), as evidenced by the multiple linear regression results (Pearson correlation = 0.906, p-value ~= 0). Upon conducting a thorough analysis of samples with the most deviated predicted values, we discovered additional potential for our method. All three samples that we analyzed exhibited maternal copy number variants that impacted approximately 460 kb, 400 kb, and 240 kb regions of the genome. Abnormal values of the telomere content can serve as an indicator of genomic malignancies.
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