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  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6869—Methods for sequencing
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  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
  • G16B20/10—Ploidy or copy number detection
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
  • G16B40/20—Supervised data analysis
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6879—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for sex determination

Definitions

• the invention relates generally to the field of non-invasive prenatal screening and diagnostics.
• the invention provides a method for detection of the presence or absence of sex chromosomal aneuploidies, particularly monosomy and trisomy of chromosome X, Klinefelter and XYY syndrome of chromosome Y, sex, and the proportion of DNA fragments of fetuses from the blood sample taken from mother in early stage of pregnancy.
• the invention provides a novel approach to calculation of fetal fraction of cell free DNA fragments which is used internally, and which can be used separately in other fields of non-invasive prenatal screening and diagnostics.
• the invention relates to single or twin pregnancies. The extension of the invention to triple or quadruple pregnancies with future technologies is contemplated.
• prenatal testing is an integral component of obstetric practice.
• the primary aim of prenatal testing is screening for fetal aneuploidies, such as trisomy of chromosome 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome).
• Other major group of abnormalities are sex chromosome aberrations (SCAs), such as monosomy X (X0, Turner syndrome), XXY (Klinefelter syndrome), XXX (triple X syndrome), and XYY (Jacob syndrome).
• SCAs Although the majority of fetuses with aneuploidy result in termination during the development of the fetus, the SCAs are rarely lethal and their phenotypic features are less severe than autosomal chromosomal aberrations. The most common monosomy X has been estimated to occur in 1-1.5% of pregnancies, and it is a common cause of first trimester pregnancy loss (approx. 23%). Therefore, the prenatal detection of SCAs is important prenatal genetic test for prenatal screening or diagnostics. Reliable invasive prenatal tests are available, however, because of their risky nature, they are currently preformed only in high- risk pregnancies.
• NIPT non-invasive prenatal testing
• CffDNA constitutes approximately less than 10% of the total circulating cell-free DNA (cfDNA) in maternal plasma, however it has recently been found that the entire fetal genome, in the form of cffDNA, is present in maternal blood and thus it is very promising material for NIPD.
• the detection of fetal SCAs such as monosomy or trisomy of X in female fetuses using NGS is done through the following process.
• a short region at one end of each DNA molecule of maternal plasma is sequenced and mapped against the reference human genome to determine the chromosomal origin of each sequence.
• the amount of the sequenced tags from the chromosome of interest e.g. chromosome X
• fetal fraction proportion of DNA fragments originated from fetus
• fetal fraction proportion of DNA fragments originated from fetus
• the method of Zimmermann et al. 2012 is based on targeted sequencing of specific polymorphic loci (SNP). The method generates multiple hypotheses how should the sequencing data look like for monosomic, disomic, or trisomic chromosome with a particular fetal fraction. Likelihood of each hypothesis is then calculated given the observed data, and the most likely hypothesis is selected.
• the method of Mazloom et al. 2013 is based on over and under representation of X and Y chromosomes when compared to a cohort of euploid samples with female fetuses in case of chromosome X, and a cohort of euploid males in case of chromosome Y.
• the method appeats to be similar to that of Chiu et al. 2008.
• No z-score or hypothesis likelihood is reported in sample diagnostics.
• a z-score on chromosome X is used internally to define classification regions (see Supporting information).
• a decision tree is used to classify a sample (45 X, 46 XX, 47 XXX, 47 XXY, 47 XYY, or 46 XY).
• the method of Liang et al. 2013, which is said to calculate trisomy of 9 , 13 , 18 , 21 s , as well as sex chromosome aberrations X0, XXX, XXY, and XYY, is based on normalized chromosomal values NCR, which equals the count of the sequences uniquely mapped to the chromosome of interest/total count of the sequences uniquely mapped to all the autosomal chromosomes.
• the NCR values are used to produce z-scores in a manner common to the art. Classification of the samples to euploid or aberrant is done by means of this z-score and various cut-off values, different for each aneuploidy.
• the method of Wang et al. 2014 is based on dividing each chromosome in to contiguous 20kbp bins. Given a training set of euploid pregnancies, a special normalized read number for each bin is calculated, and the median value is stored. Then, to ascertain the gain or loss of chromosome regions, similar bins of a test sample are compared with the stored median values by means of a fused lasso algorithm (least absolute shrinkage and selection operator).
• the level of chromosome mosaicism is reported as well, and it is calculated by means of normalized chromosome representations NCR as (NCRj - NCRf)/NCRf, where NCRj is NCR of any chromosome of a test sample, and NCRf is the mean NCR of the same chromosome in a set of reference samples.
• the document disclosed a method for prenatal screening and diagnostics of fetal chromosomal aneuploidy on the basis of NGS comprising a novel protocol for preparing sequencing libraries from a maternal sample.
• the novel approach in preparing sequencing libraries comprises the consecutive steps of end-repairing, dA-tailing and adaptor ligating said nucleic acids, and wherein said consecutive steps exclude purifying the end-repaired products prior to the dA-tailing step and exclude purifying the dA-tailing products prior to the adaptor-ligating step.
• the method allows for determining copy number variations (CNV) of any sequence of interest. No z-score was determined as a decisive value in this method. Another improvement was disclosed in WO 2011/051283 (Benz, M. et al., assigned to Lifecodexx, AG, DE). The method for non-invasive diagnosis of chromosomal aneuploidy disclosed therein is improved by the enrichment and quantification of selected cfDNA sequences in a maternal blood sample.
• fetal fraction is routinely estimated from abundance of fragments in these chromosomes. This approach is however limited to samples with a male fetus. Fraction of female fetus may be estimated from characteristics differing between fetal and maternal fragments.
• Method Sanefalcon uses more detailed information of fragment origin based on different mechanisms of fragment degradation. They used proportion of consistent fragments with precalculated genomic locations of nucleosomes as fetal fraction predictor, based on assumption that maternal fragments originate more often on positions of nucleosomes than fetal fragments.
• Another promising characteristic is a length of a fragment. Since fetal fragments tends to be shorter, profiles of maternal and fetal fragment lengths differ significantly and may be used as predictor of fetal fraction.
• the length-based method has been proposed in Yu et al. 2014 18. Fetus genome inherited genomic information evenly from father and mother. Based on origin, it differs in certain positions, most notably in point changes of nucleotides called SNP. Fragments that are consistent with SNPs specific to father are most likely of fetal fraction and may be used in the prediction. The method FetalQuant ⁇ Jiang et al. 2016 19 ) uses this information; however, it requires another laboratory assay for genetic map of parents. Application of the method is thus too time-consuming and expensive for routine diagnosis.
• the present invention provides alternative and reliable method that is applicable to the practice of non-invasive prenatal screening for sex chromosomes aneuploidies such as monosomy X (X0, Turner syndrome), XXY (Klinefelter syndrome), XXX (triple X syndrome), and XYY (Jacob syndrome). It provides simultaneous diagnosis of aneuploidy, sex and fetal fraction using single model which can be easily expanded to another chromosomal aneuploidies and disorders, such as monosomy or trisomy of 13 th , 18 th , 21 st or any other chromosome.
• the method uses internally a novel method for the calculation of fetal fraction of cfDNA fragments in maternal blood, which can be used separately in other fields than SCA determination such as (but not limited to) trisomy T21, T18, or T13 detection. Additionally, the method needs relatively low amount of sequencing data; therefore, the method is relatively cheap and would be affordable even for the small healthcare institutions.
• Aneuploidy is used in the common sense known to the person skilled in the art, it means an imbalance of genetic material caused by a loss or gain of a whole chromosome or part of chromosome. In other words, it means the presence of the entire excessive chromosome or the absence of full chromosome or partial chromosome duplication or deletions of a significant size (> 1 kbp).
• T21, T18 and T13 denote the most common types of autosomal trisomies that survive to birth in humans, which are trisomy of chromosome 21 resulting in Down syndrome, trisomy of chromosome 18 resulting in Edwards syndrome and trisomy of chromosome 13 resulting in Patau syndrome.
• X0, XXX, XXY, and XYY denote the common types of sex chromosomal aneuploidies that survive to birth in humans, which are monosomy of chromosome X resulting in Turner syndrome, trisomy of chromosome X resulting in triple X syndrome, karyotype 47 XXY resulting in Klinefelter syndrome, and karyotype 47 XYY resulting in Jacob syndrome.
• MPS massively parallel sequencing
• NGS new generation sequencing
• Sequence reads being the short DNA sequences obtained from NGS sequencing, however, long enough ⁇ e.g. at least about 30bp) to serve as sequence tags, i.e. that can be assigned unambiguously to one of the chromosomes (1-22, X, Y). Small degree of mismatch can be usually allowed (lbp).
• the tags are assigned or rather mapped reads.
• the tags are uniquely mapped to a reference genome i.e. they are assigned to a single location to the reference genome.
• Tags that can be mapped to more than one location on a reference genome, i.e., tags that do not map uniquely, are excluded from the analysis.
• Mapping means an 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 be several ways, we used the method of Liu et al 2014. As used
• the term human reference genome or reference genome refers to hgl9 sequence .
• the terms aligned or alignment refer to one or more sequences that are identified as a match in terms of the order of their nucleic acid molecules to a known sequence from a reference genome. Such alignment can be done manually or by a computer algorithms that are well known to the persons skilled in the art of molecular biology and bioinformatics. The matching of a sequence read in aligning can be a 100% sequence match or less than 100% (non-perfect match).
• Fetal fraction is the proportion of cfDNA fragments originated from the fetus compared to all sequenced fragments.
• Normal (or Gaussian) distribution is a common continuous probability distribution. It is defined by its probability density function f (x
• Artificial neural network is computational approach used in computer science and other research disciplines, which is based on a large collection of neural units (artificial neurons), loosely mimicking the way a biological brain solves problems with large clusters of biological neurons connected by axons.
• Each neural unit is connected with many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units.
• Each individual neural unit may have a summation function which combines the values of all its inputs together. There may be a threshold function or limiting function on each connection and on the unit itself, such that the signal must surpass the limit before propagating to other neurons.
• FASTQ format is a text-based file format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer. SAM is a text-based file format for storing biological sequences aligned to a reference sequence developed by Heng Li. The acronym SAM stands for Sequence Alignment/Map. It is widely used for storing data, such as nucleotide sequences, generated by Next generation sequencing technologies.
• GC content bias describes the dependence between fragment count (read coverage) and GC content found in Illumina sequencing data. This bias can dominate the signal of interest for analyses that focus on measuring fragment abundance within a genome, such as copy number estimation. The bias is not consistent between samples; and there is no consensus as to the best methods to remove it in a single sample. There are many proposed approaches to correcting this bias, such as Benjamini and Speed 2012 23 or Liao et al. 201424.
• hypothesis in the context of this patent refers to the possible status of fetal sex chromosomal aberrations together with the fetal fraction of cfDNA fragments in maternal blood, plasma or serum.
• a part of the method according to the invention is formulation of such hypotheses in probability theory, and calculating the probability of each such hypothesis based on the observations made from cfDNA fragments found in the said blood, plasma or serum.
• the present invention relates to the method for determining aneuploidy of fetal sex chromosomes from a maternal blood, plasma or serum sample comprising a mixture DNA fragments of fetal and maternal origin, wherein the mixture of DNA fragments of fetal and maternal origin are circulating cell-free DNA molecules, said method comprising four main stages: 1) obtaining and treating samples of maternal blood, 2) preparation of DNA sample and DNA library, 3) sequencing, and 4) analysis of the sequence data to obtain a prediction, or in other words, a diagnosis.
• the important part of the method is preparation of the training data, which needs to pass through the same laboratory process as the test samples (samples under examination).
• the starting point of the present method is the analysis of the maternal sample, i.e., peripheral blood, thus the method is non-invasive.
• the peripheral blood comprises cell free DNA (cfDNA) that is a mixture of fragments of maternal DNA and fetal DNA (cffDNA). Said fragments are further named also cfDNA fragments or briefly DNA fragments.
• CffDNA is what matters, therefore fetal DNA can be enriched (by selection of the shorter fragment, either in silico or physical selection as in Minarik et al. 2015 25 ).
• the total DNA sample (still comprising DNA fragments of both maternal and fetal origin, i.e., it is a mixed sample) is subjected to the massively parallel sequencing by NGS approach to obtain huge number of short sequence reads.
• These reads serve as sequence tags, i.e., they are mapped to a certain genomic region or chromosome.
• the present method determines the likelihood of observing NGS sequencing data for chromosome X and Y given a hypothesis about fetal sex chromosomes (e.g., female fetus with Turner syndrome).
• a hypothesis about fetal sex chromosomes e.g., female fetus with Turner syndrome.
• Various hypotheses relating to the various fetal SCAs are constructed, and the hypothesis with maximum likelihood is selected as the most probable case.
• the hypotheses are formulated for single or twin pregnancy, male or female fetus, with euploid or aberrant sex chromosomes, and for all fetal fractions (discrete values ranging from 5 % to 100 % with a step of 0.1 %).
• a priori fetal fraction distribution i.e., the likelihood of a particular value being the true fetal fraction of cfDNA in a given sample
• a priori occurrence of SCAs is gathered from the current research to scale the proposed hypotheses so that they reflect the real expectations (e.g., expectation of two female fetuses with Turner syndrome is less probable than the expectation of having two euploid female fetuses).
• the method is more tailored to the problem at hand when compared with methods adapted from trisomy detection such as in Chiu et al. 2008, Chiu et al. 2011, Sehnert et al. 2011, Bianchi et al. 2012, Lau et al. 2012.
• each sample i.e. test sample as well as training sample
• NGS data sequenced cfDNA fragments
• FASTQ or equivalent
• the sample's FASTQ file passed a mapping process, where the content of this file is aligned to a reference human genome (such as hgl9 26 ), and this mapping information is stored in a SAM (or equivalent) file format.
• the cfDNA fragments are further named also DNA fragments or simply cfDNA interchangeably.
• the method according to the present invention comprises a likelihood analysis of several tailor-made hypotheses about the fetus' (or fetuses', in case of twin pregnancy) sex chromosomes condition.
• the case of pregnancy i.e., single or twin, is specified by the operator before using the method (the type of pregnancy is determined by ultrasonic examination of the mother).
• the method operates in steps described below, some of which needs to be carried out only once. Gathering of the training data and hypothesis formulation needs to be performed only once, at the beginning of the implementation of the method into the practice.
• the training of the neural network model for calculation of fetal fraction distribution needs to be performed only once as well.
• fetal fraction distribution must be calculated by the trained neural network for each test sample.
• the probability of each hypothesis given the specific NGS data must be calculated for each test sample separately.
• chromosomes Z which produce reads mapped to chromosome Y from chromosome X.
• the number of chromosomes Z will depend on the number of chromosomes X.
• a female sample will have two chromosomes Z.
• a fetus with Turner syndrome will have only one chromosome Z, while a fetus with triple X syndrome will have three.
• a female with euploid female fetus will have in sum two (a part of the two will come from mother and another part will come from the fetus).
• a female with Turner female fetus will have a fraction between one and two which will depend on the fetal fraction of cfDNA fragments belonging to the fetus (the mother will supply two chromosomes Z for her part, but the fetus will only supply one; thus, the sum of the chromosomes Z will depend on the proportion of cfDNA fragments supplied by mother and fetus - fetal fraction).
• male samples or samples with male fetus will have chromosomes Z as well.
• muX three trained numbers from here on termed as muX, muY, muZ (mathematical details are stated below). Furthermore, we calculate the standard deviations of these numbers in an analogous manner (mathematical details below), which gives as additional three numbers from here on termed as sdX, sdY, sdZ.
• Tf Tin, and Te does not depend on the sample(s) to be tested for SCAs. We only require that the selection of the samples into the training sets is not biased, i.e., the samples are drawn randomly from a large population, and no sample is repeated in any of the training sets. Formulating hypotheses.
• mapping hypotheses for each sex chromosome aberrations including two euploid cases (X0, XX, XXX, XY, XXY, XYY for single pregnancies, and all their combinations in twin pregnancies (e.g., X0-X0 for twins with Turner syndrome, XO-XX for twins where one is euploid and the other has Turner syndrome, XX-XY for male and female euploid twins and so on).
• each of these hypotheses is parameterized by a value of fetal fraction of cfDNA fragments in maternal blood.
• this fetal fraction will be denoted with the symbol
• twin pregnancy there are two fetal fractions, and we will denote them with symbols fl and 2.
• Fetal fraction distribution - single pregnancy. Naturally, we expect observing fetal fraction 10% to be more probable than, say, 60%.
• fetal fraction distribution i.e., probability function assigning any discretized value of fetal fraction the probability to be the true fetal fraction of cfDNA fragments (for a particular sample), using other available sequencing data, namely cfDNA fragment lengths.
• cfDNA fragment lengths we use discretized values of fetal fractions because we do not need higher precision than 0.1% (the variation in the source data in the population is in the order of percents).
• the method of fetal fraction determination from fragment length is described in Yu et al. 2014. However, in this publication the method assigns only one value to fetal fraction of a given sample. Such approach cannot take into account the error of their estimation.
• N(muF2, sgF2 2 ) N(muF/2, sgF 2 /2).
• N(muF, sgF 2 ) is fetal fraction distribution determined as if it was a single pregnancy. Hypothesis selection.
• the likelihood of the given sample's NGS sequencing data is calculated under each hypothesis and for each fetal fraction / (resp. /i and 2), where the fetal fraction/is discretized, i.e., it starts at 5% and by a step of 0.1% goes to 100% (we require the sample to have at least 5% of fetal fraction of cfDNA fragments).
• the probability of each fetal fraction, given by the determined fetal fraction distribution is taken into account.
• hypothesis interpretation In case of single pregnancy, the interpretation of the selected hypothesis is straightforward. We discuss several such cases in examples below. In case of twin pregnancies, the interpretation is not so straightforward because some results may not be interpreted unambiguously.
• the prevalence of the SCAs in population provides assistance for the decision.
• knowing that there are only two possibilities can help the operator to suggest additional tests that provide decisive answer. More details will be given below in the examples.
• the numbers muZ and sdZ are determined analogously from the fragments mapped to chromosome Y in the training set Tf.
• the numbers muY and sdY can be determined directly from the male training set Tin because there is only one chromosome Y in any sample from this set (we assume that the training set contains only euploid males).
• the value muZ from the mean chromosome Y ratio obtained from training set Tin in order to get a better value of muY.
• sdZ from the variance of the chromosome Y ratio obtained from the training
• Tin in order to get a better value sdY .
• Our neural network consists of two layers: a base layer for input nodes and an output layer consisting of one output node (with sufficiently large training set, a more complex network with hidden layers can be designed). Moreover, each input node is connected with the output node. A detailed description of the network can be found in Example 1 below.
• the neural network is trained on the third of the samples from the training set Te.
• a sample's sequencing data namely mapped cfDNA fragments from all chromosomes, are classified according to their length, which results in a data histogram (classification lengths are limited to all lengths in base pairs from lOObp to 200bp, all other fragments were discarded).
• the neural network is trained in the usual way, known to the person skilled in the art.
• the guiding values during the training are fetal fractions obtained from cfDNA fragments mapped to chromosome Y as in the prior art.
• the last third of the samples from the training set Te is used to assess the error of the length-based fetal fraction.
• the neural network with the linear transformation can be directly used to predict fetal fraction of a given sample. Moreover, by comparing the predicted fetal fractions with known Y-based fetal fractions in last third of the training set Te, we can calculate the error of the prediction.
• N(muF, sgF ) specifying the probability distribution for the values of fetal fraction/, where muF is the prediction we got from the neural network for a given test sample, and sgF is the estimate of the error we got from the training set.
• hypotheses Formulation of hypotheses - single pregnancy
• Each of the presented hypotheses will have a code indicating how many of each chromosome X, Y, or Z is present in maternal and fetal source. It will be observed that maternal part never changes (coded as Mxxzz) because mother is always assumed to have two whole chromosomes X and two whole fictitious chromosomes Z. However, if we had any specific information regarding the chromosome distribution of the mother, we can very easily adjust the hypotheses to accommodate this change. For this reason, the following hypotheses should also be taken as examples according to which one can produce plentitude of other, perhaps case-specific, custom hypotheses.
• the part of the code belonging to fetus changes depending on fetal sex chromosome aberrations (e.g., euploid female fetus is Fxxzz, euploid male fetus is Fxzy because it has one chromosome X, one fictitious chromosome Z, and one chromosome Y and so on).
• fetal sex chromosome aberrations e.g., euploid female fetus is Fxxzz, euploid male fetus is Fxzy because it has one chromosome X, one fictitious chromosome Z, and one chromosome Y and so on.
• chromosome X there are four sources of fragments in this case: two maternal chromosomes X and two fetal chromosomes X.
• chromosome X there are three sources of fragments: two maternal chromosomes X and one fetal chromosomes X. We associate these sources with thee random variables XI, X2, and X3 all of which have the same normal distribution N(muX, sdX ).
• MxxzzFl xxzzF2xxxzzz.sdX 2 2*(1 -f 1 -f2) 2 *sdX 2 + 2*f 1 2 *sdX 2 + 3*f2 2 *sdX 2 ⁇
• MxxzzFl xxzzF2xxxzzz.muY (2+f2)*muZ
• Certain combinations of SCAs in twin pregnancies may compensate each other deficits or excesses.
• one Turner female fetus with triple X female fetus compensate the aberrations on X chromosome so that the sample would appear as two euploid female fetuses, if the fetal fractions of both fetuses are equal or close.
• the following table lists all ambiguous cases, when the fetal fraction is equal for both fetuses in a twin pregnancy Symmetric part of the table is omitted (empty cells). A dash (-) stands where there is no ambiguity.
• the NGS data cannot be, in general, interpreted unambiguously.
• the test provides the user with valuable information, because there are always only two possible interpretations.
• the user can suggest additional tests specifically targeting one of the possible interpretations to prove or disprove it.
• prevalence of the SCAs in population also provides help with the interpretation. For example, it is much more likely to have two euploid female twins rather than one with Turner and one with triple X syndrome.
• twin pregnancies we considered both fetuses to be independent. For this reason, the prevalence probabilities are multiplied. For example, the probability of having one euploid female (XX) with one triple X female (XXX) equals the prevalence of XX multiplied by the prevalence of XXX.
• the input to the maximum likelihood analysis are two numbers, x and y, specifying the ratio of DNA fragments mapped to chromosome X and Y, respectively, for a given sample. Additionally, the second part of the input is the a priori fetal fraction distribution given by the density function N(muF, sgF ), or two fetal fraction distributions given by their density functions N(muFl, sgFl 2 ) and N(muF2, sgF22 ) in case of twin pregnancy.
• N(muFl, sgFl 2 ) N(muF2, sgF2 2 ).
• this step calculates, for each hypothesis h (MxxzzFxxzz, MxxzzFxzy, MxxzzFxz, MxxzzFxxxzzz, MxxzzFxxzzy, MxxzzFxzyy) and each value of fetal fraction/ (discretized values ranging from 5% to 100% by a step of 0.1%) the probability of observing the data x and y under h and /, i.e., Pr[x ⁇ h,f] and Pr[y ⁇ h,f] .
• Pr[x ⁇ h, f] i.e., Pr[x ⁇ h,f] and Pr[y ⁇ h,f] .
• Pr[y ⁇ h, f]) is calculated as a definite integral of the probability density function N(h.muX, h.sdX 2 ) (resp. N(h.muY, h.sdY 2 )) on the interval [x - le-6, x + le-6] (resp.
• this step calculates, for each hypothesis h (MxxzzFlxxzzF2xxzz, MxxzzFlxxzzF2xzy, MxxzzFlxxzzF2xz, MxxzzFlxxzzF2xxxzzz, MxxzzFlxxzzF2xxzzy, MxxzzFlxxzzF2xzyy, MxxzzFlxzyF2xzy, MxxzzFlxzyF2xz, MxxzzFlxzyF2xxxzzz, MxxzzFlxzyF2xxzzy, MxxzzFlxzyF2xzyy, MxxzzFlxzF2xz, MxxzzFlxzF2xxxzzz, MxxzzFlxzF2xxzzy, MxxzzFlxzF2xzyy, MxxzzFlxxxzzzF2xzyy, MxxzzFlxxxzzzF2xzyy, MxxzzFlxxxzzzF2xzyy, MxxzzFlxxxzzz
• Pr[y ⁇ h, fl, fl]) is calculated as a definite integral of the probability density function N(h.muX, h.sdX 2 ) (resp. N(h.muY, h.sdY 2 )) on the interval [x - le-6, x + le-6] (resp. [y - le-6, y + le-
• h.muX (resp. h.muY) and h.sdX 2 (resp. h.sdY 2 ) are specified by the hypothesis h.
• the probability of the particular value of fetal fraction // (resp. /2) is calculated as a definite integral of the probability density function N(h.muFl, h.sdFl ) (resp. N(h.muF2, h.sdF2 )) on the interval [f - le-3, f + le-3].
• Pr[h, fl, f2 ⁇ x, y] Pr[x ⁇ h, fl, f2]* Pr[y ⁇ h, fl, f2]* Pr[fl]*Pr[f2]*Pr[h] , where Pr[h] is the prevalence of hypothesis h in the population. This value is calculated and plotted for each hypothesis h and fetal fraction// and/2. Improvements in the initial data processing
• this step markedly improved the correlation between mapping ratios of chromosomes X and Y for samples with euploid male fetus.
• the said method can be easily trained and applied to common autosomal aneuploidies such as (but not limited to) trisomy or monosomy of chromosome 13, 18 or 21.
• Such case is directly analogous to a case where the female fetus has Turner (monosomy) or triple X (trisomy) syndrome. This is because, healthy female fetus has two X chromosomes, which in this analogy corresponds to two chromosomes 21 (or any other autosome). Additionally, monosomy of chromosome 21 (or any other autosome) is an analogy of Turner syndrome, and trisomy of chromosome 21 (or any other autosome) is an analogy of triple X syndrome.
• hypotheses for fetus with Turner and triple X syndrome can be easily adjusted to correspond with the autosomal monosomy or trisomy.
• the only difference would be the number of considered hypotheses - 1) monosomy (aka Turner syndrome), 2 trisomy (aka triple X syndrome), and 3) euploid (aka euploid female fetus).
• monosomy aka Turner syndrome
• trisomy aka triple X syndrome
• euploid aka euploid female fetus
• the method of the present invention can be largely automatized.
• At least the "bioinformatics" part of the method i.e. processing of sequencing data and all subsequent determinations and calculations
• may be performed using suitable computer system such as PC equipped with a processor, peripheral input/output devices (e.g. ports, interfaces), memories (e.g. system memory, hard disk), keyboard, monitor, mouse etc., and a specific software, program for instructing the computer system to perform specific step.
• the computer system is in data communication with the sequencing system providing the sequence data, preferably in the form of plurality of sequence reads (by a wire or wireless networking, bluetooth, internet, cloud etc.). It means that the computer system is configured for receiving sequence data from the sequencing system.
• the suitable computer systems as well as means for connection with sequencing system are well known to the persons skilled in the art.
• At least part of the method can be implemented as a software code, i.e. a plurality of instructions (computer program) to be executed by a processor of a computing system.
• the code may be comprised in the computer readable medium for storage or transmission such as for example RAM, ROM, hard-drive, SDS, CD, DVD, flash memory etc.
• the code may be transmitted via any suitable wired, optical or wireless network, for example via internet.
• the whole computer programme can be downloaded by the operator (user) via the internet.
• another aspect of the present invention relates to the computer implemented method comprising all steps of calculations and determinations needed for processing of the input (sequencing data) into the output parameter(s) characterizing the diagnosis condition (fetal fraction and the most probable hypothesis, from which the presence or absence of aneuploidy as well as sex can be inferred).
• Still another aspect of the present invention relates to a computer program product comprising a computer readable medium comprising a plurality of instructions for controlling a computing system to perform at least a portion of the method according to the invention, preferably portion thereof starting with the step of receiving sequence information from the random sequencing step performed with automated sequencing system.
• the output of the present method can be transformed into plots depicting probability of hypotheses about the fetal sex chromosomes, and how this probability changes with fetal fraction (see Examples 3-11 and Figures 3-11).
• the discord between fetal fraction from the input fetal fraction distribution, and the fetal fraction from the hypotheses can exist. This is because they are both calculated from independent data. While the fetal fraction for the input fetal fraction distribution is calculated from the distribution of cfDNA fragment lengths, the fetal fraction for hypotheses is calculated from the NGS data for chromosome X and Y.
• the operator should make the decision based on the top plot as it contains all of the available information (NGS sequencing data for chromosomes X and Y, fetal fraction distribution, and SCAs prevalence in population).
• NGS sequencing data for chromosomes X and Y fetal fraction distribution
• SCAs prevalence in population a subset of the available information
• the other plots may offer some supporting information as well, especially in cases with mosaicism present, in which case the fetal fraction distribution may be very different from the most probable fetal fraction from the SCAs hypotheses.
• twin pregnancies we show only two plots for real samples (see Examples 9-10 and Figures 9-10) because of the abundance of the data, more precisely, only the top two plots from the single pregnancy test (the probability of each case is given either above or below the top of each bar). As was pointed out in the section Limitations above, interpreting results for twin pregnancies is more difficult because the same NGS data about chromosome X and Y can lead to ambiguous results (for more details see the table in section Limitations).
• a subject-matter of the present invention is a method for determining sex chromosome aneuploidy, sex and fetal fraction of one or multiple fetuses from a test sample of maternal blood, plasma or serum, as defined in the attached claims.
• Still another subject-matter of the present invention is computer implemented method comprising the steps of above method that follows the step of performing sequencing and obtaining sequence information, as defined in the attached claims.
• Still another subject-matter of the present invention is a computer program product comprising a computer readable medium comprising a plurality of instructions for controlling a computing system to perform said computer implemented method, as defined in the attached claims.
• Fig. 1 shows flow chart demonstrating the basic steps of the method according to the present invention. The references to the corresponding parts of the specification are added.
• Fig. 3 shows the results of an analysis of a sample of single pregnancy with euploid female fetus.
• Fig. 4 shows the results of an analysis of a sample of single pregnancy with euploid male fetus.
• Fig. 5 shows the results of an analysis of a sample of single pregnancy with Turner female fetus.
• Fig. 6 shows the results of an analysis of a sample of single pregnancy with Klinefelter male fetus.
• Fig. 7 shows the results of an analysis of a sample of single pregnancy with triple X female fetus.
• Fig. 8 shows the results of an analysis of a sample of single pregnancy with Jacob male fetus.
• Fig. 9 shows the results of an analysis of a sample of twin pregnancy with either two euploid male fetuses or one Turner female and one Jacob male fetus.
• Fig. 10 shows the results of an analysis of a sample of twin pregnancy with either two euploid female fetuses or one Turner female and one triple X female fetus.
• Fig. 11 shows the results of an analysis of a sample of twin pregnancy with either two Turner female fetuses.
• Our neural network as shown in Fig. 2, consists of two layers: a base layer for 101 input nodes and an output layer of one node (with sufficiently large training set, a more complex network with hidden layers can be designed). Moreover, each input node is connected with the output node.
• a sample's sequencing data namely mapped cfDNA fragments from all chromosomes, are classified according to their lengths, which results in a data histogram (fragment lengths are limited to the range from lOObp to 200bp, all other fragments are discarded).
• the input of the neural network is then the relative counts of the considered lengths for each sample.
• a set of samples with varying and known fetal fraction e.g.
• chromosome Y obtained from chromosome Y in case of samples with male fetus
• f len f Y + epsilon, where epsilon represents a random normal error
• Massively parallel sequencing is necessary for the application of the method according of the invention.
• the method was specifically developed and validated for small benchtop next generation sequencing systems to allow low initial costs for NIPT service lab setup.
• the method was validated on NextSeq500 system (Illumina, Inc., San Diego, CA, USA).
• Blood sample collection and plasma separation processing b) 10ml of peripheral blood sample should be collected from pregnant women after 11 th week of pregnancy in general EDTA containing tubes or tubes which stabilize cell free circulating nucleic acids (e.g. Streck Cell-Free DNA BCT).
• EDTA e.g. Streck Cell-Free DNA BCT
• Dual step plasma separation should be carried out not later than 2 days after collection with car to prevent white blood cell carry over
• Plasma samples are advised to be processed immediately after separation but storage is possible at -20 or -80C.
• the volume of processed plasma is advised to be at least 1 mL.
• Plasma isolated from STRECK tubes does require use of proteinase K treatment
• Next Generation Sequencing library preparation a) 30uL of isolated DNA from maternal plasma with use of MagMax Cell-Free isolation Kit should be processed in library preparation. b) Sequencing libraries should be prepared with using Illumina TruSeq Nano library preparation kit.
• Second wash Add 200 uL freshly prepared 80% EtOH and incubate 30 s.
• Paired end sequencing is performed with 2 x 35bp read setting with dual index reading.
• the Turner/Jacob case is asymmetric, and thus it can be better adjusted to the specific shown data by varying the two fetal fractions. However, this does not reflect the true probabilistic difference between these two cases (i.e., the Turner/Jacob case can be better fitted to data because it has one more free parameter). This holds for any other pair of two possibilities from section Limitations.

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